BEFORE THE PUBLIC UTILITIES COMMISSION OF NEVADA - NV Energy · 2020-07-11 · smart meter technology. The balancing authority and market resource optimization groups incorporated

BEFORE THE PUBLIC UTILITIES COMMISSION OF NEVADA

Application of NEVADA POWER COMPANY d/b/a NV Energy and SIERRA PACIFIC POWER COMPANY d/b/a NV Energy, seeking approval to add 1,001 MW of renewable power purchase agreements and 100 MW of energy storage Docket No. 18-06___ capacity, among other items, as part of their joint 2019-2038 integrated resource plan, for the three year Action Plan period 2019-2021, and the Energy Supply Plan period 2019-2021

VOLUME 8 OF 18

TECHNICAL APPENDIX DEMAND SIDE PLAN

ITEM DESCRIPTION PAGE NUMBER

DSM-13 Demand Response - Residential - NPC 2

DSM-14 Demand Response - Residential - SPPC 145

DSM-15 Schools - NPC 205

DSM-16 Schools - SPPC 297

DSM-13

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Residential Demand Response Program

NV Energy – Southern Nevada (NPC) Program Year 2017

Measurement and Verification Report March 15, 2018

Prepared for:

Prepared by:

3239 Ramos Circle Sacramento, CA 95827

916-363-8383

Page 3 of 359

Residential Demand Response 2017 – NV Energy, Southern Nevada M&V Report March 2018

TABLE OF CONTENTS

1 EXECUTIVE SUMMARY 1

2 PROGRAM OVERVIEW AND STUDY OBJECTIVES 6

3 METHODOLOGY 15

4 RESULTS 20

5 ENERGY SAVINGS CURVES 98

6 CONCLUSIONS AND RECOMMENDATIONS 99

7 APPENDIX A: DIFFERENCE-IN-DIFFERENCES METHODOLOGY FOR ECOFACTOR OPTIMIZATION SAVINGS 101

8 APPENDIX B: 2017 SAVINGS PER MONTH BY RATE CLASS 106

9 APPENDIX C: DETERMINING ENERGY SAVINGS (KWH) PER MONTH BY RATE CLASS 109

10 APPENDIX D: ECOFACTOR OPTIMIZATION MATCHING 114

11 APPENDIX E: NON-EVENT DAY CONTROL GROUPS FITS 116

12 APPENDIX F: 2017 HOURLY IMPACTS 127

Page 4 of 359


LIST OF TABLES

Table Number: Description Page Number

Table 1: DR Program Offerings per Budget Category 2 Table 2: Device Population Definitions 3 Table 3: 2017 Demand Response Events Ex Post Verified Demand Reduction 3 Table 4: Demand Response Verified 2017 Energy Savings 4 Table 5: Demand Response Potential Annual Energy Savings 4 Table 6: Report Outline 9 Table 7: Abbreviated Names, All Residential Device Subgroups 10 Table 8: Quantity of Residential Devices on 12/31/2017, Compared with Average Quantity of Residential Devices during Summer 2017 11 Table 9: Device-to-Premise Ratios 12/31/2017 18 Table 10: Residential Event Schedules (Individual Cells Represent One Phase) 20 Table 11: DP1RS Demand Reduction Results 23 Table 12: DP1RS VDR and Energy Savings 25 Table 13: DP1RM Demand Reduction Results 28 Table 14: DP1RM VDR and Energy Savings 29 Table 15: DP3CSERS Demand Reduction Results 32 Table 16: DP3CSERS VDR and Energy Savings 33 Table 17: DP3CSERM Demand Reduction Results 36 Table 18: DP3CSERM VDR and Energy Savings 37 Table 19: DP3LCRRS Demand Reduction Results 40 Table 20: DP3LCRRS VDR and Energy Savings 41 Table 21: DP3LCRRM Demand Reduction Results 44 Table 22: DP3LCRRM VDR and Energy Savings 45 Table 23: DP4LCRRS Demand Reduction Results 48 Table 24: DP4LCRRS VDR and Energy Savings 49 Table 25: DP4LCRRM Demand Reduction Results 52 Table 26: DP4LCRRM VDR and Energy Savings 53 Table 27: DP7RS Demand Reduction Results 56 Table 28: DP7RS VDR and Energy Savings 57 Table 29: DP7RM Demand Reduction Results 60 Table 30: DP7RM VDR and Energy Savings 61 Table 31: Override Rates for DP1 63 Table 32: Number of DCU Opt-Out Requests, by DCU Model 64 Table 33: Override Rates for DP7 65 Table 34: Program-Level Energy Impacts for CS1 65 Table 35: DP11RS Demand Reduction Results 69 Table 36: DP11RM Demand Reduction Results 72 Table 37: DP11RS Manage VDR and Energy Savings 73 Table 38: DP11RM Manage VDR and Energy Savings 74 Table 39: DP11RS Build VDR and Energy Savings 76 Table 40: DP11RM Build VDR and Energy Savings 77 Table 41: DP12RS Demand Reduction Results 80 Table 42: DP12RM Demand Reduction Results 83 Table 43: DP13RS Demand Reduction Results 85 Table 44: DP13RM Demand Reduction Results 87

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Table 45: DP12RS VDR and Energy Savings 88 Table 46: DP12RM VDR and Energy Savings 89 Table 47: DP13RS VDR and Energy Savings 90 Table 48: DP13RM VDR and Energy Savings 91 Table 49: Average Per Day Electricity Savings from EcoFactor Optimization (kWh) from Space Cooling (June – September) 92 Table 50: Average Per Day Electricity Savings from EcoFactor Optimization (kWh) from Space Heating (October – May) 92 Table 51: Data Used for 2017 EcoFactor Gas Savings Analysis (Therms) 93 Table 52: Override Rates for DP11 94 Table 53: CS1 and CS2 Results 96 Table 54: M&V Summary Results from 2017 Residential Demand Response Program 99 Table 55: Average Per Day Electricity Savings from EcoFactor Optimization (kWh) from Space Cooling (June – September) 103 Table 56: Average Per Day Electricity Savings from EcoFactor Optimization (kWh) from Space Heating (October – May) 104 Table 57: Treatment Regression Results 105 Table 58: Control Regression Results 105 Table 59: CS1 DR Event kWh Savings by Month by Rate Class – 2017 106 Table 60: CS2 Manage DR Event kWh Savings by Month by Rate Class – 2017 106 Table 61: CS2 Manage Energy Efficiency kWh Savings by Month by Rate Class – 2017 106 Table 62: CS2 Build DR Event kWh Savings by Month by Rate Class – 2017 107 Table 63: CS2 Build Energy Efficiency kWh Savings by Month by Rate Class – 2017 107 Table 64: CS2 Build Pilot DP12 DR Event kWh Savings by Month by Rate Class – 2017 107 Table 65: CS2 Build Pilot DP13 DR Event kWh Savings by Month by Rate Class – 2017 108 Table 66: Sample Calculation of Energy Savings 113 Table 67: Welch’s Two Sample t-test 114 Table 68: Hourly Impacts for DR Event Dates, Residential Device Populations 127

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1 EXECUTIVE SUMMARY

This report provides results of the ADM Associates Inc. (“ADM”) impact evaluation of the NV Energy (“NVE”) 2017 Residential Demand Response (“DR”) program for its Southern Nevada service territory (NPC1). The Residential Demand Response program is intended to, through the remote control of a participating customer’s HVAC2 to quickly reduce demand from the electric grid, either for economic reasons or in the event of a system emergency.

1.1 HISTORY OF THE DEMAND RESPONSE PROGRAM

NV Energy deployed residential load control switches originally in the 1980’s. The Company began deploying demand response technologies again at a limited scale in 2001. From 2001 through 2006, NV Energy conducted a technology evaluation of a variety of manufacturers’ switches, thermostats, gateways, and dispatch control systems, and built a load control capacity of 19 to 23 MW. In 2007, the program moved from a technology assessment program to a full-scale DR resource with the launch of the Cool Share program, which was based upon two-way communicating programmable thermostat (“PCTs”) technology. NV Energy expanded the overall system to over 140 MW of residential load control by the end of 2010.

In 2011 and 2012, the Company upgraded the demand response infrastructure with the installation of a demand response management system (“DRMS”) and upgraded customer technology designed to leverage smart meter technology. The balancing authority and market resource optimization groups incorporated DR in their operations and energy portfolio as a reliable and flexible peaking resource. In October of 2012, NV Energy launched its next generation residential offering, referred to as the mPowered Home Energy Management program. The Company also started customer trials of an advanced commercial offering, now referred to as the Commercial Energy Optimization program. Both offerings were deployed as integrated energy efficiency and demand response programs. In 2014, the Company also began to expand the commercial offerings with pilot installations at several sites using multiple technologies. These pilot installs grew in 2015 and has been incorporated into the Commercial and Industrial program (sometimes referred to as the “Commercial mPowered”, “C&I”, or simply “Commercial” program), serving all sizes of commercial customers.

In 2015, the residential demand response program started with six main customer offerings described above, and during the year added a small residential pilot of EcoBee two-way PCTs. In 2016, NVE rebranded its residential “mPowered” program as “PowerShift” and continued expanding, installing over 12,000 devices. During 2017, the “PowerShift Pilot” was implemented investigating the efficacy of two new Wifi smart thermostats from EcoBee and EcoFactor.

The 2017 Residential Demand Response portfolio consisted of the following customer offerings:

1. Cool Share, or CS1 2. PowerShift Home Energy Management, or CS2 3. Advanced Thermostat Pilot, or CS2 Pilot

1 NPC: Nevada Power Company or “Nevada Power” 2 HVAC: Heating, Ventilation, and Air Conditioning

Executive Summary Page 7 of 359

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It is important to describe some of the nomenclature used in this report and to note that these customer offerings fall into specific budget categories outlined in the Demand Response Program Data Sheet, which distinguishes budget categories between “Manage” and “Build” budgets. Support for all customers enrolled before 2017 fall into the Manage budget categories, while all customers newly enrolled in 2017 fall into the Build budget categories. For 2017, the customer offerings are related to budget categories as follows.

Table 1: DR Program Offerings per Budget Category

Customer Offerings Residential

Manage (Pre-2017)

Residential Build (2017)

Cool Share (CS1)

PowerShift (CS2)

PowerShift (CS2) Pilot

Cool Share, or CS1, consists of the technologies adopted since the program’s inception and installed prior to the current program year (2017). In 2012, Nevada Power continued to build DR resources through the deployment of its next-generation Home Energy Management program, which utilizes broadband communications, cloud-based optimization software from EcoFactor, thermostats produced by Computime, and gateways from Digi International. These residential installations are designated as CS2 Manage for those customers enrolled before 2017 and CS2 Build for those customers newly enrolled in 2017. In 2015, Nevada Power added the EcoBee residential pilot and in 2017 the pilot expanded into the PowerShift Pilot.

All of the residential DR components are listed in Table 2 below.

1.2 ORGANIZATION OF THE STUDY

The purposes of this measurement and verification (M&V) study are to determine the achieved peak demand savings and energy savings due to the CS1 (Cool Share) and CS2 (PowerShift Home Energy Management) customer offerings which collectively are referred to as the Residential Demand Response (DR) program. The study is organized according to the different groups of control devices, or Device Populations (DPs), comprising the program. Table 2 provides the formal organization of the DR program. CS1 consists of device populations from 1 to 7; CS2 consists of device population 11; and the PowerShift pilot consists of device population’s 13 and 14.


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Table 2: Device Population Definitions

Program Device Population Device Description

CS1 DP1 Carrier Residential Two-Way PCTs

CS1 DP3 Residential Cannon LCR 5000 Switches & CSE DCU Switches

CS1 DP4 Residential Cannon LCR 5200 Switches w/ TrueCycle CS1 DP7 Honeywell Residential One-Way PCTs

CS2 Manage (PowerShift) DP11 EcoFactor Residential Two-Way PCTs

CS2 Build (PowerShift) DP11 EcoFactor Residential Two-Way PCTs3

CS2 Build (PowerShift) Pilot DP12 EcoBee Residential Two-Way WiFi PCTs

CS2 Build (PowerShift) Pilot DP13 EcoFactor s100 Two-Way WiFi PCTs

All residential populations are further subdivided by rate class: for example, DP1 consists of DP1RM (multifamily) and DP1RS (single family). At this lowest level of organization, these subsets of DR devices are called subgroups. Altogether, CS1 has 15 different subgroups, CS2 has 2 different subgroups, and the PowerShift Pilot has 4 different subgroups.

1.3 PROGRAM RESULTS

For the NVE Demand Response program, 2017 Demand Response Events provided ex-post peak verified demand reduction (VDR) presented in the table below.

Table 3: 2017 Demand Response Events Ex Post Verified Demand Reduction

Program Device Population %NRD Available Devices kW Factor Max VDR

(kW)

CS1 (Manage)

DP1.RM 13.9 4,095 1.45 5,115 DP1.RS 8.2 28,576 2.39 62,730

DP3LCR.RM 29.7 257 0.89 161 DP3LCR.RS 27.4 1,382 1.24 1,244 DP3CSE.RM 21.6 698 0.75 410 DP3CSE.RS 14.8 7,220 0.75 4,616 DP4LCR.RM 29.5 222 0.98 153 DP4LCR.RS 29.8 899 1.66 1,048

DP7.RM 19.7 638 1.16 594 DP7.RS 11.1 4,609 1.78 7,291

CS2 Manage (PowerShift)

DP11M.RS 7.5 47,973 2.16 95,881 DP11M.RM 11.4 1,087 1.71 1,647

CS2 Build (PowerShift)

DP11B.RS 7.5 9,789 2.16 19,565 DP11B.RM 11.4 697 1.71 1,056 DP12B.RS 15.3 351 2.48 737

3 The EcoFactor devices in CS2 Build are identical to those in CS2 Manage; the only difference is that the devices in CS2 Build were installed on or after January 1, 2017.


3



(kW)

CS2 Build (PowerShift) Pilot

DP12B.RM 22.6 162 1.33 167 DP13B.RS 9.6 779 2.51 1,768 DP13B.RM 13.4 239 1.44 298

Total 109,673 204,482

For the NVE Demand Response program, 2017 DR Events and Energy Efficiency services provided ex-post verified energy savings presented in Table 4 and Table 5.

Table 4: Demand Response Verified 2017 Energy Savings4

Program Device Population 2017 DR Energy Savings (Events),

kWh

2017 EE Energy Savings

(Optimization), kWh

2017 Total Energy

Savings, kWh

Potential Annual Energy

Savings, kWh

CS1 All Legacy Residential Devices 2,973,323 2,973,323 2,973,323

CS2 (PowerShift) Manage DP11 1,614,861 15,165,738 16,780,599 16,780,599

CS2 (PowerShift) Build DP11 207,819 1,982,919 2,190,739 3,628,236

CS2 (PowerShift) Pilot Build

DP12 4,525 4,525 18,096

DP13 1,257 1,257 13,481

Total 4,801,785 17,148,657 21,950,443 23,413,735

Table 5: Demand Response Potential Annual Energy Savings5

Program Device Population kWh Factor Available Devices

kWh Savings DR

kWh Savings EE kWh Savings Total

CS1

DP1.RM 42.64 4,095 174,611 174,611 DP1.RS 69.14 28,576 1,975,745 1,975,745

DP3LCR.RM 70.82 257 18,201 18,201 DP3LCR.RS 72.35 1,382 99,988 99,988 DP3CSE.RM 47.65 698 33,260 33,260 DP3CSE.RS 31.95 7,220 230,679 230,679 DP4LCR.RM 69.34 222 15,393 15,393 DP4LCR.RS 82.54 899 74,203 74,203

DP7.RM 65.22 638 41,610 41,610 DP7.RS 67.18 4,609 309,633 309,633

CS2 (PowerShift) Manage

DP11.RS 33.92 47,973 1,627,350 15,165,738

16,793,088 DP11.RM -11.49 1,087 -12,489 -12,489

4 This table presents verified and potential energy savings. Potential savings is based on a typical (non- leap year). 5 This table represents potential annual energy savings from the program if all of the devices were installed at the

beginning of the program year, no devices leave the program and all devices function optimally. This is not to be confused with future savings values as the same population is evaluated each year.

Executive Summary 4 Page 10 of 359


Program Device Population kWh Factor Available Devices

kWh Savings DR

kWh Savings EE kWh Savings Total

CS2 (PowerShift) Build

DP11.RS 33.92 9,789 332,064 3,304,179

3,636,243 DP11.RM -11.49 697 -8,008 -8,008

CS2 (PowerShift) Pilot Build

DP12.RS 36.34 351 12,755 12,755 DP12.RM 32.97 162 5,341 5,341 DP13.RS 21.38 779 16,655 16,655 DP13.RM -13.28 239 -3,174 -3,174

Total 109,673 4,943,818 18,469,917 23,413,735

Executive Summary 5 Page 11 of 359

2 PROGRAM OVERVIEW AND STUDY OBJECTIVES

This report presents the results of ADM Associates Inc.’s (ADM) measurement and verification (M&V) study of NV Energy’s (NVE) Residential Demand Response (DR) program for 2017. In this introductory chapter, we provide a high-level summary of the DR program, ADM’s M&V methodology, and the basic structure of the remainder of this report.

Throughout, important terms will appear in boldface when introduced for the first time.

2.1 NV ENERGY’S RESIDENTIAL DEMAND RESPONSE PROGRAM

Demand Response, as the name suggests, encompasses a range of interventions and techniques utilities can use to respond to and control demand for electricity, either for economic reasons or in the event of an electric system emergency. The types of interventions used in DR programs include both tariff-based and technology-based measures.

NVE has been deploying DR technologies addressed in this report in its Southern Nevada service territory since 2001, beginning with smaller scale technology demonstrations, eventually scaling up to a system of approximately 110,000 control devices in over 76,000 homes in 2017.

Currently, the overall DR program includes two primary customer offerings:

1. CS1 or Cool Share is comprised of those customers who enrolled in NVE’s DR effort prior to 2012 with non-EcoFactor devices.

2. CS2 or PowerShift is comprised of enrollees to the DR program in 2012, 2013, 2014, 2015, 2016 and 2017 with Home Energy Management systems leveraging cloud-based software from EcoFactor.

2.1.1 About Cool Share The Cool Share program utilizes an assortment of PCTs and direct control units (DCUs) to control participants’ HVAC systems during a demand response event. Cool Share PCTs communicate over commercial paging systems. DR events increase their cooling set point by 4 degrees. Cool Share DCU’s receive a remote signal to reduce their cooling duty cycles (and hence their compressor run times) by half.

For their participation in Cool Share, customers with PCTs installed before 2012 receive a $5 bill credit per device per month over the June to September DR season; customers with DCUs receive a $7.50 bill credit per device per month over the June to September DR season. In addition to this fixed incentive, customers can also receive a tariff-based rebate, the amount of which depends on how much demand response event energy savings they can achieve, relative to a customer-specific baseline6.

6 https://www.nvenergy.com/home/saveenergy/rebates/coolshare.cfm

Program Overview and Study Objectives Page 12 of 359

6

https://www.nvenergy.com/home/saveenergy/rebates/coolshare.cfm


2.1.2 About PowerShift Home Energy Management The PowerShift program is marketed not only as a demand response program but also as a home energy management program utilizing an internet-connected system that communicates with cloud-based software.


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from EcoFactor. EcoFactor technology provides a software service that automates HVAC controls based on household patterns, with the goal of reducing HVAC energy usage.

For their participation in the PowerShift Home Energy Management program, customers receive the thermostat, installation, and EcoFactor automation service subscription free of charge7. The PowerShift customers also receive a tariff-based rebate that varies depending on the amount of demand savings achieved.

2.2 RESIDENTIAL DEMAND RESPONSE EVENTS IN 2017

Between June and September 2017, 40 residential demand response events were called. Figure 1 shows the days each of the events were called.

June July S M T W Th F Sa

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

S M T W Th F Sa 1

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

August September S M T W Th F Sa

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

S M T W Th F Sa 1 2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Residential DR Event

Figure 1: 2017 DR event dates

7 https://www.nvenergy.com/home/saveenergy/rebates/Power Shift/images/Power ShiftFAQ.pdf

Program Overview and Study Objectives 8

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https://www.nvenergy.com/home/saveenergy/rebates/Power

2.3 OBJECTIVES OF THE M&V STUDY

The objective of this study is to provide answers to each of the following questions:

1. To what extent did the two demand response programs (CS1 and CS2) reduce peak electric demand during the summer of 2017?

2. To what extent did the two demand response programs reduce energy consumption during the year of 2017?

3. For the purposes of forecasting, what factors influence the amount of demand reduction achieved in an event?

4. What was the customer response to demand response events? Specifically, at what rates did participants opt out of events or the program entirely?

5. How reliable is the demand response system? Specifically, at what rates did individual devices fail to curtail the usage of the equipment they controlled?

To provide an answer to these questions, ADM provided NVE with a research plan outlining a rigorous analytical approach, which relied on both ADM’s and NVE’s technical expertise and experience in evaluating and operating, respectively, demand response programs.

2.4 ORGANIZATION OF THIS REPORT

The remaining chapters of this report are outlined in Table 6:

Table 6: Report Outline

Chapter Number Chapter Title Description

3 Methodology Description of the methodology used to estimate residential demand reductions and energy savings.

4 Results Estimates of and for residential subgroups are provided.

5 Energy Savings Curves Describes energy savings curves used for the 2017 DR analysis.

6 Conclusions and Recommendations

Recommendations for future program years are provided in light of this year’s evaluation.

A number of technical appendices are located at the end of this report and are referred to as needed in the main body of the text.

2.5 ABBREVIATIONS FOR DEVICE SUBGROUPS

Throughout this report, control device subgroups will be referred to using the following abbreviated forms, for the sake of brevity:


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Table 7: Abbreviated Names, All Residential Device Subgroups Abbreviated

Name Device Description

DP1RS Carrier two-way PCTs in single-family homes DP1RM Same as above, but in multifamily homes DP3CSERS CSE DCUs in single-family homes DP3CSERM Same as above, but in multifamily homes DP3LCRRS Cannon LCR 5000 DCUs in single-family homes DP3LCRRM Same as above, but in multifamily homes DP4LCRRS Cannon LCR 5200 DCUs in single-family homes DP4LCRRM Same as above, but in multifamily homes DP7RS Honeywell one-way PCTs in single-family homes DP7RM Same as above, but in multifamily homes DP11RS Manage EcoFactor two-way PCTs in single-family homes, i.e., devices installed prior to 2017.

DP11RM Manage EcoFactor two-way PCTs in multi-family homes, i.e., devices installed prior to 2017.

DP11RS Build EcoFactor two-way PCTs in single-family homes, i.e., devices installed during 2017. DP11RM Build EcoFactor two-way PCTs in multi-family homes, i.e., devices installed during 2017.

DP12RS Build EcoBee two-way WiFi PCTs in single-family homes, i.e., devices installed during 2017. DP12RM Build EcoBee two-way WiFi PCTs in multifamily homes, i.e., devices installed during 2017.

DP13RS Build EcoFactor two-way WiFi PCTs in single-family homes, i.e., devices installed during 2017. DP13RM Build EcoFactor two-way WiFi PCTs in multi-family homes, i.e., devices installed during 2017.

2.6 RESIDENTIAL DEMAND RESPONSE SYSTEM SIZE

In this chapter, we explain how tracking data for the different DR programs were used to quantify the size of the system at different times during the summer. The counts provided below are of available devices, which are defined as devices registered with NVE as DR assets. An available device becomes unavailable if and only if the customer in possession of said device decides to permanently opt out of a demand response program.

Available devices are to be distinguished with active or responding devices, which are available devices that are, at a given moment, controllable by NVE. The potential for various technical failures on the part of devices means that, in theory, and in practice, responding devices are a proper subset of available devices. The proportion of non-responding devices is estimated for each subgroup and each event and is used to adjust the number of available devices down to the number of responding devices that contribute to actual demand reduction (see section 2.6.3).


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2.6.1 CS1 Device Counts The CS1 device subgroups were tracked using NV Energy’s Demand Response Management System (DRMS), which serves as a centralized hub for controlling and monitoring the entire DR system (including CS2 and C&I).

The DRMS keeps track of when new customers join a DR program and when they opt out, as well as identifiers such as meter number and device serial number. Rate class information was determined by cross-referencing the DRMS with NVE’s Banner customer database. Going by the dates provided in the DRMS, the available devices in device subgroup at a given date is:

Equation 1

This quantity can change day-to-day, so is calculated for every event . However, to provide a prospective value for system size, we have used the system size as of December 31, 2017 as shown in Table 8 below.

Table 8: Quantity of Residential Devices on 12/31/2017, Compared with Average Quantity of Residential Devices during Summer 2017

Subgroup

Summer 2017

Average Quantity

Size at 12/31/2017 ((N_g (d))

DP1RS 29,011 28,576 DP1RM 4,093 4,095

DP3CSERS 7,443 7,220 DP3CSERM 720 698 DP3LCRRS 1,399 1,382 DP3LCRRM 256 257 DP4LCRRS 933 899 DP4LCRRM 219 222

DP7RS 4,658 4,609 DP7RM 640 638

DP11 (Manage) 55,169 49,060

DP11 (Build) 7,090 10,486 DP12 (Build)

Pilot 141 513

DP13 (Build) Pilot 51 1,018

Total 111,822 109,673


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2.6.2 CS2 Device Counts The CS2 EcoFactor PCTs were tracked using the “Customer Extract” report from the DRMS. For CS2,

is the quantity of DP11 devices installed in 2017. Of the 59,546 devices enrolled in DP11 (as shown in Table 8), 10,486 were installed in 2017.

2.6.3 Residential Non-Responding Devices A device is considered “non-responding” if it does not respond to the curtailment signal sent by NVE for reasons other than the device being manually overridden (for more information related to overrides please see section 3.1.3) by the customer. Common causes of non-response are system outages, internet accessibility issues or other physical barriers that may block the signal.

Prior to the calculation of kW factors, non-responding devices were identified and removed from the sample using a combination of two algorithms: a cumulative sum (CSUM) change in slope analysis and a straight 10% decrease in load detection. Given that overrides and partial overrides cannot be considered non-responding devices or NRDs, we utilized override data to identify and exclude overrides and partial overrides from the NRD identification process.

CSUM Algorithm

When an event is called, each device is sent curtailment instructions that result in a significant load drop over the event period. This drop is illustrated in Figure 2, where an example DR event is presented with an example “normal” usage curve.

k

W

h

6

5

4

3

2

1

0

Normal Treatm…

3 2 1 0 1 2 1+ 2+ 3+ 4+ Event Hours

Figure 2: Example of site-level load shapes during event hours

Because every home has different energy usage patterns and magnitudes, when identifying which homes participated in an event it is important to look at load shapes in a way that reduces the variability in load shapes to a simple pattern. We apply a cumulative sum to each load shape to create a simple cumulative time series:

Equation 2

where x is a vector of kWh measures taken at 15-minute intervals, the interval : is the 24-hour interval from 7 am to 7 am the following day. The result of Equation 2 for each treatment site is a smoothed,


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increasing curve, as depicted in the following figure. If the device responded to the curtailment call, then there will be a significant change in slope during the event period compared to the period before the event started. To test for significance in the slope change, we apply two tests, Test 1 takes the ratio of the event period (2 hours) slope divided by the pre-period (3 hours) slope (Figure 3). A responding device is detected by a decrease in the line slope, so the ratio will be less than unity.

25

20

k 15 W

10 h

5

0

Event Hours Figure 3: Example of Site-level Cumulative Sum Slope Changes during Event Hours

Some sites can have a unique meter profiles that can confuse Test 1. Test 2 takes the average load shape of each site based on the previous 7 non-events, non-weekend days to create a “site-normal” cumulative curve to compare with the event curve (Figure 4). We calculate the slope ratios for the site-normal cumulative curve and then compare it to event ratio for those sites that failed Test 1.

30

25

20 k

W 15

h 10

5

0

Event Hours

Figure 4: Example of the CSUM Change-in-Slope Analysis used to Identify NRDs

If the ratio for the site-normal curve is greater than the ratio for the event curve, then the device is classified as responding. Any devices left over after the two tests are classified as non-responding.

Percent Reduction Algorithm

Treatment

Event Slope

Pre Slope

Ratio = Event Slope Pre Slope

3 2 1 0 1 2 1+ 2+ 3+ 4+

Normal

Treatment

Test 2: T.Ratio < N.Ratio ... Event Occurs

N.Ratio = C / A

T.Ratio = B / A

A

C

B

3 2 1 0 1 2 1+ 2+ 3+ 4+


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Since it is possible that a single NRD classification algorithm may misidentify NRDs, to reduce Type 1 errors we employ two algorithms and take the intersection of the resulting groups. The percent reduction algorithm is a straight test for a 10% reduction in kWh during the first hour of an event. The same meter data tested in by the CSUM algorithm is put through this algorithm. For each unique device, the kWh for 1-hour pre-event/pre-cool and kWh for the first hour of the event (Figure 2) are analyzed for a drop greater than 10% as follows:

Equation 3

When T1 is less than or equal to T2 we classify that device as an NRD. The 10% drop is the average value found from an extensive review of drop percentages for the different device types in the program by ADM and NV Energy.


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3 METHODOLOGY

In this chapter, we detail the methodology used to calculate the demand reductions and energy savings associated the residential demand response program.

3.1.1 Calculation of Demand Reductions In 2017, forty demand response events were called between June and September. Using 15-minute interval energy consumption data for a near-census of program participants, ADM calculated the following metrics of the DR program’s demand reduction capabilities.

The first metric is the subgroup kW factor ( ), which is the upper limit of a subgroup ’s per-device load reduction capability. For device populations that are curtailed in phases, is taken to be the maximal load reduction among all disaggregated phases.

The second metric is the season verified demand reduction ( ), which is equal to:

Equation 4

Where is the number of responding devices in subgroup ( is the average proportion of non-responding devices).

3.1.2 Calculation of DR Event Energy Savings Although intended primarily as a peak management resource, the DR program also achieves energy savings because the cooling loads shifted by the DR events do not always end up being as large as they would have been had no event been called. Often, even when cooling-demand is shifted to later in the day by a DR event (the so-called “snapback”), the home still ends up using less energy overall because of the event. The net per-device difference in energy consumption (relative to a control group baseline) for a subgroup during event is known as the subgroup-event kWh factor ( ). The program-level DR event energy savings ( ) is the sum of the subgroup-event kWh factors over all subgroups, and all events, multiplied by the number of (responding devices), similar to the following equation:

Equation 5

As with demand reductions, line losses are not factored into this quantity.

3.1.3 Manual Overrides All demand response subprograms provide a process by which the customer may override demand response curtailments. If the customer does not wish to participate in a particular event, depending on the subprogram, they may override the curtailment over the internet, by calling a customer support hotline or manually doing so on the device. Manual overrides were measured using various event tracking data sets

Results 15 Page 21 of 359


provided by NVE and its implementers. The percent of participants who overrode (or PPO) during each event is presented in the results sections.

3.1.4 Data Acquisition and Sampling Smart meter data was obtained for participants via the DRMS (around 61% of the enrolled premises were part of the delivered data). The sampling units are individual homes and apartment units, identified by a unique meter number. A unit is included in the sample if and only if the three criteria are fulfilled:

1. The home did not dis-enroll any devices from the program in 2017. That is, every device in the sample should have participated in every event. The only exception was CS2 build homes which could have been installed mid-season.

2. The home’s service class could be verified as single-family or multifamily in NVE’s records. 3. The home, even if it has multiple DR control devices, has only one kind of device. That is, if a

home has one Cannon DCU and one CSE DCU, or a PCT and a DCU, then it is excluded from the sample. On the other hand, if, say, the home has two Carrier PCTs then it is included in the sample.

Because the units of the smart meter data are in “kWh delivered” per 15-minute interval, we multiply these values by 4 to convert to average demand (in kW).

3.1.5 Aggregating Subgroup Loads The individual time series of household demand are aggregated by subgroup, after removing premises with suspected non-responding devices according to the method described in Section 2.6.3. We denote the aggregated subgroup time series by where is the time index (where time is discretized into 15-minute intervals, per the smart meter’s default time resolution).

Aggregation is useful because it smooths out the temporal noise that is present in individual time series. Moreover, despite the fact that household demand tends to vary widely from home to home, due to the large size of the samples, , as an estimate of subgroup average household demand, is virtually error-free by the central limit theorem8, especially since the sample sizes approach the population sizes.

3.1.6 Creating Control groups to Estimate Baseline Loads For each device subgroup, a control group is created to serve as a point of comparison, or a baseline, during DR event days. Meter data for over 50,000 single family homes and over 5,000 multifamily homes were obtained through NV Energy’s DRMS system. The data was used to select subgroup-specific control groups on the basis of property characteristics and load similarity. Up to five different control sites were chosen based on these characteristics. This pool of control sites was then re-matched to each subgroup, for each event, based on the total kWh usage over the previous seven (non-event) weekdays:

Equation 6

where Monday, …, Friday with event days excluded for both groups of homes.

8 Which states that the imprecision in the point estimate of a mean, based off a sample, goes to zero on the order of , where is the sample size.

Methodology Page 22 of 359

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For each treatment premise , the total is matched to the ’s from control premises using an increasing pseudo-clustering circle that expands until several control premises are matched. This control subgroup is matched for up to five premises per treatment premise (with replacement), these sites are removed from the base group and the process is repeated for each treatment premise.

This process was designed to select, for each treatment premise, the handful of homes in the non-DR control groups that match the treatment premise’s characteristics and consumption patterns as closely as possible. The resulting matched control group is significantly better fit to the treatment group than a random sample of control premises.

As with the subgroup loads, once the control groups are selected, they are aggregated to form the subgroup control time series, which we denote by .

See Appendix E for a series of illustrations depicting the load shapes of the treatment and matched control groups during DR season non-event days.

3.1.7 Event-Day Adjustment of the Control Group Load

Even with the matched control groups, there may be a consistent difference between and , which would bias the DR load impacts if one were to simply take the difference of the two.

To account for these gaps, an event-day adjustment is made to the control group load:

Equation 7

where lies within an event period. For CS1 device groups, is calculated as the ratio of the average treatment group load and the average control group load, the hour prior to the event. CS2 devices often employ a pre-cooling strategy where the home is cooled during a period of two hours

immediately prior to the event. For CS2 devices, is calculated as the

mean of the ratios of the average treatment group load and the average control group load, for the hour before the pre-cool begins and the third hour after the event ends.

3.1.8 Calculation of Demand and Energy Factors

Let be some time during an event . At the most basic level, we take the difference of and

to produce what we call the load differential:

Equation 8

With a few exceptions, subgroups are curtailed for roughly two hours9 and are expected to take another two hours to restore the pre-event indoor temperatures. This post-event period is called the “snapback” period

9 We say “roughly” because in the case of DP1, the “soft restore” feature may delay the official event end time by up to 30 minutes for subsets of those device populations.


17


because usage within the treatment group will rapidly shoot up and past the control group after the end of an event, implying negative savings for that period.

For each event , and subgroup , we calculate the average for each of these four10 (approximately)

hour-long periods. We denote these averaged differences by , where refers the event hour (which usually includes two curtailment hours, two snapback hours, and pre-cool 90 minutes of pre-cool for DP11).

For each subgroup and event , we calculate the subgroup-event kW and kWh factors. The subgroup-event kW factor is defined as:

Equation 9

that is, the larger of the first- or second-hour average load reductions normalized by the sample device-to-premise ratio :

Equation 10

which normalizes the load differentials to a per-device value. Table 9 lists the device-to-premise ratios based on the device counts at the end of the program year.

Table 9: Device-to-Premise Ratios 12/31/2017 Subgroup Premises Devices Ratio

DP1.RM 4,063 4,095 1.008

DP1.RS 19,687 28,576 1.452

DP3LCR.RM 251 257 1.024

DP3LCR.RS 1,154 1,382 1.198

DP3CSE.RM 688 698 1.015

DP3CSE.RS 5,408 7,220 1.335

DP4LCR.RM 222 222 1.000

DP4LCR.RS 653 899 1.377

DP7.RM 617 638 1.034

DP7.RS 3,477 4,609 1.326

DP11.RS 37,029 57,762 1.560

DP11.RM 1,692 1,784 1.054

DP12.RS 211 351 1.664

DP12.RM 146 162 1.110

DP13.RS 495 779 1.574

10 For DP11 and DP13, the analysis period is six hours as it also includes the two hours prior to the event (the pre-cooling period).


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Subgroup Premises Devices Ratio

DP13.RM 191 239 1.251

The subgroup-event kWh factor is defined as:

Equation 11

where the sum is over all event hours (including pre-cooling and snapback).

3.1.9 Calculation of CS2 Energy-Optimization Impacts In addition to its role as a demand response asset for NVE, the PowerShift program includes an energy-optimization service that provides users year-round energy savings through fine-tuned control of the HVAC system that also learns residents’ preferences and behaviors. These non-DR event energy impacts are estimated using econometric techniques.

Utilizing a similarly matched control group that is used to calculate demand reductions, ADM determined the electricity savings resulting from the energy-optimization service by employing a “difference in differences” method. With this method, changes in energy use for customers receiving optimization from the EcoFactor device are compared to changes in non-optimized energy use of the control group. Both groups are then compared to a baseline “pre” period occurring prior to the PowerShift participants’ receipt of their EcoFactor devices.

The change in energy usage for the different groups is determined using the results from regression analysis of the energy usage data for the participant and non-participant groups. ADM uses regression analysis to estimate the amounts of electricity used and to quantify the impacts of receiving EcoFactor optimization on energy consumption after controlling for the effects of weather and other factors. The regression analysis isolates and quantifies the effects of different factors on the changes in energy usage. Please see Appendix A for a more detailed explanation of the difference-in-differences methodology and regression specifications.


19

4 RESULTS

4.1 RESIDENTIAL EVENT SCHEDULE

Table 10 provides the scheduling of the residential event phases. To provide this information in a compact form, the start time of the first phase is provided and then offset, in minutes, of the following phases. For example, on June 5, Phase 1 began at 15:30, the second phase began at 15:40 (15:30 plus 10 minutes), the third phase began at 15:50 (15:40 plus 10 minutes), and so on. In 2017, all 40 events began at 15:30, had 8 total phases each beginning 10 minutes after the last. Each phase lasted for 2 hours.

Table 10: Residential Event Schedules (Individual Cells Represent One Phase)

Date First Phase Phase Offsets in Minutes 5-Jun 15:30 10 +10 +10 +10 +10 +10+10 6-Jun 15:30 10 +10 +10 +10 +10 +10+10 7-Jun 15:30 10 +10 +10 +10 +10 +10+10

14-Jun 15:30 10 +10 +10 +10 +10 +10+10 15-Jun 15:30 10 +10 +10 +10 +10 +10+10 16-Jun 15:30 10 +10 +10 +10 +10 +10+10 19-Jun 15:30 10 +10 +10 +10 +10 +10+10 20-Jun 15:30 10 +10 +10 +10 +10 +10+10 21-Jun 15:30 10 +10 +10 +10 +10 +10+10 22-Jun 15:30 10 +10 +10 +10 +10 +10+10 23-Jun 15:30 10 +10 +10 +10 +10 +10+10 26-Jun 15:30 10 +10 +10 +10 +10 +10+10 27-Jun 15:30 10 +10 +10 +10 +10 +10+10 28-Jun 15:30 10 +10 +10 +10 +10 +10+10 30-Jun 15:30 10 +10 +10 +10 +10 +10+10 3-Jul 15:30 10 +10 +10 +10 +10 +10+10 5-Jul 15:30 10 +10 +10 +10 +10 +10+10 6-Jul 15:30 10 +10 +10 +10 +10 +10+10 7-Jul 15:30 10 +10 +10 +10 +10 +10+10 8-Jul 15:30 10 +10 +10 +10 +10 +10+10

12-Jul 15:30 10 +10 +10 +10 +10 +10+10 13-Jul 15:30 10 +10 +10 +10 +10 +10+10 14-Jul 15:30 10 +10 +10 +10 +10 +10+10 18-Jul 15:30 10 +10 +10 +10 +10 +10+10 20-Jul 15:30 10 +10 +10 +10 +10 +10+10 21-Jul 15:30 10 +10 +10 +10 +10 +10+10 28-Jul 15:30 10 +10 +10 +10 +10 +10+10 1-Aug 15:30 10 +10 +10 +10 +10 +10+10 2-Aug 15:30 10 +10 +10 +10 +10 +10+10 7-Aug 15:30 10 +10 +10 +10 +10 +10+10 9-Aug 15:30 10 +10 +10 +10 +10 +10+10 10-Aug 15:30 10 +10 +10 +10 +10 +10+10



Date First Phase Phase Offsets in Minutes 11-Aug 15:30 10 +10 +10 +10 +10 +10+10 18-Aug 15:30 10 +10 +10 +10 +10 +10+10 25-Aug 15:30 10 +10 +10 +10 +10 +10+10 29-Aug 15:30 10 +10 +10 +10 +10 +10+10 30-Aug 15:30 10 +10 +10 +10 +10 +10+10 31-Aug 15:30 10 +10 +10 +10 +10 +10+10 5-Sep 15:30 10 +10 +10 +10 +10 +10+10 6-Sep 15:30 10 +10 +10 +10 +10 +10+10

4.2 INTERPRETING EVENT PERFORMANCE TABLES

The following sections contain tables that detail the performance of each residential device population during each 2017 demand response event. The following columns are included in each table:

Date – This column contains the date of each DR event. Event1 – This column contains the aggregated (weighted by the device counts in each phase) kW

reduction during the first event hour. Event2 – This column contains the aggregated (weighted by the device counts in each phase) kW

reduction during the second event hour. Snap1– This column contains the aggregated (weighted by the device counts in each phase) kW

reduction during the first snapback hour. Snap2 – This column contains the aggregated (weighted by the device counts in each phase) kW

reduction during the second snapback hour. kW Factor – This column contains i.e. the maximal load reduction among all disaggregated

phases. kWh Factor – This column contains the subgroup-event kWh factor ( ), i.e. net per-

device difference in energy consumption (relative to a control group baseline) for a subgroup during event .

kW Factor Phase – This column contains the number of the phase from which the kW Factor ( ) was derived.

Available Devices – This column contains the number of devices that were installed at the time of each demand response event.

Percent NRD – This column contains the percentage of devices in the analysis sample that were classified as non-responding.

Responding – This column contains the number of devices considered to be responding during the event (Available Devices * Percent NRD). Because the Percent NRD is calculated from the analysis sample and then extrapolated to Available Devices (the actual number of devices installed is typically slightly greater than the number of devices in the analysis sample because meter data for all devices is not always available), it is possible for the Responding device count to be a non-integer. Non-integer values are rounded accordingly.

4.3 CS1 RESULTS

The following sections detail all the 2017 event performance results for CS1 device populations.



4.3.1 Carrier residential thermostats (DP1RS/RM) DP1RS and DP1RM are comprised of Carrier two-way PCTs. DP1 is controlled with the DRMS, which interfaces with Carrier’s own Comfort Choice Manager (CCM) server, which in turn sends the control signals to the individual PCTs (via local cellular paging towers), and records data emitted from the PCTs (such as timestamps for manual overrides).

The curtailment strategy for DP1 devices is to initiate an immediate 4-degree increase of the thermostat’s set point. For example, if the PCT is normally scheduled to maintain indoor temperature at 75 degrees, it will increase to 79 degrees during a DR event. The 4-degree offset is maintained for the given schedule for the entire duration of the event11.

Because of its large size, DP1 is divided into 18 "DRMS zones" of around 1,000-5,000 devices each. DRMS zones are based on geography. During multi-phased events, not all DRMS zones are curtailed at the same time, thus the results reported in this section are weighted averages of the individual phases’ results, weighted per their respective sizes. Figure 5 shows the typical curtailment pattern for DP1RS. This graph shows the event day load of CCM group 1 on 7/7/2017.

11 For example, at a normal schedule of 75 F, Carrier will apply a set point of 79 F. If the schedule then changes to 72 F, perhaps if the user returns home from work, then Carrier will apply 76 F.



4.3.1.1 DP1RS Event Load Shape

Figure 5: DP1RS Phase 1 Load Shape 7/7/2017

4.3.1.2 DP1RS Demand Reduction Results

Table 11: DP1RS Demand Reduction Results

Date Event1 Event2 Snap1 Snap2 kW Factor

kWh Factor

kW Factor Phase

6/5/2017 1.52 0.89 -0.54 -0.34 1.63 1.53 2

6/6/2017 1.55 0.85 -0.63 -0.29 1.65 1.47 1

6/7/2017 1.48 0.86 -0.65 -0.25 1.65 1.43 1




kWh Factor

kW Factor Phase

6/14/2017 1.38 0.87 -0.59 -0.22 1.41 1.44 1

6/15/2017 1.69 0.98 -0.59 -0.29 1.80 1.80 1

6/16/2017 1.75 0.98 -0.55 -0.30 1.82 1.89 2

6/19/2017 2.01 1.03 -0.47 -0.30 2.16 2.27 1

6/20/2017 1.99 1.00 -0.45 -0.32 2.11 2.22 2

6/21/2017 1.92 1.00 -0.51 -0.30 2.02 2.11 1

6/22/2017 2.02 1.05 -0.52 -0.33 2.21 2.22 2

6/23/2017 1.95 0.97 -0.47 -0.32 2.06 2.13 1

6/26/2017 1.79 0.96 -0.61 -0.32 2.00 1.81 1

6/27/2017 1.67 0.87 -0.60 -0.32 1.74 1.63 1

6/28/2017 1.74 0.96 -0.62 -0.32 1.84 1.76 1

6/30/2017 1.73 0.91 -0.60 -0.34 1.88 1.69 2

7/3/2017 1.75 0.93 -0.63 -0.35 1.87 1.69 1

7/5/2017 1.95 0.97 -0.55 -0.34 2.04 2.03 1

7/6/2017 2.00 1.04 -0.47 -0.29 2.28 2.29 2

7/7/2017 2.09 0.98 -0.45 -0.32 2.39 2.29 2

7/8/2017 1.81 0.93 -0.54 -0.31 1.98 1.90 2

7/12/2017 1.84 0.98 -0.62 -0.34 2.02 1.85 1

7/13/2017 1.87 0.99 -0.63 -0.36 2.04 1.87 1

7/14/2017 1.87 0.94 -0.54 -0.35 2.06 1.93 1

7/18/2017 1.82 1.02 -0.63 -0.32 2.14 1.89 1

7/20/2017 1.32 0.78 -0.69 -0.21 1.42 1.20 1

7/21/2017 1.72 0.93 -0.62 -0.34 1.89 1.69 1

7/28/2017 1.82 0.97 -0.60 -0.34 1.92 1.86 1

8/1/2017 1.99 0.98 -0.62 -0.39 2.18 1.97 2

8/2/2017 1.83 0.94 -0.60 -0.34 1.99 1.84 1

8/7/2017 1.57 0.85 -0.69 -0.31 1.72 1.43 1

8/9/2017 1.71 0.95 -0.63 -0.30 1.86 1.73 1

8/10/2017 1.78 0.97 -0.60 -0.30 1.89 1.85 1

8/11/2017 0.17 0.19 0.06 0.06 0.37 0.48 6

8/18/2017 1.71 0.92 -0.65 -0.32 1.78 1.65 1

8/25/2017 1.54 0.84 -0.73 -0.33 1.67 1.32 1

8/29/2017 1.54 0.82 -0.69 -0.27 1.72 1.41 1

8/30/2017 1.38 0.74 -0.70 -0.24 1.58 1.18 1

8/31/2017 1.52 0.81 -0.67 -0.28 1.69 1.38 1




kWh Factor

kW Factor Phase

9/5/2017 1.66 0.90 2.00 -0.28 1.80 1.63 2

9/6/2017 1.45 0.88 -0.69 -0.27 1.69 1.38 1

4.3.1.3 DP1RS VDR and Energy Savings

Table 12: DP1RS VDR and Energy Savings

Date Available Devices

Percent NRD

Responding Devices

VDR (kW)

Energy Savings (kWh)

6/5/2017 29,236 8 26,897 43,842 41,153

6/6/2017 29,236 7 27,189 44,863 39,969

6/7/2017 29,236 9 26,605 43,898 38,045

6/14/2017 29,209 11 25,996 36,654 37,434

6/15/2017 29,209 11 25,996 46,793 46,793

6/16/2017 29,197 10 26,277 47,825 49,664

6/19/2017 29,181 9 26,555 57,358 60,279

6/20/2017 29,169 8 26,835 56,623 59,575

6/21/2017 29,154 7 27,113 54,769 57,209

6/22/2017 29,130 10 26,217 57,940 58,202

6/23/2017 29,130 8 26,800 55,207 57,083

6/26/2017 29,118 8 26,789 53,577 48,487

6/27/2017 29,100 3 28,227 49,115 46,010

6/28/2017 29,099 8 26,771 49,259 47,117

6/30/2017 29,074 7 27,039 50,833 45,696

7/3/2017 29,058 8 26,733 49,991 45,179

7/5/2017 29,024 8 26,702 54,472 54,205

7/6/2017 29,024 9 26,412 60,219 60,483

7/7/2017 29,019 9 26,407 63,113 60,473

7/8/2017 29,026 4 27,865 55,173 52,943

7/12/2017 29,013 9 26,402 53,332 48,843

7/13/2017 28,996 9 26,386 53,828 49,342

7/14/2017 28,996 6 27,256 56,148 52,605

7/18/2017 28,988 9 26,379 56,451 49,856

7/20/2017 28,977 10 26,079 37,033 31,295

7/21/2017 28,966 9 26,359 49,819 44,547

7/28/2017 28,919 8 26,605 51,083 49,486




Percent NRD

Responding Devices

VDR (kW)


8/1/2017 28,929 12 25,458 55,497 50,151

8/2/2017 28,918 9 26,315 52,368 48,420

8/7/2017 28,900 9 26,299 45,234 37,608

8/9/2017 28,900 8 26,588 49,454 45,997

8/10/2017 28,892 9 26,292 49,691 48,640

8/11/2017 28,892 10 26,003 9,621 12,481

8/18/2017 28,849 8 26,541 47,243 43,793

8/25/2017 28,832 6 27,102 45,260 35,775

8/29/2017 28,820 4 27,667 47,588 39,011

8/30/2017 28,808 3 27,944 44,151 32,974

8/31/2017 28,801 7 26,785 45,267 36,963

9/5/2017 28,777 9 26,187 47,137 42,685

9/6/2017 28,778 10 25,900 43,771 35,742



4.3.1.4 DP1RM Event Load Shape

Figure 6: DP1RM Phase 8 Load Shape 7/7/2017



4.3.1.5 DP1RM Demand Reduction Results

Table 13: DP1RM Demand Reduction Results


kWh Factor

kW Factor Phase

6/5/2017 0.84 0.53 -0.20 -0.16 1.15 1.00 2

6/6/2017 0.84 0.48 -0.26 -0.19 0.95 0.86 2

6/7/2017 0.80 0.46 -0.27 -0.12 0.84 0.86 2

6/14/2017 0.77 0.53 -0.19 -0.09 0.89 1.03 2

6/15/2017 0.98 0.64 -0.12 -0.03 1.19 1.46 3

6/16/2017 0.92 0.54 -0.16 -0.02 1.03 1.26 2

6/19/2017 0.92 0.45 -0.29 -0.17 1.15 0.91 2

6/20/2017 1.00 0.51 -0.19 -0.17 1.32 1.15 2

6/21/2017 0.92 0.46 -0.29 -0.14 1.21 0.95 1

6/22/2017 0.99 0.49 -0.26 -0.18 1.17 1.05 2

6/23/2017 0.97 0.59 -0.07 -0.03 1.29 1.45 2

6/26/2017 0.90 0.50 -0.22 -0.11 1.03 1.08 2

6/27/2017 0.79 0.38 -0.26 -0.15 0.98 0.76 2

6/28/2017 0.94 0.55 -0.14 -0.07 1.06 1.29 2

6/30/2017 0.91 0.50 -0.18 -0.06 1.12 1.18 2

7/3/2017 0.97 0.59 -0.18 -0.08 1.19 1.31 2

7/5/2017 1.00 0.62 -0.12 -0.03 1.32 1.46 2

7/6/2017 1.08 0.51 -0.25 -0.15 1.45 1.20 2

7/7/2017 0.99 0.60 -0.03 -0.05 1.28 1.51 2

7/8/2017 0.89 0.55 -0.14 -0.07 1.20 1.23 2

7/12/2017 0.98 0.55 -0.27 -0.18 1.20 1.08 3

7/13/2017 0.96 0.58 -0.18 -0.11 1.26 1.25 2

7/14/2017 0.98 0.62 -0.12 -0.01 1.31 1.48 2

7/18/2017 0.80 0.42 -0.22 -0.09 1.19 0.90 2

7/20/2017 0.84 0.58 -0.15 0.00 0.99 1.27 3

7/21/2017 0.94 0.63 -0.14 -0.05 1.12 1.40 2

7/28/2017 0.92 0.51 -0.19 -0.10 1.19 1.13 1

8/1/2017 0.98 0.56 -0.17 -0.08 1.26 1.30 2

8/2/2017 0.97 0.55 -0.16 -0.08 1.04 1.28 2

8/7/2017 0.91 0.55 -0.20 -0.04 0.97 1.22 3

8/9/2017 0.84 0.55 -0.19 -0.10 1.09 1.09 3

8/10/2017 0.98 0.61 -0.18 -0.12 1.18 1.29 3




kWh Factor

kW Factor Phase

8/11/2017 0.14 0.07 -0.04 -0.10 0.37 0.07 6

8/18/2017 0.91 0.49 -0.28 -0.11 1.13 1.02 2

8/25/2017 0.75 0.36 -0.40 -0.23 1.04 0.49 2

8/29/2017 0.72 0.34 -0.37 -0.25 1.00 0.46 2

8/30/2017 0.69 0.33 -0.33 -0.20 0.73 0.49 8

8/31/2017 0.80 0.44 -0.23 -0.10 0.93 0.92 2

9/5/2017 0.76 0.31 -0.38 -0.21 0.94 0.49 3

9/6/2017 0.78 0.51 -0.22 -0.06 1.01 1.01 2

4.3.1.6 DP1RM VDR and Energy Savings

Table 14: DP1RM VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 4,083 17 3,389 3,897 3,389

6/6/2017 4,083 14 3,511 3,336 3,020

6/7/2017 4,083 15 3,471 2,915 2,985

6/14/2017 4,087 15 3,474 3,092 3,578

6/15/2017 4,087 18 3,351 3,988 4,893

6/16/2017 4,094 15 3,480 3,584 4,385

6/19/2017 4,091 15 3,477 3,999 3,164

6/20/2017 4,095 14 3,522 4,649 4,050

6/21/2017 4,091 14 3,518 4,257 3,342

6/22/2017 4,095 17 3,399 3,977 3,569

6/23/2017 4,095 12 3,604 4,649 5,225

6/26/2017 4,099 14 3,525 3,631 3,807

6/27/2017 4,106 12 3,613 3,541 2,746

6/28/2017 4,106 15 3,490 3,700 4,502

6/30/2017 4,102 13 3,569 3,997 4,211

7/3/2017 4,095 14 3,522 4,191 4,613

7/5/2017 4,085 14 3,513 4,637 5,129

7/6/2017 4,085 12 3,595 5,212 4,314

7/7/2017 4,089 15 3,476 4,449 5,248

7/8/2017 4,096 10 3,686 4,424 4,534

7/12/2017 4,091 14 3,518 4,222 3,800




Percent NRD

Responding Devices

VDR (kW)


7/13/2017 4,090 13 3,558 4,483 4,448

7/14/2017 4,090 15 3,477 4,554 5,145

7/18/2017 4,104 11 3,653 4,347 3,287

7/20/2017 4,103 16 3,447 3,412 4,377

7/21/2017 4,099 17 3,402 3,810 4,763

7/28/2017 4,097 9 3,728 4,437 4,213

8/1/2017 4,108 14 3,533 4,451 4,593

8/2/2017 4,095 16 3,440 3,577 4,403

8/7/2017 4,087 17 3,392 3,290 4,139

8/9/2017 4,087 10 3,678 4,009 4,009

8/10/2017 4,089 16 3,435 4,053 4,431

8/11/2017 4,089 8 3,762 1,392 263

8/18/2017 4,089 16 3,435 3,881 3,503

8/25/2017 4,084 12 3,594 3,738 1,761

8/29/2017 4,089 11 3,639 3,639 1,674

8/30/2017 4,085 11 3,636 2,654 1,781

8/31/2017 4,086 15 3,473 3,230 3,195

9/5/2017 4,088 12 3,597 3,382 1,763

9/6/2017 4,094 16 3,439 3,473 3,473

4.3.2 CSE Direct Control Units (DP3CSERS/RM) CSE and Cannon LCR5000 DCUs both belong to DP3 because of their similar functionality. Both devices in DP3 are one-way communicating switches that allow for adjustable duty cycles on the connected equipment, typically air-conditioning units. These devices reduce peak demand by limiting the operating time of the equipment during the event. The devices in DP3 were programmed to limit the air conditioning equipment to 50% runtime during the events, which is manifested by the on-off pattern visible in the following figure.



4.3.2.1 DP3CSERS Event Load Shape

Figure 7: DP3CSERS Phase 5 Load Shape 7/8/2017



4.3.2.2 DP3CSERS Demand Reduction Results

Table 15: DP3CSERS Demand Reduction Results


kWh Factor

kW Factor Phase

6/5/2017 0.25 0.15 -0.02 0.06 0.25 0.44 5

6/6/2017 0.41 0.28 -0.03 0.08 0.41 0.75 5

6/7/2017 0.55 0.47 0.21 0.26 0.55 1.50 5

6/14/2017 0.31 0.22 0.03 0.08 0.31 0.64 5

6/15/2017 0.40 0.29 0.00 0.07 0.40 0.76 5

6/16/2017 0.46 0.36 -0.01 0.07 0.46 0.87 5

6/19/2017 0.55 0.40 -0.08 0.01 0.55 0.87 5

6/20/2017 0.49 0.37 -0.14 -0.04 0.49 0.67 5

6/21/2017 0.70 0.61 0.09 0.12 0.70 1.53 5

6/22/2017 0.51 0.36 -0.12 -0.01 0.51 0.74 5

6/23/2017 0.75 0.64 0.02 0.10 0.75 1.50 5

6/26/2017 0.56 0.45 0.04 0.11 0.56 1.17 5

6/27/2017 0.34 0.22 -0.10 -0.01 0.34 0.46 5

6/28/2017 0.40 0.25 -0.11 0.01 0.40 0.55 5

6/30/2017 0.44 0.33 -0.04 0.04 0.44 0.78 5

7/3/2017 0.72 0.64 0.19 0.25 0.72 1.81 5

7/5/2017 0.39 0.25 -0.15 -0.06 0.39 0.43 5

7/6/2017 0.52 0.37 -0.13 -0.02 0.52 0.74 5

7/7/2017 0.27 0.39 0.34 0.27 0.39 1.26 5

7/8/2017 0.51 0.36 -0.03 0.06 0.51 0.90 5

7/12/2017 0.30 0.14 -0.13 -0.01 0.30 0.30 5

7/13/2017 0.47 0.34 -0.07 0.03 0.47 0.77 5

7/14/2017 0.49 0.31 -0.12 -0.01 0.49 0.66 5

7/18/2017 0.28 0.13 -0.16 -0.04 0.28 0.22 5

7/20/2017 0.25 0.12 -0.02 0.05 0.25 0.40 5

7/21/2017 0.43 0.30 -0.07 0.01 0.43 0.67 5

7/28/2017 0.58 0.45 -0.01 0.06 0.58 1.08 5

8/1/2017 0.64 0.52 0.05 0.10 0.64 1.32 5

8/2/2017 0.58 0.49 -0.01 0.05 0.58 1.12 5

8/7/2017 0.61 0.49 0.19 0.24 0.61 1.53 5

8/9/2017 0.40 0.33 0.02 0.09 0.40 0.84 5

8/10/2017 0.49 0.37 -0.03 0.01 0.49 0.84 5

8/11/2017 0.25 -0.01 0.06 0.10 0.25 0.40 5




kWh Factor

kW Factor Phase

8/18/2017 0.28 0.16 -0.10 0.02 0.28 0.37 5

8/25/2017 0.30 0.18 -0.06 0.04 0.30 0.46 5

8/29/2017 0.21 0.09 -0.07 0.04 0.21 0.26 5

8/30/2017 0.30 0.17 0.00 0.11 0.30 0.58 5

8/31/2017 0.22 0.11 -0.04 0.02 0.22 0.31 5

9/5/2017 0.41 0.26 0.03 0.16 0.41 0.87 5

9/6/2017 0.13 0.17 0.14 0.13 0.17 0.58 5

4.3.2.3 DP3CSERS VDR and Energy Savings

Table 16: DP3CSERS VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 7,503 7 6,978 1,744 3,070

6/6/2017 7,503 23 5,777 2,369 4,333

6/7/2017 7,503 33 5,027 2,765 7,541

6/14/2017 7,488 23 5,766 1,787 3,690

6/15/2017 7,488 17 6,215 2,486 4,723

6/16/2017 7,489 19 6,066 2,790 5,278

6/19/2017 7,491 6 7,042 3,873 6,126

6/20/2017 7,489 1 7,414 3,633 4,967

6/21/2017 7,488 25 5,616 3,931 8,592

6/22/2017 7,487 6 7,038 3,589 5,208

6/23/2017 7,487 23 5,765 4,324 8,647

6/26/2017 7,482 24 5,686 3,184 6,653

6/27/2017 7,477 7 6,954 2,364 3,199

6/28/2017 7,476 14 6,429 2,572 3,536

6/30/2017 7,471 14 6,425 2,827 5,012

7/3/2017 7,468 31 5,153 3,710 9,327

7/5/2017 7,467 4 7,168 2,796 3,082

7/6/2017 7,467 10 6,720 3,495 4,973

7/7/2017 7,454 21 5,889 2,297 7,420

7/8/2017 7,453 9 6,782 3,459 6,104

7/12/2017 7,448 0 7,448 2,234 2,234

7/13/2017 7,443 16 6,252 2,939 4,814




Percent NRD

Responding Devices

VDR (kW)


7/14/2017 7,443 12 6,550 3,209 4,323

7/18/2017 7,438 4 7,140 1,999 1,571

7/20/2017 7,428 17 6,165 1,541 2,466

7/21/2017 7,428 20 5,942 2,555 3,981

7/28/2017 7,414 24 5,635 3,268 6,085

8/1/2017 7,415 20 5,932 3,796 7,830

8/2/2017 7,413 28 5,337 3,096 5,978

8/7/2017 7,400 34 4,884 2,979 7,473

8/9/2017 7,400 15 6,290 2,516 5,284

8/10/2017 7,398 20 5,918 2,900 4,971

8/11/2017 7,398 6 6,954 1,739 2,782

8/18/2017 7,391 7 6,874 1,925 2,543

8/25/2017 7,390 12 6,503 1,951 2,991

8/29/2017 7,386 3 7,164 1,505 1,863

8/30/2017 7,387 9 6,722 2,017 3,899

8/31/2017 7,390 4 7,094 1,561 2,199

9/5/2017 7,390 12 6,503 2,666 5,658

9/6/2017 7,394 10 6,655 1,131 3,860



4.3.2.4 DP3CSERM Event Load Shape

Figure 8: DP3CSERM Phase 5 Load Shape 7/8/2017



4.3.2.5 DP3CSERM Demand Reduction Results

Table 17: DP3CSERM Demand Reduction Results


kWh Factor

kW Factor Phase

6/5/2017 0.43 0.32 0.13 0.17 0.43 1.05 5

6/6/2017 0.24 0.14 0.07 0.06 0.24 0.50 5

6/7/2017 0.45 0.39 0.24 0.32 0.45 1.40 5

6/14/2017 0.49 0.46 0.32 0.35 0.49 1.62 5

6/15/2017 0.44 0.32 0.06 0.13 0.44 0.95 5

6/16/2017 0.52 0.46 0.21 0.26 0.52 1.45 5

6/19/2017 0.68 0.52 0.09 0.22 0.68 1.51 5

6/20/2017 0.74 0.63 0.25 0.27 0.74 1.88 5

6/21/2017 0.40 0.28 -0.03 0.11 0.40 0.76 5

6/22/2017 0.61 0.48 -0.01 0.12 0.61 1.20 5

6/23/2017 0.36 0.31 0.02 0.07 0.36 0.75 5

6/26/2017 0.46 0.40 0.16 0.19 0.46 1.21 5

6/27/2017 0.53 0.43 0.16 0.13 0.53 1.25 5

6/28/2017 0.64 0.49 0.18 0.21 0.64 1.52 5

6/30/2017 0.33 0.26 0.07 0.07 0.33 0.72 5

7/3/2017 0.31 0.23 -0.05 -0.01 0.31 0.47 5

7/5/2017 0.67 0.62 0.22 0.18 0.67 1.69 5

7/6/2017 0.57 0.53 0.16 0.24 0.57 1.50 5

7/7/2017 0.47 0.52 0.46 0.46 0.52 1.92 5

7/8/2017 0.45 0.38 0.03 0.10 0.45 0.97 5

7/12/2017 0.65 0.61 0.26 0.35 0.65 1.86 5

7/13/2017 0.34 0.24 0.03 0.09 0.34 0.69 5

7/14/2017 0.75 0.68 0.28 0.32 0.75 2.02 5

7/18/2017 0.40 0.34 0.04 0.12 0.40 0.90 5

7/20/2017 0.32 0.13 -0.01 0.02 0.32 0.45 5

7/21/2017 0.64 0.66 0.26 0.30 0.66 1.85 5

7/28/2017 0.68 0.54 0.27 0.26 0.68 1.75 5

8/1/2017 0.36 0.33 0.07 0.13 0.36 0.89 5

8/2/2017 0.59 0.56 0.15 0.19 0.59 1.49 5

8/7/2017 0.49 0.39 0.19 0.30 0.49 1.37 5

8/9/2017 0.55 0.55 0.27 0.28 0.55 1.65 5

8/10/2017 0.61 0.51 0.26 0.28 0.61 1.66 5




kWh Factor

kW Factor Phase

8/11/2017 0.33 0.17 0.22 0.20 0.33 0.91 5

8/18/2017 0.60 0.46 0.23 0.33 0.60 1.62 5

8/25/2017 0.46 0.34 0.08 0.14 0.46 1.02 5

8/29/2017 0.37 0.30 0.07 0.21 0.37 0.95 5

8/30/2017 0.16 0.20 0.05 0.16 0.20 0.56 5

8/31/2017 0.15 0.07 0.00 0.06 0.15 0.28 5

9/5/2017 0.51 0.33 0.19 0.22 0.51 1.24 5

9/6/2017 0.09 0.02 0.04 0.02 0.09 0.17 5

4.3.2.6 DP3CSERM VDR and Energy Savings

Table 18: DP3CSERM VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 724 32 492 212 517

6/6/2017 724 18 594 142 297

6/7/2017 724 32 492 222 689

6/14/2017 722 39 440 216 713

6/15/2017 722 35 469 206 446

6/16/2017 722 32 491 255 712

6/19/2017 723 24 549 374 830

6/20/2017 723 25 542 401 1,019

6/21/2017 723 8 665 266 506

6/22/2017 723 24 549 335 659

6/23/2017 723 1 716 258 537

6/26/2017 723 19 586 269 709

6/27/2017 722 28 520 276 650

6/28/2017 722 29 513 328 779

6/30/2017 722 8 664 219 478

7/3/2017 723 15 615 191 289

7/5/2017 722 26 534 358 903

7/6/2017 722 22 563 321 845

7/7/2017 719 30 503 262 966

7/8/2017 720 14 619 279 601

7/12/2017 722 27 527 343 980




Percent NRD

Responding Devices

VDR (kW)


7/13/2017 722 4 693 236 478

7/14/2017 722 29 513 384 1,035

7/18/2017 722 21 570 228 513

7/20/2017 722 16 606 194 273

7/21/2017 720 33 482 318 892

7/28/2017 717 29 509 346 891

8/1/2017 717 3 695 250 619

8/2/2017 719 32 489 288 728

8/7/2017 716 29 508 249 696

8/9/2017 716 28 516 284 851

8/10/2017 715 24 543 331 902

8/11/2017 715 12 629 208 573

8/18/2017 719 28 518 311 839

8/25/2017 717 28 516 237 527

8/29/2017 718 15 610 226 580

8/30/2017 718 7 668 134 374

8/31/2017 718 3 696 104 195

9/5/2017 718 25 539 275 668

9/6/2017 716 11 637 57 108

4.3.3 Cannon LCR 5000 Direct Control Units (DP3LCRRS/RM) The Cannon LCR 5000 DCUs are comparable to CSE DCUs in their basic functionality. The Cannon DCUs, however, are not synchronized across units as to when compressor runtime is curtailed, and different groups of individual switches work are randomized as to when they cut off the compressor. This is exhibited in Figure 9 below, which shows the aggregate load for DP3LCR. Instead of the on-off pattern exhibited by DP3CSERS in Figure 8 above, the DP3LCR curtailment shown in Figure 9 below appears to result in a sustained drop in load, which is actually the average of different proportions of DCUs that are simultaneously on and off.



4.3.3.1 DP3LCRRS Event Load Shape

Figure 9: Load Shape DP3LCRRS Phase 4 7/8/2017



4.3.3.2 DP3LCRRS Demand Reduction Results

Table 19: DP3LCRRS Demand Reduction Results


kWh Factor

kW Factor Phase

6/5/2017 0.73 0.76 0.28 0.36 0.76 2.12 4

6/6/2017 0.74 0.68 0.25 0.40 0.74 2.06 4

6/7/2017 0.66 0.68 0.33 0.39 0.68 2.06 4

6/14/2017 0.83 0.91 0.53 0.49 0.91 2.77 4

6/15/2017 0.94 0.89 0.24 0.35 0.94 2.43 4

6/16/2017 0.44 0.54 0.09 0.12 0.54 1.20 4

6/19/2017 1.21 1.18 0.23 0.22 1.21 2.83 4

6/20/2017 0.91 0.84 0.17 0.14 0.91 2.06 4

6/21/2017 0.67 0.62 0.02 0.05 0.67 1.35 4

6/22/2017 1.07 0.95 0.15 0.21 1.07 2.38 4

6/23/2017 1.24 1.10 0.11 0.05 1.24 2.50 4

6/26/2017 0.47 0.47 0.02 0.11 0.47 1.06 4

6/27/2017 0.43 0.43 0.02 0.09 0.43 0.97 4

6/28/2017 0.79 0.87 0.31 0.33 0.87 2.31 4

6/30/2017 0.93 0.99 0.40 0.38 0.99 2.71 4

7/3/2017 0.51 0.51 -0.03 0.08 0.51 1.08 4

7/5/2017 0.80 0.89 0.14 0.08 0.89 1.90 4

7/6/2017 0.84 0.73 0.04 0.05 0.84 1.66 4

7/7/2017 0.83 0.80 0.17 0.15 0.83 1.95 4

7/8/2017 0.50 0.43 -0.16 -0.08 0.50 0.69 4

7/12/2017 0.69 0.67 0.17 0.17 0.69 1.70 4

7/13/2017 0.72 0.74 0.12 0.14 0.74 1.72 4

7/14/2017 0.89 0.89 0.23 0.32 0.89 2.31 4

7/18/2017 0.62 0.59 0.18 0.27 0.62 1.66 4

7/20/2017 0.64 0.55 0.18 0.30 0.64 1.67 4

7/21/2017 0.82 0.85 0.21 0.29 0.85 2.17 4

7/28/2017 0.81 0.84 0.21 0.24 0.84 2.10 4

8/1/2017 0.73 0.81 0.13 0.16 0.81 1.82 4

8/2/2017 0.80 0.79 0.22 0.29 0.80 2.10 4

8/7/2017 0.62 0.69 0.24 0.33 0.69 1.88 4

8/9/2017 0.70 0.70 0.26 0.29 0.70 1.96 4

8/10/2017 0.83 0.83 0.28 0.33 0.83 2.26 4

8/11/2017 0.33 0.39 0.33 0.30 0.39 1.36 4




kWh Factor

kW Factor Phase

8/18/2017 0.84 0.76 0.17 0.28 0.84 2.05 4

8/25/2017 0.39 0.26 -0.06 0.09 0.39 0.69 4

8/29/2017 0.38 0.38 0.04 0.27 0.38 1.07 4

8/30/2017 0.30 0.30 0.12 0.21 0.30 0.93 4

8/31/2017 0.36 0.29 -0.04 0.08 0.36 0.69 4

9/5/2017 0.64 0.63 0.21 0.33 0.64 1.81 4

9/6/2017 0.69 0.78 0.37 0.48 0.78 2.31 4

4.3.3.3 DP3LCRRS VDR and Energy Savings

Table 20: DP3LCRRS VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 1,409 29 1,000 760 2,121

6/6/2017 1,409 23 1,085 803 2,235

6/7/2017 1,409 30 986 671 2,032

6/14/2017 1,407 42 816 743 2,260

6/15/2017 1,407 49 718 675 1,744

6/16/2017 1,406 17 1,167 630 1,400

6/19/2017 1,408 42 817 988 2,311

6/20/2017 1,406 28 1,012 921 2,085

6/21/2017 1,406 22 1,097 735 1,481

6/22/2017 1,404 37 885 946 2,105

6/23/2017 1,404 46 758 940 1,895

6/26/2017 1,404 14 1,207 568 1,280

6/27/2017 1,403 0 1,403 603 1,361

6/28/2017 1,403 34 926 806 2,139

6/30/2017 1,403 34 926 917 2,509

7/3/2017 1,404 22 1,095 559 1,183

7/5/2017 1,404 29 997 887 1,894

7/6/2017 1,404 24 1,067 896 1,771

7/7/2017 1,399 27 1,021 848 1,991

7/8/2017 1,399 13 1,217 609 840

7/12/2017 1,398 27 1,021 704 1,735

7/13/2017 1,400 34 924 684 1,589




Percent NRD

Responding Devices

VDR (kW)


7/14/2017 1,400 30 980 872 2,264

7/18/2017 1,397 19 1,132 702 1,878

7/20/2017 1,397 35 908 581 1,516

7/21/2017 1,397 43 796 677 1,728

7/28/2017 1,394 33 934 785 1,961

8/1/2017 1,394 28 1,004 813 1,827

8/2/2017 1,394 31 962 769 2,020

8/7/2017 1,393 28 1,003 692 1,886

8/9/2017 1,393 30 975 683 1,911

8/10/2017 1,394 33 934 775 2,111

8/11/2017 1,394 8 1,282 500 1,744

8/18/2017 1,392 42 807 678 1,655

8/25/2017 1,391 10 1,252 488 864

8/29/2017 1,391 13 1,210 460 1,295

8/30/2017 1,391 11 1,238 371 1,151

8/31/2017 1,392 16 1,169 421 807

9/5/2017 1,392 24 1,058 677 1,915

9/6/2017 1,391 39 849 662 1,960



4.3.3.4 DP3LCRRM Event Load Shape

Figure 10:Load Shape DP3LCRRM Phase 4 7/8/2017



4.3.3.5 DP3LCRRM Demand Reduction Results

Table 21: DP3LCRRM Demand Reduction Results


kWh Factor

kW Factor Phase

6/5/2017 0.43 0.56 0.23 0.30 0.56 1.52 4

6/6/2017 0.65 0.69 0.55 0.41 0.69 2.30 4

6/7/2017 0.32 0.37 0.24 0.29 0.37 1.23 4

6/14/2017 0.74 0.76 0.38 0.31 0.76 2.20 4

6/15/2017 0.72 0.65 0.59 0.46 0.72 2.42 4

6/16/2017 0.60 0.55 0.19 0.51 0.60 1.84 4

6/19/2017 0.52 0.50 0.31 0.43 0.52 1.76 4

6/20/2017 0.71 0.63 0.39 0.18 0.71 1.91 4

6/21/2017 0.70 0.77 0.43 0.49 0.77 2.39 4

6/22/2017 0.62 0.71 0.45 0.44 0.71 2.22 4

6/23/2017 0.62 0.62 0.27 0.44 0.62 1.94 4

6/26/2017 0.58 0.58 0.42 0.42 0.58 1.99 4

6/27/2017 0.75 0.72 0.57 0.55 0.75 2.59 4

6/28/2017 0.61 0.56 0.21 0.18 0.61 1.54 4

6/30/2017 0.64 0.49 0.33 0.41 0.64 1.88 4

7/3/2017 0.52 0.50 0.30 0.23 0.52 1.55 4

7/5/2017 0.38 0.32 0.03 0.24 0.38 0.98 4

7/6/2017 0.43 0.42 0.20 0.27 0.43 1.32 4

7/7/2017 0.68 0.65 0.30 0.29 0.68 1.93 4

7/8/2017 0.39 0.33 0.07 0.09 0.39 0.88 4

7/12/2017 0.77 0.68 0.18 0.22 0.77 1.86 4

7/13/2017 0.62 0.64 0.29 0.39 0.64 1.94 4

7/14/2017 0.61 0.59 0.06 -0.01 0.61 1.24 4

7/18/2017 0.54 0.49 0.17 0.38 0.54 1.57 4

7/20/2017 0.56 0.63 0.21 0.23 0.63 1.63 4

7/21/2017 0.61 0.60 0.44 0.57 0.61 2.21 4

7/28/2017 0.54 0.52 0.51 0.36 0.54 1.92 4

8/1/2017 0.54 0.53 0.28 0.21 0.54 1.55 4

8/2/2017 0.68 0.65 0.35 0.44 0.68 2.13 4

8/7/2017 0.63 0.54 0.15 0.16 0.63 1.47 4

8/9/2017 0.50 0.65 0.36 0.32 0.65 1.84 4

8/10/2017 0.62 0.62 0.35 0.14 0.62 1.72 4




kWh Factor

kW Factor Phase

8/11/2017 0.22 0.26 0.21 0.26 0.26 0.97 4

8/18/2017 0.48 0.37 0.21 0.26 0.48 1.32 4

8/25/2017 0.54 0.58 0.24 0.20 0.58 1.55 4

8/29/2017 0.46 0.49 0.21 0.39 0.49 1.55 4

8/30/2017 0.27 0.43 0.24 0.28 0.43 1.23 4

8/31/2017 0.43 0.46 0.22 0.53 0.46 1.64 4

9/5/2017 0.54 0.57 0.38 0.33 0.57 1.82 4

9/6/2017 0.75 0.89 0.69 0.94 0.89 3.27 4

4.3.3.6 DP3LCRRM VDR and Energy Savings

Table 22: DP3LCRRM VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 257 29 182 102 277

6/6/2017 257 26 190 131 437

6/7/2017 257 14 221 82 272

6/14/2017 254 40 152 116 335

6/15/2017 254 34 168 121 406

6/16/2017 254 36 163 98 299

6/19/2017 254 34 168 87 295

6/20/2017 254 27 185 132 354

6/21/2017 255 28 184 141 439

6/22/2017 255 28 184 130 408

6/23/2017 255 37 161 100 312

6/26/2017 255 21 201 117 401

6/27/2017 256 35 166 125 431

6/28/2017 256 29 182 111 280

6/30/2017 257 45 141 90 266

7/3/2017 257 36 164 86 255

7/5/2017 257 27 188 71 184

7/6/2017 257 26 190 82 251

7/7/2017 257 32 175 119 337

7/8/2017 257 16 216 84 190

7/12/2017 256 37 161 124 300




Percent NRD

Responding Devices

VDR (kW)


7/13/2017 257 26 190 122 369

7/14/2017 257 44 144 88 178

7/18/2017 257 19 208 112 327

7/20/2017 257 41 152 96 247

7/21/2017 256 31 177 108 390

7/28/2017 256 40 154 83 295

8/1/2017 256 18 210 113 325

8/2/2017 255 32 173 118 369

8/7/2017 255 31 176 111 259

8/9/2017 255 29 181 118 333

8/10/2017 254 34 168 104 288

8/11/2017 254 14 218 57 212

8/18/2017 256 30 179 86 237

8/25/2017 254 33 170 99 264

8/29/2017 256 14 220 108 341

8/30/2017 256 18 210 90 258

8/31/2017 256 29 182 84 298

9/5/2017 255 34 168 96 306

9/6/2017 255 34 168 150 550

4.3.4 CoMge Direct Control Units (DP4COMRS/RM) Comverge and Cannon LCR5200 DCUs both belong to DP4 because of their similar functionality. Like DP3, both devices are direct control units and operate in a similar way, differing from their counterparts in that they vary the degree of curtailment initiated based on prior cooling loads for the residence. These devices include additional control logic (the TrueCycle or Adaptive Algorithm) that allows them to learn the typical operating characteristics of the equipment and base curtailment on equipment run times just prior to the event. They adapt in order to ensure that all homes experience similar temperature increases during events. This system uses cooling comfort as the primary determinant, rather than kW loading (as is the case with DP3).

Beginning in 2014, Comverge DCUs were no longer utilized as demand response assets in Southern Nevada.

4.3.5 Cannon LCR 5200 Direct Control Units (DP4LCRRS/RM) The Cannon LCR5200 DCUs operate in a similar manner to the Comverge DCUs as described above. Like DP3LCR and DP4COM, individual DP4LCR devices are curtailed in a randomized manner.



4.3.5.1 DP4LCRRS Event Load Shape

Figure 11: DP4LCRRS Phase 4 Load Shape 7/7/2017



4.3.5.2 DP4LCRRS Demand Reduction Results

Table 23: DP4LCRRS Demand Reduction Results


kWh Factor

kW Factor Phase

6/5/2017 0.92 0.93 -0.01 -0.10 0.93 1.74 4

6/6/2017 0.98 0.93 -0.11 -0.18 0.98 1.62 4

6/7/2017 0.96 1.06 0.17 0.11 1.06 2.30 4

6/14/2017 0.80 0.87 0.18 0.11 0.87 1.96 4

6/15/2017 1.06 1.11 0.23 0.06 1.11 2.47 4

6/16/2017 1.04 1.04 0.13 -0.06 1.04 2.15 4

6/19/2017 1.10 1.20 0.11 -0.09 1.20 2.31 4

6/20/2017 1.17 1.20 0.29 0.08 1.20 2.74 4

6/21/2017 1.11 1.14 0.14 0.01 1.14 2.40 4

6/22/2017 1.17 1.20 0.12 -0.03 1.20 2.46 4

6/23/2017 1.10 1.14 0.12 -0.13 1.14 2.24 4

6/26/2017 1.30 1.22 -0.42 -0.49 1.30 1.61 4

6/27/2017 1.05 1.18 0.15 0.01 1.18 2.39 4

6/28/2017 1.10 1.10 0.17 0.09 1.10 2.46 4

6/30/2017 1.27 1.26 -0.06 -0.22 1.27 2.25 4

7/3/2017 1.07 1.05 0.07 -0.12 1.07 2.07 4

7/5/2017 0.99 1.02 -0.07 -0.28 1.02 1.67 4

7/6/2017 1.64 1.66 -0.01 -0.21 1.66 3.07 4

7/7/2017 1.21 1.25 0.30 -0.09 1.25 2.68 4

7/8/2017 0.81 0.87 0.07 -0.03 0.87 1.71 4

7/12/2017 1.04 1.09 0.10 0.12 1.09 2.34 4

7/13/2017 1.03 0.96 0.05 -0.05 1.03 1.99 4

7/14/2017 1.46 1.41 -0.14 -0.36 1.46 2.37 4

7/18/2017 0.79 0.72 -0.04 -0.09 0.79 1.38 4

7/20/2017 0.85 0.85 0.01 0.01 0.85 1.72 4

7/21/2017 1.11 1.12 0.01 -0.10 1.12 2.15 4

7/28/2017 1.09 1.18 0.04 -0.13 1.18 2.18 4

8/1/2017 1.17 1.16 0.16 0.01 1.17 2.50 4

8/2/2017 1.23 1.29 0.13 0.01 1.29 2.67 4

8/7/2017 0.93 0.94 0.04 0.00 0.94 1.92 4

8/9/2017 0.92 0.89 0.00 -0.08 0.92 1.74 4

8/10/2017 0.91 1.03 0.07 -0.10 1.03 1.91 4

8/11/2017 0.22 0.28 0.23 0.18 0.28 0.91 4




kWh Factor

kW Factor Phase

8/18/2017 0.99 0.95 0.16 0.04 0.99 2.14 4

8/25/2017 0.95 0.97 0.12 0.09 0.97 2.13 4

8/29/2017 0.63 0.68 -0.02 0.04 0.68 1.32 4

8/30/2017 0.59 0.60 -0.04 0.07 0.60 1.21 4

8/31/2017 1.05 0.92 0.01 0.16 1.05 2.14 4

9/5/2017 0.75 0.72 -0.09 -0.05 0.75 1.33 4

9/6/2017 1.00 1.13 0.00 0.07 1.13 2.19 4

4.3.5.3 DP4LCRRS VDR and Energy Savings

Table 24: DP4LCRRS VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 948 28 683 635 1,188

6/6/2017 948 36 607 595 983

6/7/2017 948 31 654 693 1,504

6/14/2017 946 37 596 519 1,168

6/15/2017 946 38 587 651 1,449

6/16/2017 946 32 643 669 1,383

6/19/2017 948 26 702 842 1,621

6/20/2017 945 27 690 828 1,890

6/21/2017 941 24 715 815 1,716

6/22/2017 939 28 676 811 1,663

6/23/2017 939 31 648 739 1,451

6/26/2017 939 42 545 708 877

6/27/2017 937 31 647 763 1,545

6/28/2017 937 30 656 721 1,614

6/30/2017 937 37 590 750 1,328

7/3/2017 937 30 656 702 1,358

7/5/2017 935 29 664 677 1,109

7/6/2017 935 35 608 1,009 1,866

7/7/2017 932 25 699 874 1,873

7/8/2017 932 8 857 746 1,466

7/12/2017 934 27 682 743 1,595

7/13/2017 933 28 672 692 1,337




Percent NRD

Responding Devices

VDR (kW)


7/14/2017 933 49 476 695 1,128

7/18/2017 933 19 756 597 1,043

7/20/2017 932 34 615 523 1,058

7/21/2017 932 37 587 658 1,262

7/28/2017 931 41 549 648 1,197

8/1/2017 931 34 614 719 1,536

8/2/2017 931 41 549 709 1,467

8/7/2017 927 31 640 601 1,228

8/9/2017 927 29 658 606 1,145

8/10/2017 924 32 628 647 1,200

8/11/2017 924 11 822 230 748

8/18/2017 922 31 636 630 1,361

8/25/2017 922 27 673 653 1,434

8/29/2017 922 10 830 564 1,095

8/30/2017 922 11 821 492 993

8/31/2017 922 32 627 658 1,342

9/5/2017 922 23 710 532 944

9/6/2017 922 39 562 636 1,232



4.3.5.4 DP4LCRRM Event Load Shape

Figure 12: DP4LCRRM Phase 4 Load Shape 7/7/2017



4.3.5.5 DP4LCRRM Demand Reduction Results

Table 25: DP4LCRRM Demand Reduction Results


kWh Factor

kW Factor Phase

6/5/2017 0.55 0.49 0.17 0.14 0.55 1.35 4

6/6/2017 0.69 0.69 0.06 0.18 0.69 1.62 4

6/7/2017 0.59 0.77 0.27 0.20 0.77 1.83 4

6/14/2017 0.60 0.59 0.40 0.43 0.60 2.02 4

6/15/2017 0.77 0.92 0.49 0.27 0.92 2.45 4

6/16/2017 0.74 0.86 0.45 0.32 0.86 2.37 4

6/19/2017 0.71 0.55 -0.07 -0.05 0.71 1.14 4

6/20/2017 0.71 0.73 0.09 0.06 0.73 1.59 4

6/21/2017 0.48 0.45 0.11 0.08 0.48 1.12 4

6/22/2017 0.65 0.66 -0.04 -0.01 0.66 1.26 4

6/23/2017 0.64 0.67 0.16 0.05 0.67 1.52 4

6/26/2017 0.54 0.74 0.31 0.22 0.74 1.81 4

6/27/2017 0.73 0.96 0.23 0.24 0.96 2.16 4

6/28/2017 0.67 0.78 0.38 0.40 0.78 2.23 4

6/30/2017 0.83 0.79 0.21 0.27 0.83 2.10 4

7/3/2017 0.70 0.82 0.21 0.16 0.82 1.89 4

7/5/2017 0.72 0.86 0.38 0.28 0.86 2.24 4

7/6/2017 0.58 0.68 0.22 0.17 0.68 1.65 4

7/7/2017 0.66 0.76 0.33 0.16 0.76 1.91 4

7/8/2017 0.64 0.66 0.15 0.18 0.66 1.63 4

7/12/2017 0.60 0.66 0.08 -0.06 0.66 1.28 4

7/13/2017 0.91 0.98 0.11 0.26 0.98 2.26 4

7/14/2017 0.67 0.72 0.30 0.04 0.72 1.73 4

7/18/2017 0.69 0.69 0.24 0.09 0.69 1.71 4

7/20/2017 0.42 0.37 0.06 0.13 0.42 0.98 4

7/21/2017 0.87 0.91 0.51 0.29 0.91 2.58 4

7/28/2017 0.81 0.96 0.43 0.16 0.96 2.36 4

8/1/2017 0.64 0.76 0.25 0.19 0.76 1.84 4

8/2/2017 0.81 0.93 0.08 0.14 0.93 1.96 4

8/7/2017 0.59 0.71 0.45 0.44 0.71 2.19 4

8/9/2017 0.58 0.76 0.26 0.08 0.76 1.68 4

8/10/2017 0.87 0.83 0.24 0.23 0.87 2.17 4




kWh Factor

kW Factor Phase

8/11/2017 0.31 0.30 0.28 0.18 0.31 1.07 4

8/18/2017 0.63 0.60 0.11 0.31 0.63 1.65 4

8/25/2017 0.39 0.14 -0.20 -0.18 0.39 0.15 4

8/29/2017 0.44 0.56 0.09 0.11 0.56 1.20 4

8/30/2017 0.64 0.49 0.13 0.17 0.64 1.43 4

8/31/2017 0.50 0.56 0.14 0.21 0.56 1.41 4

9/5/2017 0.57 0.72 0.38 0.51 0.72 2.18 4

9/6/2017 0.61 0.64 0.06 0.31 0.64 1.62 4

4.3.5.6 DP4LCRRM VDR and Energy Savings

Table 26: DP4LCRRM VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 219 28 158 87 213

6/6/2017 219 24 166 115 270

6/7/2017 219 40 131 101 240

6/14/2017 220 34 145 87 293

6/15/2017 220 37 139 128 340

6/16/2017 220 30 154 132 365

6/19/2017 220 32 150 106 171

6/20/2017 220 28 158 116 252

6/21/2017 220 24 167 80 187

6/22/2017 220 34 145 96 183

6/23/2017 220 25 165 111 251

6/26/2017 220 26 163 120 295

6/27/2017 220 31 152 146 328

6/28/2017 220 30 154 120 343

6/30/2017 220 35 143 119 300

7/3/2017 220 31 152 124 287

7/5/2017 220 34 145 125 325

7/6/2017 220 25 165 112 272

7/7/2017 219 28 158 120 301

7/8/2017 219 19 177 117 289

7/12/2017 219 30 153 101 196




Percent NRD

Responding Devices

VDR (kW)


7/13/2017 219 10 197 193 445

7/14/2017 219 31 151 109 261

7/18/2017 219 33 147 101 251

7/20/2017 219 30 153 64 150

7/21/2017 219 38 136 124 350

7/28/2017 217 38 135 129 318

8/1/2017 218 29 155 118 285

8/2/2017 218 36 140 130 273

8/7/2017 218 26 161 115 353

8/9/2017 218 32 148 113 249

8/10/2017 218 35 142 123 307

8/11/2017 218 13 190 59 203

8/18/2017 217 31 150 94 247

8/25/2017 216 27 158 62 24

8/29/2017 216 18 177 99 213

8/30/2017 216 23 166 106 238

8/31/2017 215 27 157 88 221

9/5/2017 215 32 146 105 319

9/6/2017 214 45 118 75 191

4.3.6 Honeywell Residential Thermostats (DP7RS/RM) DP7 consists of Honeywell Residential UtilityPro and ExpressStat One-Way PCTs. These devices operate in a similar manner to those in DP1, but do not provide data back to the utility. These PCTs curtail cooling loads through the adjustment of thermostat set-points, allowing for an adjustment of up to 4 degrees.



4.3.6.1 DP7RS Event Load Shape




4.3.6.2 DP7RS Demand Reduction Results



kWh Factor

kW Factor Phase

6/5/2017 1.01 0.75 -0.30 -0.05 1.01 1.40 7

6/6/2017 0.94 0.72 -0.36 -0.11 0.94 1.18 7

6/7/2017 0.87 0.65 -0.29 -0.05 0.87 1.18 7

6/14/2017 0.89 0.64 -0.34 -0.11 0.89 1.09 7

6/15/2017 1.28 0.95 -0.24 -0.05 1.28 1.94 7

6/16/2017 0.38 0.33 0.03 0.09 0.38 0.84 7

6/19/2017 1.30 0.87 -0.20 -0.06 1.30 1.90 7

6/20/2017 1.51 1.00 -0.18 -0.08 1.51 2.26 7

6/21/2017 1.41 0.91 -0.31 -0.13 1.41 1.89 7

6/22/2017 1.52 1.06 -0.14 0.02 1.52 2.47 7

6/23/2017 1.78 1.12 -0.30 -0.14 1.78 2.46 7

6/26/2017 1.06 0.71 -0.33 -0.08 1.06 1.36 7

6/27/2017 1.43 1.06 -0.17 0.01 1.43 2.33 7

6/28/2017 1.28 0.94 -0.22 -0.04 1.28 1.96 7

6/30/2017 1.27 0.84 -0.32 -0.13 1.27 1.66 7

7/3/2017 1.03 0.65 -0.35 -0.14 1.03 1.18 7

7/5/2017 1.45 0.97 -0.30 -0.13 1.45 1.99 7

7/6/2017 1.25 0.83 -0.24 -0.08 1.25 1.76 7

7/7/2017 1.20 0.74 -0.22 -0.10 1.20 1.62 7

7/8/2017 1.19 0.78 -0.27 -0.10 1.19 1.60 7

7/12/2017 1.25 0.88 -0.23 -0.03 1.25 1.86 7

7/13/2017 1.43 1.05 -0.20 -0.04 1.43 2.25 7

7/14/2017 1.19 0.80 -0.22 -0.08 1.19 1.70 7

7/18/2017 1.25 0.91 -0.24 -0.01 1.25 1.92 7

7/20/2017 0.73 0.57 -0.32 -0.08 0.73 0.91 7

7/21/2017 1.37 0.96 -0.32 -0.09 1.37 1.92 7

7/28/2017 1.31 0.88 -0.37 -0.17 1.31 1.65 7

8/1/2017 1.45 0.97 -0.26 -0.08 1.45 2.08 7

8/2/2017 1.10 0.73 -0.20 -0.01 1.10 1.62 7

8/7/2017 1.26 0.96 -0.28 0.04 1.26 1.98 7

8/9/2017 1.50 1.12 -0.24 0.01 1.50 2.39 7

8/10/2017 1.40 1.04 -0.22 0.01 1.40 2.23 7

8/11/2017 0.17 0.26 0.23 0.24 0.26 0.89 7




kWh Factor

kW Factor Phase

8/18/2017 1.18 0.84 -0.24 -0.05 1.18 1.74 7

8/25/2017 0.89 0.55 -0.43 -0.11 0.89 0.91 7

8/29/2017 0.97 0.68 -0.37 -0.07 0.97 1.22 7

8/30/2017 0.94 0.69 -0.35 -0.03 0.94 1.24 7

8/31/2017 1.13 0.87 -0.25 0.00 1.13 1.75 7

9/5/2017 1.03 0.68 -0.32 -0.07 1.03 1.32 7

9/6/2017 1.07 0.79 -0.31 -0.02 1.07 1.53 7

4.3.6.3 DP7RS VDR and Energy Savings



Percent NRD

Responding Devices

VDR (kW)


6/5/2017 4,720 4 4,531 4,577 6,344

6/6/2017 4,720 3 4,578 4,304 5,403

6/7/2017 4,720 0 4,720 4,106 5,570

6/14/2017 4,701 16 3,949 3,514 4,304

6/15/2017 4,701 17 3,902 4,994 7,570

6/16/2017 4,697 16 3,945 1,499 3,314

6/19/2017 4,696 9 4,273 5,555 8,119

6/20/2017 4,690 19 3,799 5,736 8,586

6/21/2017 4,688 16 3,938 5,552 7,443

6/22/2017 4,681 18 3,838 5,834 9,481

6/23/2017 4,681 28 3,370 5,999 8,291

6/26/2017 4,676 3 4,536 4,808 6,169

6/27/2017 4,671 18 3,830 5,477 8,924

6/28/2017 4,662 14 4,009 5,132 7,858

6/30/2017 4,662 14 4,009 5,092 6,655

7/3/2017 4,659 2 4,566 4,703 5,388

7/5/2017 4,658 12 4,099 5,944 8,157

7/6/2017 4,658 9 4,239 5,298 7,460

7/7/2017 4,654 5 4,421 5,306 7,163

7/8/2017 4,654 5 4,421 5,261 7,074

7/12/2017 4,653 14 4,002 5,002 7,443

7/13/2017 4,649 18 3,812 5,451 8,577




Percent NRD

Responding Devices

VDR (kW)


7/14/2017 4,649 6 4,370 5,200 7,429

7/18/2017 4,645 12 4,088 5,110 7,848

7/20/2017 4,643 2 4,550 3,322 4,141

7/21/2017 4,642 8 4,271 5,851 8,200

7/28/2017 4,634 16 3,893 5,099 6,423

8/1/2017 4,633 18 3,799 5,509 7,902

8/2/2017 4,631 3 4,492 4,941 7,277

8/7/2017 4,630 18 3,797 4,784 7,517

8/9/2017 4,630 18 3,797 5,695 9,074

8/10/2017 4,632 13 4,030 5,642 8,987

8/11/2017 4,632 7 4,308 1,120 3,834

8/18/2017 4,626 11 4,117 4,858 7,164

8/25/2017 4,623 4 4,438 3,950 4,039

8/29/2017 4,622 5 4,391 4,259 5,357

8/30/2017 4,624 7 4,300 4,042 5,332

8/31/2017 4,623 14 3,976 4,493 6,958

9/5/2017 4,620 9 4,204 4,330 5,550

9/6/2017 4,624 14 3,977 4,255 6,084



4.3.6.4 DP7RM Event Load Shape

Figure 14:DP7RM Phase 7 Load Shape 7/7/2017



4.3.6.5 DP7RM Demand Reduction Results



kWh Factor

kW Factor Phase

6/5/2017 1.00 0.71 -0.13 0.07 1.00 1.66 7

6/6/2017 0.58 0.45 -0.25 -0.11 0.58 0.68 7

6/7/2017 0.88 0.74 0.04 0.20 0.88 1.86 7

6/14/2017 0.88 0.59 -0.14 0.08 0.88 1.41 7

6/15/2017 0.51 0.34 -0.23 -0.16 0.51 0.45 7

6/16/2017 0.57 0.46 0.19 0.29 0.57 1.52 7

6/19/2017 0.98 0.68 -0.08 0.12 0.98 1.69 7

6/20/2017 1.06 0.77 -0.04 0.06 1.06 1.85 7

6/21/2017 0.76 0.48 -0.09 0.01 0.76 1.17 7

6/22/2017 0.96 0.63 -0.14 0.02 0.96 1.46 7

6/23/2017 1.11 0.79 0.00 0.11 1.11 2.01 7

6/26/2017 1.08 0.78 0.07 0.23 1.08 2.16 7

6/27/2017 0.64 0.40 -0.22 -0.14 0.64 0.67 7

6/28/2017 0.95 0.76 0.01 0.10 0.95 1.82 7

6/30/2017 1.06 0.99 0.12 0.24 1.06 2.41 7

7/3/2017 1.07 0.82 0.05 0.22 1.07 2.16 7

7/5/2017 1.12 0.82 -0.11 0.14 1.12 1.98 7

7/6/2017 0.80 0.54 -0.04 0.03 0.80 1.33 7

7/7/2017 1.14 0.80 0.04 0.12 1.14 2.10 7

7/8/2017 0.91 0.65 -0.13 0.04 0.91 1.47 7

7/12/2017 0.93 0.65 -0.28 0.00 0.93 1.29 7

7/13/2017 0.92 0.69 -0.10 0.14 0.92 1.65 7

7/14/2017 1.07 0.82 -0.06 0.08 1.07 1.91 7

7/18/2017 0.90 0.76 0.01 0.21 0.90 1.88 7

7/20/2017 0.80 0.60 -0.21 0.04 0.80 1.23 7

7/21/2017 1.04 0.84 -0.05 0.15 1.04 1.98 7

7/28/2017 1.16 0.88 -0.01 0.16 1.16 2.20 7

8/1/2017 1.03 0.77 -0.04 0.15 1.03 1.92 7

8/2/2017 1.13 0.82 0.03 0.09 1.13 2.07 7

8/7/2017 1.05 0.83 -0.04 0.18 1.05 2.03 7

8/9/2017 0.96 0.75 -0.04 0.13 0.96 1.80 7

8/10/2017 1.03 0.77 -0.10 0.05 1.03 1.76 7




kWh Factor

kW Factor Phase

8/11/2017 0.17 0.33 0.24 0.23 0.33 0.98 7

8/18/2017 1.02 0.76 -0.09 0.11 1.02 1.80 7

8/25/2017 0.73 0.48 -0.22 0.03 0.73 1.02 7

8/29/2017 0.82 0.57 -0.14 0.11 0.82 1.36 7

8/30/2017 0.79 0.69 0.00 0.17 0.79 1.65 7

8/31/2017 0.78 0.60 -0.08 0.17 0.78 1.48 7

9/5/2017 0.89 0.67 -0.08 0.15 0.89 1.63 7

9/6/2017 0.99 0.75 -0.15 0.14 0.99 1.72 7

4.3.6.6 DP7RM VDR and Energy Savings



Percent NRD

Responding Devices

VDR (kW)


6/5/2017 645 24 490 490 814

6/6/2017 645 9 587 340 399

6/7/2017 645 23 497 437 924

6/14/2017 642 27 469 412 661

6/15/2017 642 2 629 321 283

6/16/2017 644 34 425 242 646

6/19/2017 644 22 502 492 849

6/20/2017 644 18 528 560 977

6/21/2017 644 10 580 441 678

6/22/2017 644 19 522 501 762

6/23/2017 644 23 496 550 997

6/26/2017 644 22 502 543 1,085

6/27/2017 644 7 599 383 401

6/28/2017 643 25 482 458 878

6/30/2017 643 26 476 504 1,147

7/3/2017 641 21 506 542 1,094

7/5/2017 640 22 499 559 988

7/6/2017 640 10 576 461 766

7/7/2017 640 22 499 569 1,048

7/8/2017 640 11 570 518 837

7/12/2017 638 24 485 451 626




Percent NRD

Responding Devices

VDR (kW)


7/13/2017 639 25 479 441 791

7/14/2017 639 25 479 513 915

7/18/2017 639 17 530 477 997

7/20/2017 637 27 465 372 572

7/21/2017 637 25 478 497 946

7/28/2017 638 23 491 570 1,081

8/1/2017 638 24 485 499 931

8/2/2017 635 26 470 531 973

8/7/2017 637 19 516 542 1,047

8/9/2017 637 22 497 477 894

8/10/2017 637 24 484 499 852

8/11/2017 637 14 548 181 537

8/18/2017 636 21 502 512 904

8/25/2017 636 11 566 413 577

8/29/2017 635 13 552 453 751

8/30/2017 636 14 547 432 902

8/31/2017 636 15 541 422 800

9/5/2017 637 17 529 471 862

9/6/2017 637 26 471 467 811



4.3.7 CS1 Manual Overrides In this section, we provide statistics on CS1 customer overrides during the 2017 DR season.

4.3.7.1 Carrier Thermostat Manual Overrides (DP1)

CS1 customers with Carrier thermostats have the option of overriding the DR event set-point during events. This can be done with the physical thermostat or over the internet. When a customer decides to override the DR event, the time of override is recorded and sent to the Carrier CCM server. Table 31 provides the override rates for DP1, by event, with the median time to override.

Table 31: Override Rates for DP1

Date Event High Temperature (º F)

DP1 Participants Overriding

DP1 Median Time before Override (in Minutes)

6/5/2017 102.92 9.46% 57

6/6/2017 102.92 17.88% 53

6/7/2017 101.48 17.20% 53

6/14/2017 98.06 15.78% 55

6/15/2017 105.98 17.63% 51

6/16/2017 107.06 18.54% 50

6/19/2017 113 20.36% 46

6/20/2017 116.06 20.98% 45

6/21/2017 114.53 19.93% 45

6/22/2017 114.8 20.20% 44

6/23/2017 112.46 20.15% 43

6/26/2017 111.02 19.40% 48

6/27/2017 105.98 19.19% 47

6/28/2017 107.06 18.83% 48

6/30/2017 107.06 18.48% 47

7/3/2017 105.08 15.70% 48

7/5/2017 111.02 19.43% 45

7/6/2017 111.92 5.83% 35.5

7/7/2017 114.98 20.59% 40

7/8/2017 114.53 19.14% 44

7/12/2017 105.32 8.28% 51

7/13/2017 107.96 13.03% 48

7/14/2017 111.02 15.88% 45

7/18/2017 107.96 11.21% 51

7/20/2017 98.96 17.28% 54

7/21/2017 104 19.40% 48

7/28/2017 107.06 19.39% 48




DP1 Participants Overriding

DP1 Median Time before Override (in Minutes)

8/1/2017 107.96 20.72% 48

8/2/2017 107.06 20.62% 48

8/7/2017 102.02 19.08% 50

8/9/2017 105.98 19.01% 50

8/10/2017 107.96 20.21% 48

8/11/2017 106.52 1.25% 35

8/18/2017 104 18.78% 49

8/25/2017 100.94 18.00% 50

8/29/2017 107.06 18.14% 52

8/30/2017 105.98 17.58% 53

8/31/2017 100.94 18.99% 50

9/5/2017 105.08 19.27% 50

9/6/2017 100.94 18.59% 54

On average, 17.23% of DP1 thermostats were manually overridden during the 2017 demand response events. This is a small decrease from 2016 (22.77%). The average event median12 times-to-override decreased in 2017: 48.16 minutes in 2017 versus 48.64 minutes in 2016.

4.3.7.2 Direct Control Unit Manual Overrides (DP3/DP4)

Direct control units (DP3CSE/LCR, DP4COM/LCR) are installed on the circuitry of a home’s air conditioner or heat pump and cannot be overridden directly by the customer. Customers with DCUs who wish to opt-out of a DR event must call the NVE customer service department, which forwards requests to the DR department. Some customers request to opt-out of the program entirely when they call. NVE keeps records of when these requests are made and fulfilled. Because opt-out requests for DCUs are rare, in Table 32 we report opt-out as the absolute number of requests, rather than percentages. Only event days for which the reports indicated there were opt-outs are included in the table.

Table 32: Number of DCU Opt-Out Requests, by DCU Model Date DP3LCR DP3CSE DP4LCR

6/16/2017 1 1 0

9/1/2017 0 1 0

Grand Total 1 2 0

12 The 2017 median of event median times to override was 48 minutes.



4.3.7.3 Honeywell Thermostat Manual Overrides (DP7)

CS1 customers with a Honeywell PCT can override DR event settings on the internet. Customers can preemptively opt-out of events, or override the DR setting mid-event. Whenever a customer chooses either of these options, the time this action is taken is recorded in a log. The following table provides the number of DP7 Honeywell PCTs that overrode the DR event signal, either preemptively or mid-event. Only event days for which the reports indicated there were opt-outs are included in the table.


Date % of Participants Overriding Pre Event

% of Participants Overriding During Event % Total

6/5/2017 0.075 0.019 0.093

6/6/2017 0.075 0.019 0.093

6/7/2017 0.186 0.019 0.205

6/14/2017 0.075 0.019 0.094

6/15/2017 0.131 0.075 0.206

6/16/2017 0.169 0.075 0.243

6/19/2017 0.262 0.094 0.356

6/20/2017 0.356 0.112 0.469

6/21/2017 0.281 0.019 0.300

6/22/2017 0.376 0.019 0.394

6/23/2017 0.263 0.056 0.319

6/26/2017 0.282 0.019 0.301

6/27/2017 0.282 0.019 0.301

6/28/2017 0.245 0.151 0.396

7/5/2017 0.057 0.000 0.057

7/7/2017 0.038 0.000 0.038

7/8/2017 0.019 0.000 0.019

8/1/2017 0.019 0.000 0.019

8/10/2017 0.038 0.000 0.038

8/29/2017 0.000 0.038 0.038

4.3.8 Program-level summaries for CS1 In this section, we summarize results from the previous sections to provide program-level VDR and energy savings for all of CS1.

Table 34: Program-Level Energy Impacts for CS1

Date Program-Event VDR (kW)

Program-Event Energy Savings

(kWh)

6/5/2017 56,427 58,910





(kWh)

6/6/2017 57,056 57,038

6/7/2017 56,029 59,611

6/14/2017 47,177 54,219

6/15/2017 60,409 68,360

6/16/2017 57,790 67,244

6/19/2017 73,755 83,557

6/20/2017 73,652 83,533

6/21/2017 71,030 81,296

6/22/2017 74,213 81,962

6/23/2017 72,937 84,478

6/26/2017 67,610 69,478

6/27/2017 62,835 65,289

6/28/2017 63,277 68,877

6/30/2017 65,399 67,427

7/3/2017 64,877 68,803

7/5/2017 70,643 75,864

7/6/2017 77,214 82,832

7/7/2017 78,011 86,603

7/8/2017 70,801 74,773

7/12/2017 67,293 67,577

7/13/2017 69,137 71,944

7/14/2017 71,829 75,193

7/18/2017 70,220 67,358

7/20/2017 47,194 45,944

7/21/2017 64,485 66,778

7/28/2017 66,518 71,738

8/1/2017 71,863 75,787

8/2/2017 66,583 71,657

8/7/2017 58,662 62,058

8/9/2017 64,016 69,532

8/10/2017 64,830 72,505

8/11/2017 15,268 23,222

8/18/2017 60,312 62,096

8/25/2017 56,922 48,090

8/29/2017 59,013 51,946





(kWh)

8/30/2017 54,610 47,735

8/31/2017 56,425 52,764

9/5/2017 59,743 60,458

9/6/2017 54,696 53,611

Program-Event VDR is the summation of the VDRs across all CS1 device populations on the given event day. Similarly, Program-Event Energy Savings is the summation of the Energy Savings across all CS1 device populations on the given event day.

4.4 CS2 RESULTS

In this section, we report demand reduction and energy savings results for the CS2 Build, Manage, and Build Pilot programs (also known as “PowerShift”), for which the population of devices are EcoFactor two-way PCTs (DP11), EcoBee WiFi thermostats (DP12), and EcoFactor WiFi thermostats (DP13).

We also provide the kW factors ( ), kWh factors ( ), subgroup-event VDR, and net energy savings for CS2, for each event.

4.4.1 CS2 demand reduction results The VDR and energy savings results below were derived from the demand reductions for CS2 Build as a separate analysis was not done for the EcoFactor PCTs installed under the purview of CS2 Manage, i.e. all DP11 devices were grouped together to calculate the event performance results. Because the DP11 and DP13 CS2 devices employed the pre-cool option for all 2017 DR events, some of the tables that follow also include one or two additional columns that represent the pre-cooling prior to the event.

Figure 15 below details the hourly load profile of DP11 devices installed in single family homes during the July 7th, 2017 demand response event.



4.4.1.1 CS2 Event Load Shapes


Table 35 below details the demand reductions results by event hour for the DP11 devices installed in single family homes.



4.4.1.2 CS2 Manage and Build Demand Reduction Results


Date Pre1 Pre2 Event1 Event2 Snap1 Snap2 kW Factor

kWh Factor

kWh Factor Phase

6/5/2017 -0.18 -0.78 1.28 0.70 -0.27 -0.12 1.366 0.624 5 6/6/2017 6/7/2017

-0.17 -0.79 1.39 0.74 -0.28 -0.12 1.476 0.774 5 -0.24 -0.88 1.25 0.72 -0.22 -0.08 1.359 0.546 7

6/14/2017 -0.30 -0.87 1.11 0.69 -0.19 -0.08 1.204 0.369 8 6/15/2017 -0.25 -0.80 1.39 0.78 -0.27 -0.12 1.632 0.738 5 6/16/2017 -0.17 -0.65 1.42 0.74 -0.26 -0.12 1.489 0.952 5 6/19/2017 -0.13 -0.54 1.75 0.81 -0.26 -0.14 2.085 1.489 4 6/20/2017 -0.10 -0.50 1.66 0.73 -0.25 -0.16 1.794 1.366 4 6/21/2017 -0.14 -0.58 1.65 0.79 -0.25 -0.14 1.787 1.327 5 6/22/2017 -0.15 -0.55 1.62 0.75 -0.25 -0.13 1.794 1.295 5 6/23/2017 -0.15 -0.55 1.70 0.79 -0.25 -0.14 1.962 1.412 4 6/26/2017 -0.25 -0.70 1.47 0.75 -0.25 -0.10 1.496 0.933 5 6/27/2017 -0.26 -0.72 1.40 0.71 -0.25 -0.12 1.496 0.764 7 6/28/2017 -0.27 -0.73 1.43 0.76 -0.24 -0.12 1.522 0.835 8 6/30/2017 -0.25 -0.68 1.48 0.77 -0.23 -0.11 1.710 0.984 5 7/3/2017 -0.27 -0.68 1.51 0.81 -0.24 -0.10 1.580 1.036 4 7/5/2017 -0.25 -0.67 1.81 0.88 -0.31 -0.14 2.033 1.321 3 7/6/2017 -0.17 -0.52 1.66 0.78 -0.25 -0.13 1.762 1.360 5 7/7/2017 -0.16 -0.48 1.80 0.80 -0.25 -0.13 2.157 1.580 4 7/8/2017 -0.15 -0.47 1.64 0.82 -0.25 -0.09 1.807 1.496 4

7/12/2017 -0.25 -0.69 1.50 0.74 -0.27 -0.12 1.703 0.900 3 7/13/2017 -0.25 -0.67 1.56 0.78 -0.26 -0.12 1.762 1.043 4 7/14/2017 -0.22 -0.60 1.61 0.74 -0.29 -0.14 1.853 1.088 4 7/18/2017 -0.25 -0.65 1.41 0.71 -0.26 -0.12 1.542 0.849 4 7/20/2017 -0.51 -1.00 1.13 0.69 -0.13 -0.03 1.283 0.162 7 7/21/2017 -0.29 -0.69 1.46 0.73 -0.27 -0.12 1.528 0.816 5 7/28/2017 -0.26 -0.64 1.49 0.72 -0.24 -0.13 1.560 0.939 5 8/1/2017 -0.25 -0.63 1.66 0.78 -0.29 -0.15 1.890 1.133 4 8/2/2017 -0.25 -0.61 1.63 0.81 -0.20 -0.09 1.781 1.282 5 8/7/2017 -0.32 -0.73 1.31 0.68 -0.23 -0.10 1.412 0.609 3 8/9/2017 -0.31 -0.73 1.40 0.74 -0.24 -0.12 1.515 0.732 5

8/10/2017 -0.31 -0.69 1.48 0.75 -0.27 -0.13 1.567 0.835 5 8/11/2017 -0.28 -0.04 0.32 0.10 -0.01 -0.05 0.525 0.058 4 8/18/2017 -0.33 -0.71 1.33 0.67 -0.24 -0.10 1.451 0.622 5 8/25/2017 -0.36 -0.78 1.25 0.63 -0.27 -0.12 1.295 0.356 8



Date

8/29/2017

Pre1

-0.25

Pre2

-0.64

Event1

1.24

Event2

0.58

Snap1

-0.32

Snap2

-0.15

kW Factor

1.386

kWh Factor

0.453

kWh Factor Phase

5 8/30/2017 -0.37 -0.79 1.09 0.51 -0.35 -0.11 1.133 -0.026 4 8/31/2017 -0.33 -0.75 1.18 0.58 -0.27 -0.14 1.269 0.278 4 9/5/2017 -0.34 -0.73 1.28 0.56 -0.32 -0.16 1.327 0.298 8 9/6/2017 -0.45 -0.88 1.23 0.67 -0.20 -0.08 1.295 0.291 4

Figure 16 below details the hourly load profile of DP11 devices installed in multi-family homes during the July 7th, 2017 demand response event.



Figure 16: DP11RM Phase 3 Load Shape 7/7/2017

Table 36 below details the demand reductions results by event hour for the DP11 devices installed in multi-family homes.





kWh Factor

kWh Factor Phase

6/5/2017 -0.33 -0.92 0.96 0.42 -0.44 -0.27 1.244 -0.570 7 6/6/2017 -0.23 -0.79 1.02 0.58 -0.42 -0.27 1.282 -0.114 5 6/7/2017 -0.30 -0.93 0.94 0.47 -0.48 -0.28 1.263 -0.598 5

6/14/2017 -0.34 -0.89 0.86 0.44 -0.45 -0.28 1.179 -0.675 7 6/15/2017 -0.29 -0.83 1.07 0.48 -0.53 -0.37 1.341 -0.456 5 6/16/2017 -0.35 -0.81 1.03 0.47 -0.44 -0.18 1.436 -0.295 7 6/19/2017 -0.23 -0.66 1.15 0.36 -0.51 -0.34 1.350 -0.228 5 6/20/2017 -0.23 -0.57 1.33 0.53 -0.37 -0.28 1.567 0.408 5 6/21/2017 -0.32 -0.74 1.09 0.41 -0.48 -0.31 1.386 -0.361 7 6/22/2017 -0.23 -0.66 1.23 0.55 -0.39 -0.23 1.490 0.285 5 6/23/2017 -0.28 -0.67 1.32 0.58 -0.36 -0.22 1.585 0.370 5 6/26/2017 -0.33 -0.72 1.13 0.51 -0.32 -0.16 1.376 0.104 7 6/27/2017 -0.38 -0.86 0.92 0.33 -0.50 -0.40 1.234 -0.892 7 6/28/2017 -0.35 -0.84 1.09 0.49 -0.36 -0.26 1.375 -0.228 7 6/30/2017 -0.38 -0.82 1.19 0.64 -0.27 -0.14 1.404 0.218 7 7/3/2017 -0.31 -0.71 1.23 0.63 -0.30 -0.17 1.707 0.360 7 7/5/2017 -0.34 -0.77 1.20 0.48 -0.47 -0.30 1.489 -0.199 5 7/6/2017 -0.25 -0.56 1.37 0.63 -0.29 -0.24 1.707 0.654 5 7/7/2017 -0.28 -0.55 1.34 0.55 -0.28 -0.25 1.584 0.531 5 7/8/2017 -0.19 -0.53 1.25 0.62 -0.22 -0.09 1.451 0.844 7

7/12/2017 -0.29 -0.75 1.10 0.56 -0.37 -0.30 1.489 -0.066 5 7/13/2017 -0.33 -0.76 1.14 0.53 -0.39 -0.31 1.545 -0.133 5 7/14/2017 -0.39 -0.82 1.25 0.49 -0.43 -0.32 1.545 -0.209 4 7/18/2017 -0.30 -0.75 1.11 0.52 -0.35 -0.22 1.196 0.009 4 7/20/2017 -0.56 -1.03 0.82 0.39 -0.40 -0.31 1.053 -1.110 7 7/21/2017 -0.49 -0.91 1.02 0.47 -0.36 -0.23 1.215 -0.493 7 7/28/2017 -0.48 -0.89 1.17 0.48 -0.36 -0.27 1.480 -0.351 7 8/1/2017 -0.34 -0.75 1.33 0.62 -0.41 -0.30 1.575 0.152 4 8/2/2017 -0.45 -0.78 1.05 0.42 -0.43 -0.28 1.385 -0.455 5 8/7/2017 -0.47 -0.91 0.99 0.46 -0.41 -0.18 1.311 -0.523 5 8/9/2017 -0.46 -0.93 1.09 0.54 -0.36 -0.29 1.378 -0.399 7

8/10/2017 -0.41 -0.77 1.27 0.63 -0.32 -0.27 1.690 0.142 7 8/11/2017 -0.36 -0.29 0.24 0.01 -0.11 -0.21 0.570 -0.741 4 8/18/2017 -0.47 -0.98 0.87 0.30 -0.47 -0.36 1.044 -1.101 4 8/25/2017 -0.48 -0.99 0.80 0.22 -0.60 -0.39 1.006 -1.443 5



Date

8/29/2017

Pre1

-0.40

Pre2

-0.76

Event1

0.86

Event2

0.35

Snap1

-0.45

Snap2

-0.25

kW Factor

0.987

kWh Factor

-0.626

kWh Factor Phase

3 8/30/2017 -0.38 -0.84 0.74 0.34 -0.41 -0.19 0.807 -0.740 3 8/31/2017 -0.43 -0.85 0.85 0.39 -0.33 -0.25 0.930 -0.617 7 9/5/2017 -0.45 -0.93 0.90 0.28 -0.53 -0.36 1.139 -1.091 5 9/6/2017 -0.49 -1.02 0.89 0.45 -0.41 -0.28 1.044 -0.854 5

4.4.1.3 CS2 Manage VDR and Energy Savings

Table 37 below details VDR and energy savings by event for DP11 devices installed in single family homes from the Manage population.

Table 37: DP11RS Manage VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 53,560 8 49,275 67,293 30,763

6/6/2017 53,564 9 48,743 71,955 37,721

6/7/2017 53,564 10 48,208 65,521 26,334

6/14/2017 54,174 10 48,757 58,722 17,996

6/15/2017 54,184 8 49,849 81,339 36,796

6/16/2017 54,176 8 49,842 74,231 47,444

6/19/2017 54,177 10 48,759 101,665 72,618

6/20/2017 54,185 7 50,392 90,385 68,849

6/21/2017 54,155 8 49,823 89,040 66,134

6/22/2017 54,107 7 50,320 90,254 65,165

6/23/2017 54,108 10 48,697 95,543 68,741

6/26/2017 54,104 7 50,317 75,274 46,924

6/27/2017 54,080 4 51,917 77,663 39,672

6/28/2017 54,052 8 49,728 75,679 41,543

6/30/2017 54,035 8 49,712 84,998 48,938

7/3/2017 53,985 8 49,666 78,487 51,467

7/5/2017 53,963 12 47,487 96,566 62,737

7/6/2017 53,963 8 49,646 87,451 67,518

7/7/2017 53,929 10 48,536 104,676 76,700

7/8/2017 53,944 5 51,247 92,603 76,671

7/12/2017 53,911 9 49,059 83,562 44,164




Percent NRD

Responding Devices

VDR (kW)


7/13/2017 53,870 9 49,022 86,370 51,123

7/14/2017 53,881 8 49,571 91,834 53,945

7/18/2017 53,830 4 51,677 79,666 43,850

7/20/2017 53,808 10 48,427 62,108 7,842

7/21/2017 53,822 7 50,054 76,500 40,843

7/28/2017 53,756 7 49,993 78,008 46,935

8/1/2017 53,725 10 48,353 91,407 54,782

8/2/2017 53,630 9 48,803 86,907 62,573

8/7/2017 53,602 7 49,850 70,365 30,341

8/9/2017 53,602 7 49,850 75,529 36,474

8/10/2017 53,590 8 49,303 77,263 41,186

8/11/2017 53,590 4 51,446 26,985 2,998

8/18/2017 53,531 5 50,854 73,778 31,619

8/25/2017 53,535 6 50,323 65,178 17,924

8/29/2017 53,521 3 51,915 71,947 23,534

8/30/2017 53,526 4 51,385 58,234 -1,331

8/31/2017 53,528 5 50,852 64,539 14,159

9/5/2017 53,464 4 51,325 68,132 15,288

9/6/2017 53,476 8 49,198 63,727 14,338

Table 38 below details VDR and energy savings by event for DP11 devices installed in multi-family homes from the Manage population.

Table 38: DP11RM Manage VDR and Energy Savings


Percent NRD

Responding Devices VDR (kW)


6/5/2017 1,344 12 1,183 1,471 -674

6/6/2017 1,344 11 1,196 1,533 -136

6/7/2017 1,344 13 1,169 1,476 -699

6/14/2017 1,342 15 1,141 1,345 -770

6/15/2017 1,343 12 1,182 1,585 -539

6/16/2017 1,343 13 1,168 1,678 -344

6/19/2017 1,345 12 1,184 1,598 -270

6/20/2017 1,348 11 1,200 1,880 490

6/21/2017 1,345 9 1,224 1,697 -442




Percent NRD

Responding Devices VDR (kW)


6/22/2017 1,342 12 1,181 1,760 336

6/23/2017 1,342 14 1,154 1,830 427

6/26/2017 1,341 10 1,207 1,661 126

6/27/2017 1,339 8 1,232 1,520 -1,099

6/28/2017 1,339 13 1,165 1,602 -265

6/30/2017 1,341 13 1,167 1,638 255

7/3/2017 1,341 12 1,180 2,015 425

7/5/2017 1,341 11 1,193 1,777 -238

7/6/2017 1,341 11 1,193 2,038 781

7/7/2017 1,337 12 1,177 1,863 625

7/8/2017 1,337 7 1,243 1,804 1,050

7/12/2017 1,333 12 1,173 1,746 -78

7/13/2017 1,331 12 1,171 1,810 -155

7/14/2017 1,331 14 1,145 1,769 -239

7/18/2017 1,331 9 1,211 1,448 11

7/20/2017 1,332 14 1,146 1,207 -1,272

7/21/2017 1,333 14 1,146 1,393 -566

7/28/2017 1,329 15 1,130 1,672 -397

8/1/2017 1,330 16 1,117 1,760 170

8/2/2017 1,330 12 1,170 1,621 -533

8/7/2017 1,324 11 1,178 1,545 -616

8/9/2017 1,324 12 1,165 1,605 -465

8/10/2017 1,326 13 1,154 1,950 164

8/11/2017 1,326 8 1,220 695 -903

8/18/2017 1,319 9 1,200 1,253 -1,322

8/25/2017 1,317 9 1,198 1,206 -1,729

8/29/2017 1,316 7 1,224 1,208 -767

8/30/2017 1,315 5 1,249 1,008 -925

8/31/2017 1,314 9 1,196 1,112 -738

9/5/2017 1,312 11 1,168 1,330 -1,274

9/6/2017 1,310 13 1,140 1,190 -973

4.4.1.4 CS2 Build VDR and Energy savings



Table 39 below details VDR and energy savings by event for DP11 devices installed in single family homes from the Build population.

Table 39: DP11RS Build VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 5,153 8 4,741 6,474 2,960

6/6/2017 5,189 9 4,722 6,971 3,654

6/7/2017 5,189 10 4,670 6,347 2,551

6/14/2017 5,571 10 5,014 6,039 1,851

6/15/2017 5,614 8 5,165 8,428 3,812

6/16/2017 5,657 8 5,204 7,751 4,954

6/19/2017 5,746 10 5,171 10,783 7,702

6/20/2017 5,784 7 5,379 9,648 7,349

6/21/2017 5,824 8 5,358 9,576 7,112

6/22/2017 5,853 7 5,443 9,763 7,049

6/23/2017 5,872 10 5,285 10,369 7,460

6/26/2017 5,961 7 5,544 8,293 5,170

6/27/2017 5,998 4 5,758 8,614 4,400

6/28/2017 6,052 8 5,568 8,473 4,651

6/30/2017 6,107 8 5,618 9,606 5,531

7/3/2017 6,178 8 5,684 8,982 5,890

7/5/2017 6,219 12 5,473 11,129 7,230

7/6/2017 6,219 8 5,721 10,078 7,781

7/7/2017 6,288 10 5,659 12,205 8,943

7/8/2017 6,361 5 6,043 10,920 9,041

7/12/2017 6,441 9 5,861 9,984 5,276

7/13/2017 6,525 9 5,938 10,462 6,192

7/14/2017 6,552 8 6,028 11,167 6,560

7/18/2017 6,646 4 6,380 9,836 5,414

7/20/2017 6,666 10 5,999 7,694 971

7/21/2017 6,710 7 6,240 9,537 5,092

7/28/2017 6,930 7 6,445 10,057 6,051

8/1/2017 6,984 10 6,286 11,882 7,121

8/2/2017 7,002 9 6,372 11,347 8,170

8/7/2017 7,198 7 6,694 9,449 4,074

8/9/2017 7,198 7 6,694 10,143 4,898

8/10/2017 7,221 8 6,643 10,411 5,550




Percent NRD

Responding Devices

VDR (kW)


8/11/2017 7,221 4 6,932 3,636 404

8/18/2017 7,394 5 7,024 10,191 4,367

8/25/2017 7,609 6 7,152 9,264 2,548

8/29/2017 7,737 3 7,505 10,401 3,402

8/30/2017 7,776 4 7,465 8,460 -193

8/31/2017 7,811 5 7,420 9,418 2,066

9/5/2017 7,904 4 7,588 10,073 2,260

9/6/2017 7,961 8 7,324 9,487 2,135

Table 40 below details VDR and energy savings by event for DP11 devices installed in multi-family homes from the Build population.

Table 40: DP11RM Build VDR and Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/5/2017 468 12 412 512 -235

6/6/2017 478 11 425 545 -48

6/7/2017 478 13 416 525 -249

6/14/2017 499 15 424 500 -286

6/15/2017 502 12 442 592 -202

6/16/2017 506 13 440 632 -130

6/19/2017 510 12 449 606 -102

6/20/2017 513 11 457 715 186

6/21/2017 514 9 468 648 -169

6/22/2017 513 12 451 673 129

6/23/2017 514 14 442 701 164

6/26/2017 518 10 466 642 49

6/27/2017 524 8 482 595 -430

6/28/2017 527 13 458 631 -104

6/30/2017 534 13 465 652 101

7/3/2017 533 12 469 801 169

7/5/2017 532 11 473 705 -94

7/6/2017 532 11 473 808 310

7/7/2017 532 12 468 741 249

7/8/2017 533 7 496 719 418

7/12/2017 535 12 471 701 -31




Percent NRD

Responding Devices

VDR (kW)


7/13/2017 540 12 475 734 -63

7/14/2017 541 14 465 719 -97

7/18/2017 543 9 494 591 5

7/20/2017 543 14 467 492 -518

7/21/2017 546 14 470 570 -232

7/28/2017 552 15 469 695 -165

8/1/2017 550 16 462 728 70

8/2/2017 554 12 488 675 -222

8/7/2017 562 11 500 656 -261

8/9/2017 562 12 495 681 -197

8/10/2017 564 13 491 829 70

8/11/2017 564 8 519 296 -384

8/18/2017 569 9 518 541 -570

8/25/2017 581 9 529 532 -763

8/29/2017 592 7 551 543 -345

8/30/2017 597 5 567 458 -420

8/31/2017 599 9 545 507 -336

9/5/2017 602 11 536 610 -585

9/6/2017 601 13 523 546 -447

4.4.1.5 CS2 Build Pilot

In 2015, NV Energy began piloting new residential demand response technology, the EcoBee smart thermostat. Participation was limited to 30 (16 single-family, 14 multi-family) devices. Most devices were installed in NV Energy employee homes.

During Program Year 2017 NV Energy implemented an expanded CS2 Build pilot in conjunction with their existing Residential Demand Response program. The pilot sought to investigate the potential value of EcoBee and EcoFactor s100 thermostat devices may have for NVE’s existing Residential Demand Response program. The following section details results from the pilot’s demand response event performance during the 2017 demand response season. ADM estimated the DR event impacts associated with the pilot using the same methodology detailed in Chapter 3. Installation of the pilot devices began shortly after the beginning of the 2017 Demand Response season; device counts were very low for many early season events. The results from events with small sample sizes should be interpreted carefully and are less conclusive than later events when most of the pilot devices were installed.

Figure 17 below details the hourly load profile of DP12 devices installed in single family homes during the August 25th, 2017 demand response event.



Figure 17: DP12RS August 25, 2017 Event





Date Event1 Event2 Snap1 Snap2 kW Factor kWh Factor

kW Factor Phase

6/16/2017 -1.05 -0.67 -1.03 -0.27 -0.67 -3.02 4

6/19/2017 0.39 0.67 0.28 0.78 0.67 2.12 4

6/20/2017 1.23 1.05 -1.01 -0.85 1.23 0.42 4

6/21/2017 0.62 0.22 -1.23 -0.57 0.62 -0.96 4

6/22/2017 2.35 2.23 0.47 2.37 2.35 7.42 4

6/23/2017 2.29 0.83 -1.73 -0.63 2.29 0.76 4

6/26/2017 0.70 -0.43 -2.19 -1.60 0.70 -3.52 4

6/27/2017 0.42 -0.25 -1.46 -1.05 0.42 -2.34 4

6/28/2017 1.23 -0.03 -0.23 0.24 1.23 1.21 4

6/30/2017 0.92 0.58 -1.19 -0.86 0.92 -0.55 4

7/3/2017 2.24 1.85 -0.15 0.92 2.24 4.86 4

7/5/2017 2.22 1.56 -0.60 -1.05 2.22 2.13 4

7/6/2017 2.36 2.12 -0.27 0.19 2.36 4.40 4

7/7/2017 2.32 0.90 -1.25 -0.85 2.32 1.12 4

7/8/2017 2.48 2.15 -0.24 0.31 2.48 4.70 4

7/12/2017 1.76 1.24 -1.21 -0.57 1.76 1.22 4

7/13/2017 1.17 0.53 -1.58 -1.04 1.17 -0.92 4

7/14/2017 2.37 1.57 -0.47 -0.41 2.37 3.06 4

7/18/2017 1.55 1.05 -1.10 -0.53 1.55 0.97 4

7/20/2017 1.16 0.59 -0.90 -0.11 1.16 0.74 4

7/21/2017 1.61 0.85 -0.94 -0.68 1.61 0.84 4

7/28/2017 1.68 1.01 -0.92 -0.53 1.68 1.24 4

8/1/2017 1.70 1.09 -0.63 -0.22 1.70 1.94 4

8/2/2017 1.59 0.95 -0.84 -0.55 1.59 1.15 4

8/7/2017 1.38 0.88 -0.93 -0.56 1.38 0.77 4

8/9/2017 1.33 0.68 -1.02 -0.51 1.33 0.48 4

8/10/2017 1.61 1.01 -0.87 -0.58 1.61 1.17 4

8/11/2017 0.17 0.16 -0.05 -0.13 0.17 0.15 4

8/18/2017 1.84 1.19 -0.82 -0.39 1.84 1.82 4

8/25/2017 1.38 0.80 -1.19 -0.63 1.38 0.36 4

8/29/2017 1.26 0.74 -1.26 -0.64 1.26 0.10 4

8/30/2017 1.30 0.84 -1.04 -0.52 1.30 0.58 4

8/31/2017 1.38 0.84 -1.07 -0.53 1.38 0.62 4

9/5/2017 1.62 0.89 -1.03 -0.51 1.62 0.97 4




kW Factor Phase

9/6/2017 1.38 0.86 -1.15 -0.76 1.38 0.33 4

Figure 18 below details the hourly load profile of DP12 devices installed in multi-family homes during the August 25th, 2017 demand response event.



Figure 18: DP12RM August 25, 2017 Event






kW Factor Phase

6/30/2017 1.01 1.17 0.17 0.07 1.17 2.42 4

7/3/2017 1.22 0.58 0.23 0.12 1.22 2.15 4

7/5/2017 0.68 0.28 -0.54 -0.56 0.68 -0.14 4

7/6/2017 0.68 0.32 -0.03 -0.48 0.68 0.49 4

7/7/2017 0.70 0.47 0.11 -0.35 0.70 0.93 4

7/8/2017 0.39 0.43 0.08 -0.38 0.43 0.52 4

7/12/2017 0.49 0.17 -0.06 0.02 0.49 0.62 4

7/13/2017 0.82 0.43 -0.25 -0.30 0.82 0.70 4

7/14/2017 0.90 0.11 -0.40 -0.13 0.90 0.48 4

7/18/2017 0.74 0.62 0.02 0.20 0.74 1.58 4

7/20/2017 0.75 0.58 -0.24 -0.22 0.75 0.87 4

7/21/2017 1.24 0.62 -0.49 -0.40 1.24 0.97 4

7/28/2017 1.15 0.69 -0.28 -0.24 1.15 1.32 4

8/1/2017 1.10 0.64 -0.33 -0.32 1.10 1.09 4

8/2/2017 1.15 0.66 -0.35 -0.12 1.15 1.34 4

8/7/2017 0.90 0.52 -0.73 -0.37 0.90 0.32 4

8/9/2017 0.74 0.58 -0.48 -0.41 0.74 0.43 4

8/10/2017 1.33 0.90 -0.33 -0.34 1.33 1.56 4

8/11/2017 0.20 0.06 -0.01 0.10 0.20 0.35 4

8/18/2017 0.96 0.61 -0.68 -0.41 0.96 0.48 4

8/25/2017 0.95 0.63 -0.41 -0.22 0.95 0.95 4

8/29/2017 1.00 0.76 -0.71 -0.33 1.00 0.72 4

8/30/2017 0.89 0.81 -0.52 -0.15 0.89 1.03 4

8/31/2017 0.90 0.66 -0.70 -0.38 0.90 0.48 4

9/5/2017 1.25 0.91 -0.93 -0.43 1.25 0.80 4

9/6/2017 1.08 0.79 -0.68 -0.31 1.08 0.88 4

Figure 19 below details the hourly load profile of DP13 devices installed in single family homes during the August 25th, 2017 demand response event.



Figure 19: DP13RS August 25, 2017 Event





kWh Factor

kWh Factor Phase

7/18/2017 0.20 -0.17 1.50 1.83 0.62 1.64 1.830 5.620 4 7/20/2017 -1.33 -1.26 0.65 0.79 -0.68 0.24 0.790 -1.590 4 7/21/2017 -1.25 -0.77 1.76 1.66 -0.86 0.13 1.760 0.670 4 7/28/2017 0.20 -0.27 2.07 1.06 -0.17 -0.52 2.070 2.370 4 8/1/2017 -0.10 -0.60 1.46 1.10 -0.63 -0.24 1.460 0.990 4 8/2/2017 -0.24 -0.59 1.89 1.26 -0.61 -0.39 1.890 1.320 4 8/7/2017 -0.35 -0.94 1.39 1.07 -0.82 -0.47 1.390 -0.120 4 8/9/2017 -0.17 -0.43 2.02 1.23 -0.07 0.01 2.020 2.590 4

8/10/2017 -0.11 -0.46 1.79 1.25 -0.58 -0.54 1.790 1.350 4 8/11/2017 -0.39 0.01 0.64 0.42 -0.02 -0.12 0.640 0.540 4 8/18/2017 -0.24 -0.35 2.51 1.93 -0.19 -0.18 2.510 3.480 4 8/25/2017 -0.47 -0.65 1.27 0.84 -0.30 -0.39 1.270 0.300 4 8/29/2017 -0.30 -0.55 1.53 1.18 -0.50 -0.18 1.530 1.180 4 8/30/2017 -0.40 -0.82 1.14 0.61 -0.56 -0.15 1.140 -0.180 4 8/31/2017 -0.22 -0.57 1.39 1.19 -0.24 -0.14 1.390 1.410 4 9/5/2017 -0.45 -0.73 1.46 1.11 -0.50 -0.26 1.460 0.630 4 9/6/2017 -0.47 -0.66 1.46 1.02 -0.49 -0.04 1.460 0.820 4

Results 85

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Figure 20: DP13RM August 25, 2017 Event





kWh Factor

kWh Factor Phase

7/28/2017 -0.94 -1.32 0.49 0.03 -1.54 -1.05 0.490 -4.330 4 8/1/2017 -0.59 -0.96 1.40 0.52 -0.16 -0.51 1.400 -0.300 4 8/2/2017 -0.44 -0.74 1.44 1.37 -0.63 -0.37 1.440 0.630 4 8/7/2017 -0.21 -0.75 1.22 0.93 -0.25 -0.12 1.220 0.820 4 8/9/2017 -0.44 -0.85 0.67 0.57 -0.70 -0.65 0.670 -1.400 4

8/10/2017 -0.57 -1.07 1.00 0.56 -1.17 -0.78 1.000 -2.030 4 8/11/2017 -0.17 -0.46 -0.08 -0.39 -0.46 -0.77 -0.080 -2.330 4 8/18/2017 -0.41 -0.66 0.52 0.15 -0.95 -0.73 0.520 -2.080 4 8/25/2017 -0.58 -0.86 0.32 -0.03 -0.83 -0.37 0.320 -2.350 4 8/29/2017 -0.16 -0.46 0.99 0.39 -0.42 -0.13 0.990 0.210 4 8/30/2017 -0.67 -1.02 0.74 0.52 -0.62 -0.08 0.740 -1.130 4 8/31/2017 -0.47 -0.98 0.96 0.70 -0.59 -0.13 0.960 -0.510 4 9/5/2017 -0.11 -0.41 1.24 0.82 -0.14 -0.15 1.240 1.250 4 9/6/2017 -0.50 -0.87 1.00 0.87 -0.33 0.10 1.000 0.270 4

Results 87

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4.4.1.6 CS2 Build Pilot VDR and Energy savings

Table 45 below details VDR and energy savings by event for DP12 devices installed in single family homes.



Percent NRD

Percent Override Responding VDR (kW) Energy Savings

(kWh)

6/16/2017 3 33 0 2 -1.34 -6.04

6/19/2017 3 33 0 2 1.34 4.24

6/20/2017 3 33 0 2 2.46 0.84

6/21/2017 3 33 0 2 1.24 -1.92

6/22/2017 3 33 0 2 4.7 14.84

6/23/2017 3 33 0 2 4.58 1.52

6/26/2017 5 25 0 4 2.8 -14.08

6/27/2017 7 0 0 7 2.94 -16.38

6/28/2017 7 0 0 7 8.61 8.47

6/30/2017 12 0 33.33 12 11.04 -6.6

7/3/2017 15 0 10 15 33.6 72.9

7/5/2017 17 8 7.69 16 35.52 34.08

7/6/2017 17 0 15.38 17 40.12 74.8

7/7/2017 20 7 7.14 19 44.08 21.28

7/8/2017 23 0 18.75 23 57.04 108.1

7/12/2017 30 4 11.54 29 51.04 35.38

7/13/2017 54 12 9.38 48 56.16 -44.16

7/14/2017 56 9 24.24 51 120.87 156.06

7/18/2017 84 14 9.8 72 111.6 69.84

7/20/2017 89 12 15.62 78 90.48 57.72

7/21/2017 100 15 12.68 85 136.85 71.4

7/28/2017 142 15 10.87 121 203.28 150.04

8/1/2017 165 13 14.42 144 244.8 279.36

8/2/2017 172 10 17.14 155 246.45 178.25

8/7/2017 214 4 12.07 205 282.9 157.85




Percent NRD


(kWh)

8/9/2017 214 5 11.36 203 269.99 97.44

8/10/2017 219 5 13.64 208 334.88 243.36

8/11/2017 219 11 0 195 33.15 29.25

8/18/2017 254 6 11.94 239 439.76 434.98

8/25/2017 269 5 13.33 256 353.28 92.16

8/29/2017 296 2 12.5 290 365.4 29

8/30/2017 296 2 16.06 290 377 168.2

8/31/2017 301 2 2.33 295 407.1 182.9

9/5/2017 303 7 0 282 456.84 273.54

9/6/2017 304 7 0 283 390.54 93.39

Table 46 below details VDR and energy savings by event for DP12 devices installed in multi-family homes.



Percent NRD

Percent Override Responding VDR

(kW) Energy Savings

(kWh)

6/6/2017 5 50 0 3 3.03 3.57

6/7/2017 5 43 0 3 2.07 6.03

6/14/2017 5 36 0 3 2.1 1.98

6/15/2017 5 15 0 4 3.04 6.48

6/16/2017 5 46 0 3 1.59 1.5

6/19/2017 5 38 0 3 0.42 -0.6

6/20/2017 5 15 0 4 2.84 3.72

6/21/2017 5 46 0 3 2.64 2.43

6/22/2017 5 31 0 3 1.05 -2.1

6/23/2017 5 15 0 4 2.04 1.88

6/26/2017 5 50 0 3 2.79 0.54

6/27/2017 5 29 0 4 4 5.76

6/28/2017 5 33 0 3 2.43 2.16

6/30/2017 6 47 13.33 3 3.51 7.26

7/3/2017 6 13 6.67 5 6.1 10.75

7/5/2017 6 40 0 4 2.72 -0.56

7/6/2017 6 27 6.67 4 2.72 1.96

7/7/2017 7 12 6.25 6 4.2 5.58




Percent NRD


(kW) Energy Savings

(kWh)

7/8/2017 7 6 12.5 7 3.01 3.64

7/12/2017 10 20 0 8 3.92 4.96

7/13/2017 12 24 14.29 9 7.38 6.3

7/14/2017 14 30 15 10 9 4.8

7/18/2017 21 25 0 16 11.84 25.28

7/20/2017 24 19 15.62 19 14.25 16.53

7/21/2017 19 21 12.12 15 18.6 14.55

7/28/2017 43 10 11.48 39 44.85 51.48

8/1/2017 47 17 18.84 39 42.9 42.51

8/2/2017 55 16 11.43 46 52.9 61.64

8/7/2017 80 9 12.99 73 65.7 23.36

8/9/2017 80 13 11.76 70 51.8 30.1

8/10/2017 84 14 14.12 72 95.76 112.32

8/11/2017 84 7 0 78 15.6 27.3

8/18/2017 99 9 13.21 90 86.4 43.2

8/25/2017 116 8 17.7 107 101.65 101.65

8/29/2017 129 6 12.17 121 121 87.12 8/30/2017 129 6 8.7 121 107.69 124.63 8/31/2017 129 6 0 121 108.9 58.08 9/5/2017 130 19 0 105 131.25 84 9/6/2017 129 12 0 114 123.12 100.32

Table 47 below details VDR and energy savings by event for DP13 devices installed in single family homes.



Percent NRD


(kWh)

7/18/2017 8 0 1 8 15 45

7/20/2017 18 27 2 13 10 -21

7/21/2017 25 12 0 22 39 15

7/28/2017 39 7 5 36 75 85

8/1/2017 49 14 3 42 61 42

8/2/2017 53 19 4 43 81 57

8/7/2017 70 5 6 67 93 -8




Percent NRD


(kWh)

8/9/2017 70 4 6 67 135 174

8/10/2017 72 4 7 69 124 93

8/11/2017 72 10 0 65 42 35

8/18/2017 90 13 7 78 196 271

8/25/2017 95 16 6 80 102 24

8/29/2017 123 6 11 116 177 137

8/30/2017 128 6 7 120 137 -22

8/31/2017 151 6 3 142 197 200

9/5/2017 167 8 0 154 225 97

9/6/2017 196 6 0 184 269 151

Table 48 below details VDR and energy savings by event for DP13 devices installed in multi-family homes.



Percent NRD


(kWh)

7/28/2017 14 19 1 11 5 -48

8/1/2017 14 22 1 11 15 -3

8/2/2017 16 22 0 12 17 8

8/7/2017 19 22 2 15 18 12

8/9/2017 19 5 2 18 12 -25

8/10/2017 20 10 2 18 18 -37

8/11/2017 20 14 0 17 -1 -40

8/18/2017 23 0 0 23 12 -48

8/25/2017 26 5 2 25 8 -59

8/29/2017 35 7 5 33 33 7

8/30/2017 36 15 0 31 23 -35

8/31/2017 41 19 2 33 32 -17

9/5/2017 47 14 0 40 50 50

9/6/2017 59 14 0 51 51 14



4.4.2 CS2 EcoFactor Optimization Energy Savings

4.4.2.1 Electricity Savings

The following table details average daily electricity savings associated with EcoFactor Optimization services. All values were determined using the methods described in Appendix A.

Table 49: Average Per Day Electricity Savings from EcoFactor Optimization (kWh) from Space Cooling (June – September)

Treatment Group Control Group

Average per Premise Daily Electricity Savings associated with EcoFactor Optimization

Average Daily Post-Installation

Period Consumption

(kWh)

Average Daily Pre-Installation Period

Consumption (kWh)


Period Consumption

(kWh)


Consumption (kWh)

51.38 51.74 53.70 50.18 3.89

The annual per premise energy savings associated with EcoFactor optimization during space cooling is 475 kWh (122 days x 3.89 kWh).

Table 50: Average Per Day Electricity Savings from EcoFactor Optimization (kWh) from Space Heating (October – May)




Period Consumption

(kWh)


Consumption (kWh)


Period Consumption

(kWh)


Consumption (kWh)

24.42 25.34 24.87 25.58 0.22

The annual per premise energy savings associated with EcoFactor optimization during space heating is 53 kWh (243 days x 0.22 kWh). The total annual per premise energy savings associated with the EcoFactor optimization is 477 kWh (475 kWh + 53 kWh – 51 kWh13).

4.4.2.2 Potential Gas Savings

In the absence of gas consumption data for EcoFactor customers in Southern Nevada, to develop an estimate for potential natural gas savings from EcoFactor Optimization, ADM leveraged gas consumption data from Northern Nevada and analyzed data from the following sources:

The Energy Information Administration (EIA) report on Nevada residential gas consumption for the years 2010, 2011, 2012, 2013, 2014, 2015, 2016, and 2017.14

13 Because monthly data was used, DR event days could not be excluded from the analysis; 51 kWh was the average per premise weighted average of net energy impacts from the DR events of DP11RS and DP11RM.

14 http://www.eia.gov/dnav/ng/hist/n3010nv2m.htm


http://www.eia.gov/dnav/ng/hist/n3010nv2m.htm


Public Utilities Commission of Nevada (PUCN) fact sheet “Energy Conservation, Natural Gas” (http://pucweb1.state.nv.us/PDF/Consumer/gascon.pdf).

PUCN document entitled, “Volume 7 of 22, Demand Side Plan Technical Appendix: DSM-4 Saturation Surveys” from Docket No. 10-07003, Application of Sierra Pacific Power Company d/b/a NV Energy for approval of its 2011-2030 Triennial Integrated Resource Plan.15

ADM utilized 8 years (2009 – 2017) of monthly gas consumption data from Northern Nevada to develop a weather-normalized model that calculates monthly natural gas consumption as a function of heating degree days. Using the weather-normalized model and monthly heating degree data for 2017 from the Weather Underground, ADM calculated monthly natural gas consumption for a typical residence in Las Vegas during months in which space heating occurs. ADM then applied a base load scalar derived from the sources mentioned above to determine what portion of monthly consumption is attributed to monthly base loads (domestic hot water, cooking, and other household uses that do not cause significant variances relative to annual consumption) and what portion is attributed to space heating. Finally, in 2017 a robust econometric analysis was used to calculate the gas savings associated with EcoFactor optimization in Northern Nevada using a large sample of gas consumption data. It was found that natural gas consumption for residential heating decreased approximately 9.2% after an EcoFactor thermostat was installed. Therefore, ADM applied the same savings percentage for gas savings in southern Nevada, i.e., we took the product of 9.2 percent relative savings and applied it to each of the monthly space heating consumption values. After summing across all months we found that an EcoFactor measure likely saved approximately 24.61 therms per year per southern Nevada home during 2017. The table below provides a summary of the data used to develop estimates for annual gas savings resulting from the implementation of EcoFactor programmable thermostats.

Table 51: Data Used for 2017 EcoFactor Gas Savings Analysis (Therms)

Month: Jan Feb Mar Apr May Oct Nov Dec

Total Gas Consumption 112.12 78.60 63.31 52.67 50.54 51.34 65.97 102.94 Base Load Consumption 43.97 30.82 24.83 20.65 19.82 20.13 25.87 40.37

Space Heating Consumption 68.15 47.78 38.48 32.01 30.72 31.20 40.10 62.57 Energy Savings 4.78 3.35 2.70 2.24 2.15 2.19 2.81 4.39

4.4.3 CS2 Overrides (DP11) CS2 customers with an EcoFactor PCT can override the DR event setting either online, via a smart phone/tablet app, or on the physical thermostat. Like the Carrier PCTs, these events are recorded in a log. The table below provides the override rates for DP11.

15 http://pucweb1.state.nv.us/PDF/AxImages/DOCKETS_2010_THRU_PRESENT/2010-7/2626.pdf


http://pucweb1.state.nv.us/PDF/AxImages/DOCKETS_2010_THRU_PRESENT/2010-7/2626.pdf

http://pucweb1.state.nv.us/PDF/Consumer/gascon.pdf



Overrides during First Pre cool

Hour)

Overrides during Second Pre cool

Hour

Overrides in First Event Hour

Overrides in Second Event

Hour

Total Event Overrides

6/5/2017 102.92 1.27% 8.00% 13.58% 18.92% 31.16% 6/6/2017 102.92 1.29% 7.78% 13.83% 18.40% 30.89% 6/7/2017 101.48 1.26% 8.15% 13.25% 17.71% 29.59%

6/14/2017 98.06 1.34% 8.34% 11.27% 17.00% 27.22% 6/15/2017 105.98 1.30% 5.49% 13.58% 17.80% 29.99% 6/16/2017 107.06 1.44% 8.26% 14.78% 17.72% 30.90% 6/19/2017 113 1.45% 8.74% 17.73% 18.57% 34.46% 6/20/2017 116.06 1.62% 8.94% 18.11% 18.58% 34.78% 6/21/2017 114.53 1.48% 8.25% 16.70% 17.99% 32.95% 6/22/2017 114.8 1.44% 8.44% 16.89% 18.03% 33.10% 6/23/2017 112.46 1.43% 8.43% 16.98% 17.27% 32.45% 6/26/2017 111.02 1.37% 8.46% 15.32% 17.90% 31.65% 6/27/2017 105.98 1.32% 8.03% 14.75% 17.23% 30.46% 6/28/2017 107.06 1.25% 8.74% 14.42% 17.40% 30.44% 6/30/2017 107.06 1.40% 8.31% 14.90% 16.79% 30.26% 7/3/2017 105.08 1.55% 8.65% 14.73% 16.50% 29.81% 7/5/2017 111.02 1.28% 8.17% 15.77% 17.87% 31.93% 7/6/2017 111.92 1.35% 8.20% 16.14% 17.09% 31.59% 7/7/2017 114.98 1.65% 8.48% 17.74% 16.06% 32.02% 7/8/2017 114.53 1.89% 9.46% 16.10% 16.15% 30.70%

7/12/2017 105.32 1.41% 7.49% 14.80% 17.25% 30.62% 7/13/2017 107.96 1.32% 7.63% 15.56% 17.87% 31.89% 7/14/2017 111.02 1.46% 8.27% 16.46% 16.96% 31.66% 7/18/2017 107.96 1.26% 8.19% 14.58% 18.14% 31.27% 7/20/2017 98.96 1.46% 9.41% 11.66% 16.37% 26.75% 7/21/2017 104 1.39% 8.13% 15.50% 17.51% 31.33% 7/28/2017 107.06 1.48% 7.81% 15.90% 17.16% 31.45% 8/1/2017 107.96 1.27% 7.70% 16.60% 18.54% 33.43% 8/2/2017 107.06 1.35% 7.73% 16.30% 18.28% 32.98% 8/7/2017 102.02 1.45% 8.14% 14.29% 18.01% 30.91% 8/9/2017 105.98 1.29% 8.08% 14.82% 18.16% 31.52%

Results 94

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Overrides during First Pre cool

Hour)

Overrides during Second Pre cool

Hour

Overrides in First Event Hour

Overrides in Second Event

Hour

Total Event Overrides

8/10/2017 107.96 1.26% 7.90% 15.52% 17.38% 31.39% 8/18/2017 104 1.35% 8.08% 15.03% 17.17% 30.68% 8/25/2017 100.94 1.31% 8.37% 13.73% 16.93% 29.39% 8/29/2017 107.06 1.31% 8.55% 13.45% 18.04% 30.18% 8/30/2017 105.98 1.31% 8.92% 12.50% 17.67% 28.88% 8/31/2017 100.94 1.16% 8.49% 13.38% 17.46% 29.59% 9/5/2017 105.08 1.24% 8.33% 14.53% 18.09% 31.17% 9/6/2017 100.94 1.27% 8.89% 13.07% 17.79% 29.50%

Results 95

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4.5 COMBINED RESIDENTIAL RESULTS (CS1 AND CS2)

The table below summarizes the combined CS1 and CS2 results by event day.

Table 53: CS1 and CS2 Results

Date VDR kWh Savings from DR

Available Devices

Responding Devices

Overall %NRD

6/5/2017 132,178 91,724 110,269 100,229 9.11%

6/6/2017 138,060 98,229 110,319 99,182 10.10%

6/7/2017 129,899 87,548 110,319 97,046 12.03%

6/14/2017 113,783 73,008 111,262 96,986 12.83%

6/15/2017 152,352 108,228 111,319 98,643 11.39%

6/16/2017 142,083 119,168 111,351 99,304 10.82%

6/19/2017 188,407 163,504 111,434 99,630 10.59%

6/20/2017 176,280 160,408 111,465 101,929 8.56%

6/21/2017 171,990 153,932 111,448 100,282 10.02%

6/22/2017 176,663 154,641 111,393 100,664 9.63%

6/23/2017 181,379 161,269 111,414 97,899 12.13%

6/26/2017 153,480 121,747 111,484 101,072 9.34%

6/27/2017 151,226 107,832 111,477 105,333 5.51%

6/28/2017 149,662 114,702 111,494 100,350 10.00%

6/30/2017 162,294 122,252 111,508 100,803 9.60%

7/3/2017 155,162 126,754 111,499 99,997 10.32%

7/5/2017 180,819 145,499 111,467 98,949 11.23%

7/6/2017 177,590 159,222 111,467 100,979 9.41%

7/7/2017 197,497 173,119 111,468 98,913 11.26%

7/8/2017 176,847 161,953 111,571 105,225 5.69%

7/12/2017 163,286 116,908 111,592 100,801 9.67%

7/13/2017 168,513 129,041 111,614 99,580 10.78%

7/14/2017 177,318 135,362 111,653 101,460 9.13%

7/18/2017 161,761 116,638 111,692 104,157 6.75%

7/20/2017 118,694 52,967 111,664 99,028 11.32%

7/21/2017 152,485 111,915 111,707 100,364 10.15%

7/28/2017 156,950 124,162 111,784 100,516 10.08%

8/1/2017 177,640 137,930 111,828 97,892 12.46%

8/2/2017 167,133 141,645 111,725 99,027 11.37%



Date VDR kWh Savings from DR

Available Devices

Responding Devices

Overall %NRD

8/7/2017 140,677 95,596 111,849 99,423 11.11%

8/9/2017 151,975 110,241 111,849 101,350 9.39%

8/10/2017 155,282 119,474 111,854 99,997 10.60%

8/11/2017 46,879 25,337 111,854 104,615 6.47%

8/18/2017 146,074 96,191 111,910 103,177 7.80%

8/25/2017 133,102 66,069 112,107 104,005 7.23%

8/29/2017 143,112 77,771 112,221 107,436 4.26%

8/30/2017 122,769 44,866 112,257 106,708 4.94%

8/31/2017 132,000 67,915 112,291 104,531 6.91%

9/5/2017 139,888 76,148 112,296 104,090 7.31%

9/6/2017 129,645 68,664 112,373 100,792 10.31%


5 ENERGY SAVINGS CURVES

The following section details the use of energy savings curves in the 2017 NPC Residential Demand Response program. For a more general discussion of energy savings curves please see Appendix C (Chapter 9).

Demand response for device populations in the residential sector was achieved through the direct load control of energy management devices that employ software-based optimization to reduce energy usage. The various residential devices can save energy during “event” and “non-event” days. The following sections describe ADM’s development of hourly energy savings curves through a combination of methods including the averaging of whole-house meter data and M&V.

5.1 CS1 AND CS2 ENERGY SAVINGS CURVES

The residential demand response and energy optimizing thermostat (DP11 or EcoFactor devices) is the basis for the future of the residential demand response program. The device can achieve energy savings through several mechanisms. In the cooling season, the device saves energy by running the air handler for several minutes after the compressive cooling cycle has ended; this effectively turns the air handler into a direct evaporative cooler and achieves a cooling “boost” after each air conditioning cycle. The thermostat can also save energy by altering the thermostat set points throughout the whole calendar year.

ADM modeled the demand response performance by reviewing M&V results. All energy savings associated with CS1 and CS2 demand response occur during DR event related hours. As such, the relevant energy savings curve for each CS1/CS2 device population is derived from the particular device population’s event performance (including pre-cooling, event hour reductions and snapback). All the other hours in the demand response energy savings curve which were not pre-cool, event or snapback hours were assigned a value of zero as there is not savings that occurs which is attributable to the program.

In addition to the CS1 and CS2 energy savings curves described above, ADM also modeled hourly energy efficiency impacts of EcoFactor Optimization as a percentage of the whole-house usage for a typical customer. The CS2 population of the DR program is made up of participants with “smart thermostats.” As described in section 2, the CS2 savings curve for the 2017 MV report was derived from the normalized load shape of the participants. Because these residential EE curves are to be used in forecasting exercises by NV energy, ADM decided to derive these curves from the whole-house meter data of CS2 participants on non-event days in 2014 and 2015 to include the most complete datasets available. This meter data was averaged across the two years, except for event days. The few calendar days that shared an event day during the two program years were interpolated from the previous non-event day.

Given that the HVAC system controlled by the smart thermostat dominates a typical NVE customer's whole-house load profile, ADM believes that this aggregated whole-house load profile provides an accurate curve for forecasting energy savings into hourly bins.

The energy savings and demand reduction impacts are combined through simple addition, with the energy savings curves normalized to represent the typical customer.

Energy Savings Curves Page 104 of 359

98

6 CONCLUSIONS AND RECOMMENDATIONS

The results from this evaluation study of the Residential Demand Response Program summarized in the following table, which indicates that the DR program can provide significant kW reductions during critical peak hours, allowing NV Energy greater flexibility in resource management.

Table 54: M&V Summary Results from 2017 Residential Demand Response Program


(kW)

CS1 (Manage)

DP1.RM 13.9 4,095 1.45 5,115 DP1.RS 8.2 28,576 2.39 62,730

DP3LCR.RM 29.7 257 0.89 161 DP3LCR.RS 27.4 1,382 1.24 1,244 DP3CSE.RM 21.6 698 0.75 410 DP3CSE.RS 14.8 7,220 0.75 4,616 DP4LCR.RM 29.5 222 0.98 153 DP4LCR.RS 29.8 899 1.66 1,048

DP7.RM 19.7 638 1.16 594 DP7.RS 11.1 4,609 1.78 7,291

CS2 Manage (PowerShift)

DP11M.RS 7.5 47,973 2.16 95,881 DP11M.RM 11.4 1,087 1.71 1,647

CS2 Build (PowerShift)

DP11B.RS 7.5 9,789 2.16 19,565 DP11B.RM 11.4 697 1.71 1,056

CS2 Build (PowerShift) Pilot

DP12B.RS 15.3 351 2.48 737 DP12B.RM 22.6 162 1.33 167 DP13B.RS 9.6 779 2.51 1,768 DP13B.RM 13.4 239 1.44 298

Total 109,673 204,482

Accounting for non-responding devices and participant overrides, during a Demand Response Event NV Energy, the Residential Demand Response program could achieve a demand reduction of 204,482 kW if all devices were to operate simultaneously and achieve their maximum demand reduction during the same hour. This represents a cooling load that the DR program can shift to off-peak hours during critical peak demand periods on NVE’s southern Nevada grid, without taking into account T&D line loss.

Like in previous years, the Residential Carrier and EcoFactor Two Way PCTs provided the bulk of the demand reduction during 2017. They enable customers to override without needing to contact a call center or go online. This does cause an overall increase in overrides when compared to other systems, which decreases the kW Factor. However, this potentially makes program participation more appealing as customers know in advance that they have the option to override a curtailment without significant inconvenience. As such, providing this degree of control could induce higher overall participation.

By the end of 2017 DR season, the number of available residential devices in Southern Nevada totaled 109,673, an increase of 2,913 from the end of 2016. This increase was primarily attributable to the continued recruitment for the CS2 residential program and the CS2 PowerShift Pilot.

ADM provides the following recommendations to improve the residential DR components in future years:

Conclusions and Recommendations Page 105 of 359

99


We recommend the continued review of the inclusion of “pre-cooling” as a component of the residential program design. Past analyses seem to indicate that while pre-cooling doesn’t have a significant impact on the magnitude of an event curtailment, it can, however, erode event related energy savings. Pre-cooling may increase customer comfort during an event and as such customer satisfaction metrics may need to be investigated before making any decisions.

We recommend establishing quarterly workshops between ADM, NVE and its Demand Response implementers. This would allow for better project tracking, addressing of various data issues, updates regarding system performance and a periodic review of the implementation operations.

We recommend NVE continue to utilize process evaluation efforts to better understand possible market transformation related to the slightly downward trend of energy efficiency savings associated with the smart thermostat optimization services.

Similarly, we recommend NVE continue to utilize process evaluation efforts to understand participant’s behaviors related to overriding demand response events.


100

7 APPENDIX A: DIFFERENCE-IN-DIFFERENCES METHODOLOGY FOR ECOFACTOR OPTIMIZATION SAVINGS

Calculating the energy savings associated with the EcoFactor Optimization via the difference-in-differences methodology relies on the average consumption for both the test and control groups during both the period before and after the EcoFactor thermostat was installed. ADM utilized regression analysis to estimate these values. Devices installed since the inception of the program were included in the analysis because the algorithms used to optimize energy usage require some time to learn the behaviors/patterns of the participants.

The basic specification for the regression modeling is illustrated as follows. Consider modeling the energy use of a customer who is benefitting from EcoFactor optimization. In simplest terms, average daily electricity use can be separated between weather-sensitive and non-weather-sensitive factors. A model to represent this is:

Equation 12

Where:

is average daily use of electricity for period t for a customer; is cooling degree days during day ;

is heating degree days during day ; is an error term; is the intercept term; and are regression coefficients showing the changes in use that occurs for a change in either

cooling degree days or heating degree days.

Ultimately, by filtering the premises that are analyzed, the above regression equation will provide us with four energy consumption estimates for .

Equation 13

16 The number of cooling degree days per month was calculated using hourly weather data from the Weather Underground. First the number of cooling degree days for each hour was calculated by subtracting the base temperature of 72 degrees from the actual temperature (if the actual temperature was lower than the base temperature than there were 0 cooling degree days during that hour). Next, the average number of cooling degree days per day was calculated for each day.

17 The number of heating degree days per month was calculated using hourly weather data from the Weather Underground. First the number of heating degree days for each hour was calculated by subtracting the actual temperature from the base temperature of 65 (if the base temperature was lower than the actual temperature than there were 0 heating degree days during that hour). Next, the average number of heating degree days per day was calculated for each day.

Appendix A 101 Page 107 of 359


– The average daily consumption of the test group prior to the installation of the EcoFactor thermostat.

- The average daily consumption of the test group after the installation of the EcoFactor thermostat.

- The average daily consumption of the control group prior to the installation of the EcoFactor thermostat.

- The average daily consumption of the control group after the installation of the EcoFactor thermostat.

By subtracting the estimated average consumption after the thermostat was installed from the average consumption before the thermostat was installed we obtain a basic estimate of the impact of the EcoFactor Optimization on energy consumption. However, this estimate does not control for potential time-varying factors that may bias the estimate (e.g. changes in the economy).

Equation 14

In order to control for the time-varying factors, the analysis is expanded to include the control group. The implicit assumption for the difference-in-differences analysis is that a change in energy use in response to a change in weather conditions (and other time-varying factors like the economy) would be the same for the control group and the test group in the absence of the optimization. If this assumption holds, then the change in energy usage of the control group in response to a change in weather conditions can be applied to predict what the (counterfactual) energy use of the test group would have been under the changed weather conditions in the absence of the optimization. This allows the difference between actual post-optimization energy use of the test group and the counterfactual predicted energy use to be calculated as the savings attributable to the optimization.

The difference-in-difference equation takes the following form:

Equation 15

Where is the average energy savings attributed to the EcoFactor optimization algorithm.

The regression equations were estimated by applying estimation procedures that take into account both the cross-sectional and the time-series dimensions of the data. In particular, regression models were estimated by pooling cross-sectional observations (i.e., participants who had their EcoFactor devices installed in 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017 and control group members) with time-series observations (i.e., monthly consumption from January 2010 – February 2018).

A “fixed-effects” specification was used for the panel regression modeling. The purpose of this specification is to control for those determinants of a household’s electricity use that are constant over time.



The basic idea underlying this specification is that each customer household acts as its own control for omitted variables that account for the heterogeneity across households. This is the case for both household characteristics that are easily measured (like house size and age) and for characteristics more difficult to measure (like interest in conservation, etc.). Time-varying variables were handled by measuring and putting them as covariates in a “fixed effects” regression model.

Conceptually, the “fixed effects” regression analysis involved applying a least squares dummy variable (LSDV) covariance estimate procedure. In this approach, a binary dummy variable was created for each customer in the sample, with the variable assigned a value of 1 for each observation that is associated with the customer and a value of 0 for each observation that is not. The full set of these dummy variables was included in the regression analysis. In effect, the equation estimated contained a unique constant term for each customer that captures the effects of all the determinants of that customer’s electricity use that are constant over time. This approach automatically controlled for differences among households that influence the average level of consumption across customer households. The specification of customer- specific effects allows the regression model to capture much of the baseline differences across customers while obtaining reliable estimates of the effects of the optimization.

There were several significant advantages to using this fixed-effects panel model.

First, the precision associated with the model is generally high. Second, it has been our experience that these models tend to be more robust with respect to outliers.

Standard statistical tests and regression diagnostics were used to evaluate the performance of the models. Each model was screened for implausible results. The statistical tests and diagnostics included evaluating the t-statistics for estimated coefficients and the adjusted for equation fit and examining residuals from the fitted models.

The following table details the daily electricity savings associated with the EcoFactor Optimization services.

Table 55: Average Per Day Electricity Savings from EcoFactor Optimization (kWh) from Space Cooling (June – September)




Period Consumption

(kWh)


Consumption (kWh)


Period Consumption

(kWh)


Consumption (kWh)

51.38 51.74 53.70 50.18 3.89




Table 56: Average Per Day Electricity Savings from EcoFactor Optimization (kWh) from Space Heating (October – May)




Period Consumption

(kWh)


Consumption (kWh)


Period Consumption

(kWh)


Consumption (kWh)

24.42 25.34 24.87 25.58 0.22

The annual per premise energy savings associated with EcoFactor optimization during space heating is 53 kWh (243 days x 0.22 kWh). The total annual per premise energy savings associated with the EcoFactor optimization is 477 kWh (475 kWh + 53 kWh – 51 kWh18)

18 Because monthly data was used, DR event days could not be excluded from the analysis; 51 kWh was the average per premise weighted average of net energy impacts from the DR events of DP11RS and DP11RM.


Table 57: Treatment Regression Results

Month Period Intercept CDD HDD Average CDD

Average HDD AEC Adjusted R-

squared Number of

Observations

Number of Cross-

Sections

Hausman P-Value

Winter (October Post 27.80*** NA -0.70*** NA 4.87 24.42 0.05 242,316 13,245 0.00 – May) Pre 29.85*** NA -0.71*** NA 6.35 25.34 0.07 673,653 13,872 1.00

Summer (June – Post 12.60*** 2.06*** NA 18.84 NA 51.38 0.42 84,413 12,246 0.12 September) Pre 14.69*** 2.12*** NA 17.44 NA 51.74 0.25 229,071 13,871 0.00

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 58: Control Regression Results



squared Number of

Observations

Number of Cross-

Sections

Hausman P-Value

Winter (October Post 27.20*** NA -0.59*** NA 3.96 24.87 -0.01 174,393 11,649 0.04 – May) Pre 29.51*** NA -0.63*** NA 6.29 25.58 0.05 605,333 11,863 0.00


Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Appendix A 105

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8 APPENDIX B: 2017 SAVINGS PER MONTH BY RATE CLASS

This appendix provides monthly savings by rate class during 2017 for the NPC Residential DR Program.

Table 59: CS1 DR Event kWh Savings by Month by Rate Class – 2017

Rate Class Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

RS - - - - - 1,070,788 854,762 646,574 118,124

- - - 2,690,247

RM - - - - - 107,327 96,804 67,541 11,402

- - - 283,074

Total - - - - - 1,178,116 951,566 714,114 129,525

- - - 2,973,322

Table 60: CS2 Manage DR Event kWh Savings by Month by Rate Class – 2017


RS - - - - - 691,574 603,995 303,711 28,070

- - - 1,627,350

RM - - - - - (3,238) (179) (6,843) (2,102)

- - - (12,362)

GS - - - - - (33) (2) (70) (21)

- - - (126)

Total - - - - - 688,302 603,814 296,798 25,947

- - - 1,614,861

Table 61: CS2 Manage Energy Efficiency kWh Savings by Month by Rate Class – 2017


RS 802,412 653,473 724,898 763,395 1,127,262 1,949,258 2,281,676 2,062,728 1,649,960 1,039,934 734,465 886,874 14,676,336

RM 26,601 21,663 24,031 25,308 37,370 64,620 75,641 68,382 54,698 34,475 24,348 29,401 486,540

GS 156 127 141 149 220 380 445 402 322 203 143 173 2,862

Total 829,169 675,263 749,071 788,852 1,164,852 2,014,259 2,357,762 2,131,513 1,704,980 1,074,612 758,956 916,448 15,165,738

Appendix B 106

Page 112 of 359


Table 62: CS2 Build DR Event kWh Savings by Month by Rate Class – 2017


RS - - - - - 82,965 80,964 45,763 4,641

- - - 214,334

RM - - - - - (1,483) (113) (3,677) (1,176)

- - - (6,449)

GS - - - - - (14) (1) (38) (12)

- - - (65)

Total - - - - - 81,467 80,850 42,048 3,454

- - - 207,819

Table 63: CS2 Build Energy Efficiency kWh Savings by Month by Rate Class – 2017


RS 141 4,314 21,248 28,861 87,402 227,166 309,978 314,169 273,046 187,328 142,075 177,535 1,665,454

RM 6 189 1,502 3,569 10,834 30,485 38,761 36,828 31,057 20,583 15,480 19,053 195,680

GS 2 22 47 50 73 166 222 201 161 103 119 144 1,231

Total 149 4,525 22,797 32,480 98,309 257,818 348,961 351,198 304,264 208,014 157,674 196,732 1,982,919

Table 64: CS2 Build Pilot DP12 DR Event kWh Savings by Month by Rate Class – 2017


RS - - - - - (12) 878 2,028 395 - - - 3,290

RM - - - - - 63 181 773 219 - - - 1,235

Total - - - - - 51 1,059 2,801 614 - - - 4,525

Appendix B 107

Page 113 of 359


Table 65: CS2 Build Pilot DP13 DR Event kWh Savings by Month by Rate Class – 2017


RS - - - - - 125 1,107 266 - - - 1,498 125

RM - - - - - (61) (256) 75 - - - (241) (61)

Total - - - - - 65 851 341 - - - 1,257 65

Appendix B 108

Page 114 of 359

9 APPENDIX C: DETERMINING ENERGY SAVINGS (KWH) PER MONTH BY RATE CLASS

This chapter provides a detailed description of ADM’s analytical steps for determining the energy (kWh) savings per month per rate class values that are provided in the M&V reports for program year 2017.19

9.1 APPORTIONMENT OF ANNUAL ENERGY SAVINGS BY RATE CLASS

NV Energy’s DSM programs generally include populations of customers from more than one rate class. NV Energy tracks the rate class for each identifiable customer participating in DSM programs. However, participant information is not known for certain DSM programs, such as the Consumer Electronics and Plug Loads program or other “upstream” or “midstream” programs where incentives are provided through contractual arrangements with manufacturers or distributors of the rebated products. For DSM programs for which participant information is not known, ADM collected participant information at the point of sale or conducted customer surveys to identify the proportions of participants that belong to various rate classes.

9.2 APPORTIONMENT OF ANNUAL ENERGY SAVINGS BY MONTH

ADM developed a methodology that utilizes energy savings curves to calculate the portion of annual energy savings that occurs during each month of the year. An energy savings curve describes the temporal nature of energy savings. For example, on any given day the energy savings achieved by a LED exit sign are approximately 1/365 of the verified annual energy savings for that LED exit sign. On the other hand, an efficient air conditioner may not save any energy during the month of January but may achieve 35 percent of its annual energy savings in the month of July alone. ADM constructed appropriate energy savings curves from metered data collected during M&V of NV Energy DSM programs (or other programs if appropriate), customer billing data, calibrated DOE2 simulations and engineering calculations. The energy savings curves were coupled with project implementation dates on a record-by-record basis to produce accurate determinations of the energy savings achieved for each month of the year.

9.3 HIGH-LEVEL SUMMARY OF ADM’S CALCULATION METHODOLOGY

Monthly energy (kWh) savings for each program were calculated by applying an appropriate hourly or daily energy savings curve to each program participant’s ex post verified energy savings, then aggregating kWh savings for each month. The energy savings curve distributes a participant’s energy savings over time. Its shape is, therefore, dependent on not only the measure installed (i.e., lighting vs. HVAC) but also on the building type and sometimes its location.

The overall process by which ADM calculated monthly kWh savings was to (1) download from DSM Central all program tracking data, i.e., ex ante expected kWh savings, measure type, measure completion date, rate class, etc., (2) calculate ex post values per participant, (3) assign an energy savings curve to each participant’s ex post savings to distribute ex post energy savings by rate class over each of the 8,760 hours

19 The Public Utilities Commission of Nevada (PUCN) requires NV Energy to report energy (kWh) savings per month and by rate class for each Demand Side Management (DSM) program.

Appendix C 109 Page 115 of 359


in a year, and (4) aggregate ex post verified savings for the purpose of presenting savings by month and by rate class.

ADM also calculated first-year kWh savings for each program by combining measure startup date (from DSM Central/Demand Response Management System) with the aforementioned process. A detailed description of the steps involved in tabulating first-year kWh savings is provided in section D.5 below.

9.4 ENERGY SAVINGS CURVES

9.4.1 Definition

The phrase ‘energy savings curve’ is used to describe the temporal dependence of energy savings. The curves are typically hourly (1 × 8760 array), daily (1 × 365 array), or monthly (1 × 12 array). The energy savings curves are often normalized such the sum of all array elements is unity. When normalized, each element describes the fraction of annual savings that is expected to occur in a given hour, day, or month.

9.4.2 Nomenclature

Note that if the term ‘load shape’ is encountered in the spreadsheets that are used to tally monthly energy savings by program and rate class, one should take it to be the same as ‘energy savings curve’ as described herein. The reason for the usage of the term ‘load shape’ is twofold:

Energy savings curves are differential load shapes describing differences in electricity loads resulting from the implementation of energy efficiency measures; in other words, energy savings curves indicate the shape over time of electricity that is saved or not used. Note also that energy that is not used due to energy efficiency actions (i.e., “saved” energy) is sometimes called “Negawatts” – a “Negawatt” saved is meant to represent the negative form of a “Megawatt” of power that would have been used if the energy efficiency actions had not occurred.

An energy savings curve for a measure may or may not be synchronous with the load curve of the base case technology against which savings are determined.

There are energy efficiency measures (EEMs) for which the normalized savings curve is synchronous and proportional to the normalized load shape or curve of the base case technology. Examples of such EEMs include “smart thermostats” like EcoFactor versus a standard programmable thermostat as it is assumed that (1) there are null or negligible interactive effects and (2) pre- and post-retrofit usage schedules are identical. If the savings curve for an EEM is synchronous with the base case technology load shape, then the two curves have identical shapes.

For other EEMs, the energy savings curve is asynchronous with the load curve of the base case technology. Examples of EEMs with asynchronous savings curves include economizers, occupancy sensors, and control systems. For such measures, the shape of the energy savings curve is different from the shape of the base case technology.

As part of our evaluation effort, ADM determines for each EEM whether to use normalized energy savings curves that are either synchronous or asynchronous with the normalized load shape of the base case technology.



9.5 TABULATING MONTHLY ENERGY (KWH) SAVINGS PER RATE CLASS

Normalized daily energy savings curves are utilized for this task. A normalized daily energy savings curve is comprised of 365 daily fractions summing to exactly 1 (unity). For each measure, ADM determines ex post annual kWh savings, which is then multiplied by each of the 365 daily energy savings curve fractions to disaggregate annual kWh into 365 daily kWh bins.

9.5.1 First-Year kWh Savings

‘First-year’ kWh savings are savings that occur during the same calendar year in which a conservation program was implemented. For NV Energy a program year is the same as a calendar year. Thus ‘first-year’ kWh savings for a measure installed during the 2017 program year are equal to that measure’s kWh savings during the 2017 calendar year.

The following calculations are performed to tabulate ‘first-year’ kWh savings attributable to a particular customer rate class. For any given 2017 NV Energy program:

For each rate class, for each day of 2017, identify all measures that have been implemented (or ‘installed’ or ‘started up’) by the end of the prior day.

For each rate class, for each day of 2017, for all measures that have been installed by the prior day, multiply the ex post verified ‘typical-year’ annualized kWh savings20 for each measure type by that measure’s daily kWh bin. In other words, multiply the measure-level annual kWh by the measure-level daily bin from the appropriate energy savings curve.

For each rate class, tally all measure-level daily kWh savings to determine program-level daily kWh savings.

For each rate class, for any given month of 2017, tally all measure-level daily kWh savings occurring during that month to determine program-level monthly kWh savings during the 2017 calendar year.

For each rate class, the first-year kWh savings is the program-level monthly kWh savings for that rate class summed across all 12 months of 2017.

20 ‘Typical-year’ annualized kWh savings is 365 consecutive days of energy savings – usually a full calendar year other than Leap Year – attributed to an energy efficiency measure(s) for which ex post verified kWh savings will occur during a multi-year measure life. For example, an NV Energy conservation measure installed during the 2017 program year (i.e., during the 2017 calendar year) will normally provide kWh savings starting on its date of installation. ‘First-year’ savings is the savings that occurs during the 2017 calendar year. ‘Full-year’ savings is the savings occurring during subsequent calendar years. .



9.5.2 ‘Typical-Year’ Energy (kWh) Savings

‘Typical-year’ energy (kWh) savings represents 365 consecutive days of energy savings attributed to a measure(s) or program for which ex post verified savings will occur across a multi-year measure life.21

The following calculations are performed to tabulate ‘typical-year’ energy (kWh) savings attributable to a particular customer rate class. For any given 2017 NV Energy program, all measures would have been implemented or installed during calendar year 2017.

For each rate class, for each hour (or day) of 2017 and subsequent years, multiply ex post verified ‘typical-year’ energy (kWh) savings for each measure type by that measure’s hourly (or daily) kWh bin. In other words, multiply the measure-level annual kWh by the measure-level hourly (or daily) bin from the appropriate energy savings curve.22

For each rate class, tally all measure-level hourly (or daily) kWh savings to determine program-level hourly (or daily) kWh savings.

For each rate class, for any given month, sum all measure-level hourly (or daily) kWh savings occurring in that month to determine program-level monthly kWh savings.

For each rate class, ‘typical-year’ kWh savings is the program-level monthly kWh savings for that rate class summed across all 365 days of any non-Leap Year.

For any given program, ‘full-year’ kWh savings for a Leap Year will be marginally higher than ‘full-year’ kWh savings for a ‘typical year’ or non-Leap Year. Thus we always use a non-Leap Year when we quantify ‘typical-year’ kWh savings.

Following is an example of the determination of daily kWh savings generated by a program. Let’s consider a hypothetical program that targets two energy efficiency (EE) measures: residential lighting and residential cooling. For this hypothetical program, the table below provides a simple comparison of the measures’ respective:

‘typical-year’ energy savings;

21 The distinction between ‘typical year’ and ‘full year’ is that a ‘typical year’ is a 365-day year. A Leap Year is not a ‘typical year’ – instead, a Leap Year is a ‘full year’ that has 366 days. In M&V reports, the kWh savings tables (which show monthly savings per rate class) usually indicate titles such as “First Year 2016” (Leap Year), “Full Year 2017”, “Full Year 2018” and “Full Year 2019”.

21 When tallying kWh savings per month per rate class, the use of hourly bins or daily bins is equally correct and accurate. ADM typically uses daily bins (which are created from hourly bins) in our kW guru™ Excel files simply because a workstation processor can complete the billions of computations in a large kW guru™ file relatively faster when the number of computations is based on 365 daily bins instead of 8760 hourly bins per calendar year. Hourly bins in kW guru™ files (i.e., the 8760 hourly bins per ‘typical year’) exist for the following two purposes: 1) they are summed across the 24 hours of each day to create the aforementioned daily bins; and 2) they provide the hourly resolution that enables us to analyze and report critical peak demand (kW) savings per month per rate class for any specified kW-reporting period. 22 The daily bin value for February 1 represents the February 1 daily fraction of ‘typical-year’ annual energy (kWh) savings.



daily bin value in its energy savings curve for a specific day – February 1st – of any given year after the EE measures were installed;

energy (kWh) savings during February 1st of any given year after the EE measures were installed.

In Table 66 below, the assumption is that 1,000,000 kWh of annual energy savings (‘typical-year’ savings as reported in M&V reports) were achieved through distribution of CFLs and 500,000 kWh of annual (‘typical-year’) energy savings were achieved through implementation of air conditioning (AC) related EE measures (like an EcoFactor “smart” thermostat that optimizes AC usage). Energy (kWh) savings on June 1st are obtained by multiplying ‘typical-year’ kWh savings by the entries corresponding to June 1st in the respective normalized energy savings curves.

Following is a sample calculation of energy savings achieved for a given rate class on June 1 for a hypothetical program targeting residential lighting and space cooling.

Table 66: Sample Calculation of Energy Savings Comparison for “Indoor Lighting” vs. “Space Cooling” Measures

EE Measure = “Indoor Lighting”

EE Measure = “Space Cooling”

‘Typical-year’ energy savings (annual kWh): 1,000,000 500,000

Jun. 1 daily bin value in each EE measure’s energy savings curve: 0.0010 0.0035

Jun. 1 energy (kWh) savings in a typical year: 1,000 1,750

For each program, such calculations are performed for each rate class, energy savings curve and hour (or day). Hourly (or daily) results are then aggregated at the monthly level.

9.5.3 Leap Year Savings

To account for the extra day in February in Leap Years, one of the following methods is used. Either method produces accurate and very similar ex post verified energy savings determinations for Leap Years.

Energy savings during the month of February in a Leap Year is taken to be equal to 29/28 of energy savings during the month of February in a typical non-Leap Year.

Or, energy savings on the day of February 29 in a Leap Year is assumed to be the same as energy savings on the previous day (February 28).


10 APPENDIX D: ECOFACTOR OPTIMIZATION MATCHING

This appendix details the matching procedures employed to create the matched control group used to estimate EcoFactor optimization energy savings.

Propensity score matching is a method by which the control group is “matched” to the treatment group via a propensity score, which is essentially an estimate, derived from observed characteristics, of a particular customer’s likelihood of participating in the PowerShift program. The logit model below was used to estimate the propensity scores for all customers.

Equation 16

Where:

is a binary variable that is 1 if the customer is a NVE Demand Response program participant and 0 if they are a non-participant;

is a continuous variable that captures the customer’s pre-EcoFactor installation, weather normalized (consumption divided by degree days), average daily consumption;

is an error term; is a coefficient showing the changes in propensity to participate in the NVE Demand Response

program that occurs for a change in the variable;

After the propensity scores were estimated, for each treatment premise , a k-nearest neighbors algorithm is used to find the closest propensity score from among the control premises. It should also be noted that in addition to the propensity scores, treatment members and control group members were matched exactly with respect to their rate classes. This procedure was implemented in R using the “MatchIt” package23 and matches were selected with replacement.

As with the subgroup loads, once the control groups are selected and confirmed, they are aggregated to form the subgroup control time series, which we denote by .

In order to ensure the quality of the matching procedure, Welch’s Two Sample t-test was conducted. The results are in the Table 67 below:

Table 67: Welch’s Two Sample t-test

Independent Variable

Treatment Control Welch Test T

Statistic

Welch Test P-Value Pre-Matching Mean

Post Matching Mean

Pre-Matching Mean

Post Matching Mean

kWhN 2.74 2.74 4.35 2.73 0.564 0.573

23 https://cran.r-project.org/web/packages/MatchIt/MatchIt.pdf

Appendix D 114 Page 120 of 359

https://cran.r-project.org/web/packages/MatchIt/MatchIt.pdf


Because the p-value is greater than 0.05 we fail to reject the null hypothesis and conclude that the true difference in means is not statistically significantly different than zero.

Appendix D 115 Page 121 of 359

11 APPENDIX E: NON-EVENT DAY CONTROL GROUPS FITS

The figures below depict the load curves of the various device populations and the DR-event matched control groups during non-event days.

Figure 21: DP1RS Non-Event Day Load Shapes

Appendix E 116 Page 122 of 359


Figure 22: DP1RM Non-Event Day Load Shapes



Figure 23: DP3CSERS Non-Event Day Load Shapes



Figure 24: DP3CSERM Non-Event Day Load Shapes



Figure 25: DP3LCRRS Non-Event Day Load Shapes



Figure 26: DP3LCRRM Non-Event Day Load Shapes



Figure 27: DP4LCRRS Non-Event Day Load Shapes



Figure 28: DP4LCRRM Non-Event Day Load Shapes



Figure 29: DP7RS Non-Event Day Load Shapes



Figure 30: DP7RM Non-Event Day Load Shapes



Figure 31: DP11 Non-Event Day Load Shapes


12 APPENDIX F: 2017 HOURLY IMPACTS

The following table provides the hourly load impacts associated with 2017 DR events by date and device population for all hours of DR event dates.

Table 68: Hourly Impacts for DR Event Dates, Residential Device Populations

Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population

DP4LCR. DP7.R DP7. RS M RS

DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

6/5/2017 13:00 0 0 0 0 0 0 0 0 0 0 -28 -95 -79 -992 0 0 0 0 -1194

6/5/2017 14:00 0 0 0 0 0 0 0 0 0 0 -198 -1269 -557 -13299 0 0 0 0 -15323

6/5/2017 15:00 1606 27320 0 0 0 0 0 0 0 0 -203 -2313 -572 -24241 0 0 0 0 1596

6/5/2017 16:00 2270 29942 214 1778 79 724 87 633 493 4572 363 5704 1023 59792 0 0 0 0 107673

6/5/2017 17:00 373 -1586 157 1017 102 754 77 638 354 3387 67 2525 190 26471 0 0 0 0 34526

6/5/2017 18:00 -592 -

11243 62 -170 43 277 27 -8 -60 -1358 -162 -1164 -457 -12196 0 0 0 0 -27001

6/5/2017 19:00 -229 -3333 81 397 55 358 23 -71 32 -250 -82 -495 -232 -5185 0 0 0 0 -8931

6/6/2017 13:00 0 0 0 0 0 0 0 0 0 0 -34 -130 -93 -1356 0 0 0 0 -1613

6/6/2017 14:00 0 0 0 0 0 0 0 0 0 0 -156 -1186 -430 -12336 0 0 0 0 -14109

6/6/2017 15:00 1662 27871 0 0 0 0 0 0 0 0 -156 -2254 -430 -23432 0 0 0 0 3261

6/6/2017 16:00 2267 30230 144 2368 125 792 114 594 341 4286 409 6142 1126 63863 3 -1 0 0 112800

6/6/2017 17:00 72 -3981 82 1644 132 729 114 565 268 3265 129 2679 355 27855 2 -1 0 0 33908

6/6/2017 18:00 -719 -

11071 39 -156 104 269 10 -64 -145 -1656 -152 -1154 -420 -11997 -1 -1 0 0 -27113

6/6/2017 19:00 -245 -3112 34 470 78 434 30 -111 -60 -469 -85 -493 -235 -5126 0 0 0 0 -8892

6/7/2017 13:00 0 0 0 0 0 0 0 0 0 0 -41 -167 -112 -1736 0 0 0 0 -2056

6/7/2017 14:00 0 0 0 0 0 0 0 0 0 0 -223 -1519 -615 -15792 0 0 0 0 -18149

6/7/2017 15:00 1513 26187 0 0 0 0 0 0 0 0 -178 -2768 -492 -28783 0 0 0 0 -4521

6/7/2017 16:00 2047 29067 220 2756 71 651 78 627 437 4071 361 5569 994 57903 2 0 0 0 104851

6/7/2017 17:00 121 -4274 195 2351 81 663 100 693 366 3060 70 2636 194 27413 1 0 0 0 33670

Appendix F 127

Page 133 of 359


Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

6/7/2017 18:00 -582 -

10372 115 1050 55 332 35 107 21 -1347 -171 -901 -473 -9369 1 1 0 0 -21499

6/7/2017 19:00 -63 -2647 155 1297 65 384 26 69 99 -219 -70 -344 -193 -3576 1 0 0 0 -5016

6/14/2017 13:00 0 0 0 0 0 0 0 0 0 0 -35 -256 -92 -2499 0 0 0 0 -2881

6/14/2017 14:00 0 0 0 0 0 0 0 0 0 0 -214 -1960 -565 -19143 0 0 0 0 -21881

6/14/2017 15:00 1515 23100 0 0 0 0 0 0 0 0 -204 -2844 -537 -27784 0 0 0 0 -6754

6/14/2017 16:00 2287 27449 218 1793 113 675 86 476 412 3525 347 5354 915 52305 2 0 0 0 95959

6/14/2017 17:00 232 -2486 201 1319 116 742 86 517 276 2547 77 2642 203 25813 1 1 0 0 32287

6/14/2017 18:00 -470 -8936 139 179 58 436 58 108 -63 -1345 -171 -769 -451 -7510 -1 1 0 0 -18737

6/14/2017 19:00 -77 -1728 150 459 48 404 62 66 34 -431 -89 -326 -235 -3181 0 1 0 0 -4843

6/15/2017 13:00 0 0 0 0 0 0 0 0 0 0 -34 -168 -90 -1628 0 0 0 0 -1920

6/15/2017 14:00 0 0 0 0 0 0 0 0 0 0 -197 -1637 -517 -15867 0 0 0 0 -18219

6/15/2017 15:00 1890 28829 0 0 0 0 0 0 0 0 -148 -2656 -389 -25737 0 0 0 0 1790

6/15/2017 16:00 2697 32124 210 2448 121 673 107 625 321 4960 432 6870 1133 66578 3 0 0 0 119302

6/15/2017 17:00 581 -1379 149 1818 110 637 127 658 211 3703 82 3139 215 30423 3 0 0 0 40478

6/15/2017 18:00 -227 -

10203 26 -21 98 173 68 135 -146 -950 -211 -1229 -552 -11907 1 -1 0 0 -24944

6/15/2017 19:00 -104 -2548 59 444 77 248 38 40 -103 -164 -124 -521 -325 -5048 0 -1 0 0 -8030

6/16/2017 13:00 0 0 0 0 0 0 0 0 0 0 -44 -148 -114 -1423 0 0 0 0 -1728

6/16/2017 14:00 0 0 0 0 0 0 0 0 0 0 -220 -1270 -573 -12230 0 0 0 0 -14293

6/16/2017 15:00 1872 30396 0 0 0 0 0 0 0 0 -164 -1930 -425 -18579 0 0 0 0 11170

6/16/2017 16:00 2437 32805 256 2803 97 519 114 663 242 1503 424 7044 1101 67817 1 -2 0 0 117825

6/16/2017 17:00 334 -856 227 2154 89 639 133 665 198 1299 88 3013 229 29003 1 -1 0 0 37214

6/16/2017 18:00 -292 -

10072 101 -88 30 110 69 84 82 108 -163 -1226 -423 -11801 -1 -2 0 0 -23485

6/16/2017 19:00 -21 -2469 123 396 83 132 49 -37 122 366 -51 -542 -133 -5219 0 -1 0 0 -7201

6/19/2017 13:00 0 0 0 0 0 0 0 0 0 0 -19 -82 -49 -780 0 0 0 0 -929

6/19/2017 14:00 0 0 0 0 0 0 0 0 0 0 -171 -985 -441 -9342 0 0 0 0 -10939

Appendix F 128

Page 134 of 359


Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

6/19/2017 15:00 1997 35767 0 0 0 0 0 0 0 0 -83 -1072 -215 -10171 0 0 0 0 26223

6/19/2017 16:00 2308 36194 375 3829 86 991 105 763 489 5558 447 8306 1155 78797 0 1 0 0 139405

6/19/2017 17:00 -57 914 285 2804 84 967 82 835 338 3743 36 3217 94 30520 0 1 0 0 43864

6/19/2017 18:00 -848 -9683 50 -560 51 186 -10 77 -38 -875 -210 -1199 -541 -11370 -1 1 0 0 -24970

6/19/2017 19:00 -287 -2675 120 46 72 176 -7 -65 59 -266 -110 -651 -284 -6177 0 2 0 0 -10050

6/20/2017 13:00 0 0 0 0 0 0 0 0 0 0 -15 -48 -39 -456 0 0 0 0 -559

6/20/2017 14:00 0 0 0 0 0 0 0 0 0 0 -157 -854 -405 -8056 0 0 0 0 -9472

6/20/2017 15:00 2262 35542 0 0 0 0 0 0 0 0 -34 -1095 -87 -10325 0 0 0 0 26263

6/20/2017 16:00 2417 35606 399 3652 132 920 113 812 563 5767 530 8264 1363 77919 3 2 0 0 138462

6/20/2017 17:00 259 1154 345 2719 118 851 115 832 407 3818 125 3113 321 29357 2 2 0 0 43538

6/20/2017 18:00 -670 -

10077 135 -1026 74 167 14 203 -21 -679 -161 -1335 -414 -12588 -1 -2 0 0 -26381

6/20/2017 19:00 -352 -2887 143 -351 33 144 9 56 28 -287 -99 -711 -254 -6705 0 -2 0 0 -11235

6/21/2017 13:00 0 0 0 0 0 0 0 0 0 0 -54 -84 -137 -784 0 0 0 0 -1059

6/21/2017 14:00 0 0 0 0 0 0 0 0 0 0 -208 -1052 -532 -9848 0 0 0 0 -11639

6/21/2017 15:00 1996 34679 0 0 0 0 0 0 0 0 -132 -1451 -339 -13593 0 0 0 0 21160

6/21/2017 16:00 2311 35686 271 3952 130 727 79 794 443 5585 466 8176 1193 76574 2 1 0 0 136389

6/21/2017 17:00 -183 87 183 3427 142 675 74 813 282 3605 59 3367 151 31532 2 0 0 0 44216

6/21/2017 18:00 -771 -

10198 -19 490 79 15 19 106 -51 -1219 -206 -1290 -527 -12083 -1 -2 0 0 -25659

6/21/2017 19:00 -221 -2826 71 685 91 57 13 4 5 -520 -97 -662 -249 -6200 -1 -1 0 0 -9852

6/22/2017 13:00 0 0 0 0 0 0 0 0 0 0 -23 -110 -59 -1022 0 0 0 0 -1214

6/22/2017 14:00 0 0 0 0 0 0 0 0 0 0 -157 -1103 -401 -10263 0 0 0 0 -11923

6/22/2017 15:00 2015 35959 0 0 0 0 0 0 0 0 -89 -1428 -227 -13293 0 0 0 0 22938

6/22/2017 16:00 2452 35914 332 3600 113 953 94 787 495 5882 512 8158 1310 75932 1 5 0 0 136538

6/22/2017 17:00 17 162 262 2551 132 844 95 812 328 4088 125 3260 319 30344 1 4 0 0 43344

6/22/2017 18:00 -685 -

10253 -7 -858 83 136 -6 84 -74 -536 -164 -1256 -420 -11691 -2 1 0 0 -25649

Appendix F 129

Page 135 of 359


Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

6/22/2017 19:00 -297 -3464 65 -49 80 182 -1 -21 12 66 -69 -596 -177 -5545 -2 5 0 0 -9812

6/23/2017 13:00 0 0 0 0 0 0 0 0 0 0 -36 -79 -93 -729 0 0 0 0 -937

6/23/2017 14:00 0 0 0 0 0 0 0 0 0 0 -179 -1057 -457 -9807 0 0 0 0 -11500

6/23/2017 15:00 2102 34711 0 0 0 0 0 0 0 0 -72 -1311 -184 -12169 0 0 0 0 23076

6/23/2017 16:00 2684 35148 255 4343 99 936 106 717 552 5991 508 8339 1297 77374 2 5 0 0 138354

6/23/2017 17:00 646 60 219 3668 98 837 111 743 393 3750 150 3328 384 30883 2 2 0 0 45273

6/23/2017 18:00 -156 -

10044 12 142 43 81 26 81 1 -1008 -135 -1270 -344 -11780 0 -3 0 0 -24352

6/23/2017 19:00 -130 -3031 50 547 70 41 8 -86 51 -448 -60 -597 -153 -5541 -2 -1 0 0 -9283

6/26/2017 13:00 0 0 0 0 0 0 0 0 0 0 -40 -196 -102 -1796 0 0 0 0 -2135

6/26/2017 14:00 0 0 0 0 0 0 0 0 0 0 -221 -1713 -560 -15673 0 0 0 0 -18167

6/26/2017 15:00 1832 31774 0 0 0 0 0 0 0 0 -105 -2182 -265 -19967 0 0 0 0 11087

6/26/2017 16:00 2415 32896 274 3198 116 567 87 702 542 4839 476 7638 1205 69880 2 3 0 0 124840

6/26/2017 17:00 187 -2191 238 2551 116 559 120 663 391 3222 117 3275 297 29960 1 -2 0 0 39504

6/26/2017 18:00 -434 -

11325 91 244 84 15 50 -226 35 -1513 -114 -1202 -288 -10995 -1 -8 0 0 -25587

6/26/2017 19:00 -239 -2877 110 639 85 130 35 -265 116 -384 -58 -511 -147 -4675 -2 -6 0 0 -8048

6/27/2017 13:00 0 0 0 0 0 0 0 0 0 0 -50 -216 -126 -1960 0 0 0 0 -2353

6/27/2017 14:00 0 0 0 0 0 0 0 0 0 0 -255 -1846 -640 -16786 0 0 0 0 -19527

6/27/2017 15:00 1623 31390 0 0 0 0 0 0 0 0 -196 -2416 -492 -21966 0 0 0 0 7944

6/27/2017 16:00 2104 32357 276 2398 125 602 111 678 382 5490 392 7529 983 68448 4 3 0 0 121883

6/27/2017 17:00 -124 -3278 226 1505 120 613 145 766 239 4095 39 3193 97 29034 2 -2 0 0 36672

6/27/2017 18:00 -696 -

11459 83 -680 95 22 34 99 -131 -648 -213 -1283 -534 -11664 0 -10 0 0 -26986

6/27/2017 19:00 -172 -3106 69 -50 91 130 36 6 -86 31 -151 -597 -379 -5426 0 -7 0 0 -9611

6/28/2017 13:00 0 0 0 0 0 0 0 0 0 0 -48 -234 -121 -2104 0 0 0 0 -2507

6/28/2017 14:00 0 0 0 0 0 0 0 0 0 0 -234 -1867 -583 -16813 0 0 0 0 -19497

6/28/2017 15:00 1882 31009 0 0 0 0 0 0 0 0 -167 -2333 -415 -21009 0 0 0 0 8967

Appendix F 130

Page 136 of 359


Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

6/28/2017 16:00 2505 33103 328 2543 110 739 103 725 460 5116 464 7554 1155 68023 3 9 0 0 122938

6/28/2017 17:00 467 -2556 253 1613 102 804 121 726 370 3776 116 3305 288 29762 1 0 0 0 39148

6/28/2017 18:00 -407 -

11401 93 -709 38 287 58 108 4 -875 -149 -1194 -371 -10751 -1 -2 0 0 -25271

6/28/2017 19:00 9 -3429 105 104 32 310 61 59 48 -148 -86 -611 -214 -5499 -1 2 0 0 -9259

6/30/2017 13:00 0 0 0 0 0 0 0 0 0 0 -34 -156 -85 -1392 0 0 0 0 -1668

6/30/2017 14:00 0 0 0 0 0 0 0 0 0 0 -248 -1715 -612 -15313 0 0 0 0 -17889

6/30/2017 15:00 1991 31279 0 0 0 0 0 0 0 0 -140 -2240 -345 -19998 0 0 0 0 10547

6/30/2017 16:00 2341 32259 217 2844 92 856 119 749 510 5061 502 7836 1238 69952 3 11 0 0 124588

6/30/2017 17:00 387 -2632 172 2096 69 916 113 742 475 3344 182 3487 449 31129 4 7 0 0 40938

6/30/2017 18:00 -414 -

11736 44 -254 47 374 30 -32 54 -1274 -110 -1159 -271 -10343 1 -14 0 0 -25059

6/30/2017 19:00 -133 -3628 43 303 59 355 39 -129 114 -523 -53 -547 -132 -4883 0 -10 0 0 -9126

7/3/2017 13:00 0 0 0 0 0 0 0 0 0 0 -38 -203 -94 -1791 0 0 0 0 -2126

7/3/2017 14:00 0 0 0 0 0 0 0 0 0 0 -209 -1784 -516 -15736 0 0 0 0 -18244

7/3/2017 15:00 1947 30960 0 0 0 0 0 0 0 0 -93 -2167 -230 -19113 0 0 0 0 11304

7/3/2017 16:00 2800 32585 191 3692 85 555 106 698 546 4647 535 8026 1322 70794 6 34 0 0 126622

7/3/2017 17:00 403 -2861 142 3289 83 559 125 685 418 2943 151 3626 372 31979 3 28 0 0 41944

7/3/2017 18:00 -434 -

12105 -29 995 51 -23 32 44 24 -1619 -119 -1204 -294 -10623 1 -2 0 0 -25305

7/3/2017 19:00 -231 -3801 -8 1286 39 88 25 -81 114 -635 -56 -506 -137 -4460 1 14 0 0 -8348

7/5/2017 13:00 0 0 0 0 0 0 0 0 0 0 -47 -137 -117 -1204 0 0 0 0 -1505

7/5/2017 14:00 0 0 0 0 0 0 0 0 0 0 -229 -1678 -568 -14707 0 0 0 0 -17183

7/5/2017 15:00 2025 34233 0 0 0 0 0 0 0 0 -134 -1714 -331 -15018 0 0 0 0 19061

7/5/2017 16:00 2779 34678 358 2764 72 802 104 658 559 5926 506 9101 1252 79750 2 35 0 0 139346

7/5/2017 17:00 635 -641 331 1828 60 878 125 676 408 3959 103 3749 254 32851 1 25 0 0 45240

7/5/2017 18:00 -346 -

11285 118 -1061 5 145 55 -44 -51 -1233 -214 -1542 -529 -13512 -2 -9 0 0 -29504

7/5/2017 19:00 41 -2991 97 -453 46 79 41 -185 73 -525 -83 -641 -207 -5616 -2 -17 0 0 -10345

Appendix F 131

Page 137 of 359


Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

7/6/2017 13:00 0 0 0 0 0 0 0 0 0 0 -30 -113 -74 -994 0 0 0 0 -1212

7/6/2017 14:00 0 0 0 0 0 0 0 0 0 0 -166 -1273 -411 -11158 0 0 0 0 -13009

7/6/2017 15:00 2544 35685 0 0 0 0 0 0 0 0 -33 -1404 -81 -12300 0 0 0 0 24411

7/6/2017 16:00 2633 35782 320 3514 82 892 95 1001 460 5318 580 8869 1436 77723 3 40 0 0 138750

7/6/2017 17:00 98 1245 301 2516 79 770 113 1014 312 3532 178 3624 440 31758 1 36 0 0 46019

7/6/2017 18:00 -644 -9611 86 -887 37 43 36 -8 -24 -1024 -131 -1364 -325 -11951 0 -4 0 0 -25771

7/6/2017 19:00 -319 -3156 133 -162 52 51 29 -129 19 -306 -82 -619 -203 -5424 -2 3 0 0 -10114

7/7/2017 13:00 0 0 0 0 0 0 0 0 0 0 -42 -89 -103 -769 0 0 0 0 -1002

7/7/2017 14:00 0 0 0 0 0 0 0 0 0 0 -179 -1137 -443 -9865 0 0 0 0 -11624

7/7/2017 15:00 2053 37012 0 0 0 0 0 0 0 0 -18 -1081 -45 -9379 0 0 0 0 28541

7/7/2017 16:00 2679 35194 239 1601 119 843 104 847 567 5316 548 9386 1354 81431 4 43 0 0 140276

7/7/2017 17:00 746 939 263 2284 115 818 119 875 400 3277 150 3692 371 32028 3 17 0 0 46096

7/7/2017 18:00 -167 -9575 234 1985 53 170 51 210 21 -974 -133 -1287 -330 -11162 1 -23 0 0 -20926

7/7/2017 19:00 -68 -3533 231 1573 51 150 24 -62 56 -446 -76 -677 -187 -5875 -2 -16 0 0 -8856

7/8/2017 13:00 0 0 0 0 0 0 0 0 0 0 -14 -129 -33 -1102 0 0 0 0 -1278

7/8/2017 14:00 0 0 0 0 0 0 0 0 0 0 -140 -1151 -346 -9867 0 0 0 0 -11504

7/8/2017 15:00 1750 33924 0 0 0 0 0 0 0 0 -48 -1057 -118 -9062 0 0 0 0 25391

7/8/2017 16:00 2646 34138 285 3479 85 610 113 689 519 5247 578 9184 1426 78757 3 57 0 0 137815

7/8/2017 17:00 431 -1461 238 2453 73 524 117 748 373 3429 189 3908 467 33514 3 49 0 0 45054

7/8/2017 18:00 -267 -

10763 20 -204 15 -193 26 56 -71 -1183 -91 -1368 -225 -11731 1 -5 0 0 -25984

7/8/2017 19:00 -54 -3144 58 398 18 -89 32 -24 19 -438 -46 -496 -114 -4257 -2 7 0 0 -8134

7/12/2017 13:00 0 0 0 0 0 0 0 0 0 0 -54 -172 -131 -1455 0 0 0 0 -1812

7/12/2017 14:00 0 0 0 0 0 0 0 0 0 0 -202 -1822 -495 -15431 0 0 0 0 -17950

7/12/2017 15:00 2176 32085 0 0 0 0 0 0 0 0 -133 -2192 -327 -18566 0 0 0 0 13042

7/12/2017 16:00 2528 33622 342 2235 125 707 91 708 447 4999 492 8098 1205 68578 4 51 0 0 124233

Appendix F 132

Page 138 of 359


Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

7/12/2017 17:00 31 -2594 320 1081 111 678 101 745 313 3478 116 3335 284 28244 1 36 0 0 36282

7/12/2017 18:00 -695 -

11656 132 -959 29 171 12 71 -134 -921 -169 -1404 -413 -11892 -1 -35 0 0 -27865

7/12/2017 19:00 -290 -2970 183 -91 36 173 -10 80 1 -125 -84 -640 -206 -5422 0 -16 0 0 -9381

7/13/2017 13:00 0 0 0 0 0 0 0 0 0 0 -41 -209 -100 -1747 0 0 0 0 -2097

7/13/2017 14:00 0 0 0 0 0 0 0 0 0 0 -231 -1761 -559 -14722 0 0 0 0 -17272

7/13/2017 15:00 2026 33139 0 0 0 0 0 0 0 0 -124 -2131 -300 -17823 0 0 0 0 14787

7/13/2017 16:00 2480 33741 231 2966 117 661 179 695 439 5436 478 8685 1157 72625 7 55 0 0 129952

7/13/2017 17:00 428 -2637 165 2172 124 683 193 646 325 3972 142 3657 343 30577 4 25 0 0 40815

7/13/2017 18:00 -385 -

11860 23 -469 56 109 21 33 -47 -735 -179 -1433 -433 -11985 -2 -75 0 0 -27362

7/13/2017 19:00 -159 -3472 58 165 75 127 52 -33 71 -139 -110 -672 -266 -5615 -3 -49 0 0 -9970

7/14/2017 13:00 0 0 0 0 0 0 0 0 0 0 -48 -144 -117 -1200 0 0 0 0 -1509

7/14/2017 14:00 0 0 0 0 0 0 0 0 0 0 -238 -1613 -575 -13428 0 0 0 0 -15854

7/14/2017 15:00 1931 33744 0 0 0 0 0 0 0 0 -139 -1838 -337 -15301 0 0 0 0 18059

7/14/2017 16:00 2799 34156 388 3173 87 871 101 693 515 5201 510 8918 1233 74250 9 120 0 0 133025

7/14/2017 17:00 645 -1150 352 1988 84 869 108 671 394 3494 108 3519 261 29298 1 80 0 0 40723

7/14/2017 18:00 -231 -

11206 141 -758 9 223 45 -70 -26 -962 -196 -1657 -473 -13792 -4 -24 0 0 -28979

7/14/2017 19:00 -26 -3637 164 -83 -1 308 6 -170 39 -327 -106 -733 -256 -6106 -1 -21 0 0 -10949

7/18/2017 13:00 0 0 0 0 0 0 0 0 0 0 -37 -229 -88 -1875 0 0 0 0 -2229

7/18/2017 14:00 0 0 0 0 0 0 0 0 0 0 -220 -1941 -529 -15905 0 0 0 0 -18595

7/18/2017 15:00 1809 32519 0 0 0 0 0 0 0 0 -127 -2180 -306 -17861 0 0 0 0 13855

7/18/2017 16:00 2086 33829 233 1989 112 697 102 597 472 5095 489 8425 1179 69023 12 113 0 12 124453

7/18/2017 17:00 129 -2640 192 966 101 674 103 549 402 3734 142 3449 343 28255 10 76 0 15 36484

7/18/2017 18:00 -564 -

10902 24 -1112 34 202 36 -34 7 -975 -168 -1479 -405 -12115 0 -80 0 5 -27531

7/18/2017 19:00 -193 -3407 67 -277 79 299 13 -65 113 -41 -78 -694 -189 -5684 3 -39 0 13 -10093

7/20/2017 13:00 0 0 0 0 0 0 0 0 0 0 -71 -395 -172 -3223 0 0 0 0 -3861

Appendix F 133

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Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

7/20/2017 14:00 0 0 0 0 0 0 0 0 0 0 -350 -3443 -842 -28116 0 0 0 0 -32750

7/20/2017 15:00 1763 22799 0 0 0 0 0 0 0 0 -246 -4228 -591 -34523 0 0 0 0 -15025

7/20/2017 16:00 2361 25526 190 1556 85 586 64 519 373 3334 357 6591 860 53824 15 90 0 9 96330

7/20/2017 17:00 513 -5115 75 723 94 506 57 521 278 2596 58 3253 139 26561 11 46 0 11 30318

7/20/2017 18:00 -194 -9760 -9 -116 33 159 9 7 -100 -1471 -165 -609 -397 -4973 -5 -70 0 -9 -17660

7/20/2017 19:00 -55 -2280 12 308 36 277 19 6 19 -339 -102 -152 -246 -1242 -4 -8 0 3 -3752

7/21/2017 13:00 0 0 0 0 0 0 0 0 0 0 -64 -216 -153 -1750 0 0 0 0 -2183

7/21/2017 14:00 0 0 0 0 0 0 0 0 0 0 -310 -2196 -743 -17816 0 0 0 0 -21065

7/21/2017 15:00 1922 30508 0 0 0 0 0 0 0 0 -197 -2426 -473 -19680 0 0 0 0 9653

7/21/2017 16:00 2500 32027 308 2595 108 651 118 652 500 5849 441 8498 1057 68934 19 136 0 38 124394

7/21/2017 17:00 541 -2942 321 1774 106 674 123 660 404 4091 104 3523 250 28574 9 72 0 36 38286

7/21/2017 18:00 -251 -

11812 123 -445 77 163 70 10 -25 -1335 -154 -1534 -369 -12446 -7 -79 0 -19 -28015

7/21/2017 19:00 -122 -3465 141 53 101 232 40 -62 68 -398 -58 -632 -139 -5126 -6 -57 0 3 -9430

7/28/2017 13:00 0 0 0 0 0 0 0 0 0 0 -57 -208 -135 -1629 0 0 0 0 -2029

7/28/2017 14:00 0 0 0 0 0 0 0 0 0 0 -296 -1997 -698 -15661 0 0 0 0 -18652

7/28/2017 15:00 2291 31801 0 0 0 0 0 0 0 0 -173 -2274 -409 -17836 0 0 0 0 13400

7/28/2017 16:00 2533 33468 343 3269 82 759 108 593 568 5086 478 8923 1129 69981 45 202 18 75 127568

7/28/2017 17:00 165 -2061 273 2527 80 778 128 644 432 3396 121 3664 285 28732 27 122 12 39 39313

7/28/2017 18:00 -556 -

11197 135 -102 78 194 58 22 -3 -1450 -161 -1433 -381 -11238 -11 -111 -7 -6 -26159

7/28/2017 19:00 -239 -2843 128 331 55 230 21 -71 82 -669 -89 -719 -210 -5637 -9 -63 -1 -19 -9701

8/1/2017 13:00 0 0 0 0 0 0 0 0 0 0 -35 -217 -83 -1685 0 0 0 0 -2019

8/1/2017 14:00 0 0 0 0 0 0 0 0 0 0 -221 -1836 -523 -14287 0 0 0 0 -16867

8/1/2017 15:00 1897 33623 0 0 0 0 0 0 0 0 -108 -1967 -255 -15306 0 0 0 0 17885

8/1/2017 16:00 2728 34050 255 3791 113 728 99 716 503 5530 553 9772 1313 76039 42 243 23 61 136476

8/1/2017 17:00 424 -2158 223 3092 111 810 117 713 377 3704 143 3874 339 30143 25 156 13 46 42093

Appendix F 134

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Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

8/1/2017 18:00 -413 -

12002 49 299 58 126 38 98 -20 -981 -178 -1769 -422 -13763 -13 -90 6 -27 -28981

8/1/2017 19:00 -88 -3278 88 622 44 159 29 10 73 -328 -101 -822 -239 -6395 -13 -32 3 -10 -10270

8/2/2017 13:00 0 0 0 0 0 0 0 0 0 0 -62 -232 -145 -1794 0 0 0 0 -2233

8/2/2017 14:00 0 0 0 0 0 0 0 0 0 0 -272 -1878 -641 -14548 0 0 0 0 -17339

8/2/2017 15:00 1809 32289 0 0 0 0 0 0 0 0 -139 -1939 -327 -15023 0 0 0 0 16671

8/2/2017 16:00 2575 32596 289 3138 119 772 113 682 532 4916 463 9660 1090 74846 53 247 25 81 132092

8/2/2017 17:00 428 -2316 273 2662 114 756 131 712 387 3260 64 4120 151 31920 31 148 24 54 42840

8/2/2017 18:00 -403 -

11301 73 -44 62 207 11 70 15 -894 -173 -1130 -407 -8755 -17 -130 -1 -26 -22815

8/2/2017 19:00 12 -3352 93 280 77 285 20 8 42 -37 -100 -582 -235 -4511 -6 -85 1 -16 -8092

8/7/2017 13:00 0 0 0 0 0 0 0 0 0 0 -54 -312 -125 -2352 0 0 0 0 -2843

8/7/2017 14:00 0 0 0 0 0 0 0 0 0 0 -307 -2601 -710 -19597 0 0 0 0 -23215

8/7/2017 15:00 1674 27455 0 0 0 0 0 0 0 0 -209 -2842 -484 -21414 0 0 0 0 4181

8/7/2017 16:00 2416 29302 251 3003 112 620 96 592 540 4778 451 8216 1044 61911 66 284 21 92 113681

8/7/2017 17:00 311 -4626 200 2397 94 684 114 602 428 3630 78 3470 181 26148 38 180 17 71 33930

8/7/2017 18:00 -337 -

11666 97 908 26 240 73 29 -21 -1054 -162 -1326 -375 -9989 -53 -190 0 -54 -23800

8/7/2017 19:00 69 -3063 148 1167 28 338 72 0 92 143 -64 -531 -147 -4001 -26 -114 2 -31 -5892

8/9/2017 13:00 0 0 0 0 0 0 0 0 0 0 -52 -250 -121 -1883 0 0 0 0 -2306

8/9/2017 14:00 0 0 0 0 0 0 0 0 0 0 -309 -2445 -716 -18422 0 0 0 0 -21892

8/9/2017 15:00 1841 30436 0 0 0 0 0 0 0 0 -208 -2935 -482 -22119 0 0 0 0 6533

8/9/2017 16:00 2508 32198 282 2570 90 686 86 610 476 5689 514 8785 1190 66199 51 271 19 136 122206

8/9/2017 17:00 363 -2858 283 2061 118 692 112 589 378 4233 123 3847 286 28988 40 139 18 83 39393

8/9/2017 18:00 -471 -

10929 136 153 66 253 38 2 -21 -918 -168 -1479 -390 -11148 -33 -208 -5 -5 -25119

8/9/2017 19:00 -272 -3304 142 578 58 287 11 -54 60 32 -100 -663 -232 -4998 -29 -104 -4 0 -8589

8/10/2017 13:00 0 0 0 0 0 0 0 0 0 0 -53 -225 -121 -1690 0 0 0 0 -2089

8/10/2017 14:00 0 0 0 0 0 0 0 0 0 0 -264 -2413 -610 -18127 0 0 0 0 -21414

Appendix F 135

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Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

8/10/2017 15:00 2059 31280 0 0 0 0 0 0 0 0 -107 -2655 -247 -19948 0 0 0 0 10382

8/10/2017 16:00 2607 32801 336 2932 104 773 123 573 498 5615 572 9243 1322 69447 96 334 31 124 127377

8/10/2017 17:00 508 -1892 278 2143 103 772 118 642 371 4168 161 3912 372 29392 65 209 22 86 41323

8/10/2017 18:00 -538 -

10725 138 -181 58 267 34 47 -47 -884 -158 -1649 -366 -12386 -24 -180 -10 -40 -26592

8/10/2017 19:00 -244 -3239 150 72 24 303 32 -63 23 22 -85 -737 -196 -5535 -25 -120 -3 -37 -9618

8/11/2017 13:00 0 0 0 0 0 0 0 0 0 0 -50 -250 -117 -1877 0 0 0 0 -2293

8/11/2017 14:00 0 0 0 0 0 0 0 0 0 0 -250 -2348 -577 -17641 0 0 0 0 -20815

8/11/2017 15:00 528 3322 0 0 0 0 0 0 0 0 -14 706 -31 5305 0 0 0 0 9816

8/11/2017 16:00 207 5041 207 1702 48 427 58 177 95 735 78 1937 180 14555 16 33 1 42 25495

8/11/2017 17:00 -4 2594 106 -34 58 504 58 235 181 1116 -12 624 -28 4687 5 31 -4 27 10119

8/11/2017 18:00 -246 1393 135 431 47 427 53 185 134 983 -71 -60 -164 -452 0 -10 -5 -1 2785

8/11/2017 19:00 -268 -79 122 712 57 381 33 150 126 1047 -78 -253 -180 -1902 7 -24 -11 -8 -149

8/18/2017 13:00 0 0 0 0 0 0 0 0 0 0 -63 -320 -144 -2352 0 0 0 0 -2880

8/18/2017 14:00 0 0 0 0 0 0 0 0 0 0 -342 -2664 -779 -19547 0 0 0 0 -23333

8/18/2017 15:00 1901 29627 0 0 0 0 0 0 0 0 -252 -2980 -575 -21863 0 0 0 0 5859

8/18/2017 16:00 2352 32359 313 1956 85 676 95 625 507 4871 399 8731 907 64066 86 441 23 196 118468

8/18/2017 17:00 18 -3047 240 1080 67 617 91 601 382 3453 13 3677 29 26982 54 285 16 151 34544

8/18/2017 18:00 -592 -

11779 117 -657 37 135 17 101 -45 -997 -217 -1541 -494 -11309 -62 -197 -11 -15 -27482

8/18/2017 19:00 -247 -3347 170 145 48 228 46 22 55 -177 -123 -634 -280 -4653 -36 -91 -6 -14 -8874

8/25/2017 13:00 0 0 0 0 0 0 0 0 0 0 -67 -361 -149 -2571 0 0 0 0 -3148

8/25/2017 14:00 0 0 0 0 0 0 0 0 0 0 -353 -2965 -787 -21105 0 0 0 0 -25210

8/25/2017 15:00 1550 28120 0 0 0 0 0 0 0 0 -269 -3502 -599 -24927 0 0 0 0 373

8/25/2017 16:00 1850 29488 237 1924 92 487 61 638 414 3965 376 8430 837 60012 101 353 23 101 109265

8/25/2017 17:00 -279 -5728 173 1175 99 329 23 653 275 2459 -12 3375 -26 24023 68 204 15 67 26809

8/25/2017 18:00 -1116 -

13072 38 -393 42 -78 -31 76 -124 -1902 -287 -1728 -638 -12298 -44 -303 -5 -24 -31858

Appendix F 136

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Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

8/25/2017 19:00 -333 -3312 70 275 34 112 -28 60 16 -485 -159 -713 -354 -5077 -23 -162 5 -31 -10078

8/29/2017 13:00 0 0 0 0 0 0 0 0 0 0 -50 -250 -108 -1747 0 0 0 0 -2154

8/29/2017 14:00 0 0 0 0 0 0 0 0 0 0 -295 -2243 -646 -15700 0 0 0 0 -18885

8/29/2017 15:00 1550 29479 0 0 0 0 0 0 0 0 -165 -2881 -361 -20166 0 0 0 0 7455

8/29/2017 16:00 1831 29262 229 1487 100 453 78 528 458 4275 429 8635 938 60451 121 364 38 177 109639

8/29/2017 17:00 -293 -5419 181 636 106 464 100 562 319 2979 43 3211 93 22476 91 216 18 136 25767

8/29/2017 18:00 -1085 -

11511 44 -533 47 49 16 -19 -77 -1612 -208 -2110 -455 -14771 -86 -367 -8 -58 -32678

8/29/2017 19:00 -361 -3083 124 281 85 320 20 29 59 -293 -111 -1000 -242 -7000 -40 -185 1 -21 -11397

8/30/2017 13:00 0 0 0 0 0 0 0 0 0 0 -56 -375 -121 -2611 0 0 0 0 -3163

8/30/2017 14:00 0 0 0 0 0 0 0 0 0 0 -295 -3184 -640 -22168 0 0 0 0 -26286

8/30/2017 15:00 1386 26168 0 0 0 0 0 0 0 0 -232 -3913 -505 -27246 0 0 0 0 -4343

8/30/2017 16:00 1796 26386 107 2016 58 377 107 486 438 4029 374 7585 813 52819 107 376 42 136 97874

8/30/2017 17:00 -297 -6141 134 1154 91 373 81 497 375 2973 48 2652 103 18465 99 245 37 74 20852

8/30/2017 18:00 -868 -

11112 36 6 50 141 21 -35 0 -1500 -196 -2315 -427 -16121 -63 -301 1 -67 -32684

8/30/2017 19:00 -239 -2314 105 759 60 260 29 53 96 -142 -75 -638 -164 -4446 -18 -151 18 -18 -6826

8/31/2017 13:00 0 0 0 0 0 0 0 0 0 0 -58 -302 -126 -2092 0 0 0 0 -2578

8/31/2017 14:00 0 0 0 0 0 0 0 0 0 0 -315 -2872 -682 -19903 0 0 0 0 -23772

8/31/2017 15:00 1555 27175 0 0 0 0 0 0 0 0 -219 -3581 -475 -24817 0 0 0 0 -362

8/31/2017 16:00 2100 28080 102 1596 79 417 78 661 424 4471 431 8320 933 57662 108 407 49 197 105871

8/31/2017 17:00 250 -4654 47 812 83 344 87 574 326 3444 71 3244 154 22487 80 249 40 168 27598

8/31/2017 18:00 -524 -

10882 -1 -330 41 -48 22 6 -39 -999 -159 -1853 -343 -12843 -85 -316 -3 -33 -28353

8/31/2017 19:00 -208 -2968 39 161 95 95 32 101 94 13 -94 -940 -204 -6516 -46 -155 11 -20 -10501

9/5/2017 13:00 0 0 0 0 0 0 0 0 0 0 -66 -284 -142 -1946 0 0 0 0 -2439

9/5/2017 14:00 0 0 0 0 0 0 0 0 0 0 -324 -2945 -698 -20152 0 0 0 0 -24119

9/5/2017 15:00 1792 29203 0 0 0 0 0 0 0 0 -248 -3429 -533 -23461 0 0 0 0 3324

Appendix F 137

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Date and Hour

DP1.R M

DP1. RS

DP3CSE. RM

DP3CSE. RS

DP3LCR. RM

DP3LCR. RS

DP4LCR. RM

Device Population


DP11B. RM

DP11B. RS

DP11M. RM

DP11M. RS

DP12R M

DP12 RS

DP13R M

DP13. RS

System Total

9/5/2017 16:00 1816 30426 279 2683 91 679 83 532 470 4315 421 8930 906 61097 131 457 55 224 113317

9/5/2017 17:00 -445 -4169 178 1692 96 661 104 509 353 2843 -4 3135 -8 21447 95 253 38 170 26739

9/5/2017 18:00 -1034 -

10643 102 194 64 218 55 -63 -42 -1343 -259 -2169 -556 -14842 -97 -292 -2 -77 -30709

9/5/2017 19:00 -378 -2372 115 1074 57 355 74 -34 83 -285 -126 -1037 -271 -7095 -44 -144 -2 -40 -10028

9/6/2017 13:00 0 0 0 0 0 0 0 0 0 0 -62 -409 -134 -2777 0 0 0 0 -3382

9/6/2017 14:00 0 0 0 0 0 0 0 0 0 0 -362 -3693 -778 -25085 0 0 0 0 -29918

9/6/2017 15:00 1641 25074 0 0 0 0 0 0 0 0 -256 -4286 -550 -29113 0 0 0 0 -7491

9/6/2017 16:00 2166 28156 55 843 126 585 73 568 463 4274 447 8543 961 58026 123 388 81 271 105796

9/6/2017 17:00 148 -4604 16 1140 150 660 75 636 356 3152 77 3755 165 25506 90 243 74 188 31564

9/6/2017 18:00 -409 -

10361 22 957 116 309 8 2 -75 -1227 -188 -1252 -405 -8501 -78 -323 13 -91 -21404

9/6/2017 19:00 -119 -3221 15 875 158 404 37 39 63 -91 -107 -556 -231 -3775 -36 -212 35 -7 -6757

Appendix F 138

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DSM-14

Page 145 of 359

Residential Demand Response NV Energy – Northern Nevada (SPPC)

Program Year 2017

Measurement and Verification Report

March 15, 2018

Prepared for:

Prepared by:


916-363-8383

Page 146 of 359

Residential Demand Response 2017 – NV Energy, Northern Nevada M&V Report March 2018

TABLE OF CONTENTS

1 EXECUTIVE SUMMARY 1

2 PROGRAM OVERVIEW AND METHODOLOGY 4

3 RESULTS 17

4 ENERGY SAVINGS CURVES 34

5 CONCLUSIONS AND RECOMMENDATIONS 35

6 APPENDIX A: DIFFERENCE-IN-DIFFERENCE METHODOLOGY FOR ECOFACTOR OPTIMIZATION SAVINGS 37

7 APPENDIX B: 2017 SAVINGS PER MONTH BY RATE CLASS 42

8 APPENDIX C: DETERMINING ENERGY SAVINGS (KWH) PER MONTH BY RATE CLASS 44

9 APPENDIX D: 2017 HOURLY IMPACTS 49

10 APPENDIX E: MATCHING 56

Page 147 of 359


LIST OF TABLES

Table Number: Description Table 1: DR Program Offerings per Budget Category 2 Table 2: Device Population Definitions 2 Table 3: 2017 Demand Response Events Ex Post Verified Demand Reduction 2 Table 4: Demand Response Verified 2017 Energy Savings 3 Table 5: Demand Response Potential Annual Energy Savings 3 Table 6: Report Outline 6 Table 7: Residential PowerShift Override Rates 7 Table 8: DP11D1 Demand Reduction Results 18 Table 9: DP11D1 Manage VDR and DR energy savings 19 Table 10: DP11D1 Build VDR and DR Energy Savings 20 Table 11: DP11DM1 Demand Reduction Results 23 Table 12. DP11DM1 Manage VDR and DR energy savings 24 Table 13: DP11DM1 Build VDR and DR energy savings 25 Table 14: DP12D1 Demand Reduction Results 28 Table 15: DP12D1 VDR and Energy Savings 28 Table 16: DP12 DM1 Demand Reduction Results 29 Table 17: DP12 DM1 Demand Reduction Results 31 Table 18: Average Per-Day Electricity Savings from EcoFactor Optimization (kWh) from Space Cooling (June – September) 32 Table 19: Average Per-Day Electricity Savings from EcoFactor Optimization (kWh) from Space Heating (October – May) 32 Table 20: Average Per-Month Savings from EcoFactor Optimization (therms) from Space Heating (October – April) 33 Table 21: M&V Summary Results from 2017 Demand Response Program 35 Table 22: Average Per-Day Electricity Savings from EcoFactor Optimization (kWh) from Space Cooling (June – September) 39 Table 23: Average Per-Day Electricity Savings from EcoFactor Optimization (kWh) from Space Heating (October – May) 39 Table 24: Treatment Regression Results 41 Table 25: Control Regression Results 41 Table 26: Residential PowerShift Manage DR Event kWh Savings by Month by Rate Class – 2017 42 Table 27: Residential PowerShift Build DR Event kWh Savings by Month by Rate Class – 2017 42 Table 28: Residential PowerShift Manage Energy Efficiency kWh Savings by Month by Rate Class – 2017

42 Table 29: Residential PowerShift Build Energy Efficiency kWh Savings by Month by Rate Class – 2017

42 Table 30: Residential PowerShift Build Pilot DR Event kWh Savings by Month by Rate Class – 2017 43 Table 31: Sample Calculation of Energy Savings 48 Table 32: Hourly Impacts for DR Event Dates, Residential Device Populations 49 Table 33: Welch’s Two Sample t-test 56

Page 148 of 359

1 EXECUTIVE SUMMARY

This report presents the results of the ADM Associates Inc. (ADM) impact evaluation of the NV Energy (“NVE”) 2017 Residential Demand Response (“DR”) program for its Northern Nevada service territory (SPPC1). The primary objective of the DR program is to enable NV Energy, during periods of critical peak demand at the system or grid level, to employ DR devices to curtail DR participants’ air conditioner (“AC”) loads. By curtailing those AC loads and effectively shifting them to later, non-critical peak demand hours, NV Energy can achieve the following objectives.

Improve the efficiency of its grid.

Avoid certain energy costs that would otherwise be incurred during critical peak demand periods (when electricity cost is typically highest).

Potentially defer the construction of new transmission and generation capacity.

1.1 HISTORY OF THE DEMAND RESPONSE PROGRAM

Since 2001, NV Energy has offered a Demand Reduction or DR program (known previously as the Air Conditioning Load Management program) in its Southern territory, in which customers volunteer to have a curtailment device installed on their AC. In 2011, a residential pilot program was approved and tested in the Northern service territory. The trial utilized a Home Energy Management system supported by cloud-based optimization software from technology vendor EcoFactor.2 The program was expanded to over 2,700 homes in 2015, which participated in 20 DR events that DR season. SPPC is continuing to build NVE’s DR resource through the installation of the next-generation Home Energy Management program. The EcoFactor device installations in 2015 were designated simply as “mPowered”. In 2016, NVE rebranded “mPowered” as “PowerShift” and continued recruiting into program, adding over 1,800 devices. During 2017, an “PowerShift Pilot” was implemented investigating the efficacy of a new Wifi smart thermostat from EcoBee.

It is important to describe some of the nomenclature used in this report and to note that these customer offerings fall into specific budget categories outlined in the Demand Response Program Data Sheet, which distinguishes budget categories between “Manage” and “Build” budgets. Support for all customers enrolled before 2017 fall into the Manage budget categories, while all customers newly enrolled in 2017 fall into the Build budget categories. For 2017, the customer offerings are related to budget categories as follows:

1 SPPC: Sierra Pacific Power Company or “Sierra” 2 In this report, the Home Energy Management system may be referred to as “EcoFactor devices” or simply

“EcoFactor”. The PCTs are provided by Computime and broadband gateways are provided by Digi International.


1


Table 1: DR Program Offerings per Budget Category

Residential Residential Customer Offerings Manage Build

(Pre-2017) (2017)

PowerShift

PowerShift Pilot

1.2 ORGANIZATION OF THE STUDY

The purposes of this measurement and verification (“M&V”) study are to determine the achieved peak demand and energy savings due to the Residential PowerShift program, which we will refer to as the Demand Response or DR program. The study is organized according to the different groups of control devices, or Device Populations (“DPs”), comprising the program. Table 2 provides the formal organization of the DR system.

Table 2: Device Population Definitions

Program Device Population Device Description

PowerShift Manage DP11 EcoFactor Two-Way PCTs

PowerShift Build DP11 EcoFactor Two-Way PCTs

PowerShift Build Pilot DP12 EcoBee WiFi Thermostats

The residential populations are further subdivided by rate class: for example, DP11 consists of DP11DM 1 (multifamily) and DP11D1 (single family). At this lowest level of organization, these subsets of DR devices are called subgroups. Altogether, DP11 has 4 and the PowerShift Pilot has 2 different subgroups.

1.3 PROGRAM RESULTS

For the NVE Demand Response program, 2017 Demand Response Events provided ex post peak verified demand reduction (VDR) presented in Table 3.

Table 3: 2017 Demand Response Events Ex Post Verified Demand Reduction

Program Device Population %NRD Available

Devices kW Factor Max VDR (kW)

Res (PowerShift) Manage DP11D1 10.07 4,350 1.85 7,237 DP11DM1 13.85 143 1.1 136

Res (PowerShift) Build DP11D1 10.07 2,670 1.85 4,442 DP11DM1 13.85 126 1.1 119

Res (PowerShift) Build Pilot DP12D1 14.69 747 2.85 1,816 DP12DM1 23.11 137 1.20 126

Total 8,173 13,877

For the NVE Demand Response program, 2017 DR Events and Energy Efficiency services provided ex post verified energy (kWh) savings presented in Table 4 below.


2


Table 4: Demand Response Verified 2017 Energy Savings

Program Device Population

2017 DR Energy Saving (kWh)

2017 EE Energy Savings (kWh)

2017 Total Energy Savings (kWh)

Potential Annual Energy

Savings(kWh)

Res (PowerShift) Manage DP11 -8,736 1,197,651 1,188,915 1,188,915

Res (PowerShift) Build DP11 -4,395 360,910 356,515 747,063

Res (PowerShift) Build Pilot DP12 828 828 14,664

Total -12,303 1,558,561 1,546,258 1,950,641

The following table provides an estimate of potential annual energy savings from the program if all of the following hypothetical conditions were achieved:

a) If all of the devices had been installed previous to the program year b) If no devices were to leave the program during the program year c) If all devices were to function optimally during the program year

The values provided in the following table do not represent verified savings for 2017 and do not represent verified savings for any future year.3

Table 5: Demand Response Potential Annual Energy Savings

Program Device Population

kWh Factor

Available Devices

kWh Savings DR

kWh Savings EE

kWh Savings Total

Res (PowerShift) Manage

DP11D1 -1.18 4,350 -5,136 1,152,390 1,147,254

DP11DM1 -25.17 143 -3,600 45,261 41,661

Res (PowerShift) Build

DP11D1 -1.18 2,670 -3,153 713,583 710,430

DP11DM1 -25.17 126 -3,172 39,804 36,632

Res (PowerShift) Build Pilot

DP12D1 20.43 747 15,261 15,261

DP12DM1 -4.36 137 -597 -597

Total 8,173 -397 1,951,038 1,950,641

3 During any given program year, ADM evaluates the whole population of DR devices, including DR devices which were installed previous to the program year and DR devices which have been installed during the program year.


3

2 PROGRAM OVERVIEW AND METHODOLOGY

This report presents the results of the ADM Associates Inc. (“ADM”) measurement and verification (“M&V”) study of NV Energy’s 2017 Residential Demand Response (“DR”) program. In this introductory chapter, we provide a high-level summary of the DR program, ADM’s M&V methodology, and the basic structure of the remainder of this report.

Throughout, important terms will appear in boldface when introduced for the first time.

2.1 NV ENERGY’S DEMAND RESPONSE PROGRAM

Demand Response, as the name suggests, encompasses a range of interventions and techniques utilities can use to respond to and control demand for electricity, either for economic reasons or in the event of an emergency. The types of interventions used in DR programs include both tariff-based and technology-based measures.

NVE began deploying DR technologies in its Northern Nevada service territory in 2011, beginning with the small-scale pilot of 64 residences. During 2015, the residential program component continued recruiting additional participants and by the end of 2015, there were over 3,000 participating devices in more than 2,600 homes. In 2016, NVE rebranded “mPowered” as “PowerShift” and continued recruiting into the program, adding over 1,800 devices. During 2017, the “PowerShift Pilot” was implemented investigating the efficacy of new Wifi smart thermostats from EcoBee.

2.1.1 About Residential PowerShift The residential PowerShift program is marketed not only as a DR program but also as a home energy management program utilizing an internet-connected EcoFactor powered programmable communicating thermostat (PCT). EcoFactor technology provides a software service that optimizes HVAC control operations based on household patterns, in order to potentially reduce wasteful energy usage.

For their participation in PowerShift, customers receive the programmable and controllable thermostat, installation, and automation service subscription free of charge4. The PowerShift customers also receive a tariff-based rebate that varies depending on the amount of demand savings achieved.

2.2 DEMAND RESPONSE EVENTS IN 2017

Between June and September 2017, 26 residential PowerShift demand response events were called. In

Figure 1, calendars of the summer months are shown with the event days shaded. All residential events began at 4 pm and lasted for two hours.

4 https://www.nvenergy.com/home/saveenergy/rebates/Power Shift/images/Power ShiftFAQ.pdf

Program Overview and Methodology Page 152 of 359

4

https://www.nvenergy.com/home/saveenergy/rebates/Power

June July S M T W Th F Sa

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

S M T W Th F Sa 1

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

August September S M T W Th F Sa

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

S M T W Th F Sa 1 2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Residential DR Event

Figure 1: Residential DR Event Dates

2.3 OBJECTIVES OF THE M&V STUDY

The objective of this study is to provide answers to each of the following questions:

1. To what extent did the residential demand response program reduce peak electric demand during the summer of 2017? 2. To what extent did the residential demand response program reduce energy consumption during the year of 2017? 3. For the purposes of forecasting, what factors influence the amount of demand reduction achieved in an event? 4. What was the customer response to demand response events? Specifically, at what rates did participants opt out of events or the program

entirely?

Program Overview and Methodology 5

Page 153 of 359

5. How reliable is the demand response system? Specifically, at what rates did individual devices fail to curtail the usage (i.e. Non-Responding Devices) of the equipment they controlled?

To provide an answer to these questions, ADM provided NVE with a research plan outlining a rigorous analytical approach, which relied on both ADM’s and NVE’s technical expertise and experience in evaluating and operating, respectively, demand response programs.

2.4 ORGANIZATION OF THIS REPORT

The remaining chapters of this report are outlined in Table 6 below.

Table 6: Report Outline

Chapter Number Chapter Title Description

3 PowerShift Results Estimates of kW factor and kWh factor for PowerShift are provided.

4 Energy Savings Curves Describes energy savings curves used for the 2017 DR analysis.

5 Conclusions and Recommendations

Recommendations for future program years are provided in light of this year’s evaluation.

2.5 DEMAND RESPONSE SYSTEM SIZE

In this section, we explain how tracking data for the DR program were used to quantify the size of the system at different times during the summer. The counts provided below are of available devices, which are defined as devices registered with NVE as DR assets. An available device becomes unavailable if and only if the customer in possession of said device decides to permanently opt out of a demand response program.

Available devices are to be distinguished with active or responding devices, which are available devices that are, at a given moment, controllable by NVE. The potential for various technical failures on the part of devices means that, in theory, and in practice, responding devices are a proper subset of available devices. The proportion of non-responding devices is estimated for each subgroup and each event and is used to adjust the number of available devices down to the number of responding devices that contribute actual demand reduction (see Equation 1).

2.5.1 Residential PowerShift Device Counts The PowerShift devices were tracked using NV Energy’s Demand Response Management System (DRMS), which serves as a centralized hub for controlling and monitoring the entire DR system.

The DRMS keeps track of when new customers join a DR program and when they opt out, as well as identifiers such as meter number and device serial number. Rate class information was determined by cross-referencing the DRMS with NVE’s Banner customer database. Utilizing the dates provided in the DRMS, the available devices in device subgroup at a given date is:


6


Equation 1

This quantity can change day-to-day, so is calculated for every event . However, to provide a specific value for system size, we have used the system size as of December 31, 2017 in Table 3 above, i.e., 8,173 (in both Build and Manage) available devices. Because this number does not come directly from the events, it should be considered as a “best case scenario” value.

The PowerShift PCTs were tracked using the “Customer Extract” report from the DRMS. For PowerShift, = 8,173 is the number of devices installed by the end of the 2017 calendar year.

2.5.2 Manual Overrides Residential program participants with an EcoFactor PCT can override the DR event setting either online, via a smart phone/tablet app, or on the physical thermostat.These override events are recorded in a log. Table 7 below provides the override rates for participants with an EcoFactor PCT.

Table 7: Residential PowerShift Override Rates

Date Percent of Overrides

during First Pre-cool Hour

Percent of Overrides during Second Pre-

cool Hour

Percent of Overrides in First Event Hour

Percent of Overrides in Second Event

Hour 6/16/2017 1.13% 2.71% 4.52% 14.46%

6/19/2017 1.21% 2.62% 4.66% 19.41% 6/20/2017 1.43% 2.45% 4.15% 16.90% 6/21/2017 1.20% 2.80% 3.73% 15.86% 6/23/2017 0.99% 2.20% 3.71% 15.96% 6/30/2017 0.67% 3.08% 3.11% 14.46% 7/3/2017 0.95% 3.38% 3.68% 14.62% 7/5/2017 0.68% 3.35% 3.03% 15.72% 7/6/2017 1.32% 4.89% 2.51% 13.36% 7/7/2017 0.76% 3.46% 3.73% 15.61%

7/12/2017 0.73% 3.16% 3.03% 14.97% 7/13/2017 0.65% 3.12% 3.47% 16.45% 7/14/2017 0.84% 3.10% 3.21% 14.98% 7/19/2017 0.47% 2.75% 2.20% 13.58% 7/20/2017 0.55% 2.63% 2.16% 14.77% 7/28/2017 1.00% 3.00% 3.54% 15.62% 8/1/2017 1.10% 3.03% 3.26% 16.86% 8/2/2017 0.82% 2.78% 3.14% 14.79%

8/3/2017 0.64% 3.58% 3.20% 13.89%

8/4/2017 0.75% 3.61% 3.07% 13.06%

8/18/2017 0.72% 2.84% 3.27% 12.69%

8/25/2017 0.79% 2.95% 3.60% 13.47%

8/29/2017 0.65% 3.39% 2.89% 12.47%

8/30/2017 0.45% 3.26% 3.10% 14.56%

8/31/2017 0.55% 2.63% 2.94% 15.38%

9/5/2017 0.49% 4.16% 2.12% 10.78%


7


2.5.3 Residential PowerShift Non-Responding Devices A device is considered “non-responding” if it does not respond to the curtailment signal sent by NVE for reasons other than the device being manually overridden by the customer. Common causes of non-response are system outages, internet accessibility issues or other physical barriers that may block the signal.

Prior to the calculation of kW factors, non-responding devices were identified and removed from the sample using a combination of two algorithms: a cumulative sum (CSUM) change in slope analysis and a straight 10% decrease in load detection. Given that overrides and partial overrides cannot be considered non-responding devices or NRDs, we utilized override data to identify and exclude overrides and partial overrides from the NRD identification process.

CSUM Algorithm

When an event is called, each device is sent curtailment instructions that result in a significant load drop over the event period. This drop is illustrated in Figure 2, where an example DR event is presented with an example “normal” usage curve.

k

W

h

6

5

4

3

2

1

0

Normal Treatm…

3 2 1 0 1 2 1+ 2+ 3+ 4+ Event Hours

Figure 2: Example of Site-level Load Shapes during Event Hours

Because every home has different energy usage patterns and magnitudes, when identifying which homes participated in an event it is important to look at load shapes in a way that reduces the variability in load shapes to a simple pattern. We apply a cumulative sum to each load shape to create a simple cumulative time series:

Equation 2

where x is a vector of kWh measures taken at 15-minute intervals, the interval : is the 24-hour interval from 7 am to 7 am the following day. The result of Equation 2 for each treatment site is a smoothed, increasing curve, as depicted in the following figure. If the device responded to the curtailment call, then there will be a significant change in slope during the event period compared to the period before the event started. To test for significance in the slope change, we apply two tests, Test 1 takes the ratio of the event period (2 hours) slope divided by the pre-period (3 hours) slope (Figure 3). A responding device is detected by a decrease in the line slope, so the ratio will be less than unity.


8


25

20

k 15

W 10 h

5

0

Event Hours

Figure 3: Example of Site-level Cumulative Sum Slope Changes during Event Hours

Some sites can have a unique meter profiles that can confuse Test 1. Test 2 takes the average load shape of each site based on the previous 7 non-events, non-weekend days to create a “site-normal” cumulative curve to compare with the event curve (Figure 4). We calculate the slope ratios for the site-normal cumulative curve and then compare it to event ratio for those sites that failed Test 1.

30

25

20 k

W 15

h 10

5

0

Event Hours

Figure 4: Example of the CSUM Change-in-Slope Analysis used to Identify NRDs

If the ratio for the site-normal curve is greater than the ratio for the event curve, then the device is classified as responding. Any devices left over after the two tests are classified as non-responding.

Percent Reduction Algorithm

Since it is possible that a single NRD classification algorithm may misidentify NRDs, to reduce Type 1 errors we employ two algorithms and take the intersection of the resulting groups. The percent reduction algorithm is a straight test for a 10% reduction in kWh during the first hour of an event. The same meter data tested in by the CSUM algorithm is put through this algorithm. For each unique device, the kWh for

Treatment

Event Slope

Pre Slope

Ratio = Event Slope Pre Slope

3 2 1 0 1 2 1+ 2+ 3+ 4+

Normal

Treatment

Test 2: T.Ratio < N.Ratio ... Event Occurs

N.Ratio = C / A

T.Ratio = B / A

A

C

B

3 2 1 0 1 2 1+ 2+ 3+ 4+


9


1-hour pre-event/pre-cool and kWh for the first hour of the event (Figure 2) are analyzed for a drop greater than 10% as follows:

Equation 3

When T1 is less than or equal to T2 we classify that device as an NRD. The 10% drop is the average value found from an extensive review of drop percentages for the different device types in the program by ADM and NV Energy.

2.6 METHODOLOGY

The following sections provide a high-level overview of the methodology used to calculate the demand reductions and energy savings associated the residential demand response program.

2.6.1 Calculation of Demand Reductions In 2017, twenty-six residential demand response events were called between June and September. Based on load data for the program participants, ADM calculated the following metrics of the DR program’s demand reduction capabilities.

The first metric is the subgroup kW factor ( ), which is the upper limit of a subgroup ’s per-device load reduction capability. The second metric is the season verified demand reduction ( ), which is equal to:

Equation 4

Where is the number (averaged over the summer) of responding devices in subgroup ( is the average proportion of non-responding devices). In this report, only the end-of-line demand reductions are reported, and line losses are not included in the computation of the demand reduction achieved.

2.6.2 Calculation of DR Event Energy Savings Although intended primarily as a peak management resource, the DR program also achieves energy savings because the cooling loads shifted by the DR events do not always end up being as large as they would have been had no event been called. Often, even when cooling demand is shifted to later in the day by a DR event (the so-called “snapback”), the home still ends up using less energy overall because of the event. The net per-device difference in energy consumption (relative to a control group baseline) for a subgroup during event is known as the subgroup-event kWh factor ( ). The program-level DR event energy savings ( ) is the sum of the subgroup-event kWh factors over all subgroups, and all events, multiplied by the number of (responding devices), similar to the following equation:


10


Equation 5

As with demand reductions, line losses are not factored into this quantity.

2.6.3 Creating Control Groups to Estimate Baseline Loads For each device subgroup, a control group is created to serve as a point of comparison, or a baseline, during DR event days.

Meter data for over 10,000 single family homes and multi family was obtained through NV Energy’s MDMS5 system. The data was used to select event-specific control groups on the basis of property characteristics and load similarity. Direct matching of the PowerShift and control properties was based on zip code. Propensity score matching was used to further match PowerShift and control properties based on square footage and age of homes. Up to five different control sites were chosen based on these characteristics. This pool of control sites was then re-matched to each subgroup, for each event, based on the total kWh usage over the previous seven (non-event) weekdays:

Equation 6

where Monday, …, Friday with event days excluded for both groups of homes.

For each treatment premise , the total is matched to the ’s from control premises using an increasing pseudo-clustering circle that expands until several control premises are matched. This control subgroup is matched for up to five premises per treatment premise. These sites are removed from the base group and the process is repeated for each treatment premise.

This process was designed to select, for each treatment premise, specific homes in the non-DR control groups that match the treatment premise’s characteristics and consumption patterns as closely as possible. The resulting matched control group is a significantly better fit to the treatment group than a random sample of control premises.

As with the subgroup loads, once the control groups are selected, they are aggregated to form the subgroup control time series, which we denote by .

2.6.4 Event-Day Adjustment of the Control Group Load

Past analyses had a consistent difference between and , which would bias the DR load impacts if one were to simply take the difference of the two.

Even with the matched control groups, there may be a consistent difference between and , which would bias the DR load impacts if one were to simply take the difference of the two.

To account for these gaps, an event-day adjustment is made to the control group load:

5 MDMS: Meter Data Management System


11

mailto:9.:@s@y


Equation 7

where lies within an event period. is calculated as the ratio of the average treatment group load and the average control group load, the third hour before the event. Ecofactor devices employed a pre-cooling strategy where the home is cooled during a period of two hours immediately prior to the event for all DR events in 2017. This precool period is the reason is calculated the third hour before the event.

The matching analysis described above produced excellent matching for the previous seven non-event days between the control and DR sites. Figures 5 and 6 shows how similar the usage is between the treatment and control groups. Because the EcoFactor devices provide constant control adjustments to maximize energy savings, the DR site curves are expected to have a slightly different shape than non-EcoFactor controlled sites.

The figures below depict examples of the treatment and control group load shapes during non-event days.


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Figure 5: Control and Treatment Load Shapes 6/26/2017 – 7/17/2017


Page 161 of 359


Figure 6: Control and Treatment Load Shapes 8/2/2017 – 8/17/2017


Page 162 of 359

2.6.5 Calculation of Demand and Energy Factors

Let be some time during an event . At the most basic level, we take the difference of and to produce what we call the load differential :

Equation 8

A given DR event typically includes two hours of curtailment, during which the treatment group energy usage is expected to be significantly less than control group energy usage. Prior to the two-hour curtailment, EcoFactor thermostats provide two hours of pre-cooling; during pre-cooling hours, treatment group energy usage may exceed control group energy usage, implying negative savings for that period. Immediately following the two-hour curtailment is a two-hour “snapback” period, during which treatment group energy usage typically increases as treatment group homes recover normal cooling configurations; as treatment group homes return to normal temperatures, those homes may use more energy than control group homes, implying negative savings for the treatment group during the snapback period.

For each event , we calculate the average for each of those six (approximately) hour-long periods. We denote these averaged differences by , where refers the event hour (which usually includes two curtailment hours, two snapback hours, and two hours of pre-cool).

For each event , we calculate the event kW and kWh factors. The event kW factor is defined as:

Equation 9

In other words, the larger of the first- or second-hour average load reductions normalized by the sample device-to-premise ratio :

Equation 10

This procedure normalizes the load differentials to a per-device value. In 2017, the average device to premise ratio was 1.20.

The event kWh factor is defined as:

Equation 11

Where the sum is over all event hours (including pre-cooling and snapback).


15


2.6.6 Calculation of Energy-Optimization Impacts In addition to its role as a demand response asset for NVE, the PowerShift program is marketed as an energy-optimization service that provides users year-round energy savings through fine-tuned control of the HVAC system that also learns residents’ preferences and behaviors. These non-DR event energy impacts are estimated using econometric techniques.

Utilizing a similar matching procedure (see Appendix F) as that used to create the control group used to calculate demand reductions, ADM determined the electricity savings resulting from the energy-optimization service by employing a “difference in differences” method. With this method, changes in energy use for customers receiving optimization from the EcoFactor device are compared to changes in non-optimized energy use of the control group. Both groups were then compared to a baseline “pre” period occurring prior to the PowerShift participants’ receipt of their EcoFactor devices.

The change in energy usage for the different groups was determined using the results from regression analysis of the energy usage data for the participant and non-participant groups. ADM used regression analysis to estimate the amounts of electricity used and to quantify the impacts of receiving EcoFactor optimization on energy consumption after controlling for the effects of weather and other factors. The regression analysis isolates and quantifies the effects of different factors on the changes in energy usage. Please see Appendix A for a more detailed explanation of the difference-in-differences methodology and regression specifications.


16

3 RESULTS

In this section, we report demand reduction and energy savings results for the Build, Manage, and Build Pilot programs (also known as “PowerShift”), for which the population of devices are EcoFactor two-way PCTs (DP11), and EcoBee WiFi thermostats (DP12).

We also provide the kW factors ( ), kWh factors ( ), subgroup-event VDR, and net energy savings for each event.

3.1 DP11D1 DEMAND REDUCTION RESULTS

Figure 7 below shows the actual control and participant curves for customers with the D1 (single-family) rate class during one of the 2017 DR events.

Figure 7: DP11D1 Load Shape 7/20/2017



The following tables provide the 2017 demand reduction results for kW savings per hour for all pre-cool, event, and snapback hours. Positive values represent energy savings; negative values represent extra energy usage compared to baseline energy consumption.

Table 8: DP11D1 Demand Reduction Results


kWh Factor

6/16/2017 -0.20 -0.56 1.05 0.70 -0.21 -0.10 1.053 0.696

6/19/2017 -0.27 -0.85 1.46 0.74 -0.47 -0.29 1.459 0.323

6/20/2017 -0.25 -0.86 1.85 1.10 -0.38 -0.22 1.849 1.243

6/21/2017 -0.36 -0.95 1.46 0.92 -0.43 -0.17 1.459 0.472

6/23/2017 -0.27 -0.96 1.36 0.83 -0.51 -0.29 1.359 0.157

6/30/2017 -0.65 -1.10 1.29 0.85 -0.38 -0.14 1.293 -0.133

7/3/2017 -0.60 -0.94 1.60 1.00 -0.38 -0.12 1.599 0.555

7/5/2017 -0.63 -0.99 1.31 0.85 -0.33 -0.14 1.309 0.066

7/6/2017 -0.56 -1.00 0.96 0.66 -0.34 -0.14 0.961 -0.423

7/7/2017 -0.60 -1.00 1.57 0.95 -0.38 -0.21 1.566 0.323

7/12/2017 -0.75 -1.11 1.29 0.83 -0.36 -0.17 1.292 -0.273

7/13/2017 -0.68 -1.03 1.40 0.90 -0.31 -0.12 1.400 0.157

7/14/2017 -0.59 -0.98 1.33 0.75 -0.41 -0.16 1.325 -0.058

7/19/2017 -0.68 -0.92 0.83 0.64 -0.21 -0.09 0.828 -0.431

7/20/2017 -0.64 -0.92 0.88 0.59 -0.26 -0.11 0.878 -0.455

7/28/2017 -0.60 -0.99 1.24 0.74 -0.39 -0.17 1.243 -0.174

8/1/2017 -0.51 -0.90 1.54 0.81 -0.47 -0.26 1.540 0.215

8/2/2017 -0.59 -0.96 1.41 0.83 -0.46 -0.17 1.408 0.066

8/3/2017 -0.58 -0.93 1.21 0.68 -0.48 -0.17 1.212 -0.266

8/4/2017 -0.63 -0.89 1.39 0.79 -0.36 -0.14 1.395 0.166

8/18/2017 -0.68 -0.99 1.16 0.64 -0.41 -0.13 1.159 -0.414

8/25/2017 -0.65 -0.91 1.02 0.62 -0.36 -0.16 1.018 -0.447

8/29/2017 -0.70 -1.04 0.94 0.60 -0.44 -0.17 0.936 -0.820

8/30/2017 -0.74 -1.06 0.98 0.56 -0.41 -0.20 0.977 -0.861

8/31/2017 -0.65 -0.94 1.04 0.63 -0.31 -0.12 1.043 -0.339

9/5/2017 -0.75 -0.99 0.99 0.62 -0.37 -0.04 0.994 -0.530



3.2 DP11D1 VDR AND DR ENERGY SAVINGS

Table 9: DP11D1 Manage VDR and DR energy savings


Percent NRD

Responding Devices

VDR (kW)


6/16/2017 4,654 13 4,049 4,263 2,819

6/19/2017 4,654 9 4,235 6,181 1,370

6/20/2017 4,653 15 3,955 7,311 4,918

6/21/2017 4,653 13 4,048 5,906 1,913

6/23/2017 4,649 10 4,184 5,687 659

6/30/2017 4,649 23 3,580 4,628 (475)

7/3/2017 4,652 15 3,954 6,323 2,195

7/5/2017 4,649 12 4,091 5,356 271

7/6/2017 4,649 13 4,045 3,887 (1,709)

7/7/2017 4,649 7 4,324 6,769 1,397

7/12/2017 4,645 11 4,134 5,342 (1,130)

7/13/2017 4,644 15 3,947 5,525 621

7/14/2017 4,641 13 4,038 5,351 (234)

7/19/2017 4,642 7 4,317 3,574 (1,859)

7/20/2017 4,645 10 4,181 3,669 (1,904)

7/28/2017 4,642 6 4,363 5,424 (759)

8/1/2017 4,635 8 4,264 6,568 918

8/2/2017 4,635 8 4,264 6,002 282

8/3/2017 4,628 8 4,258 5,162 (1,131)

8/4/2017 4,615 7 4,292 5,987 713

8/18/2017 4,615 10 4,154 4,814 (1,719)

8/25/2017 4,615 7 4,292 4,371 (1,919)

8/29/2017 4,610 8 4,241 3,968 (3,476)




Percent NRD

Responding Devices

VDR (kW)


8/30/2017 4,609 2 4,517 4,413 (3,889)

8/31/2017 4,608 8 4,239 4,423 (1,439)

9/5/2017 4,609 8 4,240 4,213 (2,247)

Table 10: DP11D1 Build VDR and DR Energy Savings


Percent NRD

Responding Devices

VDR (kW)


6/16/2017 1,267 13 1,102 1,160 768

6/19/2017 1,284 9 1,168 1,705 378

6/20/2017 1,296 15 1,102 2,036 1,370

6/21/2017 1,296 13 1,128 1,645 533

6/23/2017 1,303 10 1,173 1,594 185

6/30/2017 1,313 23 1,011 1,307 (134)

7/3/2017 1,334 15 1,134 1,813 629

7/5/2017 1,355 12 1,192 1,561 79

7/6/2017 1,360 13 1,183 1,137 (500)

7/7/2017 1,360 7 1,265 1,980 409

7/12/2017 1,366 11 1,216 1,571 (332)

7/13/2017 1,384 15 1,176 1,647 185

7/14/2017 1,399 13 1,217 1,613 (71)

7/19/2017 1,401 7 1,303 1,079 (561)

7/20/2017 1,421 10 1,279 1,122 (582)

7/28/2017 1,435 6 1,349 1,677 (235)

8/1/2017 1,491 8 1,372 2,113 295

8/2/2017 1,512 8 1,391 1,958 92




Percent NRD

Responding Devices

VDR (kW)


8/3/2017 1,519 8 1,397 1,694 (371)

8/4/2017 1,556 7 1,447 2,019 240

8/18/2017 1,556 10 1,400 1,623 (580)

8/25/2017 1,597 7 1,485 1,513 (664)

8/29/2017 1,627 8 1,497 1,400 (1,227)

8/30/2017 1,653 2 1,620 1,583 (1,395)

8/31/2017 1,658 8 1,525 1,591 (518)

9/5/2017 1,664 8 1,531 1,521 (811)

3.3 DP11DM1 DEMAND REDUCTION RESULTS

Figure 8 below shows the actual control and participant curves for customers with the DM1 (multi-family) rate class during one of the 2017 DR events.



Figure 8: DP11DM1 Load Shape 7/20/2017

The following table provides the 2017 demand reduction results for kW savings per hour for all pre-cool, event, and snapback hours. Positive values represent energy savings; negative values represent extra energy usage compared to baseline energy consumption.



Table 11: DP11DM1 Demand Reduction Results


kWh Factor

6/16/2017 -0.25 -0.36 0.63 0.39 -0.14 -0.04 0.632 0.247

6/19/2017 -0.55 -1.04 0.78 0.38 -0.41 -0.20 0.780 -1.046

6/20/2017 -0.39 -0.90 1.10 0.76 -0.23 -0.21 1.095 0.128

6/21/2017 -0.48 -0.81 0.88 0.58 -0.39 -0.28 0.878 -0.503

6/23/2017 -0.45 -1.05 0.76 0.55 -0.37 -0.25 0.760 -0.799

6/26/2017 0.09 0.16 0.49 0.54 0.10 0.26 0.543 1.638

6/30/2017 -0.69 -1.02 0.56 0.25 -0.71 -0.51 0.563 -2.122

7/3/2017 -0.70 -1.01 0.75 0.17 -0.54 -0.26 0.750 -1.589

7/5/2017 -0.84 -1.08 0.48 0.12 -0.59 -0.41 0.484 -2.319

7/6/2017 -0.54 -0.63 0.61 0.33 -0.23 0.03 0.612 -0.434

7/7/2017 -0.52 -0.83 0.95 0.38 -0.48 -0.52 0.947 -1.026

7/12/2017 -0.73 -1.15 0.38 0.21 -0.78 -0.58 0.385 -2.655

7/13/2017 -0.72 -1.06 0.67 0.33 -0.54 -0.26 0.671 -1.579

7/14/2017 -0.53 -0.89 0.89 0.44 -0.45 -0.17 0.888 -0.711

7/19/2017 -0.49 -0.80 0.42 0.03 -0.47 -0.35 0.424 -1.658

7/20/2017 -0.52 -0.79 0.52 0.31 -0.40 -0.20 0.523 -1.086

7/28/2017 -0.51 -0.78 0.87 0.51 -0.34 -0.25 0.873 -0.510

8/1/2017 -0.57 -0.96 1.00 0.57 -0.35 0.07 0.997 -0.237

8/2/2017 -0.56 -0.66 1.04 0.64 -0.19 -0.16 1.036 0.109

8/3/2017 -0.57 -0.90 0.75 0.44 -0.33 -0.22 0.750 -0.819

8/4/2017 -0.53 -0.88 0.76 0.37 -0.12 -0.10 0.760 -0.503

8/18/2017 -0.76 -1.13 0.75 0.49 -0.42 -0.22 0.750 -1.283

8/25/2017 -0.76 -1.17 0.50 0.26 -0.38 -0.24 0.503 -1.796

8/29/2017 -0.64 -1.08 0.74 0.57 -0.24 -0.03 0.740 -0.671

8/30/2017 -0.63 -1.19 0.63 0.44 -0.43 -0.19 0.632 -1.372

8/31/2017 -0.75 -1.32 0.39 0.01 -0.56 -0.35 0.395 -2.576



3.4 DP11DM1 VDR AND DR ENERGY SAVINGS

Table 12. DP11DM1 Manage VDR and DR energy savings


Percent NRD

Responding Devices

VDR (kW)


6/16/2017 153 22 119 75 29

6/19/2017 153 16 129 100 (134)

6/20/2017 153 14 132 144 17

6/21/2017 153 12 135 118 (68)

6/23/2017 153 16 129 98 (103)

6/26/2017 153 25 115 62 188

6/30/2017 153 11 136 77 (289)

7/3/2017 153 12 135 101 (214)

7/5/2017 153 10 138 67 (319)

7/6/2017 153 11 136 83 (59)

7/7/2017 153 16 129 122 (132)

7/12/2017 152 14 131 50 (347)

7/13/2017 152 12 134 90 (211)

7/14/2017 152 11 135 120 (96)

7/19/2017 152 15 129 55 (214)

7/20/2017 152 17 126 66 (137)

7/28/2017 153 13 133 116 (68)

8/1/2017 153 12 135 134 (32)

8/2/2017 153 7 142 147 15

8/3/2017 153 7 142 107 (117)

8/4/2017 153 8 141 107 (71)

8/18/2017 153 15 130 98 (167)

8/25/2017 153 16 129 65 (231)




Percent NRD

Responding Devices

VDR (kW)


8/29/2017 153 17 127 94 (85)

8/30/2017 153 15 130 82 (178)

8/31/2017 153 18 125 50 (323)

Table 13: DP11DM1 Build VDR and DR energy savings


Percent NRD

Responding Devices

VDR (kW)


6/16/2017 49 22 38 24 9

6/19/2017 50 16 42 33 (44)

6/20/2017 50 14 43 47 6

6/21/2017 50 12 44 39 (22)

6/23/2017 51 16 43 33 (34)

6/26/2017 52 25 39 21 64

6/30/2017 57 11 51 29 (108)

7/3/2017 56 12 49 37 (78)

7/5/2017 57 10 51 25 (119)

7/6/2017 57 11 51 31 (22)

7/7/2017 61 16 51 49 (53)

7/12/2017 61 14 52 20 (139)

7/13/2017 63 12 55 37 (88)

7/14/2017 63 11 56 50 (40)

7/19/2017 64 15 54 23 (90)

7/20/2017 64 17 53 28 (58)

7/28/2017 66 13 57 50 (29)

8/1/2017 66 12 58 58 (14)

8/2/2017 65 7 60 63 7




Percent NRD

Responding Devices

VDR (kW)


8/3/2017 65 7 60 45 (50)

8/4/2017 65 8 60 45 (30)

8/18/2017 65 15 55 41 (71)

8/25/2017 66 16 55 28 (100)

8/29/2017 67 17 56 41 (37)

8/30/2017 67 15 57 36 (78)

8/31/2017 67 18 55 22 (142)

3.5 DP12D1 DEMAND REDUCTION RESULTS

Figure 9 below details the hourly load profile of DP12D1 devices installed in single family homes during the August 29th, 2017 demand response event.



Figure 9: DP12D1 Load Shape 8/29/2017

Table 14 below details the demand reductions results by event hour for the EcoBee device installed in single family homes.



Table 14: DP12D1 Demand Reduction Results


kWh Factor

kW Factor Phase

7/28/2017 0.28 0.24 -0.24 -0.22 0.28 0.06 0

8/1/2017 2.85 1.60 -0.25 0.21 2.85 4.41 0

8/2/2017 2.51 1.53 -0.02 -0.01 2.51 4.01 0

8/3/2017 1.18 0.44 -1.19 -0.29 1.18 0.14 0

8/4/2017 1.89 1.35 -0.12 0.06 1.89 3.18 0

8/18/2017 1.92 1.86 -0.22 0.10 1.92 3.66 0

8/25/2017 2.32 2.03 -0.06 -0.07 2.32 4.22 0

8/29/2017 1.31 1.32 -0.63 0.12 1.32 2.12 0

8/30/2017 2.09 1.85 0.07 0.38 2.09 4.39 0

8/31/2017 1.05 0.49 -0.90 -0.48 1.05 0.16 0

9/5/2017 1.78 1.40 -0.15 0.31 1.78 3.34 0

3.6 DP12D1 VDR AND DR ENERGY SAVINGS

Table 15 below details VDR and energy savings by event for EcoBee devices installed in single family homes.

Table 15: DP12D1 VDR and Energy Savings


Percent NRD Percent Override Responding VDR

(kW) Energy Savings

(kWh) 7/19/2017 12 14 14.29 11 31.35 48.51 7/20/2017 13 14 14.29 11 31.35 48.51 7/28/2017 15 29 7.14 11 27.61 44.11 8/1/2017 20 17 8.33 17 20.06 2.38 8/2/2017 20 8 7.69 18 34.02 57.24 8/3/2017 27 33 5.56 18 34.56 65.88 8/4/2017 29 10 5 26 60.32 109.72

8/18/2017 31 5 9.52 29 38.28 61.48 8/25/2017 32 5 4.76 30 62.7 131.7 8/29/2017 32 0 66.67 32 33.6 5.12 8/30/2017 70 12 0 62 110.36 207.08

3.7 DP12DM1 DEMAND REDUCTION RESULTS




Figure 10: DP12DM1 Load Shape 8/29/2017


Table 16: DP12 DM1 Demand Reduction Results


kWh Factor

kW Factor Phase

6/16/2017 -0.11 0.18 -0.16 -0.30 0.18 -0.39 0




kWh Factor

kW Factor Phase

6/19/2017 0.24 0.11 -0.14 -0.69 0.24 -0.48 0

6/20/2017 0.19 0.16 -0.58 -0.60 0.19 -0.83 0

6/21/2017 0.55 0.24 -0.59 -0.54 0.55 -0.34 0

6/23/2017 0.62 0.81 -0.17 0.04 0.81 1.30 0

6/26/2017 0.03 -0.06 -0.46 -0.31 0.03 -0.80 0

6/30/2017 0.27 -0.08 -0.79 -0.77 0.27 -1.37 0

7/3/2017 0.05 0.05 -0.41 -0.57 0.05 -0.88 0

7/5/2017 0.99 0.43 0.35 0.53 0.99 2.30 0

7/6/2017 0.20 0.12 -0.43 -0.13 0.20 -0.24 0

7/7/2017 0.47 0.35 -0.26 0.11 0.47 0.67 0

7/12/2017 0.24 -0.07 -0.63 -0.70 0.24 -1.16 0

7/13/2017 0.19 0.17 -0.80 -0.35 0.19 -0.79 0

7/14/2017 0.01 -0.13 -0.94 -0.79 0.01 -1.85 0

7/19/2017 0.92 0.25 -0.15 0.22 0.92 1.24 0

7/20/2017 0.54 0.30 -0.66 -0.51 0.54 -0.33 0

7/28/2017 0.34 0.26 -0.96 -0.58 0.34 -0.94 0

8/1/2017 0.27 0.20 -1.17 -1.00 0.27 -1.70 0

8/2/2017 0.40 0.23 -1.25 -0.88 0.40 -1.50 0

8/3/2017 0.07 0.11 -0.77 -0.81 0.11 -1.40 0

8/4/2017 0.33 0.24 -0.52 -0.35 0.33 -0.30 0

8/18/2017 1.00 1.14 -0.15 -0.19 1.14 1.80 0

8/25/2017 1.10 1.20 -0.08 -0.17 1.20 2.05 0

8/29/2017 0.88 0.67 -0.06 0.34 0.88 1.83 0

8/30/2017 0.45 0.39 -0.79 -0.61 0.45 -0.56 0

8/31/2017 0.58 0.58 -0.55 -0.24 0.58 0.37 0

9/5/2017 0.67 0.39 -0.79 -0.33 0.67 -0.06 0

3.8 DP12DM1 VDR AND DR ENERGY SAVINGS

Table 17 below details VDR and energy savings by event for EcoBee devices installed in multi-family homes.



Table 17: DP12 DM1 Demand Reduction Results


Percent NRD


(kW) Energy Savings

(kWh)

6/16/2017 11 11 0 10 1.8 -3.9

6/19/2017 11 26 0 8 1.92 -3.84

6/20/2017 11 32 0 7 1.33 -5.81

6/21/2017 11 37 0 7 3.85 -2.38

6/23/2017 11 16 0 9 7.29 11.7

6/26/2017 11 37 0 7 0.21 -5.6

6/30/2017 11 11 26.32 10 2.7 -13.7

7/3/2017 11 39 5.56 7 0.35 -6.16

7/5/2017 11 37 10.53 7 6.93 16.1

7/6/2017 11 26 5.26 8 1.6 -1.92

7/7/2017 11 37 5.26 7 3.29 4.69

7/12/2017 12 25 10 9 2.16 -10.44

7/13/2017 12 25 5 9 1.71 -7.11

7/14/2017 12 20 10 10 0.1 -18.5

7/19/2017 13 15 7.69 11 10.12 13.64

7/20/2017 13 19 4.76 11 5.94 -3.63

7/28/2017 13 43 7.14 7 2.38 -6.58

8/1/2017 13 13 0 11 2.97 -18.7

8/2/2017 14 27 0 10 4 -15

8/3/2017 17 23 7.69 13 1.43 -18.2

8/4/2017 17 23 0 13 4.29 -3.9

8/18/2017 19 24 0 14 15.96 25.2

8/25/2017 27 14 0 23 27.6 47.15

8/29/2017 28 9 0 25 22 45.75

8/30/2017 29 17 0 24 10.8 -13.44

8/31/2017 29 9 30.43 26 15.08 9.62

9/5/2017 29 9 0 26 17.42 -1.56



3.9 ECOFACTOR OPTIMIZATION ENERGY SAVINGS

3.9.1 Electricity Savings The following two tables detail the daily electricity savings associated with the EcoFactor Optimization services during the summer and winter months. All values were estimated using the methods described in Appendix A.

Table 18: Average Per-Day Electricity Savings from EcoFactor Optimization (kWh) from Space Cooling (June – September)

Treatment Group Control Group Average per Premise Daily

Electricity Savings associated with EcoFactor

Optimization


Period Consumption

(kWh)

Average Daily Pre-Installation

Period Consumption

(kWh)


Period Consumption

(kWh)


Period Consumption

(kWh) 28.96 28.08 29.37 26.31 2.18


Table 19: Average Per-Day Electricity Savings from EcoFactor Optimization (kWh) from Space Heating (October – May)



Optimization


Period Consumption

(kWh)


Period Consumption

(kWh)


Period Consumption

(kWh)


Period Consumption

(kWh)

20.35 21.67 21.17 22.27 0.22

The annual per premise energy savings associated with EcoFactor optimization during space heating is 53 kWh (243 days x 0.22 kWh). The total annual per premise energy savings associated with the EcoFactor optimization is 321 kWh (266 kWh + 53 kWh – (-2 kWh6)).

3.9.2 Potential Gas Savings

Table 20 below details the average monthly therms savings associated with the EcoFactor Optimization services. All values were estimated using the methods described in Appendix A.

6 Because monthly data was used, DR event days could not be excluded from the analysis; -2 kWh was the average per premise weighted average of net energy impacts from the DR events of DP11D1 and DP11DM1.



Table 20: Average Per-Month Savings from EcoFactor Optimization (therms) from Space Heating (October – April)

Treatment Group Control Group Average per Premise Daily Gas Savings

associated with EcoFactor

Optimization


Period Consumption

(therms)


Consumption (therms)


Period Consumption

(therms)


Consumption (therms)

2.82 3.03 3.22 3.19 0.24

The annual per premise energy savings associated with EcoFactor optimization during space heating is 52.12 therms (213 days x 0.24 therms/day).


4 ENERGY SAVINGS CURVES

The following section details the use of energy savings curves in the 2017 SPPC Residential Demand Response program. For an expanded discussion of energy savings curves please see Appendix C (Chapter 8).

Demand response for device populations in the residential and commercial sectors was achieved by controlling energy management devices that employ software-based optimization to reduce energy usage. As such, the programs are expected to achieve energy impacts on both “event” and “non-event” days. ADM developed hourly savings curves for these programs through a combination of methods including energy simulation, analysis of load research data, and M&V. The development of the savings curves is discussed below.

The residential demand response and energy optimizing thermostat (the EcoFactor device) is the critical technology for current and future residential demand response programs. The EcoFactor device can achieve energy savings through several mechanisms. In the cooling season, the device saves energy by running the air handler for several minutes after the compressive cooling cycle has ended. Doing so effectively turns the air handler into a direct evaporative cooler and achieves a cooling “boost” after each air conditioning cycle. The thermostat can also save energy by altering the thermostat set points throughout the whole calendar year.

ADM modeled the demand response performance by reviewing M&V results. All energy savings associated with PowerShift demand response events occur during DR event related hours. As such, the relevant energy savings curve for each is derived from the thermostat’s event performance (including pre-cooling, event hour reductions and snapback). All the other hours in the demand response energy savings curve which were not pre-cool, event or snapback hours were assigned a value of zero as there are not savings that occur which is attributable to the program.

In addition to the energy savings curves described above, ADM also modeled hourly energy efficiency impacts of EcoFactor Optimization as a percentage of the whole-house usage for a typical customer. The savings curve for the 2017 MV report was derived from the normalized load shape of the participants. Because these residential EE curves are to be used in forecasting exercises by NV energy, ADM decided to derive these curves from the whole-house meter data of participants on non-event days in 2014 and 2015 to include the most complete datasets available. This meter data was averaged across the two years, except for event days. The few calendar days that shared an event day during the two program years were interpolated from the previous non-event day.

Given that the HVAC system controlled by the smart thermostat dominates a typical NVE customer's whole-house load profile, ADM believes that this aggregated whole-house load profile provides an accurate curve for forecasting energy savings into hourly bins.

The energy savings and demand reduction impacts are combined through simple addition, with the energy savings curves normalized to represent the typical customer.

Energy Savings Curves Page 182 of 359

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5 CONCLUSIONS AND RECOMMENDATIONS

The results from this evaluation study of the Residential Demand Response Program are summarized in Table 21, which indicates that the DR program can provide significant kW reductions during critical peak hours, allowing NV Energy greater flexibility in resource management.

Table 21: M&V Summary Results from 2017 Demand Response Program

Program Device Population %NRD Available

Devices kW Factor Max VDR (kW)

Res (PowerShift) Manage DP11D1 10.07 4,350 1.85 7,237

DP11DM1 13.85 143 1.1 136

Res (PowerShift) Build DP11D1 10.07 2,670 1.85 4,442

DP11DM1 13.85 126 1.1 119

Res (PowerShift) Build Pilot DP12D1 14.69 747 2.85 1,816

DP12DM1 23.11 137 1.20 126 Total 8,173 13,877

Accounting for non-responding devices and participant overrides, during a Residential Demand Response Event NV Energy could achieve a demand reduction of 13,877 kW if all devices were to operate simultaneously and achieve their maximum demand reduction during the same hour. This represents a cooling load that the DR program can shift to off-peak hours during critical peak demand periods on NVE’s northern Nevada grid, without taking into account T&D line losses.

By the end of 2017 DR season, the number of residential available devices in Northern Nevada totaled 8,173 an increase of 3,372 devices from the end of the 2017 DR season. This increase was primarily attributable to the heavy recruiting for the PowerShift residential program.

ADM provides the following recommendations to improve the residential DR components in future years:

We recommend the continued review of the inclusion of “pre-cooling” as a component of the residential program design. Past analyses indicated that pre-cooling doesn’t significantly improve the kW reduction during curtailment hours, yet pre-cooling erodes kWh savings for the DR event as a whole. Pre-cooling may increase customer comfort during an event, and as such, customer satisfaction metrics may need to be investigated before determining future pre-cooling strategies and tactics.

We recommend establishing quarterly workshops between ADM, NVE and its Demand Response implementers. This would allow for better project tracking, addressing of various data issues, updates regarding system performance and a periodic review of the implementation operations.

We recommend NVE continue to utilize process evaluation efforts to better understand possible market transformation related to the slightly downward trend of energy efficiency savings associated with the smart thermostat optimization services.


35


Similarly, we recommend NVE continue to utilize process evaluation efforts to understand participant’s behaviors related to overriding demand response events.


36

6 APPENDIX A: DIFFERENCE-IN-DIFFERENCE METHODOLOGY FOR ECOFACTOR OPTIMIZATION SAVINGS

Calculating the energy savings associated with the EcoFactor Optimization via the difference-in-differences methodology relies on the average consumption for both the test and control groups during both the period before and after the EcoFactor thermostat was installed. ADM utilized regression analysis to estimate these values. Devices installed since the inception of the program were included in the analysis because the algorithms used to optimize energy usage require some time to learn the behaviors/patterns of the participants.

The basic specification for the regression modeling is illustrated as follows. Consider modeling the energy use of a customer who is benefitting from EcoFactor optimization. In simplest terms, average daily electricity use can be separated between weather-sensitive and non-weather-sensitive factors. A model to represent this is:

Equation 12

Where:

is average monthly use of electricity for period t for a customer; is daily cooling degree days during day ;

8 is daily heating degree days during day ; is an error term; s the intercept term; and are regression coefficients showing the changes in use that occurs for a change in

either heating degree days or cooling degree days.

Ultimately, by filtering the premises that are analyzed, the above regression equation will provide us with four energy consumption estimates for .

Equation 13

7 The number of cooling degree days per month was calculated using hourly weather data from the Weather Underground. First the number of cooling degree days for each hour was calculated by subtracting the base temperature of 72 degrees from the actual temperature (if the actual temperature was lower than the base temperature than there were 0 cooling degree days during that hour). Next, the average number of cooling degree days per day was calculated for each day.

8 The number of heating degree days per month was calculated using hourly weather data from the Weather Underground. First the number of heating degree days for each hour was calculated by subtracting the actual temperature from the base temperature of 65 (if the base temperature was lower than the actual temperature than there were 0 heating degree days during that hour). Next, the average number of heating degree days per day was calculated for each day.

Appendix A Page 185 of 359

37


– The average daily consumption of the test group prior to the installation of the EcoFactor thermostat.

- The average daily consumption of the test group after the installation of the EcoFactor thermostat.

- The average daily consumption of the control group prior to the installation of the EcoFactor thermostat.

- The average monthly consumption of the control group after the installation of the EcoFactor thermostat.

By subtracting the estimated average consumption after the thermostat was installed from the average consumption before the thermostat was installed we obtain a basic estimate of the impact of the EcoFactor Optimization on energy consumption. However, this estimate does not control for potential time-varying factors that may bias the estimate (e.g., changes in the economy).

Equation 14

In order to control for the time-varying factors, the analysis is expanded to include the control group. The implicit assumption for the difference-in-differences analysis is that a change in energy use in response to a change in weather conditions (and other time-varying factors like the economy) would be the same for the control group and the test group in the absence of the optimization. If this assumption holds, then the change in energy usage of the control group in response to a change in weather conditions can be applied to predict what the (counterfactual) energy use of the test group would have been under the changed weather conditions in the absence of the optimization. This allows the difference between actual post-optimization energy use of the test group and the counterfactual predicted energy use to be calculated as the savings attributable to the optimization.

The difference-in-difference equation takes the following form:

Equation 15

Where is the average energy savings attributed to the EcoFactor optimization algorithm.

The regression equations were estimated by applying estimation procedures that take into account both the cross-sectional and the time-series dimensions of the data. In particular, regression models were estimated by pooling cross-sectional observations (i.e., participants who had their EcoFactor devices installed in 2013, 2014, 2015, 2016, 2017 and control group members) with time-series observations (i.e., monthly consumption from January 2010 – January 2018).

A “fixed-effects” specification was used for the panel regression modeling. The purpose of this specification is to control for those determinants of a household’s electricity use that are constant over time. The basic idea underlying this specification is that each customer household acts as its own control, both for household characteristics that are easily measured (like house size and age) and for characteristics more difficult to measure (like interest in conservation, etc.). Time-varying variables were handled by measuring and putting them as covariates in a “fixed effects” regression model.


38


Conceptually, the “fixed effects” regression analysis involved applying a least squares dummy variable (LSDV) covariance estimate procedure. In this approach, a binary dummy variable was created for each customer in the sample, with the variable assigned a value of 1 for each observation that is associated with the customer and a value of 0 for each observation that is not. The full set of these dummy variables was included in the regression analysis. In effect, the equation estimated contained a unique constant term for each customer that captures the effects of all the determinants of that customer’s electricity use that are constant over time. This approach automatically controlled for differences among households that influence the average level of consumption across customer households. The specification of customer- specific effects allows the regression model to capture much of the baseline differences across customers while obtaining reliable estimates of the effects of the optimization.

There were several significant advantages to using this fixed-effects panel model.

First, the precision associated with the model is generally high. Second, it has been our experience that these models tend to be more robust with respect to

outliers. Standard statistical tests and regression diagnostics were used to evaluate the performance of the models. Each model was screened for implausible results. The statistical tests and diagnostics included evaluating the t-statistics for estimated coefficients and the for equation fit and examining residuals from the fitted models.

The following tables detail the daily electricity savings associated with the EcoFactor Optimization services.

Table 22: Average Per-Day Electricity Savings from EcoFactor Optimization (kWh) from Space Cooling (June – September)



Optimization


Period Consumption

(kWh)


Period Consumption

(kWh)


Period Consumption

(kWh)


Period Consumption

(kWh)

28.96 28.08 29.37 26.31 2.18


Table 23: Average Per-Day Electricity Savings from EcoFactor Optimization (kWh) from Space Heating (October – May)



Optimization


Period Consumption

(kWh)


Period Consumption

(kWh)


Period Consumption

(kWh)


Period Consumption

(kWh)

20.35 21.67 21.17 22.27 0.22


39


The annual per premise energy savings associated with EcoFactor optimization during space heating is 53 kWh (243 days x 0.22 kWh). The total annual per premise energy savings associated with the EcoFactor optimization is 321 kWh (266 kWh + 53 kWh – (-2 kWh9)).

9 Because monthly data was used, DR event days could not be excluded from the analysis; -2 kWh was the average per premise weighted average of net energy impacts from the DR events of DP11D1 and DP11DM1.


40

Table 24: Treatment Regression Results



squared Number of

Observations

Number of Cross-

Sections

Hausman P-Value

Winter (October Post 20.48*** NA -0.01*** NA 15.38 20.35 -0.10 11,347 1,058 0.81 – May) Pre 21.54*** NA 0.01*** NA 16.51 21.67 -0.02 83,723 1,430 0.84

Summer (June – Post 18.34*** 2.11*** NA 5.03 NA 28.96 0.25 4,162 943 0.15 September) Pre 19.65*** 2.35*** NA 3.58 NA 28.08 0.19 28,835 1,430 0.00

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Table 25: Control Regression Results



squared Number of

Observations

Number of Cross-

Sections

Hausman P-Value

Winter (October Post 22.23*** NA -0.08*** NA 13.48 21.17 -0.17 9,717 1,446 0.35 – May) Pre 20.58*** NA 0.10 NA 16.36 22.27 -0.01 87,382 1,464 0.29


Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Appendix A 41

Page 189 of 359

7 APPENDIX B: 2017 SAVINGS PER MONTH BY RATE CLASS

This appendix provides monthly savings by rate class during 2017 for the SPPC Residential DR Program.

Table 26: Residential PowerShift Manage DR Event kWh Savings by Month by Rate Class – 2017


D1 - - - - - 11,793 (2,164) (12,478) (2,287) - - - (5,136)

DM1 - - - - - (544) (1,804) (1,220) (32) - - - (3,600)

Total - - - - - 11,249 (3,968) (13,698) (2,319) - - - (8,736)

Table 27: Residential PowerShift Build DR Event kWh Savings by Month by Rate Class – 2017


D1 - - - - - 3,486 (1,041) (4,391) (888) - - - (2,835)

DM1 - - - - - (205) (770) (567) (15) - - - (1,558)

GS1 5 (1) (5) (1) (2)

Total - - - - - 3,286 (1,812) (4,964) (905) - - - (4,395)

Table 28: Residential PowerShift Manage Energy Efficiency kWh Savings by Month by Rate Class – 2017 Rate Class Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

D1 96,515 80,680 82,826 76,119 74,827 106,575 132,604 118,260 104,080 81,921 91,614 106,369 1,152,390 DM1 3,791 3,169 3,253 2,990 2,939 4,186 5,208 4,645 4,088 3,218 3,598 4,178 45,261 Total 100,306 83,849 86,079 79,109 77,765 110,761 137,813 122,904 108,167 85,139 95,213 110,546 1,197,651

Table 29: Residential PowerShift Build Energy Efficiency kWh Savings by Month by Rate Class – 2017 Rate Class Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

D1 36 1,189 8,084 11,323 15,379 30,412 42,622 42,777 40,927 37,947 50,074 62,149 342,920 DM1 1 41 559 663 822 1,442 2,247 2,111 1,940 1,778 2,690 3,445 17,740 GS1 - - 1 21 21 30 37 33 29 23 26 31 250 Total 37 1,230 8,644 12,006 16,222 31,883 44,906 44,921 42,897 39,748 52,790 65,625 360,910

Appendix B

Page 190 of 359

42


Table 30: Residential PowerShift Build Pilot DR Event kWh Savings by Month by Rate Class – 2017 Rate Class Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

D1 - - - - - - (16) 600 234 - - - 818 DM1 - - - - - (32) (26) 69 (2) - - - 10 Total - - - - - (32) (42) 669 232 - - - -

Appendix B

Page 191 of 359

43

8 APPENDIX C: DETERMINING ENERGY SAVINGS (KWH) PER MONTH BY RATE CLASS


8.1 APPORTIONMENT OF ANNUAL ENERGY SAVINGS BY RATE CLASS

NV Energy’s DSM programs generally include populations of customers from more than one rate class. NV Energy tracks the rate class for each identifiable customer participating in DSM programs. However, participant information is not known for certain DSM programs, such as the Residential High-Efficiency Lighting program or other “upstream” or “midstream” programs where incentives are provided through contractual arrangements with manufacturers or distributors of the rebated products. For DSM programs for which participant information is not known, ADM collected participant information at the point of sale or conducted customer surveys to identify the proportions of participants that belong to various rate classes.

8.2 APPORTIONMENT OF ANNUAL ENERGY SAVINGS BY MONTH

ADM developed a methodology that utilizes energy savings curves to calculate the portion of annual energy savings that occurs during each month of the year. An energy savings curve describes the temporal nature of energy savings. For example, on any given day the energy savings achieved by an LED exit sign are approximately 1/365 of the verified annual energy savings for that LED exit sign. On the other hand, an efficient air conditioner may not save any energy during the month of January but may achieve 35 percent of its annual energy savings in the month of July alone. This is similar to the case of the energy savings associated with EcoFactor’s optimization algorithms. In the case of demand response measures, the savings occur only during event days and thus the curves were constructed accordingly. The energy savings curves were coupled with project implementation dates on a record-by-record basis to produce accurate determinations of the energy savings achieved for each month of the year.

8.3 HIGH-LEVEL SUMMARY OF ADM’S CALCULATION METHODOLOGY

Monthly energy (kWh) savings for each program were calculated by applying an appropriate hourly or daily energy savings curve to each program participant’s ex post verified energy savings, then aggregating kWh savings for each month. The energy savings curve distributes a participant’s energy savings over time. Its shape is, therefore, dependent on not only the measure installed (i.e., lighting vs. HVAC) but also on the building type and sometimes its location.

The overall process by which ADM calculated monthly kWh savings was to (1) download from NV Energy’s DSM Central tracking data, i.e., ex ante expected kWh savings, measure type, measure completion date, rate class, etc., (2) calculate ex post values per participant, (3) assign an energy savings curve to each participant’s ex post savings to distribute ex post energy savings by rate class over each of the 8,760 hours in a year, and (4) aggregate ex post verified savings for the purpose of presenting savings by month and by rate class.

10 The Public Utilities Commission of Nevada (PUCN) requires NV Energy to report energy (kWh) savings per month and by rate class for each Demand Side Management (DSM) program.

Appendix C Page 192 of 359

44


ADM also calculated first-year kWh savings for each program by combining measure startup date (from NVE’s DSM Central/Demand Response Management System tracking data) with the aforementioned process. A detailed description of the steps involved in tabulating first-year kWh savings is provided in the section below.

8.4 ENERGY SAVINGS CURVES

8.4.1 Definition

The phrase ‘energy savings curve’ is used to describe the temporal dependence of energy savings. The curves are typically hourly (1 × 8760 array), daily (1 × 365 array), or monthly (1 × 12 array). The energy savings curves are often normalized such the sum of all array elements is unity. When normalized, each element describes the fraction of annual savings that is expected to occur in a given hour, day, or month.

8.4.2 Nomenclature

Note that if the term ‘load shape’ is encountered in the spreadsheets that are used to tally monthly energy savings by program and rate class, one should take it to be the same as ‘energy savings curve’ as described herein. The reason for the usage of the term ‘load shape’ is twofold:


An energy savings curve for a measure may or may not be synchronous with the load curve of the base case technology against which savings are determined.

There are energy efficiency measures (EEMs) for which the normalized savings curve is synchronous and proportional to the normalized load shape or curve of the base case technology. Examples of such EEMs include “smart thermostats” like EcoFactor versus a standard programmable thermostat as it is assumed that (1) there is null or negligible interactive effects and (2) pre- and post-retrofit usage schedules are identical. If the savings curve for an EEM is synchronous with the base case technology load shape, then the two curves have identical shapes.

For other EEMs, the energy savings curve is asynchronous with the load curve of the base case technology. Examples of EEMs with asynchronous savings curves include economizers, occupancy sensors, and control systems. For such measures, the shape of the energy savings curve is different from the shape of the base case technology.

As part of our evaluation effort, ADM determines for each EEM whether to use normalized energy savings curves that are either synchronous or asynchronous with the normalized load shape of the base case technology.


45


8.5 TABULATING MONTHLY ENERGY (KWH) SAVINGS PER RATE CLASS


8.5.1 First-Year kWh Savings

‘First-year’ kWh savings are savings that occur during the same calendar year in which a conservation program was implemented. For NV Energy a program year is the same as a calendar year. Thus ‘first-year’ kWh savings for a measure installed during the 2017 program year are equal to that measure’s kWh savings during the 2017 calendar year.


For each rate class, for each day of 2017, identify all measures that have been implemented (or ‘installed’ or ‘started up’) by the end of the prior day.

For each rate class, for each day of 2017, for all measures that have been installed by the prior day, multiply the ex post verified ‘typical-year’ annualized kWh savings11 for each measure type by that measure’s daily kWh bin. In other words, multiply the measure-level annual kWh by the measure-level daily bin from the appropriate energy savings curve.




8.5.2 ‘Typical-Year’ Energy (kWh) Savings

‘Typical-year’ energy (kWh) savings represents 365 consecutive days of energy savings attributed to a measure(s) or program for which ex post verified savings will occur across a multi-year measure life.12

11 ‘Typical-year’ annualized kWh savings is 365 consecutive days of energy savings – usually a full calendar year other than Leap Year – attributed to an energy efficiency measure(s) for which ex post verified kWh savings will occur during a multi-year measure life. For example, an NV Energy conservation measure installed during the 2017 program year (i.e., during the 2017 calendar year) will normally provide kWh savings starting on its date of installation. ‘First-year’ savings is the savings that occurs during the 2017 calendar year. ‘Full-year’ savings is the savings occurring during subsequent calendar years.

12 The distinction between ‘typical year’ and ‘full year’ is that a ‘typical year’ is a 365-day year. A Leap Year is not a ‘typical year’ – instead, a Leap Year is a ‘full year’ that has 366 days. In M&V reports, the kWh savings tables


46






For each rate class, ‘typical-year’ kWh savings is the program-level monthly kWh savings for that rate class summed across all 365 days of any non-Leap Year.

For any given program, ‘full-year’ kWh savings for a Leap Year will be marginally higher than ‘full-year’ kWh savings for a ‘typical year’ or non-Leap Year. Thus we always use a non-Leap Year when we quantify ‘typical-year’ kWh savings.

Following is an example of the determination of daily kWh savings generated by a program. Let’s consider a hypothetical program that targets two energy efficiency (EE) measures: residential lighting and residential cooling. For this hypothetical program, the table below provides a simple comparison of the measures’ respective:

‘typical-year’ energy savings; daily bin value in its energy savings curve for a specific day – February 1st – of any given year after

the EE measures were installed; energy (kWh) savings during February 1st of any given year after the EE measures were installed.

In Table 31 below, the assumption is that 1,000,000 kWh of annual energy savings (‘typical-year’ savings as reported in M&V reports) were achieved through distribution of CFLs and 500,000 kWh of annual (‘typical-year’) energy savings were achieved through implementation of air conditioning (AC) related EE measures (like an EcoFactor “smart” thermostat that optimizes AC usage). Energy (kWh) savings on June

(which show monthly savings per rate class) usually indicate titles such as “First Year 2017”, “Full Year 2018”, “Full Year 2019” and “Full Year 2020 (Leap Year)”.12 When tallying kWh savings per month per rate class, the use of hourly bins or daily bins is equally correct and accurate. ADM typically uses daily bins (which are created from hourly bins) in our kW guru™ Excel files simply because a workstation processor can complete the billions of computations in a large kW guru™ file relatively faster when the number of computations is based on 365 daily bins instead of 8760 hourly bins per calendar year. Hourly bins in kW guru™ files (i.e., the 8760 hourly bins per ‘typical year’) exist for the following two purposes: 1) they are summed across the 24 hours of each day to create the aforementioned daily bins; and 2) they provide the hourly resolution that enables us to analyze and report critical peak demand (kW) savings per month per rate class for any specified kW-reporting period. 13 The daily bin value for February 1 represents the February 1 daily fraction of ‘typical-year’ annual energy (kWh) savings.


47


1st are obtained by multiplying ‘typical-year’ kWh savings by the entries corresponding to June 1st in the respective normalized energy savings curves.

Following is a sample calculation of energy savings achieved for a given rate class on June 1 for a hypothetical program targeting residential lighting and space cooling.

Table 31: Sample Calculation of Energy Savings

Comparison for “Indoor Lighting” vs. “Space Cooling” Measures




Jun. 1 daily bin value in each EE measure’s energy savings curve: 0.0010 0.0035

Jun. 1 energy (kWh) savings in a typical year: 1,000 1,750


8.5.3 Leap Year Savings

To account for the extra day in February in Leap Years, one of the following methods is used. Either method produces accurate and very similar ex post verified energy savings determinations for Leap Years.




48

9 APPENDIX D: 2017 HOURLY IMPACTS

Table 31 below provides the hourly load impacts associated with 2017 DR events by date and device population for all hours of DR event dates.

Table 32: Hourly Impacts for DR Event Dates, Residential Device Populations

Date and Hour DP11D1B

Device Population

DP11DM1B DP11D1M DP11DM1M System Total

6/16/2017 14:00 -219 -9 -816 -29 -1073

6/16/2017 15:00 -601 -14 -2241 -42 -2898

6/16/2017 16:00 1138 24 4241 75 5477

6/16/2017 17:00 766 15 2856 47 3685

6/16/2017 18:00 -220 -5 -821 -17 -1063

6/16/2017 19:00 -108 -1 -403 -4 -517

6/19/2017 14:00 -309 -23 -1133 -70 -1536

6/19/2017 15:00 -979 -44 -3595 -133 -4751

6/19/2017 16:00 1684 33 6185 99 8001

6/19/2017 17:00 855 16 3139 48 4058

6/19/2017 18:00 -543 -18 -1993 -53 -2607

6/19/2017 19:00 -333 -8 -1223 -25 -1589

6/20/2017 14:00 -269 -17 -979 -52 -1317

6/20/2017 15:00 -933 -39 -3395 -118 -4485

6/20/2017 16:00 2004 48 7292 144 9489

6/20/2017 17:00 1193 33 4341 100 5667

6/20/2017 18:00 -414 -10 -1507 -29 -1960

6/20/2017 19:00 -235 -9 -856 -27 -1128

6/21/2017 14:00 -395 -21 -1439 -65 -1921

6/21/2017 15:00 -1063 -36 -3870 -109 -5079

6/21/2017 16:00 1630 39 5933 118 7721

6/21/2017 17:00 1025 26 3728 78 4856

Appendix D Page 197 of 359

49



Device Population


6/21/2017 18:00 -481 -18 -1749 -53 -2301

6/21/2017 19:00 -186 -12 -677 -38 -913

6/23/2017 14:00 -315 -20 -1139 -59 -1532

6/23/2017 15:00 -1107 -46 -4005 -135 -5293

6/23/2017 16:00 1569 33 5675 98 7374

6/23/2017 17:00 953 24 3448 72 4497

6/23/2017 18:00 -584 -16 -2114 -48 -2762

6/23/2017 19:00 -336 -11 -1217 -32 -1596

6/30/2017 14:00 -722 -35 -2548 -94 -3399

6/30/2017 15:00 -1230 -52 -4342 -139 -5763

6/30/2017 16:00 1439 29 5080 77 6625

6/30/2017 17:00 946 13 3339 34 4332

6/30/2017 18:00 -426 -36 -1503 -96 -2061

6/30/2017 19:00 -157 -26 -555 -70 -808

7/3/2017 14:00 -710 -35 -2460 -95 -3299

7/3/2017 15:00 -1106 -50 -3833 -136 -5125

7/3/2017 16:00 1883 37 6528 101 8548

7/3/2017 17:00 1181 8 4093 23 5305

7/3/2017 18:00 -446 -27 -1545 -73 -2091

7/3/2017 19:00 -147 -13 -509 -34 -703

7/5/2017 14:00 -740 -43 -2553 -115 -3451

7/5/2017 15:00 -1160 -56 -4003 -147 -5365

7/5/2017 16:00 1528 25 5273 66 6892

7/5/2017 17:00 999 6 3447 17 4469

7/5/2017 18:00 -383 -31 -1322 -81 -1817

7/5/2017 19:00 -160 -22 -553 -57 -792

7/6/2017 14:00 -707 -28 -2439 -75 -3249

7/6/2017 15:00 -1261 -33 -4351 -87 -5732


50



Device Population


7/6/2017 16:00 1207 32 4165 84 5487

7/6/2017 17:00 829 17 2861 45 3752

7/6/2017 18:00 -426 -12 -1471 -31 -1940

7/6/2017 19:00 -178 2 -615 5 -786

7/7/2017 14:00 -720 -26 -2473 -68 -3287

7/7/2017 15:00 -1204 -42 -4136 -107 -5489

7/7/2017 16:00 1872 48 6432 123 8475

7/7/2017 17:00 1144 19 3932 49 5145

7/7/2017 18:00 -455 -25 -1563 -63 -2105

7/7/2017 19:00 -252 -27 -867 -68 -1213

7/12/2017 14:00 -887 -38 -3004 -96 -4024

7/12/2017 15:00 -1301 -59 -4406 -151 -5917

7/12/2017 16:00 1509 19 5109 50 6687

7/12/2017 17:00 966 11 3271 27 4275

7/12/2017 18:00 -425 -40 -1440 -101 -2006

7/12/2017 19:00 -195 -30 -662 -75 -963

7/13/2017 14:00 -814 -39 -2725 -96 -3675

7/13/2017 15:00 -1235 -58 -4134 -141 -5567

7/13/2017 16:00 1686 36 5645 90 7458

7/13/2017 17:00 1091 18 3652 44 4804

7/13/2017 18:00 -381 -29 -1277 -72 -1760

7/13/2017 19:00 -148 -14 -495 -35 -691

7/14/2017 14:00 -762 -29 -2549 -72 -3412

7/14/2017 15:00 -1258 -49 -4210 -120 -5638

7/14/2017 16:00 1703 49 5697 120 7570

7/14/2017 17:00 973 25 3257 61 4316

7/14/2017 18:00 -538 -25 -1800 -62 -2424

7/14/2017 19:00 -204 -9 -682 -23 -919


51



Device Population


7/19/2017 14:00 -865 -26 -2853 -63 -3807

7/19/2017 15:00 -1161 -43 -3830 -103 -5136

7/19/2017 16:00 1048 22 3457 54 4582

7/19/2017 17:00 810 1 2670 3 3485

7/19/2017 18:00 -257 -25 -848 -61 -1192

7/19/2017 19:00 -117 -18 -385 -44 -565

7/20/2017 14:00 -855 -27 -2794 -66 -3742

7/20/2017 15:00 -1225 -41 -4002 -99 -5366

7/20/2017 16:00 1167 27 3815 66 5076

7/20/2017 17:00 787 16 2573 39 3414

7/20/2017 18:00 -344 -21 -1124 -52 -1541

7/20/2017 19:00 -145 -10 -475 -25 -655

7/28/2017 14:00 -789 -29 -2573 -68 -3459

7/28/2017 15:00 -1286 -44 -4195 -104 -5629

7/28/2017 16:00 1624 49 5295 116 7083

7/28/2017 17:00 965 29 3148 68 4210

7/28/2017 18:00 -505 -19 -1647 -46 -2218

7/28/2017 19:00 -225 -14 -735 -34 -1008

8/1/2017 14:00 -692 -33 -2138 -77 -2939

8/1/2017 15:00 -1243 -55 -3842 -130 -5270

8/1/2017 16:00 2115 57 6536 135 8843

8/1/2017 17:00 1121 33 3463 78 4695

8/1/2017 18:00 -654 -20 -2022 -46 -2743

8/1/2017 19:00 -358 4 -1107 9 -1453

8/2/2017 14:00 -819 -33 -2513 -80 -3446

8/2/2017 15:00 -1334 -39 -4095 -94 -5562

8/2/2017 16:00 1955 61 6003 147 8166

8/2/2017 17:00 1144 38 3513 91 4786


52



Device Population


8/2/2017 18:00 -626 -11 -1924 -27 -2588

8/2/2017 19:00 -229 -9 -703 -23 -964

8/3/2017 14:00 -815 -34 -2502 -82 -3432

8/3/2017 15:00 -1292 -54 -3966 -129 -5440

8/3/2017 16:00 1689 44 5187 106 7027

8/3/2017 17:00 948 26 2912 63 3950

8/3/2017 18:00 -674 -20 -2068 -47 -2808

8/3/2017 19:00 -227 -13 -696 -31 -967

8/4/2017 14:00 -854 -32 -2623 -76 -3585

8/4/2017 15:00 -1205 -52 -3699 -124 -5078

8/4/2017 16:00 1883 45 5783 107 7818

8/4/2017 17:00 1068 21 3280 51 4421

8/4/2017 18:00 -485 -7 -1489 -16 -1996

8/4/2017 19:00 -189 -6 -580 -15 -789

8/18/2017 14:00 -1000 -41 -2908 -99 -4048

8/18/2017 15:00 -1451 -61 -4218 -146 -5875

8/18/2017 16:00 1696 41 4933 97 6767

8/18/2017 17:00 931 27 2708 64 3731

8/18/2017 18:00 -612 -23 -1780 -55 -2470

8/18/2017 19:00 -191 -12 -555 -28 -785

8/25/2017 14:00 -981 -41 -2789 -98 -3908

8/25/2017 15:00 -1357 -64 -3860 -150 -5431

8/25/2017 16:00 1513 27 4304 65 5910

8/25/2017 17:00 920 14 2617 33 3584

8/25/2017 18:00 -544 -21 -1547 -50 -2161

8/25/2017 19:00 -229 -13 -653 -30 -925

8/29/2017 14:00 -1139 -35 -3180 -82 -4436

8/29/2017 15:00 -1701 -59 -4748 -137 -6645


53



Device Population


8/29/2017 16:00 1524 40 4255 94 5913

8/29/2017 17:00 988 31 2758 73 3850

8/29/2017 18:00 -707 -13 -1972 -31 -2723

8/29/2017 19:00 -289 -2 -808 -4 -1102

8/30/2017 14:00 -1126 -35 -3128 -82 -4371

8/30/2017 15:00 -1621 -67 -4505 -155 -6348

8/30/2017 16:00 1499 35 4164 82 5780

8/30/2017 17:00 867 25 2408 58 3358

8/30/2017 18:00 -624 -24 -1735 -57 -2440

8/30/2017 19:00 -299 -10 -830 -24 -1163

8/31/2017 14:00 -990 -41 -2739 -95 -3865

8/31/2017 15:00 -1447 -72 -4005 -167 -5690

8/31/2017 16:00 1599 21 4426 50 6097

8/31/2017 17:00 966 0 2674 1 3642

8/31/2017 18:00 -471 -31 -1304 -71 -1878

8/31/2017 19:00 -180 -19 -498 -44 -740

9/5/2017 14:00 -1182 -36 -3211 -81 -4510

9/5/2017 15:00 -1566 -52 -4254 -117 -5989

9/5/2017 16:00 1571 45 4267 102 5985

9/5/2017 17:00 983 26 2670 60 3739

9/5/2017 18:00 -595 -11 -1617 -25 -2248

9/5/2017 19:00 -60 13 -162 29 -180

8/17/2016 18:00 -122 1 -658 2 -778

8/17/2016 19:00 18 4 99 13 135

8/19/2016 14:00 -250 -4 -1341 -9 -1604

8/19/2016 15:00 -491 -7 -2629 -20 -3147

8/19/2016 16:00 812 8 4349 22 5191

8/19/2016 17:00 512 -2 2745 -4 3252


54



Device Population


8/19/2016 18:00 -74 -5 -397 -12 -489

8/19/2016 19:00 33 -2 178 -5 204

8/24/2016 14:00 -220 -3 -1154 -7 -1383

8/24/2016 15:00 -323 -8 -1690 -19 -2040

8/24/2016 16:00 644 7 3373 17 4041

8/24/2016 17:00 535 6 2802 14 3358

8/24/2016 18:00 83 3 432 6 523

8/24/2016 19:00 160 0 837 1 998


55

10 APPENDIX E: MATCHING

This appendix details the matching procedures employed to create the matched control group used to estimate EcoFactor optimization energy savings.

Propensity score matching is a method by which the control group is “matched” to the treatment group via a propensity score, which is essentially an estimate, derived from observed characteristics, of a particular customer’s likelihood of participating in the PowerShift program. The logit model below was used to estimate the propensity scores for all customers.

Equation 16

Where:

is a binary variable that is 1 if the customer is a NVE Demand Response program participant and 0 if they are a non-participant;

is a continuous variable that captures the customer’s pre-EcoFactor installation, weather normalized (consumption divided by degree days), average daily consumption;

is an error term; is a coefficient showing the changes in propensity to participate in the NVE Demand Response

program that occurs for a change in the variable;

After the propensity scores were estimated, for each treatment premise , a k-nearest neighbor’s algorithm is used to find the closest propensity score from among the control premises. It should also be noted that in addition to the propensity scores, treatment members and control group members were matched exactly with respect to their rate codes. This procedure was implemented in R using the “MatchIt” package14

and matches were selected with replacement.

As with the subgroup loads, once the control groups are selected and confirmed, they are aggregated to form the subgroup control time series, which we denote by .

In order to ensure the quality of the matching procedure, Welch’s Two Sample t-test was conducted. The results are in the Table 33 below:

Table 33: Welch’s Two Sample t-test

Independent Variable

Treatment Control Welch Test T

Statistic

Welch Test P-Value Pre-Matching Mean

Post Matching Mean

Pre-Matching Mean Post Matching Mean

kWhN 1.67 1.68 3.57 1.68 -0.015 0.987

Because the p-value is greater than 0.05 we fail to reject the null hypothesis and conclude that the true difference in means is not statistically significantly different than zero.

14 https://cran.r-project.org/web/packages/MatchIt/MatchIt.pdf


https://cran.r-project.org/web/packages/MatchIt/MatchIt.pdf

DSM-15

Page 205 of 359

Schools Program

NV Energy – Southern Nevada (NPC) Program Year 2017


Prepared for:

Prepared by:


916-363-8383

Page 206 of 359

TABLE OF CONTENTS Section Title ....................................................................................................... Page 1. Executive Summary .....................................................................................1

2. Program Background ...................................................................................2

3. M&V Methodology .....................................................................................4

4. Energy Impact Findings .............................................................................17

5. Key Findings and Recommendations ........................................................19

Appendix A: Site-Level Analyses ...................................................................................22

Appendix B: Savings per Month by Rate Class ..............................................................78

Appendix C: Calculation Methodolody, Critical Peak Demand (kW) Savings ..............80

Appendix D: Determining Energy Savings per Month by Rate Class ............................84

Page 207 of 359

1. EXECUTIVE SUMMARY

This measurement and verification (“M&V”) report provides measured and verified energy impacts achieved by the Schools Program energy efficiency program that NV Energy offered in the southern Nevada service territory during 2017.

The main features of the approach used for the impact evaluation for this program are as follows:

M&V data was collected through review of program data and on-site inspections. Based on data provided by NV Energy, a sample design was developed for on-site data collection for the impact evaluation. The sample size provides ex post verified energy savings for southern Nevada with 6.6 percent precision at the 90 percent confidence level.

On-site visits were utilized to verify measure installation; to determine measure operating parameters, and to collect data for determining ex post verified energy (kWh) and critical peak demand (kW) savings. Facility staff were interviewed to determine the operating hours of the installed system and to locate any additional benefits or shortcomings associated with the installed system. For certain M&V sites, lighting equipment, heating, ventilation, and air conditioning (“HVAC”) equipment, or other measures were monitored to obtain accurate information on hours of operation.

Ex post verified energy impacts for the 2017 Schools Program in southern Nevada are shown in Table 1-1. The realization rate for the program in southern Nevada is 101.1 percent.1 Ex post verified energy impacts totaled: 12,542,061 kWh annual savings; and kWh “first-year” savings during the 2017 calendar year. Summer critical peak demand savings are kW.

Table 1-1. Summary of Annual kWh Savings, Southern Nevada Schools Program

First Year (2017) Ex Post kWh Savings

Annual Ex Ante kWh Savings

Annual Ex Post kWh Savings

Realization Rate

8,498,008 12,406,224 12,542,061 101.1%

1 The realization rate is the ratio of ex post verified energy (kWh) savings to ex ante expected energy (kWh) savings, i.e., at the program level: 12,542,061 kWh ex post ÷ 12,406,224kWh ex ante = 1.01 or 101.1%.


1

2. PROGRAM BACKGROUND

This chapter provides an overview of the structure of the Schools Program. In addition, a summary of the evaluation approach is provided in this chapter.

2.1 DESCRIPTION OF THE SCHOOLS PROGRAM

The Schools Program was designed to facilitate energy efficiency and peak demand reduction in the public schools served by NV Energy. The program’s approach was to identify existing barriers to energy reduction in the school districts participating in the program and to recommend and implement solutions to overcome those barriers. Services included selected combinations of:

Benchmarking school energy performance;

Workshops to develop master plans;

Training and technical assistance to identify efficiency improvements in existing buildings;

Dissemination of best practices from highly energy efficient school districts; and,

Designing and building high energy efficient and LEED schools through both new construction and retrofit projects.

During 2017, a total of 136 projects were implemented in southern Nevada with total ex ante expected savings of 12,406,224 kWh.

Table 2-1 shows the distribution of ex ante expected savings by measure type. For the Schools Program in southern Nevada, approximately 90 percent of ex ante expected savings are associated with lighting retrofit measures.

Table 2-1. Ex Ante Expected Savings per Measure, Southern Nevada Schools Program

Measure Category Project Count

Total Ex Ante kWh Savings

Percent of Total Ex Ante kWh Savings

Faucet Aerators HVAC Lighting New Construction

15 28 72 21

14,028 5,581,380 3,997,752 2,813,064

0% 45% 32% 23%

Total 136 12,406,224 100%

2.2 OVERVIEW OF EVALUATION APPROACH

The overall objective of the impact evaluation of the Schools Program in southern Nevada was to determine the energy savings (kWh) and summer critical peak demand (kW) reductions resulting from program activities during 2017.

Program Background Page 209 of 359

2

Schools Program – 2017 NV Energy, Southern Nevada M&V Report March 2018

The approach for the impact evaluation had the following main features:

Available documentation (e.g., audit reports, savings calculation work papers, network reports, etc.) was reviewed for a sample of sites, with close attention to calculation procedures and documentation of savings calculations.

On-site data collection was conducted for a sample of 25 sites to verify measure installation and capture the data necessary for determining savings and demand reductions.

A comprehensive lighting operation study was conducted using monitoring data from 16 K-12 schools in northern and southern Nevada. Annual hours of operation determined for southern Nevada are described in detail in section 3.1.4 of this M&V report. Those hours of operation for southern Nevada were used in both ex ante expected savings and ex post verified savings calculations.

A lighting operation study was conducted on four college and university campuses in southern Nevada, these hours of operation are described in section 3.1.4 of this M&V report. Those hours of operation for southern Nevada were used in ex post verified savings calculations.

Annual kWh savings were determined using the following techniques:

o Analysis of lighting savings was accomplished using an engineering model that utilizes information on operating parameters collected on-site and, if appropriate, industry standards.

o For HVAC measures, the original analyses used to calculate the ex ante expected savings were reviewed and the operational settings and inputs of the calculations were verified. For custom measures or relatively more complex measures, simulations with the DOE-2 energy analysis model were used to determine energy use and savings from the installed measures.


3

3. M&V METHODOLOGY

This chapter addresses the calculation of annual kWh savings and summer critical peak demand kW savings resulting from measures installed in customer facilities under the Schools Program in southern Nevada during 2017.

3.1. METHODOLOGY FOR CALCULATING GROSS SAVINGS

The methodology used for calculating gross savings is described in this section.

3.1.1 SAMPLING PLAN

Data used to calculate the gross savings achieved through the Schools Program in southern Nevada were collected for a sample of sites at which projects were completed during 2017. Data provided by NV Energy through DSM Central showed that during 2017, a total of 136 projects were completed at school sites in southern Nevada, providing ex ante expected savings of 12,406,224 kWh annually.

Inspection of ex ante data on kWh savings for individual sites provided by the implementation contractor indicated that the distribution of savings was positively skewed, with a relatively small number of sites accounting for a high percentage of the projected savings. A sample design for selecting projects using the Dalenius-Hodges stratified random sampling method was used that took such skewness into account and allowed savings to be determined with 10 percent relative precision (or better) at the 90 percent confidence level. Sampling for this program was performed at the project level. Projects were categorized into sampling strata as shown in Table 3-1. The actual precision achieved for the sample is 6.6 percent. For sites included in the M&V sample, each of the associated projects implemented at the site was included in the M&V impact analysis.

M&V sampling and data collection were relatively concurrent with program implementation. ADM used a near real-time process whereby a portion of the sample was selected periodically as projects in the program were completed. The timing of sample selection was contingent upon the timing of the completion of projects during the program year.

Table 3-1 shows the strata boundaries, total ex post verified energy savings, the coefficient of variation, and the number of sample sites for the southern Nevada Schools Program sample for each stratum.

M&V Methodology Page 211 of 359

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Table 3-1. Population Statistics by Sample Stratum for Southern Nevada Schools Program

Sampling Criteria: Sampling Strata: Totals: 1 2 3 4 5

Strata Boundaries (Ex Post kWh)

146-17,286

25,610-95,865

103,046-173,709

175421- 275,204

317,143-813,193

747-1,108,731

Number of Projects 56 39 18 14 9 136

Total Ex Post kWh Savings 280,824 2,204,807 2,542,583 3,066,758 4,500,960 12,595,932 Average Ex Post kWh Savings 5,015 56,534 141,255 219,054 500,107 92,617 Standard Dev of Ex Post kWh savings 5,006 22,837 16,563 30,424 165,511 541,008

Coefficient of Variation 1.00 0.40 0.12 0.14 0.33 5.84

Sample n 6 7 2 2 8 25 Statistical Precision of kWh Savings Estimation at 90% Level of Confidence 6.6%

3.1.2 REVIEW OF DOCUMENTATION After the sample was selected, NV Energy’s program implementation contractor provided documentation pertaining to those projects. The first step in the evaluation effort was to review this documentation and other program materials that were relevant to the evaluation effort.

For each project, the available documentation (e.g., audit reports, savings calculation work papers, etc.) for each rebated measure was reviewed, with close attention to calculation procedures and documentation of savings calculations. Documentation reviewed for all projects in the sample included program forms, databases, reports, billing system data, weather data, and any other potentially useful data. Each project was reviewed to determine whether the following types of information had been provided:

Documentation for the equipment changed, including (1) descriptions, (2) schematics, (3) performance data, and (4) other supporting information;

Documentation for the new equipment installed, including (1) descriptions, (2) schematics, (3) performance data, and (4) other supporting information; and,

Information about the savings calculation methodology, including (1) what methodology was used, (2) specifications of assumptions and sources for these specifications, and (3) correctness of calculations.

If there was uncertainty regarding a project or apparently incomplete project documentation, ADM staff contacted the implementation contractor to obtain further information to ensure the development of an appropriate site-specific M&V plan.


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3.1.3 ON-SITE DATA COLLECTION PROCEDURES On-site visits were used to collect data that were used in calculating savings impacts. Visits to the sampled sites were used to collect primary data on the facilities participating in the program.

When sites were selected for the M&V sample, ADM notified NV Energy program management and concurrently provided the Schools Program implementation team a list of projects for which ADM would attempt to schedule M&V activities. This notification also served as a request to the implementation contractor for any documentation relating to the projects, including school name, site address or other premise identification, as well as the respective contact information for the school representative with whom ADM needed to schedule M&V activities.

During an on-site visit, the field staff accomplished three major tasks:

First, ADM field staff verified the implementation status of all measures for which customers received incentives. ADM verified that the energy efficiency measures were indeed installed, that they were installed correctly and that they still functioned properly.

Second, ADM field staff collected the physical data needed to analyze the energy savings that have been realized from the installed improvements and measures. Data was collected using a form that was prepared specifically for the project in question after an in-house review of the project file.

Third, ADM field staff interviewed the contact personnel at the facility to obtain additional information on the installed system to complement the data collected from other sources.

At some sites, monitoring was conducted to gather more information on the operating hours of the installed measures. Monitoring was conducted at sites where it was judged deemed that the monitored data would be useful for further refinement and higher accuracy of savings calculations; if, project documentation allowed for detailed calculations monitoring was not implemented.

3.1.4 PROCEDURES FOR CALCULATING kWh SAVINGS FROM MEASURES INSTALLED THROUGH SCHOOLS PROJECTS

ADM uses measure-specific methodologies to analyze and determine gross savings per project. The project-specific analyses and savings calculations can be found in Appendix A. Table 3-2 below provides a summary of typical M&V methodologies for determining energy savings.


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Table 3-2. Typical M&V Methodologies for Determining Energy Savings

Type of Measure Method to Determine Savings

Lighting Monitoring or project documentation data to determine pre-and post-installation measure wattages and fixture hours-of-use.

HVAC (including packaged units, chillers, cooling towers, controls/EMS)

Equivalent full load hour method to calculate savings resulting from HVAC measures. Equivalent full load hours were derived using eQuest models using corresponding weather files.2

The methodologies outlined in Table 3-2 guided the implementation contractor’s calculations of ex ante estimated energy savings, as well as the M&V analysis of ex post verified energy savings. After determining ex post verified energy savings, ADM calculated program-level kWh savings by applying a ratio procedure in which the achieved savings for the M&V sample sites were imputed to the whole population of program participants.

Energy savings realization rates were calculated for each project for which on-site data collection and engineering analysis/building simulations were conducted.3 Sites with relatively high or low realization rates were further analyzed to determine the reasons for the discrepancy between ex ante expected and ex post verified energy savings. Information on how realization rates were determined is included in the site-level M&V analyses presented in Appendix A.

The following discussion describes procedures used for calculating savings from various measure types. Site-specific information on savings calculations is contained in Appendix A.

Analyzing Savings from Lighting Measures: Lighting measures that were examined included retrofits of existing fixtures, and lamps and/or ballasts with energy efficient fixtures. These types of measures reduce demand, while not affecting operating hours. Lighting control strategies that might include the addition of energy conserving control technologies such as motion sensors or daylighting controls were also examined. These measures typically involve a reduction in hours of operation and/or a lower electric current passing through the fixtures.

Annual lighting hours of operation were determined by a comprehensive lighting study performed by ADM. Over 80 lighting loggers were deployed in 16 schools in both Nevada territories to determine annual operation hours by territory, type of school (elementary, middle, and high) and

2 A calculation of pre and post energy consumption using full load hours which represent the equivalent time a unit will spend operating at peak capacity during a typical year

3 The realization rate is the ratio of ex post verified energy (kWh) savings to ex ante expected energy (kWh) savings. At the project level: ex post verified energy (kWh) savings is the achieved savings that has been measured and verified for each M&V site; ex ante expected energy (kWh) savings was provided by the implementation team (through the project application procedure, and as recorded in the tracking system for the program) previous to the M&V activities that led to the determination of the achieved savings.


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by space type (used for gym retrofits). From this study, ADM determined the following blended annual lighting hours of operation for southern Nevada whole-school retrofits:

Elementary 2,515 Middle 3,202 High 2,804

The following blended hours are representative of lighting operation for gym retrofits:


Table 3-3 presents lighting operating hours specific to various space types in the southern territory:

Table 3-3. Annual Lighting Operating Hours by Space Type, NPC

Space Type Classroom Hallway Restroom Library

Work Room/ Storage

Office Lab Music Room/

Theater Elementary 2,546 2,258 2,590 2,998 568 4,303 2,958 3,243 Middle 3,148 4,823 3,464 2,374 568 3,046 3,256 2,044 High 2,135 4,440 4,743 4,085 568 4,297 2,545 3,227

Lighting hours for lights with an occupancy sensor were reduced by 30 percent, based on engineering calculations that are consistent with the most recent industry literature.4

During the calendar year 2015, ADM initiated a study to determine annual lighting hours of use for colleges and universities. Between July 30th and November 3rd, 63 lighting loggers were deployed on the UNLV campus and the three largest CSN campuses. The logger measurements yielded the average hours of use shown in Table 3-4.

Table 3-4. Annual Lighting Hours of Use (“HOU”) by Space Type, Higher Education

Space Type HOU Logger Count Hallways, Entrances, and Restrooms 7,710 14 Classrooms, Offices, Labs, and Other Rooms without Occupancy Sensors 2,922 34

The higher education hours of use study will continue to be updated throughout the 2018 evaluation by logging sampled projects at higher education facilities.

4 Study referenced for lighting occupancy sensor hours reduction of 30%: Levine, M., Geller, H., Koomey, J., Nadel S., Price, L., “Electricity Energy Use Efficiency: Experience with Technologies, Markets and Policies” ACEEE, 1992. Lighting control savings fraction of 30% is consistent with current programs offered by National Grid, Northeast Utilities, Long Island Power Authority, NYSERDA, and Energy Efficiency Vermont.


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Analyzing the savings from lighting measures requires data for retrofitted fixtures on (1) wattages before and after retrofit and (2) hours of operation before and after the retrofit.

ADM uses per-fixture baseline demand, retrofit demand, and appropriate post-retrofit operating hours to calculate annual energy savings for sampled fixtures of each usage type. Annual energy savings for each sample fixture is calculated using the following formula:

Where: kWhpre = amount of kWh used by preexisting fixtures kWhpost = amount of kWh used by post-installation fixtures

To calculate total savings attributable to lighting measures, including impacts on HVAC operation, savings from lighting measures in conditioned spaces are adjusted by region-specific and building type-specific heating and cooling interaction factors.

Analyzing Savings from HVAC Measures: Savings for HVAC measures installed at a facility are derived by using equivalent full load hours developed through DOE-2 simulations. DOE-2 is an industry standard building energy analysis and load simulation program. DOE-2 combines a description of the building layout, constructions, usage, and conditioning systems with hourly weather data to perform an hourly simulation of building energy use. The DOE-2 simulations allow calculation of the primary and secondary effects of lighting measures on energy use. Each simulation calculates HVAC energy and demand usage to be expected under different assumptions about equipment and/or construction conditions.

For the analysis of HVAC measures, the data collected through on-site visits and monitoring are utilized. Using these data, ADM calculates energy savings for the energy efficient equipment and measures installed in each of the participant facilities by developing independent assessments of the savings through engineering calculations. By using an equivalent full load hour method for the analysis, the energy use associated with the end use affected by the measure(s) being analyzed can be quantified. After having made these calculations, the energy use that would have been observed without the measure(s) is determined. The difference between what would have been observed and the end use energy of the implemented measure is the energy savings for the HVAC measure.

Heating and Cooling Interaction Factor: Installing energy efficient lighting in air-conditioned spaces saves electricity in two ways: first by reducing lighting electrical loads; and second by introducing less heat in conditioned spaces, hence incrementally decreasing space cooling loads. The relatively low heat output from energy efficient lighting also incrementally increases space heating loads. Space heating and cooling impacts of energy efficient lighting are described using a ratio that is known as the heating and cooling


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interaction factor (“HCIF”). HCIF was included in the calculations to determine energy savings (kWh) associated with the facility’s HVAC equipment. The HCIF was incorporated into the energy impact analysis for each M&V sample site (see Appendix A for all the site-level analyses).

The HCIF value represents the ratio of a) total post-retrofit kWh savings and b) lighting-only kWh savings. For example, if HCIF = 1.01, then post-retrofit kWh usage for heating and cooling decreased overall, thus improving by 1 percent the overall kWh savings provided by the energy efficient lighting measure. Conversely, if HCIF = 0.99, then post-retrofit kWh usage for heating and cooling increased overall, thus reducing by 1 percent the overall kWh savings provided by the energy efficient lighting measure. HCIF for this program was developed in tandem with the energy savings curves; the development of both methods is explained in section 3.1.5 below.

3.1.5 DETERMINING ENERGY SAVINGS CURVES

ADM utilized measure-specific Energy Savings Curves (“Curves”) to determine program-level energy (kWh) savings per month per rate class and program-level critical peak demand (kW) savings per month per rate class. The Curves were derived from Curves published through the California End Use Survey (“CEUS”).5 To adapt CEUS Curves to southern Nevada schools, ADM modified the CEUS Curves as follows:

Curves were aligned with the Clark County School District instructional calendar.

HCIF specific to southern Nevada was incorporated into the Curve for interior lighting measures. This modification improves the shape and accuracy of the interior lighting Curve but does not change annual energy (kWh) savings given that the Curve was normalized before being employed to disaggregate kWh savings into 8,760 hourly bins per rate class. A detailed explanation of the HCIF derivation is provided below (in this same section, immediately following Figure 2).

5 The California Commercial End-Use Survey (CEUS) is a comprehensive study of energy loads or end-uses in the commercial building sector. Itron performed the Survey under contract to the California Energy Commission (CEC); Pacific Gas & Electric, San Diego Gas and Electric, Southern California Edison, Southern California Gas Company and the Sacramento Municipal Utility District participated in the Survey. The Survey captured detailed building systems data, building geometry, electricity and gas usage, thermal shell characteristics, equipment inventories, operating schedules and other commercial building characteristics. A stratified random sample of 2,800 commercial facilities was targeted and a sample of 2,790 was actually completed. Commercial premises were weighted and aggregated to building segment results. Available study results include: floor stocks; fuel types and weights; electricity and natural gas consumption; energy-use indices (EUIs); energy intensities; and 16-day hourly end-use load profiles for twelve categories of commercial (i.e., non-residential) building types, including schools. For an in-depth explanation of how CEUS conducted the monitoring and calculations, please refer to: http://www.energy.ca.gov/2006publications/CEC-400-2006-005/CEC-400-2006-005.PDF.


10

http://www.energy.ca.gov/2006publications/CEC-400-2006-005/CEC-400-2006-005.PDF


Assessing the Applicability of CEUS Curves: Appropriate Curves are essential to the M&V task of allocating kWh and critical peak kW savings to hourly and monthly bins for each rate class that participated in the 2017 program. ADM explored using an Curve that was specific to the 2017 Schools Program. ADM had conducted a monitoring study in Nevada schools in 2011; therefore, we examined the feasibility of using the monitored lighting and HVAC schedules to create appropriate Curves.6 However, after examining the logger data from the 2011 study, we found that the monitored lighting and HVAC schedules closely matched the respective CEUS Curves for schools. ADM determined that the CEUS curves, after being modified to align with the actual instructional calendar for Clark County Schools, would provide the most accurate Curves for this program.

Alignment of CEUS Curve with Clark County School District Calendar: Most of the schools in the 2017 Schools Program follow a traditional nine-month schedule. CEUS curves for lighting and HVAC measures were based predominantly on traditional nine-month schedules; for example, the CEUS curve for interior lighting depicts monthly energy usage declines in June and reaches a nadir in July, then increases during August and September until it reaches a peak in October. The CEUS lighting curve is the dark line shown in Figure 2 on the following page.

Given that the CEUS data indicates that all schools were out-of-session during at least part of July, and most schools were out-of-session during all of July, ADM used CEUS data for July to “model” all out-of-session weekdays for Clark County’s summer vacation weeks. In other words, ADM modified the CEUS curves to incorporate the actual summer schedule for Clark County Schools. Because the CEUS lighting schedules during July weekdays closely match ADM’s 2011 lighting study data for summertime non-instructional days, ADM created each Curve for the PY2017 Schools Program by transposing weekday savings from the corresponding July CEUS curve onto all of Clark County’s out-of-session summer days. Therefore, compared to the corresponding CEUS curves, ADM’s PY2017 Schools Program Curves have lower summertime kWh savings.

For the interior lighting curve, Figure 1 depicts the difference in kWh savings when comparing an in-session weekday (e.g., the lighter line, representing September 10) versus out-of-session weekday (e.g., the darker line, representing July 15).

6 The 2011 monitoring study is described in Section 3.1.4 above.


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0.00%

0.01%

0.02%

0.03%

0.04%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Percen

tAnn

ualSavings

CEUS Out of Session vs In Session (Hour on Weekday)

15 Jul 10 Sep

Figure 1. CEUS Interior Lighting Savings Curve, out of session vs. in session weekday

Figure 2 below compares the original CEUS interior lighting curve to the one ADM modified to better represent the Clark County School District Calendar; the differences are not dramatic, but the ADM curve does show lower lighting energy savings during June and August.

Figure 2. Comparison of original CEUS curve to one ADM modified to reflect the Clark County School District Calendar


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Incorporating HCIF Factors into the Savings Curve: The HCIF was incorporated into the energy impact analysis for each M&V sample site (see Appendix A for site-level analyses). The overall savings curve also reflects secondary heating and cooling impacts in a manner that is consistent with the site-specific HCIF calculations. The methodology employed is described by ASHRAE7, which calculates HVAC impacts by estimating cooling and heating equipment efficiencies and applying specific thermal fractions for cooling and heating.8

kWh impactAC = kWh savingsLighting x Thermal FractionAC ÷ MCOPElectric AC

Equation 1

kWh impactHeating = kWh savingsLighting x Thermal FractionHeat ÷ MCOPElectricHeat

Equation 3 Where:

kWh savingsLighting = Verified annual energy savings from lighting retrofit. Thermal FractionAC = Fraction of the lighting impact that results in avoided cooling. ADM uses bin calculators to determine this fraction.9

Thermal FractionHeat = Fraction of the lighting impact that results in an increased need to heat the affected space. ADM uses bin calculators to determine this fraction. MCOPElectricAC = Marginal coefficient of performance, A/C system. Only the electric portion of cooling energy usage informs MCOP.

MCOPElectricHeat = Marginal coefficient of performance, heating system. Only the electric portion of heating energy usage informs MCOP10

All variables related to heating and air conditioning are modified for Las Vegas Valley climate, categorized as a “warm” climate zone. In figures below, the modified CEUS lighting shape depicted by gray dashes (“- - - -”) is normalized to unity after the interactive effects are incorporated.

In summary, for the PY2017 Schools Program, a total of three Curves were utilized to calculate critical peak demand (kW) savings. The three Curves (interior lighting, exterior lighting, and

7 Calculating Lighting and HVAC Interactions”, Robert Rundquist, PE, Karl F. Johnson and Donald J. Aumann, PE, ASHRAE Journal, November 1993. 8 Thermal fraction is the ratio of annual lighting energy contributing to cooling (or heating) load. 9 Bin calculators determine over each hour of the year the fraction of avoided or increased thermal load due to the

efficient lighting measure. 10 For example, MCOPElectricHeat may have approximate values near unity for electric resistive heating, near 2 for electric heat pumps, and 15 for forced air gas furnaces/boilers. The 1/15 in the latter case represents the electric energy usage of the air handlers, but is cast in the form of COP.


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HVAC) were derived by modifying CEUS curves to fit the school district calendar. The modified Curves are appropriate for Clark County schools. Figure 3 provides an example of the modified Curves; the Figure 3 depiction represents average daily savings curves for interior lighting, exterior lighting, and HVAC in a Clark County school.

0.00%

0.01%

0.02%

0.03%

0.04%

0.05%

0.06%

0.07%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Percen

tofA

nnua

lSavings

Daily Savings Curves

Interior Lighting Exterior Lighting HVAC

Figure 3. Average Daily Savings Curves, Clark County

Figure 4 below shows the final program-level annual savings Curve.

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%

1 2 3 4 5 6 7 8 9 10 11 12

Percen

tAnn

ualSavings

Month

Schools Annual Savings Curve

Figure 4. Program-Level Annual Savings Curve, NPC Schools Program

Table 3-5 lists the energy savings curves and the source of those curves which were employed to allocate kWh and critical peak kW savings per month and per rate class for this program.


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Table 3-5. Energy Savings Curves Specific to this 2017 Program Energy Savings Curve Source / Modification Applicability

Program-level curve for PY2017 Schools Program

CEUS interior lighting, exterior lighting and cooling savings curves, modified to represent the Clark County School District calendar.

Southern Nevada schools on the Clark County School District calendar.

3.1.6 CALCULATING FIRST-YEAR KWH SAVINGS

First-year kWh savings were calculated by determining the percentage of the year remaining when each measure was installed. We used measure startup data from NV Energy’s DSM Central database to calculate the number of days left in the calendar year as of the measure startup date. For each measure, the number of days remaining in the year was then used along with the normalized energy savings curve described above to determine the share of annualized kWh savings realized during the 2017 calendar year. First-year kWh savings was summed by month across each customer rate class in the program population to determine first-year kWh savings per month per rate class. The first-year kWh savings table is provided in Table B-1 in Appendix B.

3.1.7 CALCULATION OF CRITICAL PEAK DEMAND (kW) SAVINGS

The critical peak demand period per month for Nevada Power is defined as the hour in each month when system load is most likely to reach a critical peak. The critical peak demand hour per month is shown below in Table 3-5. The summer critical peak demand period is defined as the hour ending at 17:00 hours or 5:00 pm in the month of July. In other words, based on ADM’s analysis of historical data, system load is most likely to reach an annual maximum level on any given July day during the hour ending at 17:00 hours or 5:00 pm.

Table 3-6 Critical Peak Demand Period per Month, NPC Month Hour (NPC) Ending at: January 19 19:00

February 19 19:00 March 20 20:00 April 20 20:00 May 17 17:00 June 17 17:00 July 17 17:00

August 17 17:00 September 17 17:00

October 19 19:00 November 19 19:00 December 19 19:00

Critical peak demand (kW) savings are calculated by month and rate class utilizing ex post verified program savings and appropriate measure-level, 8760-hour energy savings curves. For each 2017 participant in this program, ex post annualized energy savings per measure is allocated to the participant’s rate class, and to the specific energy savings curve for that measure. The result is a

M&V Methodology 15 Page 222 of 359


two-dimensional matrix providing per-rate-class savings per hour for all 8,760 hours of the typical calendar year. The results are then inspected to identify the maximum critical peak demand (kW) savings per month, i.e., to answer the question: “For each month of a typical year, what is the maximum kW savings during the hour specified in Table 3-6?”

Summer critical peak demand savings is defined as the maximum kW reduction that could be expected during any day in July during the hour ending at 5:00 pm. For this program, verified summer critical peak demand reduction is 2,749 kW. The complete ex post critical peak demand (kW) savings by month and by rate class are provided in Appendix B. For more information on how ADM calculates summer critical peak demand savings, please see Appendix C.

3.1.8 DETERMINATION of EFFECTIVE USEFUL LIFE (EUL) and LIFETIME SAVINGS

ADM analyzed various data to determine the Effective Useful Life (“EUL”) for each category of measures installed by NV Energy’s 2017 Schools Programs. ADM subsequently employed its EUL determinations to calculate lifetime energy (kWh) savings per measure category and at the program level. To determine EUL values for the Lighting Retrofits, and HVAC Retrofits measure categories, ADM utilized the California Database for Energy Efficient Resources (“DEER”).11

Note the following measure-specific issues related to the EUL values provided in this M&V report:

For the HVAC Retrofits category, all measures installed by the 2017 Schools Program have the same EUL value in DEER, i.e., 15 years.

For the various Lighting Retrofit measures installed by the 2017 Schools Program, DEER provides EUL values such as approximately four years for qualified CFLs; 12 years for CFL fixtures, which in ADM’s judgment is also appropriate for LED fixtures, given that DEER does not provide an EUL value for LED fixtures; 15 years for fluorescent fixtures and exterior lighting; and 16 years for LED exit lights. ADM used the appropriate EUL values from DEER to calculate the respective weighted average EUL values for Lighting Retrofits measure category, which are 11.8 years for NPC.

To determine program-level EUL values, ADM calculated weighted averages across all measure categories; EUL calculations and lifetime energy (kWh) savings per measure category are provided in the following chapter. ADM concluded that the weighted EUL is 12.45 years.

11 http://www.deeresources.com/deer0911planning/downloads/EUL_Summary_10-1-08.xls


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http://www.deeresources.com/deer0911planning/downloads/EUL_Summary_10-1-08.xls

https://DEER).11

4. ENERGY IMPACT FINDINGS

This chapter presents the results and findings from the field data collection and energy savings analysis.

4.1 RESULTS OF EX POST VERIFIED SAVINGS CALCULATIONS

To calculate annual kWh savings and summer critical peak kW reductions for the 2017 southern Nevada Schools Program, data were collected and analyzed for a sample of 25 sites. The data were analyzed using the methods described in Section 3.1 to calculate project energy savings and peak kW reductions and to determine the realization rates for the program in southern Nevada. The results of that analysis are reported in this section. Project-level analyses are presented in Appendix A.

4.1.1 Ex Post Verified kWh Savings

The ex post verified kWh savings of the southern Nevada Schools Program during 2017 are summarized by sampling stratum in Table 4-1. Overall, the achieved ex post verified savings of 12,542,061 kWh was equal to approximately 101 percent of ex ante savings.

Table 4-1. Summary of Annual Energy (kWh) Savings by Stratum

Stratum Ex Ante kWh Savings

Ex Post kWh Savings

Realization Rate

Strata 1 273,771 273,771 100.0%

Strata 2 2,189,158 2,211,837 101.0%

Strata 3 2,560,402 2,564,591 100.2%

Strata 4 3,012,685 2,987,617 99.2%

Strata 5 4,370,208 4,504,245 103.1%

Total 12,406,224 12,542,061 101.1%

Table 4-2 provides site-level ex ante expected and ex post verified savings for the 2017 southern Nevada Schools Program.

Energy Impact Findings Page 224 of 359

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Table 4-2. Ex Ante Expected and Ex Post Annual Verified kWh Savings for Sampled Projects

Facility Name Ex Ante kWh Savings

Ex Post kWh Savings

Project Realization Rate

Fay Herron ES 982 982 100% College of Southern Nevada, N. Las Vegas, Bldg. T 10,355 10,357 100% McCall ES 12,408 12,407 100% Antonello ES 12,408 12,407 100% Agassi Preparatory Academy 27,967 25,519 91% Betsy A. Rhodes Elementary School 30,869 30,868 100% College of Southern Nevada, N. Las Vegas, Bldg. C 45,659 45,664 100% John Bass Elementary School 64,693 64,697 100% Sunrise Acres Elementary School 83,588 83,563 100% Harley A. Harmon Elementary School 92,358 99,364 108% Fay Herron Elementary School 95,708 95,734 100% D'Vorre And Hal Ober Elementary School 138,101 138,092 100% John Bass Elementary School 165,787 166,832 101% CSN, N. Las Vegas Campus, Building C 166,971 166,990 100% William & Mary Scherkenbach Elementary School 175,171 175,173 100% Charles And Phyllis Frias Elementary School 189,235 189,244 100% Southwest Career & Technical Academy (SW CTA) 203,995 200,714 98% Cashman, James Middle School 307,930 298,266 97% Woodbury Middle School 354,180 348,430 98% Martin, Roy W. MS 376,719 348,436 92% Clifford Lawrence Middle School 396,610 396,503 100% Rancho High School 575,507 516,279 90% Palo Verde HS 583,282 808,693 139% Lawrence & Heidi Canarelli Middle School 602,444 602,437 100% Sierra Vista HS 794,425 794,462 100%

Subtotal for M&V Sample 5,507,352 5,632,113 102.3%

Not Sampled 6,898,872 6,909,948 100.2%

Total 12,406,224 12,542,061 101.1%

First-year savings were calculated using the methodology presented in Section 3.1.5. First-year energy savings for Schools Program projects implemented in southern Nevada in 2017 totaled 8,498,008 kWh. First-year and annual ex post verified kWh savings of the southern Nevada Schools Program during 2017 are summarized in Table 4-3.

Table 4-3. First Year and Annual kWh Savings, Southern Nevada Schools Program



8,498,008 12,542,061

See Appendix B for tables presenting the kWh savings for the Schools Program in NV Energy’s southern Nevada service territory broken out by month by rate class for the years 2017 through 2020.


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4.1.2 Ex Post Verified kW Savings

The ex post verified summer critical peak demand reduction for the Schools Program in NV Energy’s southern Nevada service territory is kW.

The complete ex post determination for critical peak demand savings (kW savings) per month and per rate class are provided in Appendix B. Further information on ADM’s methodology for calculating critical peak demand savings can be found in Appendix C.

4.1.3 Effective Useful Life (EUL) and Lifetime Energy (kWh) Savings

As described in section 3.1.8 in the previous chapter, ADM determined the Effective Useful Life (EUL) for each category of measures installed by NV Energy’s 2017 Schools Programs. ADM subsequently employed EUL values to calculate lifetime energy (kWh) savings per measure category and at the program level.12 As shown in Table 4-4 below, ADM determined program-level lifetime savings of 156,094,958 kWh. Program-level EUL was then taken to be lifetime kWh divided by annual kWh, or 156,094,958 divided by 12,542,061, i.e., 12.45 years. ADM thus determined weighted average EUL of 12.45 years for the 2017 program.

Table 4-4. EUL and Lifetime Energy Savings, NPC Schools Program, PY2017

Measure Category Ex Post Verified Annual kWh Savings

EUL (years)

Ex Post Verified Lifetime kWh Savings

Faucet Aerators 5,681,052 11.43 64,934,429 HVAC 4,047,948 13.41 54,282,982 Lighting 2,799,032 13.13 36,751,295 New Construction 14,028 9.00 126,252 Total Energy (kWh) Savings; Weighted Average EUL 12,542,061 12.45 156,094,958

12 To determine measure level EUL for new construction sites, ex post annual savings were categorized into either lighting or HVAC savings.


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https://level.12

5. KEY FINDINGS AND RECOMMENDATIONS

This chapter presents key findings and recommendations.

5.1 KEY FINDINGS

At the program level, ex post verified energy (kWh) savings determined by ADM did not vary significantly from ex ante estimated energy (kWh) savings reported by NV Energy’s implementation contractor (“IC”). Typical variances between ex post verified energy savings and ex ante were the result of wattage differences between site plans and actual installation. As ADM, has noted in previous evaluations of this program, the implementation team (i.e., CLEAResult and NV Energy program management) has generally worked very effectively to achieve a good understanding of M&V approaches and algorithms for the various measures implemented by the program.

5.2 RECOMMENDATIONS

This section presents ADM’s recommendations for the evaluation of the 2017 Schools Program.

5.2.1 Recommendation 1

As has occurred in previous years, the Schools Program implementation team should seek – and ADM should provide as soon as possible in 2018 – M&V review and feedback related to the ex ante estimated energy (kWh) savings values per measure for program year 2018 (“PY2018”).


For PY2017, the NPC program continues to include “higher education”. This participant category may require relatively significant M&V efforts during 2018. For these PY2018 participants, the Schools Program implementation team should endeavor to keep ADM informed relative to types of facilities and measures that may be new to the program and for which applications are anticipated. To minimize M&V impacts on program participants, ADM should be included in pre-project discussions with participants for sites that have significant sources of variability and may require monitoring. At the date of this M&V report, these recommended coordination activities are underway for PY2018.


Fixture counts for each individual space, room, or section of hallway are necessary for effective M&V work in the buildings found in Schools, colleges and universities. In past reports, ADM had suggested providing more specific per space counts because secondary inspections had to be conducted to verify quantities. During PY2017, appropriate per-space counts were provided and no sites needed a secondary inspection. For PY2018, ADM recommends that per-space fixture counts continue to be provided in contractor’s project documentation.

Key Findings and Recommendations Page 227 of 359

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During PY2017, several projects had hours of operation that varied significantly from the deemed hours used to calculate ex ante savings. These projects typically included dedicated office buildings, warehouses or exterior lighting. We recommend that for these atypical building types, hours of operation should be obtained on a per-site basis.


During PY2017, several retro-commissioning sites became a part of the sample. For retro-commissioning sites with setpoint changes, ADM has concerns about the longevity of these setpoints. ADM recommends that we check the setpoints for these sites midway through PY2018 to verify the persistence of savings.


The Schools Program will add several large whole school and new construction projects in PY2018. As such, it is extremely important to understand the program population as early in the year as possible. To date, projects have been included in the program population for sampling purposes upon completion. In PY2017, this resulted in several sites being added in January. For the upcoming program year, earlier notice of both the number and estimated savings of program sites will be critical for the M&V effort. This could be achieved through access to a database of upcoming projects, or other regular update methods. This data would be used to determine sample strata boundaries and to update sample size through the program year.


21

APPENDIX A: SITE-LEVEL ANALYSES

Site Sunrise Acres ES Address 211 N 28th St. Las Vegas, NV 89101 Project Number NPC-2017Schools_1207206

Executive Summary

The customer received incentives from NV Energy for the installation of an Air Cooled Helical Rotary chiller. The realization rate for this project is 100 percent.

Project Description

The elementary school installed a 237-ton air cooled chiller with an above code efficiency. The main purpose of the retrofit was to increase the energy efficiency of the school and reduce frequent maintenance issues with the old unit. The baseline used is IECC 201213.

Measurement and Verification Effort

During the M&V visit, ADM staff verified the installation of the new chiller. In order to calculate the savings due to the chiller, ADM used the Equivalent Full Load Hour method. ADM used the following equation to calculate the HVAC energy savings:

Where: kWhsavings = Annual energy savings

Ton = Rated capacity of the unit

EFLH = Equivalent full load hours

13 IECC 2012 Table 403.2.3(7) Water Chilling Packages, Efficiency Requirements


22

M&V Report Schools Program – 2017 NV Energy, Southern Nevada

March 2018

kW/ton B

kW/ton A

= Baseline equipment part load efficiency

= As-Built equipment part load efficiency

HVAC Chiller Savings Calculations

# of Units Tons Equipment

Type

Baseline Part Load Eff

Eff Type

Unit's Part Load Eff

Eff Type EFLH CAF

Expected Total kWh

Realized Total kWh

Realization Rate EUL Lifetime

Savings

1 237 Air Cooled

Helical Rotary

0.941 kW/ton 0.596 kW/ton 1,023 1.000 83,588 83,563 100% 20 1,671,268

Results

The project level realization rate is 100 percent.


23


March 2018

Site Address Project Number

William & Mary Scherkenbach Elementary School 9371 Iron Mountain Rd, Las Vegas NV 89143 NPC-2017Schools_1213407

Executive Summary

Under project NPC-2017Schools_1213407, William & Mary Scherkenbach Elementary received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

Clark County School District completed a lighting retrofit project at William & Mary Scherkenbach Elementary School. This school is part of the performance contract between the District and Ameresco which uses energy savings to pay for the cost of the project. A subcontractor performed the lighting retrofit in both the interior and exterior areas across the entire school. Existing lights were fluorescent, incandescent, and high intensity discharge lamps which were retrofitted to new LEDs. The majority of tubular lamps were 14W LEDs, with some high ceiling areas utilizing 18W LEDs.


During the M&V visit, ADM staff verified equipment installation and determined the lighting operating schedule. The baseline lighting operating hours were verified through an interview with facility staff. The post-implementation lighting hours were verified through an interview with facility staff.

Lighting retrofit energy savings are calculated as:


N = Number of fixtures


24


kW = kW of each fixture

h = Indicates hour of year

HCIFh = HVAC interactive factor for hour h

fh = the fraction of hour h that the lights are on

base = denotes pre-installation state

as-built = denotes post-installation state

The table shown below presents expected and realized energy savings for the lighting retrofit installed under the project.

Measure Expected

kWh Savings

Realized kWh

Savings


Savings

LED Retrofit 175,171 175,173 100% 12.89 2,257,281

Results

The project-level realization rate is 100 percent.


25


March 2018


Bass ES 10377 Rancho Destino Rd. Las Vegas, NV 89183 NPC-2017Schools_1213408

Executive Summary

The customer received incentives from NV Energy for the installation of an Air-Cooled chiller. The realization rate for this project is 100 percent.

Project Description

The elementary school installed a 244-ton air cooled chiller with an above code efficiency. The main purpose of the retrofit was to increase the energy efficiency of the school and reduce frequent maintenance issues with the old unit. The baseline used is IECC 201214.


During the M&V visit, ADM staff verified the installation of the new chiller. In order to calculate the savings due to the chiller, ADM used the Equivalent Full Load Hour method. ADM used the following equation to calculate the HVAC energy savings:



EFLH = Equivalent full load hours

kW/ton B = Baseline equipment part load efficiency

kW/ton A = As-Built equipment part load efficiency

14 IECC 2012 Table 403.2.3(7) Water Chilling Packages, Efficiency Requirements


26


HVAC Chiller Savings Calculations

# of Units Tons Equipment

Type


Eff Type


Eff Type EFLH CAF

Expected Total kWh

Realized Total kWh


Savings

1 244 Air Cooled 0.941 kW/ton 0.682 kW/ton 1,023 1.000 64,693 64,697 100% 20 1,293,940

Results

The project level kWh and kW savings are 64,697 and 22.2 respectively. The project level realization rate is 100 percent.


27


March 2018


Harmon ES 5351 Hillsboro lane, Las Vegas, NV 89120 NPC-2017Schools_1213409

Executive Summary

Under project NPC-2017Schools_1213409, Harmon Elementary School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 108 percent.

Project Description

The customer installed retrofits on a variety of lighting fixtures with LEDs. (962) T8 fixtures to (962) LED fixtures (39) HPS fixtures to (39) LED fixtures (26) CFL fixtures to (26) LED fixtures (2) incandescent to (2) LED fixtures


During the M&V visit, ADM staff verified equipment installation. The baseline lighting operating hours were verified to fit the 9-month school schedule baseline. The post-implementation lighting hours did not change from the baseline operating hours.

Lighting savings are calculated as:

Where:

kWhsavings = Annual energy savings



h = hour of year


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The table shown below presents expected and realized energy savings for the lighting retrofits installed under the project.

Lighting Savings Summary

Measure Expected kWh

Ex-Ante Savings Expected kWh

Ex-Post Savings Realization Rate

Total Lifetime kWh Savings

Lighting 92,358 99,364 108% 1,277,328

Results



29


March 2018


Fay Herron ES 2421 N Kenneth Rd, North Las Vegas, NV 89030 NPC-2017Schools_1213410

Executive Summary

Under project 1213410, Herron Elementary School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer installed retrofits on a variety of lighting fixtures with LEDs.


During the M&V visit, ADM staff verified equipment installation. The baseline lighting operating hours were verified to fit the 9-month school schedule baseline. The post-implementation lighting hours did not change from the baseline operating hours.

Lighting savings are calculated as:

Where:

kWhsavings = Annual energy savings









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The table shown below presents expected and realized energy savings for the lighting retrofits installed under the project.

Lighting Savings Summary

Measure Expected kWh

Ex-Ante Savings Expected kWh

Ex-Post Savings Realization Rate

Total Lifetime kWh Savings

LED Retrofits 95,708 95,734 100% 1,301,525

Results

The project-level realization rate is 100 percent. Quantities and hours of use matched up with ex-ante hours. There is no discrepancy between what was found on site and what was reported.


31


March 2018


Andre Agassi College Prep Academy 1201 W Lake Mead Blvd., Las Vegas, NV 89106 NPC-2017Schools_1213411

Executive Summary

Under project NPC-2017Schools_1213411, Andre Agassi College Prep Academy received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 91 percent.

Project Description

The customer retrofitted: (13) 100W Metal Halides to (13) 60W LEDs; and (41) 6L 54W T5 de-lamped to (41) 4L 54W T5











32





Lighting Retrofit Savings Calculations

Measure Quantity (Fixtures) Wattage

Hours Expected

kWh Savings

Realized kWh

Savings HCIF Realization

Rate EUL Lifetime Savings Old New Old New

Outdoor Lighting 100W MH to LED De-lamping 6L 54W T5 to 4L 54W T5

13

41

13

41

124

362

60

234

4,313

3,572

3,588

22,465

3,588

21,931

1.00

1.17

100%

98%

11.59

15.00

41,600

328,959

Total 26,053 25,519 98% 14.52 370,559

Results

The ex ante estimated savings of 26,053 kWh is found in the Post Inspection Report. Based on that data, the project-level realization rate would be 98 percent. However, a different ex ante estimated savings of 27,967 kWh is found in DSM Central, causing the realization rate to change to 91 percent (i.e., 25,519 ÷ 27,967 = 91 percent). At this point, ADM decided to use the estimated savings from DSM Central as the final ex ante kWh savings.

The variation between the ex post and ex ante (kWh) was caused by the lighting retrofit, the ex ante utilized 42 de-lamping fixtures, while the ex post utilized 41 de-lamping fixtures, as noted in the site visit verification.


33


March 2018


John C. Bass Elementary School 10377 Rancho Destino Rd, Las Vegas NV 89183 NPC-2017Schools_1224043

Executive Summary

Under project NPC-2017Schools_1224043, John C. Bass Elementary received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 101 percent.

Project Description

Clark County School District completed a lighting retrofit project at John C. Bass Elementary School. This school is part of the performance contract between the District and McKinstry which uses energy savings to pay for the cost of the project. A subcontractor performed the lighting retrofit in both the interior and exterior areas across the entire school. Existing lights were fluorescent, incandescent, and high intensity discharge lamps which were retrofitted to new LEDs. The majority of tubular lamps were 18W LEDs.








34








Measure Expected

kWh Savings

Realized kWh

Savings


Savings

LED Retrofit 165,787 166,832 101% 13.74 2,292,546

Results



35


March 2018


Lawrence & Heidi Canarelli Middle School 7808 S. Torrey Pines Drive NPC-2017Schools_1224044

Executive Summary Under project NPC-2017Schools_1224044, Lawrence & Heidi Canarelli Middle School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description The customer retrofitted:

(33) Compact 4-pin, 2 lamp x 26W to (33) 7W LED (3) 60W incandescent lamp to (3) 8W LED (46) 75W incandescent lamp to (46) 12W LED (10) 50W High Pressure Sodium to (10) 18W LED (8) 150W incandescent lamp to (8) 19W LED (60) 4-lamp, 4-foot T8 x 32W (total, 112W) to (60) 24W LED (81) 3-lamp compact long twin mag x 40W (total, 133W) to (81) 28W LED (88) 4-lamp, 4-foot T8 x 32W (total, 112W) to (88) 31W LED (269) 2-lamp, 4-foot T8 x 32W (total, 58W) to (269) 36W LED (383) 3-lamp, 4-foot T8 x 32W (total, 85W) to (383) 36W LED (971) 4-lamp, 4-foot T8 x 32W (total, 112W) to (971) 36W LED (12) Metal Halide 175W to (12) 37W LED (3) 2-lamp, 4-foot T8 x 32W (total, 58W) to (3) 51W LED (3) 8-lamp, 4-foot T8 x 32W (total, 224W) to (3) 76W LED (57) 4-lamp, 4-foot T8 x 32W (total, 112W) to (57) 76W LED (60) 4-lamp, 45.8” T5 HO 54W (total, 230W) to (60) 108W LED (40) Metal Halide 400W to (40) 109W LED (56) Metal Halide 250W to (56) 113W LED (37) Metal Halide 400W to (37) 126W LED


36















Measure Quantity

(Fixtures)

Wattage per Fixture

or LED Pre

Hours Post

Hours

Expected kWh

Savings

Realized kWh


Rate EUL, years

Lifetime kWh

Savings Old New Old New

Compact 4-pin, 2 lamp x 26W to 7W LED

33 33 51 7 3,674 3,674 6,178 6,177 1.14 1.00 13.4 75,913

60W incandescent lamp to 8W LED 3 3 60 8 568 568 104 104 1.17 1.00 15.0 1,555

50W High Pressure Sodium to 18W LED

10 10 66 18 4,313 4,313 2,070 2,070 1.00 1.00 11.4 23,520

75W incandescent lamp to 12W LED 46 46 75 12 1,306 1,306 5,952 5,951 1.17 1.00 15.0 89,270

150W incandescent lamp to 19W LED

8 8 150 19 3,876 3,876 4,753 4,753 1.17 1.00 12.6 60,082

2-lamp, 4-foot T8 x 32W (total, 58W) to 36W LED

269 269 58 36 2,653 2,653 21,309 21,312 1.17 1.00 14.2 295,899


3 3 58 51 568 568 14 14 1.17 1.00 15.0 209


37


Measure Quantity

(Fixtures)

Wattage per Fixture

or LED Pre

Hours Post

Hours

Expected kWh

Savings

Realized kWh


Rate EUL, years

Lifetime kWh


3-lamp, 4-foot T8 x 32W (delamped to 58W) to 36W LED

129 129 58 36 2,377 2,377 7,365 7,365 1.17 1.00 15.0 110,479


254 254 85 36 3,511 3,511 54,722 54,720 1.17 1.00 13.8 639,738

3-lamp compact long twin mag x 40W (total, 133W) to 28W LED

81 81 133 28 3,735 3,735 41,835 41,835 1.17 1.00 13.0 482,619


60 60 112 24 3,148 3,148 13,611 13,613 1.17 1.00 15.0 204,194


88 88 112 31 3,148 3,148 18,378 18,377 1.17 1.00 15.0 275,662


971 971 112 36 2,879 2,879 217,639 217,617 1.17 1.00 14.9 3,170,700


57 57 112 76 568 568 1,351 1,364 1.17 1.01 15.0 20,455


3 3 224 76 4,823 4,823 2,505 2,505 1.17 1.00 10.2 25,455

4-lamp, 45.8” T5 HO 54W (total, 230W) to 108W LED

60 60 230 108 3,876 3,876 42,011 42,011 1.17 1.00 12.6 531,105

Metal Halide 175W to 37W LED

12 12 208 37 4,313 4,313 8,850 8,850 1.00 1.00 11.4 100,548


56 56 288 113 4,313 4,313 42,267 42,267 1.00 1.00 11.4 480,200


40 40 453 109 4,313 4,313 59,347 59,347 1.00 1.00 11.4 674,240


37 37 453 126 4,313 4,313 52,183 52,183 1.00 1.00 11.4 592,851

Total 2,220 2,220 102 40 2,760 2,760 602,444 602,437 1.123 1.00 13.0 7,854,694

Results The project-level realization rate is 101 percent for this lighting retrofit. ADM determined ex post energy savings from pre and post fixture wattages and lighting measure model numbers that were confirmed by the site contact during the on-site M&V visit to this school. For hours of use, each room was analyzed as a separate line item and assigned room-specific hours of use; each unique measure was also analyzed as a separate line item. As such, the M&V analysis includes a total of 279 lines, each representing a unique measure/room combination.


38


The table shown above provides a summary of the M&V analysis and presents the expected and realized energy savings per measure for the lighting retrofit installed under this project.

The 101 percent realization rate results from a relatively small variance related to the old fixture wattage of the measure “3-lamp, 4-foot T8 x 32W (total, 85W)” for which the ex ante wattage for a subset of those fixtures was reported as 58W instead of 85W.


39


March 2018


Rancho High School. 1900 Searles Ave. Las Vegas, NV, 89101 NPC-2017Schools_1232389

Executive Summary

Under project NPC-2017Schools_1232389, Rancho High School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 90 percent.

Project Description

The customer retrofitted: (96) T5 fixture to (96) LED fixtures; and (303) Metal Halide to (303) LED fixtures.









40








Measure

Quantity (Fixtures) Wattage

Hours Expected

kWh Savings

Realized kWh



T5 Linear Fluorescent to LED T5 Linear Fluorescent to LED Metal Halide to LED Metal Halide to LED Metal Halide to LED Metal Halide to LED Metal Halide to LED Metal Halide to LED Metal Halide to LED Metal Halide to LED Metal Halide to LED Metal Halide to LED Metal Halide to LED

37 59 88

128 36 7 9 4

10 8

10 1 2

37 59 88

128 36 7 9 4

10 8

10 1 2

33 33

453 453 453 453 453 453 453 453 453 453 453

20 20 80 80 80 80 80 80 80 80 80 80 80

4,550 4,550 4,550 4,550 2,488 2,488 2,488 2,488 2,488 2,488 2,488 2,488 2,488

2,561 4,083

174,738 254,165 57,915 11,261 14,479

6,435 16,087 12,870 16,087

1,609 3,217

2,561 4,083

174,739 254,165 33,406

6,496 8,352 3,712 9,279 7,424 9,279

928 1,856

1.17 1.17 1.17 1.17 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

100% 100% 100% 100%

58% 58% 58% 58% 58% 58% 58% 58% 58%

10.99 10.99 10.99 10.99 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00

28,13944,870

1,920,2042,793,024

501,09397,435

125,27355,677

139,192111,354139,19213,91927,838

Total 575,507 516,279 90% 11.62 5,997,210

Results

The project-level realization rate is 90 percent. For the lighting retrofit, the ex post savings do not match the ex ante savings because school staff reported different hours for exterior lights than the hours reported in the ex ante data (Post Inspection Report). Specifically, the clocks controlling exterior lighting were re-programmed after the retrofit to be off from 11 PM to 4 AM. This exterior lighting schedule change, independent of the lighting retrofit, resulted in a 58 percent realization rate for that subset of measures.


41


March 2018


Charles and Phyllis Frias Elementary School 5800 W. Broken Top Ave, Las Vegas, NV 89141 NPC-2017Schools_1224045

Executive Summary

Under project NPC-2017Schools_1224045 Charles and Phyllis Frias Elementary School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (18) 1-lamp, 4-foot T8 x 32W (total, 31W) to 20W LED (1) 2-lamp, 2-foot T8 x 32W (total, 33W) to 17W LED (4) 2-lamp, 4-foot T8 x 32W (total, 58W) to 20W LED (288) 2-lamp, 4-foot T8 x 32W (total, 58W) to 36W LED (12) 3-lamp, 4-foot T8 x 32W (delamped to 58W) to 24W LED (497) 4-lamp, 4-foot T8 x 32W (delamped to 83W) to 36W LED (24) 3-lamp, 4-foot T8 x 32W (total, 85W) to 36W LED (2) 3-lamp, 4-foot T8 x 32W (total, 85W) to 40W LED (3) 3-lamp, 4-foot T8 x 32W (total, 85W) to 60W LED (51) 4-lamp, 4-foot T8 x 32W (total, 112W) to 36W LED (2) 4-lamp, 4-foot T8 x 32W (total, 112W) to 40W LED (51) 4-lamp, 4-foot T8 x 32W (total, 112W) to 72W LED (42) Compact Multi, 4-pin, 42W lamp (total, 46W) to 10W LED (8) 90W incandescent lamp to 17W LED (16) Metal Halide 100W to 26W LED (6) Metal Halide 150W to 7W LED


42


(8) Metal Halide 175W to 18W LED (35) Metal Halide 250W to 79W LED (10) Metal Halide 250W to 89W LED (21) Metal Halide 400W to 126W LED (6) Metal Halide 400W to 160W LED














Measure Quantity

(Fixtures)

Wattage per Fixture

or LED Pre

Hours Post

Hours

Expected kWh

Savings

Realized kWh


Rate EUL, years

Lifetime kWh



18 18 31 20 2,258 2,258 523 523 1.17 1.00 15 7,846


1 1 33 17 568 568 11 11 1.17 0.97 15 159


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Measure Quantity

(Fixtures)

Wattage per Fixture

or LED Pre

Hours Post

Hours

Expected kWh

Savings

Realized kWh


Rate EUL, years

Lifetime kWh



4 4 58 20 2,258 2,258 401 402 1.17 1.00 15 6,023


288 288 58 36 1,637 1,637 14,881 14,889 1.17 1.00 15 222,593


12 12 58 24 4,303 4,303 2,054 2,054 1.17 1.00 12 23,868


497 497 83 36 2,171 2,171 54,227 54,227 1.17 1.00 14 779,009


24 24 85 36 1,244 1,244 2,721 2,719 1.17 1.00 15 40,789


2 2 85 40 4,303 4,303 453 453 1.17 1.00 12 5,265


3 3 85 60 4,303 4,303 378 378 1.17 1.00 12 4,388


51 51 112 36 1,783 1,783 7,867 7,867 1.17 1.00 15 118,010


2 2 112 40 4,303 4,303 725 725 1.17 1.00 12 8,424


51 51 112 72 3,192 3,192 7,618 7,619 1.17 1.00 15 114,280

Compact Multi, 4-pin, 42W lamp (total, 46W) to 10W LED

42 42 46 10 2,261 2,261 3,833 3,835 1.16 1.00 15 54,351

90W incandescent lamp to 17W LED

8 8 90 17 568 568 388 388 1.17 1.00 15 5,822


44


Measure Quantity

(Fixtures)

Wattage per Fixture

or LED Pre

Hours Post

Hours

Expected kWh

Savings

Realized kWh


Rate EUL, years

Lifetime kWh



16 16 124 26 4,313 4,313 6,763 6,763 1.00 1.00 12 78,400


6 6 183 7 4,313 4,313 4,555 4,555 1.00 1.00 12 52,800


8 8 208 18 3,286 3,286 5,921 5,921 1.09 1.00 13 72,059


35 35 288 79 4,313 4,313 31,550 31,550 1.00 1.00 12 365,750


10 10 288 89 4,313 4,313 8,583 8,583 1.00 1.00 12 99,500


21 21 453 126 4,313 4,313 29,617 29,617 1.00 1.00 12 343,350


6 6 453 160 2,998 2,998 6,166 6,166 1.17 1.00 15 92,497

Total 1,105 1,105 81 35 2,090 2,090 189,235 189,244 1.09 1.00 13.2 2,495,183

Results

The project-level realization rate is 100 percent. For the lighting retrofit, the ex post ADM determined by using pre and post fixture wattages and models confirmed by the site contact during the field visit. For hours of use each room was broken up into its own line item and assigned room specific hours of use.


45


March 2018


Clifford Lawrence Middle School 7808 S. Torrey Pines Dr., Las Vegas NV 89139 NPC-2017Schools_1224046

Executive Summary

Under project NPC-2017Schools_1224046, Clifford Lawrence Middle received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

Clark County School District completed a lighting retrofit project at Clifford Lawrence Middle School. This school is part of the performance contract between the District and McKinstry which uses energy savings to pay for the cost of the project. A subcontractor performed the lighting retrofit in both the interior and exterior areas across the entire school. Existing lights were fluorescent, incandescent, and high intensity discharge lamps which were retrofitted to new LEDs. The majority of tubular lamps were 18W LEDs.








46








Measure Expected

kWh Savings

Realized kWh

Savings


Savings

LED Retrofit 396,610 396,503 100% 13.48 5,344,415

Results



47


March 2018


D’Vorre and Hal Ober Elementary School 3035 Desert Marigold Ln, Las Vegas NV 89135 NPC-2017Schools_1224049

Executive Summary

Under project NPC-2017Schools_1224049, D’Vorre and Hall Ober Elementary received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

Clark County School District completed a lighting retrofit project at D’Vorre and Hal Ober Elementary School. This school is part of the performance contract between the District and Ameresco which uses energy savings to pay for the cost of the project. A subcontractor performed the lighting retrofit in interior areas across the entire school campus. Existing lights were fluorescent, incandescent, and high intensity discharge lamps which were retrofitted to new light emitting diodes (LEDs). The areas retrofitted were interior spaces only, and the majority of tubular lamps were 14W LEDs. There were some high ceiling areas that utilized 18W lamps.







48









Measure Expected

kWh Savings

Realized kWh

Savings


Savings

LED Retrofit 138,101 138,092 100% 14.61 2,017,871

Results



49


March 2018


Betsy A. Rhodes Elementary School 7350 Tealwood Street, Las Vegas, NV 89131 NPC-2017Schools_1224050

Executive Summary

Under project NPC-2017Schools_1224050, Betsy A. Rhodes Elementary School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (32) 100W Metal Halide Wallpack to 15W LED; and (2) 100W Metal Halide Wallpack to 25W LED; and (6) 70W HPS to 10W LED; and (9) 400W Metal Halide to 124W LED.








50










Hours Expected

kWh Savings

Realized kWh



100W Metal Halide Wallpack to 15W LED 32 32 124 15 4,313 15,044 15,044 1.00 100% 11.59 174,400

100W Metal Halide Wallpack to 25W LED 2 2 124 25 4,313 854 854 1.00 100% 11.59 9,900

70W HPS to 10W LED 6 6 95 10 4,313 2,200 2,200 1.00 100% 11.59 25,500 400W Metal Halide to 124W LED 9 9 453 124 4,313 12,771 12,771 1.00 100% 11.59 148,050

Total 30,869 30,868 100% 11.59 357,850

Results



51


March 2018


Sierra Vista High School 800 W. Robindale Rd. Las Vegas, NV 89113 NPC-2017Schools_1226429

Executive Summary

Under project NPC-2017Schools_1226429, Sierra Vista High School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted:

(3,494) T8 fixtures to (3,494) LED fixtures (96) T5 fixtures to (96) LED fixtures (396) Metal Halide to (396) LED fixtures (61) Incandescent fixtures to (61) LED fixtures (28) Halogen fixtures to (28) LED fixtures (172) CFL fixtures to (172) LED fixtures (3) Occupancy sensors.







52










Measure Expected

kWh Savings

Realized kWh

Savings


Savings

Total 794,425 794,462 100% 14 10,979,029

Results



53


March 2018


CSN, N. Las Vegas Campus, Building C 3200 East Cheyenne Ave, North Las Vegas, NV 89030 NPC-2017Schools_1230125

Executive Summary

Under project NPC-2017Schools_1230125, CSN, N. Las Vegas Campus, Building C. received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (767) T8 fixtures to (767) LED fixtures; and (28) metal halide to (28) LED fixtures (33) Halogen to (33) LED fixtures (52) CFL fixtures to (52) LED fixtures








54









Measure Expected

kWh Savings

Realized kWh

Savings


Savings

Total 166,971 166,990 100% 11.90 1,987,543

Results



55


March 2018


Antonello ES 1101 W Tropical Parkway, North Las Vegas, NV 89031 NPC-2017Schools_1232390

Executive Summary

Under project NPC-2017Schools_1232390, Antonello ES received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (60) T8 fixtures to (60) LED fixtures; and (6) HPS fixtures to (6) LED fixtures.











56







Hours Expected

kWh Savings

Realized kWh



T8 Linear Fluorescent to LED 20 20 112 30 1,782 3,420 3,420 1.17 100% 15.00 51,295



High Pressure Sodium to LED 2 2 95 12 4,313 716 716 1.00 100% 11.59 8,300



Total 12,408 12,407 100% 14.41 178,786

Results



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March 2018


McCall ES 800 E. Carey Ave., North Las Vegas, NV 89030 NPC-2017Schools_1232400

Executive Summary

Under project NPC-2017Schools_1232400, McCall ES received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (60) T8 fixtures to LED fixtures; and (6) HPS fixtures to (6) LED.











58







Hours Expected

kWh Savings

Realized kWh









Total 12,408 12,407 100% 14.41 178,786

Results



59


Site College of Southern Nevada, N. Las Vegas, Building T Address 3200 East Cheyenne Avenue, North Las Vegas, NV 89030 Project Number NPC-2017Schools_1239105

Executive Summary

Under project NPC-2017Schools_1239105, College of Southern Nevada, N. Las Vegas, Building T. received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (54) T8 fixtures to (54) LED fixtures











60







Hours Expected

kWh Savings

Realized kWh









Total 10,355 10,357 100% 15.00 155,362

Results



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March 2018


Fay Herron Elementary School. 2421 North Kenneth, North Las Vegas, NV 89030 NPC-2017Schools_1243868

Executive Summary

Under project NPC-2017Schools_1243868, Fay Herron Elementary School. received incentives from NV Energy for a faucet aerator project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (46) .35 GPM aerators (51) 1 GPM aerators (5) 1.5 GPM aerators


During the M&V visit, ADM staff verified equipment installation.

Faucet Aerator retrofit energy savings are calculated as:

Where:

kWh = Annual energy savings

%ElectricDHW = proportion of water heating supplied by electric resistance heating

GPM_base = Average flow rate, in gallons per minute, of the baseline faucet “as-used”

GPM_low = Average flow rate, in gallons per minute, of the low-flow faucet aerator “as-used”

Usage = Estimated usage of mixed water (mixture of hot water from water heater line and cold-water line) per faucet (gallons per year

EPG_electric = Energy per gallon of mixed water used by faucet (electric water heater)

= (8.33 * 1.0 * (WaterTemp - SupplyTemp)) / (RE_electric * 3412)


62


= 0.0795 kWh/gal for Bath, 0.0969 kWh/gal for Kitchen, 0.0919 kWh/gal for unknown

8.33 = Specific weight of water (lbs./gallon)

1.0 = Heat Capacity of water (btu/lb-°F)

WaterTemp = Assumed temperature of mixed water = 86F for Bath, 93F for Kitchen 91F for Unknown165

SupplyTemp = Assumed temperature of water entering building = 54.1°F 166

RE_electric = Recovery efficiency of electric water heater = 98% 167

3412 = Converts Btu to kWh (Btu/kWh)

ISR = In service rate of faucet aerators dependent on install method as listed in table below168

The table shown below presents expected and realized energy savings for the aerator retrofit installed under the project.

Aerator Retrofit Savings Calculations

Area Old Gpm

New GPM Count

Expected Savings

Realized Savings Realization Rate Lifetime savings

RR by P-6 (F) - High Use



RR by P-6 (M) - High Use



RR a/f p-6 (F) - Staff Use

RR a/f p-6 (F) - Staff Use

RR a/f p-6 (M) - Staff Use

70's Bldg (4 aud) - Classroom Use



RR by 73 (M) - High Use



RR by 73 (F) - High Use




80's Bldg's (2 aud) - Classroom Use

80's Bldg's (2 aud) - Classroom Use

Kitchen - Staff Use


KG/20's Bldg (2 aud) - Classroom Use



RR b/t KG22-KG21 (M) - General Use

RR b/t KG20-KG21 (M) - General Use

0.50

1.50

2.20

0.50

2.20

0.50

0.50

2.20

0.50

1.00

1.50

2.20

0.50

0.50

1.25

0.50

1.50

1.75

2.00

2.20

2.20

2.00

1.00

2.20

2.20

2.00

0.50

0.50

0.35

0.35

0.35

0.35

0.35

0.35

0.35

0.35

0.35

1.00

1.00

1.00

0.35

0.35

0.35

0.35

1.00

1.00

1.00

1.00

1.00

1.50

1.00

1.00

1.00

1.00

0.35

0.35

1.00

3.00

2.00

1.00

3.00

2.00

1.00

1.00

1.00

1.00

3.00

4.00

1.00

1.00

1.00

3.00

1.00

2.00

1.00

4.00

4.00

1.00

6.00

1.00

1.00

1.00

2.00

2.00

7.12

54.58

39.91

7.12

59.86

14.24

7.12

19.95

7.12

0.00

23.73

51.78

7.12

7.12

17.09

21.36

7.91

20.34

11.87

51.78

51.78

5.93

0.00

12.94

12.94

11.87

14.24

14.24

7.12

54.58

39.91

7.12

59.86

14.24

7.12

19.95

7.12

0.00

23.73

51.78

7.12

7.12

17.09

21.36

7.91

20.34

11.87

51.78

51.78

5.93

0.00

12.94

12.94

11.87

14.24

14.24

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

64.07

491.22

359.19

64.07

538.78

128.14

64.07

179.59

64.07

0.00

213.57

465.98

64.07

64.07

153.77

192.21

71.19

183.06

106.79

465.98

465.98

53.39

0.00

116.49

116.49

106.79

128.14

128.14


63


Area Old Gpm

New GPM Count

Expected Savings

Realized Savings Realization Rate Lifetime savings




60's Bldg (1 aud) - Classroom Use 40's and 50's Bldg (2 aud) - Classroom Use 40's and 50's Bldg (2 aud) - Classroom Use 40's and 50's Bldg (2 aud) - Classroom Use RR in Multipurpose Rm Bldg (M) - High Use RR in Multipurpose Rm Bldg (F) - High Use


RR by 95 (F) - High Use

90's Classrooms (1 aud) - Classroom Use

90's Classrooms (1 aud) - Classroom Use

Staff Lounge - Staff Use

RR by Staff Lounge (M) - Staff Use

RR by Staff Lounge (F) - Staff Use

Health Office RR (U) - General Use

Health Office - Staff Use Staff RR off Ed Training Center (U) - Staff Use Staff RR off Ed Training Center (U) - Staff Use Pumping Rm off Ed Training Center (F) - Staff Use

Ed Training Center - Staff Use

Outside access Parent Center RR a/f Main Office (U) - General Use

1.50

1.00

1.50

2.20

1.00

2.20

2.20

0.50

0.50

0.50

0.50

1.00

1.50

1.50

2.00

1.25

2.00

2.20

2.20

2.20

2.20

2.20

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

0.35

0.35

0.35

0.35

1.00

1.00

1.50

0.35

0.35

0.35

1.50

0.35

0.35

1.50

1.50

0.35

4.00

1.00

2.00

1.00

1.00

4.00

4.00

6.00

5.00

2.00

2.00

3.00

2.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

1.00

31.64

0.00

15.82

12.94

0.00

51.78

51.78

42.71

35.60

14.24

14.24

0.00

15.82

0.00

19.58

17.09

19.58

7.55

19.95

19.95

7.55

7.55

15.42

31.64

0.00

15.82

12.94

0.00

51.78

51.78

42.71

35.60

14.24

14.24

0.00

15.82

0.00

19.58

17.09

19.58

7.55

19.95

19.95

7.55

7.55

15.42

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

284.76

0.00

142.38

116.49

0.00

465.98

465.98

384.43

320.36

128.14

128.14

0.00

142.38

0.00

176.20

153.77

176.20

67.95

179.59

179.59

67.95

67.95

138.82

Total 982 982 100% 8836.43

Results



64


Site Southwest Career & Technical Academy (SW CTA) Address 7050 W. Shelbourne Avenue, Las Vegas, NV 89113 Project Number NPC-2017Schools_124577

Executive Summary

This project involved programming the water circulation loop with temperature reset controls, optimizing the heat rejection operation, and reducing variable frequency drive (VFD) minimum speed. The kWh realization rate for this project is 98 percent.

Project Description

Ameresco performed retro-commissioning at SW CTA to test and evaluate all system functionality. The SW CTA system has a chilled loop with a waterside economizer to provided chilled water cooling and hot water boilers that provide hot water heating. Additionally, the facility has a water loop heat pump that circulates water throughout the facility to provide space heat rejection into a space that requires heating, such as using the heat produced from a data center to heat up a classroom that has large, inefficient windows. The SW CTA HVAC units are controlled by a building automation system (BAS) with programmed setpoints and occupied operating hours. The commissioning agent discovered the system setpoints and schedules were not optimally programmed based on the equipment and BAS capability. This project has one associated energy efficiency measure (EEM):

EEM 1: Plant RCx: This measure includes the following BAS adjustments to the plant equipment: Water circulation loop head setpoint set from fixed to valve reset, head setpoint location changed from entering loop to at coils, temperature range changed from 75-75 to 55-85 , operation changed from standby to demand, and loop minimum flow changed from 50 percent to 20 percent; heat rejection fluid cell control changed from minimum cells to maximum cells, minimum flow reduced from 100 percent to 50 percent, maximum flow increased from 200 percent to 100 percent, and minimum VFD speed reduced from 40 percent to 30 percent; and the minimum pump speed for the boiler hot water increased from 20 percent to 40 percent.


During the M&V process, ADM staff performed a desk review of the project. For the estimated savings associated with EEM 1 (Plant RCx) savings occur due to optimizing plant heating and cooling strategies.

Savings estimate for this measure is calculated using a calibrated eQuest energy model. The baseline energy model was first run with actual weather data corresponding to the available billing periods and then the model inputs were adjusted, within reason, such that the model would adequately estimate the as-built facility. After the model was calibrated, typical model year, version 3 (TMY3) weather data was used to create a baseline energy usage year. Then a parametric run was performed to accurately calculate the saving associated with each EEM. The first parametric run implemented the adjustments stated to the BAS affecting the air handler unit (AHU) zone controls as stated in EEM 1 above.

This site retro-commissioning work was performed as part of a large school retrofit program. For that reason, instead of modeling all the schools individually they were separated into batches of


65


schools that have very similar building systems. Savings were calculated for a single building in each batch and then normalized by square footage and applied to all schools in that batch. For SW CTA it was in a batch with Veterans Tribute CTA. ADM staff performed the desk review on the Veterans Tribute CTA energy model and then applied the realization rate to SW CTA.

Results

Verified kWh Gross Savings/Realization Rates

Measure Type Ex Ante Savings

(kWh/yr.)

Ex Post Savings

(kWh/yr.)

Realization Rate EUL

Lifetime Savings

(kWh/yr.) Retro Commissioning 203,995 200,714 98% 8 1,605,714

Totals 203,995 200,714 98% 8 1,605,714

The project-level kWh realization rate is 98 percent. A desk review of the ex ante savings was deemed reasonable. ADM made a few minor adjustments to the energy model’s occupancy, insulation, and reduced the AHU runtime to more accurately represent the actual building which would account for the small difference in the realization rate.


66


Site Woodbury Middle School Address 3875 East Harmon Avenue, Las Vegas, NV, 89121 Project Number NPC-2017Schools_1245180

Executive Summary

This project involved re-programming HVAC system fan schedules and programming more appropriate cooling and heating setpoints with a custodian setback. The kWh realization rate for this project is 98 percent.

Project Description

Ameresco performed retro-commissioning at Woodbury Middle School to test and evaluate all systems functionality. Air Handlers at Woodbury Middle School are controlled by a BAS with programmed setpoints and occupied operating hours, but some of the setpoints and schedules were overridden and not operating as stated by the BAS schedules. Part of the retro-commissioning process involved releasing system overrides and testing zones to ensure proper operation. Additionally, a custodial setback schedule was programmed which broadens the heating and cooling setpoint range during non-class hours but while the building is still in occupied mode. This project has two associated energy efficiency measures:

EEM 1: HVAC Reduction: Programmed Portable Air Handling Unit thermostats to operate based on the school schedule of 7 am to 6 pm Monday through Friday instead of operating 24 hours a day.

EEM 2: AHU Zone RCx: This measure includes the following BAS adjustments to the main building AHUs: Increased cooling setpoint from 70 to 73, added a custodian cooling setback to 75 at 5 pm, and a custodian heating setback from 65 to 68 at 5 pm.


During the M&V process, ADM staff performed a desk review of the project. For the estimated savings associated with EEM 1 (HVAC Reduction) savings occur due to a reduction in total fan runtime. For the estimated savings associated with EEM 2 (AHU Zone RCx), savings will occur due to optimizing the zone heating and cooling setpoints.

Savings estimates for both measures are calculated using a calibrated eQuest energy model. The baseline energy model was first run with actual weather data corresponding to the available billing periods and then the model inputs were adjusted, within reason, such that the model would adequately estimate the as-built facility. After the model was calibrated, TMY3 weather data was used to create a baseline energy usage year. Then subsequent parametric runs were performed to accurately calculate the saving associated with each EEM. The first parametric run implemented the adjustments stated to the BAS affecting the portable thermostat controls as stated in EEM 1 above. The second parametric run implemented the adjustments to the air handling units as stated in EEM 2 above. Savings for the second measure use the energy model from the first measure as the baseline since these two measures will have an impact on each other.


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Results



(kWh/yr.)

Ex Post Savings

(kWh/yr.)


Lifetime Savings


Totals 354,180 348,430 98% 8 2,787,440

The project-level kWh realization rate is 98 percent. A desk review of the ex ante savings was deemed reasonable. ADM made a few minor adjustments to the energy model’s occupancy, insulation, AHU weekend schedule, and cooling setpoints to more accurately represent the actual building which would account for the small difference in the realization rate.


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Cashman, James Middle School 4622 W Desert Inn Rd, Las Vegas, NV 89102 NPC-2017Schools_1245185

Executive Summary

This project involved re-programming HVAC system fan schedules and programming more appropriate cooling and heating setpoints with a custodian setback. The kWh realization rate for this project is 97 percent.

Project Description

Ameresco performed retro-commissioning at Cashman Middle School to test and evaluate all system functionality. Air Handlers at Cashman Middle School are controlled by a BAS with programmed setpoints and occupied operating hours, but some of the setpoints and schedules were overridden and not operating as stated by the BAS schedules. Part of the retro-commissioning process involved releasing system overrides and testing zones to ensure proper operation. Additionally, a custodial setback schedule was programmed which broadens the heating and cooling setpoint range during non-class hours but while the building is still in occupied mode. This project has two associated energy efficiency measures:

EEM 1: HVAC Reduction: Programmed Portable Air Handling Unit thermostats to operate based on the school schedule of 5 am to 6 pm Monday through Friday instead of operating 24 hours a day. Programmed main building air handling units to stay OFF over the weekends.

EEM 2: AHU Zone RCx: This measure includes the following BAS adjustments to the main building AHUs: Increased cooling setpoint from 69 to 73 , decreased the heating setpoint from 67 to 66 , and added a custodian cooling setback from 73 to 79 after 6 pm.

In addition to the two measures, additional work at the site, included: Calibrated zone thermostats, and released all BAS overrides.


During the M&V process, ADM staff performed a desk review of the project. For the estimated savings associated with EEM 1 (HVAC Reduction) savings occur due to a reduction in total fan runtime. For the estimated savings associated with EEM 2 (AHU Zone RCx), savings will occur due to optimizing the zone heating and cooling setpoints.

Savings estimates for both measures are calculated using a calibrated eQuest energy model. The baseline energy model was first run with actual weather data corresponding to the available billing periods and then the model inputs were adjusted, within reason, such that the model would adequately estimate the as-built facility. After the model was calibrated, TMY3 weather data was used to create a baseline energy usage year. Then subsequent parametric runs were performed to accurately calculate the saving associated with each EEM. The first parametric run implemented the adjustments stated to the BAS affecting the portable thermostat controls as stated in EEM 1 above. The second parametric run implemented the adjustments to the air handling units as stated in EEM 2 above. Savings for the second measure use the energy model from the first measure as the baseline since these two measures will have an impact on each other.


69


Results



(kWh/yr.)

Ex Post Savings

(kWh/yr.)


Lifetime Savings


Totals 307,930 298,266 97% 8 2,386,128

The project-level kWh realization rate is 97 percent. A desk review of the ex ante savings was deemed reasonable. ADM made a few minor adjustments to the energy models occupancy, insulation, and cooling setpoints to more accurately represent the actual building which would account for the small difference in the realization rate.


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Site Palo Verde High School Address 333 Pavilion Center Drive, Las Vegas, NV 89144 Project Number NPC-2017Schools_1245187

Executive Summary

This project involved re-programming HVAC system fan schedules, adjusting heating/cooling setpoints, adding custodial setbacks, and implementing a SAT reset. The kWh realization rate for this project is 139 percent.

Project Description

Ameresco performed retro-commissioning at Palo Verde High School to test and evaluate all system functionality. The Palo Verde High School system has a chilled loop with a waterside economizer to provided chilled water cooling and hot water boilers that provide hot water heating. There is a mix of multi-zone units and single zone units. Palo Verde High School HVAC is controlled by a BAS with programmed setpoints and occupied operating hours. The commissioning agent discovered the system setpoints and schedules were not optimally programmed based on the equipment and BAS capability. This project has one associated energy efficiency measures:

EEM 1: AHU Zone RCx: This measure includes the following BAS adjustments: AHU turn OFF during unoccupied hours; multi zone cooling setpoint increased from 71 to 76 and heating setpoint from 67 to 66, single zone cooling setpoint increased from 70 to 76 and heating setpoint from 70 to 68; custodian cooling setback set to 78 and heating setback to 64; cooling SAT reset set to warmest zone and heating SAT reset set to coldest zone; and an outside air temperature cutoff of 65 for cooling and 60 for heating.


During the M&V process, ADM staff performed a desk review of the project. For the estimated savings associated with EEM 1 (AHU Zone RCx) savings occur due to optimizing zone heating and cooling strategies.

Savings estimate for this measure is calculated using a calibrated eQuest energy model. The baseline energy model was first run with actual weather data corresponding to the available billing periods and then the model inputs were adjusted, within reason, such that the model would adequately estimate the as-built facility. After the model was calibrated, TMY3 weather data was used to create a baseline energy usage year. Then a parametric run was performed to accurately calculate the saving associated with each EEM. The first parametric run implemented the adjustments stated to the BAS affecting the AHU zone controls as stated in EEM 1 above.

This site retro-commissioning work was performed as part of a large school retrofit program. For that reason, instead of modeling all the schools individually they were separated into batches of schools that have very similar building systems. Savings were calculated for a single building in each batch and then normalized by square footage and applied to all schools in that batch. For Palo Verde, it was in a batch with the Desert Pines High School. ADM staff performed the desk review on the Desert Pines energy model and then applied the EEM percentage savings to Palo Verde High School.


71


Results



(kWh/yr.)

Ex Post Savings

(kWh/yr.)


Lifetime Savings


Totals 583,282 808,693 139% 8 6,469,547

The project-level kWh realization rate is 138.6 percent. A desk review of the ex ante savings was deemed reasonable. The high realization rate is because the eQuest model provided had a larger savings percentage associated for the EEM than the ex-ante results stated. ADM made a few changes to the energy model to more closely represent the building energy usage, but these changes do not account for the high realization rate.


72


Site Address

Martin, Roy W. Middle School 200 N 28th St, Las Vegas, NV 89101

Project Number NPC-2017Schools_1245188

Executive Summary

This project involved re-programming HVAC system fan schedules, adjusting heating/cooling setpoints, adding a supply air temperature (SAT) reset and adding plant temperature reset controls. The kWh realization rate for this project is 92.5 percent.

Project Description

Ameresco performed Retro commissioning at Martin Middle School to test and evaluate all system functionality. The Martin Middle School system has three chillers with a water side economizer to provided chilled water cooling and three hot water boilers that provide hot water heating. Martin Middle School HVAC is controlled by a BAS with programmed setpoints and occupied operating hours. The commissioning agent discovered the system setpoints and schedules were not optimally programmed based on the equipment and BAS capability. This project has two associated energy efficiency measures:

EEM 1: AHU Zone RCx: This measure includes the following BAS adjustments: AHU turn OFF during unoccupied hours, custodian cooling/heating setbacks set to 77/64, global cooling setpoint increased from 71 to 75 , multizone units custodian cooling/heating setbacks set to 79/66 , HVAC cool control changed from constant to warmest zone, cooling SAT reset set to airflow, and a cooling SAT reset max temperature set to 65 .

EEM 2: Plant RCx: This measure includes the following BAS adjustments for the facility heating and chilled water systems: condenser water loop cooling setpoint reset; maximum and minimum reset temperatures set to 75 and 45 respectively; and a chilled water loop head reset based on valve position.


During the M&V process, ADM staff performed a desk review of the project. For the estimated savings associated with EEM 1 (AHU Zone RCx) savings occur due to optimizing zone heating and cooling strategies. For the estimated savings associated with EEM 2 (Plant RCx), savings will occur due to optimizing the chilled and condenser water reset strategies.

Savings estimates for both measures are calculated using a calibrated eQuest energy model. The baseline energy model was first run with actual weather data corresponding to the available billing periods and then the model inputs were adjusted, within reason, such that the model would adequately estimate the as-built facility. After the model was calibrated, TMY3 weather data was used to create a baseline energy usage year. Then subsequent parametric runs were performed to accurately calculate the saving associated with each EEM. The first parametric run implemented the adjustments stated to the BAS affecting the AHU zone controls as stated in EEM 1 above. The second parametric run implemented the adjustments to the central plant equipment as stated in EEM 2 above. Savings for the second measure use the energy model from the first measure as the baseline since these two measures will have an impact on each other.


73


Results



(kWh/yr.)

Ex Post Savings

(kWh/yr.)


Lifetime Savings

(kWh/yr.) Retro Commissioning 376,719 348,436 92.49% 8 2,787,488

Totals 376,719 348,436 92.49% 8 2,787,488

The project-level kWh realization rate is 92.5 percent. A desk review of the ex ante savings was deemed reasonable. ADM made a few minor adjustments to the energy model’s occupancy and insulation to more accurately represent the actual building which would account for the small difference in the realization rate.


74


Site College of Southern Nevada, N. Las Vegas, Building C Address 3200 East Cheyenne Avenue, North Las Vegas, NV, 89030 Project Number NPC-2017Schools_1245210

Executive Summary

Under project NPC-2017Schools_1245210, College of Southern Nevada, N. Las Vegas, Bldg. C received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (66) T8 fixtures to (66) LED fixtures; and (11) Metal halide to (11) LED (16) Incandescent fixtures to (16) LED fixtures. (104) CFL to (104) led fixtures (2) LED fixtures to (2) LED Fixtures (10) U-tube fluorescent fixtures removed.







75











Hours Expected

kWh Savings

Realized kWh



T8 Linear Fluorescent to LED

T8 Linear

2 2 18 9 2,992 65 65 1.21 100% 15.00 973

Fluorescent to LED

Compact

1 1 31 15 2,992 58 58 1.21 99% 15.00 865

Fluorescent to LED

T8 Linear

14 14 51 18 2,992 1,665 1,666 1.21 100% 15.00 24,985

Fluorescent to LED

LEDs to LED

1 1 58 12 2,992 166 166 1.21 100% 15.00 2,488

T8 Linear

2 2 26 12 2,992 101 101 1.21 100% 15.00 1,514

Fluorescent to LED

Standard

44 44 58 18 2,992 6,346 6,345 1.21 100% 15.00 95,182

Incandescent to LED Metal Halide to

16 16 50 7 2,992 2,480 2,480 1.21 100% 15.00 37,207

LED

T5 Linear

6 6 208 16 2,992 4,154 4,153 1.21 100% 15.00 62,301

Fluorescent to LED

Compact

9 9 63 40 2,992 746 746 1.21 100% 15.00 11,195

Fluorescent to LED

U Tube

3 - 27 - 2,992 292 292 1.21 100% 15.00 4,381

Fluorescent removed Compact

10 10 72 40 2,992 1,153 1,154 1.21 100% 15.00 17,306

Fluorescent to LED

T8 Linear

25 25 51 18 2,992 2,974 2,974 1.21 100% 15.00 44,616

Fluorescent to LED

T8 Linear

2 2 85 30 2,992 396 397 1.21 100% 15.00 5,949

Fluorescent to LED

T8 Linear

2 2 85 30 2,992 396 397 1.21 100% 15.00 5,949

Fluorescent to LED

T8 Linear

2 2 85 30 2,992 396 397 1.21 100% 15.00 5,949

Fluorescent to LED

T8 Linear

2 2 85 30 2,992 396 397 1.21 100% 15.00 5,949

Fluorescent to LED 2 2 85 30 2,992 396 397 1.21 100% 15.00 5,949


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Hours Expected

kWh Savings

Realized kWh



T8 Linear Fluorescent to LED

T8 Linear

2 2 85 30 2,992 396 397 1.21 100% 15.00 5,949

Fluorescent to LED

T8 Linear

2 2 85 30 2,992 396 397 1.21 100% 15.00 5,949

Fluorescent to LED

T8 Linear

2 2 85 30 2,992 396 397 1.21 100% 15.00 5,949

Fluorescent to LED

Compact

2 2 85 30 2,992 396 397 1.21 100% 15.00 5,949

Fluorescent to LED

Compact

5 5 16 7 7,710 418 418 1.21 100% 6.49 2,711

Fluorescent to LED

Metal Halide to

14 14 30 9 4,313 1,268 1,268 1.00 100% 11.59 14,700

LED

Metal Halide to

3 2 453 39 4,313 5,525 5,525 1.00 100% 11.59 64,050

LED

Compact

7 3 453 150 4,313 11,736 11,736 1.00 100% 11.59 136,050

Fluorescent to LED

Compact

8 8 16 7 4,313 311 311 1.00 100% 11.59 3,600

Fluorescent to LED

Compact

31 31 16 7 7,710 2,151 2,151 1.00 100% 6.49 13,950

Fluorescent to LED 7 7 16 7 7,710 486 486 1.00 100% 6.49 3,150

Total 45,659 45,664 100% 13.02 594,763

Results



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APPENDIX B: SAVINGS PER MONTH BY RATE CLASS

2017: Energy Savings (kWh) per Month per Rate Class (First Year) Rate Tariff Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

GS - 8,183 27,401 31,376 72,505 81,564 84,451 174,577 171,417 128,625 93,023 71,701 944,824

LGS-1 132 27,467 39,414 35,292 36,844 29,913 40,123 110,314 142,972 152,440 172,501 165,490 952,900

LGS-2S 4,417 101,288 254,435 297,684 556,482 648,067 647,920 986,998 919,039 612,204 395,774 286,429 5,710,737

LGS-3S - - - - 11,805 111,090 120,952 201,518 185,867 124,043 79,827 54,446 889,547

LGS-3P - - - - - - - - - - - - -

Total 4,548 136,937 321,250 364,352 677,636 870,634 893,446 1,473,406 1,419,295 1,017,312 741,124 578,065 8,498,008

2018: Energy Savings (kWh) per Month per Rate Class (Full Year) Rate Tariff Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

GS 85,829 84,834 106,287 106,102 150,365 133,592 128,003 181,354 166,678 132,183 89,829 72,258 1,437,313

LGS-1 190,103 168,264 187,304 176,587 180,857 147,972 137,118 166,121 178,298 196,738 173,311 169,399 2,072,070

LGS-2S 373,270 391,220 516,606 527,520 835,277 765,664 744,300 1,080,486 960,286 689,986 425,664 310,449 7,620,729

LGS-3S 66,063 70,268 93,939 96,412 156,197 144,030 140,385 204,690 180,816 127,428 76,951 54,769 1,411,949

LGS-3P - - - - - - - - - - - - -

Total 715,265 714,585 904,136 906,620 1,322,696 1,191,259 1,149,806 1,632,651 1,486,077 1,146,335 765,755 606,875 12,542,061


GS 86,246 85,992 102,825 109,944 153,074 128,776 130,433 179,210 171,648 129,045 87,272 72,848 1,437,313

LGS-1 190,481 168,601 183,204 181,426 180,759 144,045 139,761 164,177 182,982 196,703 169,331 170,600 2,072,070

LGS-2S 375,644 398,942 498,415 547,340 854,754 737,439 757,484 1,068,227 988,398 667,639 413,642 312,805 7,620,729

LGS-3S 66,509 71,758 90,575 100,063 159,999 138,699 142,839 202,385 186,091 123,071 74,781 55,177 1,411,949

LGS-3P - - - - - - - - - - - - -

Total 718,880 725,293 875,018 938,773 1,348,587 1,148,959 1,170,518 1,613,999 1,529,119 1,116,459 745,027 611,429 12,542,061

Appendix B

Page 285 of 359

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2020: Energy Savings (kWh) per Month per Rate Class (Full Year and Leap Year) Rate Tariff Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

GS 86,568 88,309 105,673 111,122 148,222 134,006 131,040 173,006 176,880 127,489 84,563 72,323 1,439,202

LGS-1 191,282 172,577 184,173 182,592 177,075 146,963 140,968 162,166 187,682 197,420 165,465 167,430 2,075,794

LGS-2S 376,598 410,930 517,226 553,934 826,012 770,200 759,957 1,027,267 1,018,451 655,644 400,500 314,044 7,630,763

LGS-3S 66,657 73,968 94,201 101,298 154,560 144,959 143,269 194,491 191,746 120,707 72,393 55,558 1,413,808

LGS-3P - - - - - - - - - - - - -

Total 721,106 745,785 901,273 948,946 1,305,869 1,196,129 1,175,235 1,556,930 1,574,760 1,101,261 722,920 609,356 12,559,567

Critical Peak Demand Reduction (kW) per Month per Rate Class Rate Tariff Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

GS 66.8 67.2 66.5 62.9 343.5 295.8 295.4 387.5 417.8 96.4 70.3 64.2

LGS-1 200.0 200.0 125.3 180.7 220.4 145.9 164.2 175.2 240.1 192.5 199.1 199.8

LGS-2S 280.5 283.1 316.4 314.5 2,117.7 1,893.6 1,862.1 2,501.9 2,617.1 523.5 306.9 262.3

LGS-3S 49.2 49.7 57.2 57.5 403.6 363.2 356.2 480.5 500.1 97.5 54.4 45.6

LGS-3P - - - - - - - - - - - -

Total 596.5 600.0 565.5 615.6 3,085.1 2,698.4 2,677.8 3,545.1 3,775.1 909.9 630.6 571.9

Appendix B

Page 286 of 359

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APPENDIX C: CALCULATION METHODOLOGY, CRITICAL PEAK DEMAND (KW) SAVINGS

C.1. OVERVIEW OF CALCULATION METHODOLOGY FOR KW SAVINGS

This section provides a description of analytical steps employed to determine critical peak demand savings per month per rate class for NV Energy’s 2017 DSM programs. For the 2017 M&V reports, demand (kW) reduction per month per rate class is determined using essentially the same methodology that is used to disaggregate annual energy (kWh) savings into monthly kWh savings per rate class. Please see the following Appendix D for a more detailed description of the methodology for determining energy (kWh) savings per month per rate class.

M&V reports for 2017 DSM programs do not provide critical peak demand (kW) savings for the 2017 calendar year. To do so would provide an incomplete, potentially misleading picture of critical peak kW savings, because each monthly kW reduction value would represent only a fraction of the total population of measures that are installed during the program year as a whole. Instead, M&V reports for 2017 DSM programs provide monthly critical peak kW savings values for 2018 – and for subsequent years for the life of the measures installed – which are representative of the whole population of measures installed by each program during the 2017 calendar year. This approach for reporting “typical” (or “full year”) coincident peak kW reduction is the preferred approach for impact evaluations. For this program, Table C-5 in the preceding section provides the full-year, or 2018 calendar-year, values for critical peak kW savings per month and per rate class.

C.2. ANALYTICAL STEPS AT THE MEASURE LEVEL

At the measure level, for every record (i.e., individual measure) in NV Energy’s DSM Central, ADM assigns an appropriate normalized 8,760 energy savings curve. A normalized energy savings curve is comprised of 8,760 hourly fractions summing to exactly 1 (unity).15 For each measure, ADM determines ex post annual kWh savings, which is then multiplied by each of the 8,760 hourly fractions to disaggregate the annual kWh into 8,760 hourly kW bins.

C.3. ANALYTICAL STEPS AT THE PROGRAM LEVEL

To determine program-level demand (kW) reduction for a given hourly kW bin, ADM sums the hourly kW bin across all measures in the program. For example, the program-level kW reduction for the hour ending at 5 PM on the 200th day of the year is the sum of kW for all measures in the

15 ADM has developed a library of normalized energy savings curves that are appropriate for Northern and Southern Nevada. Many of the residential energy savings curves were derived from NV Energy’s program-specific data, while others were derived from data provided in the 2008 California Database of Energy Efficiency Resources (2008 DEER).


80

https://unity).15


program during that hour on that day.

To determine monthly critical peak demand (kW) reduction for the program, ADM inspects program-level kW reduction during the one-hour critical peak demand period that is defined for each month of the year. The following table provides the monthly critical peak demand periods for NPC and Sierra, which were determined from ADM’s analysis of peak system load data provided by NV Energy.

Table C-1. Critical Peak Demand Period per Month, NV Energy

Month Critical Peak Period, NPC

Hour Ending at:

Critical Peak Period, Sierra

Hour Ending at:

January 19 19:00 19 19:00

February 19 19:00 19 19:00

March 20 20:00 20 20:00

April 20 20:00 21 21:00

May 17 17:00 17 17:00

June 17 17:00 17 17:00

July 17 17:00 17 17:00

August 17 17:00 17 17:00

September 17 17:00 17 17:00

October 19 19:00 20 20:00

November 19 19:00 19 19:00

December 19 19:00 19 19:00

For example, the critical peak demand period for July is the hour from 16:00:01 or 4:00:01 PM to 17:00:00 or 5:00:00 PM. To determine July’s program-level critical peak kW savings, ADM inspects average hourly kW reduction during 4:00:01 to 5:00:00 PM for every day in July: the highest value represents July’s critical peak kW savings. The same procedure is followed for all months of the year. Summer critical peak demand savings is defined as July’s critical peak kW savings; the rationale for doing so is that historical data reveals that during any given year, NV Energy’s peak system demand in either territory will typically occur during a July day between 4:00:01 to 5:00:00 PM.

To determine the monthly kW reduction per rate class, each program-level monthly critical peak kW savings value is disaggregated into rate class bins by correlating monthly kW savings for a given measure to the measure’s assigned customer rate class as listed in NV Energy’s DSM Central.

Calculations for energy (kWh) savings – and for demand (kW) reduction – per month per rate class require complex algorithms that are executed in massive Excel files, which are also known as kW guru™ files.


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C.4. ANALYSIS OF SYSTEM-LEVEL CRITICAL PEAK DEMAND PERIODS

ADM analyzed NV Energy’s system-level critical peak hours to determine a consistent reference for peak demand impacts of M&V evaluation of all NV Energy programs. ADM’s analysis encompassed Sierra Pacific Power Company (“Sierra”) in the north and Nevada Power Company (“NPC”) in the south.

Hourly system load data from 1985 through 2011 for Sierra and from 1999 through 2011 for NPC was provided by NV Energy. In analyzing the hourly load data, it was determined that the system peaks for Sierra in 1985 were only half of what they have been in the more recent ten-year period. The percentage change in daily system peaks between summer and winter were smaller in the 80’s and 90’s than in the more recent ten-year period. Therefore, ADM concluded that the use of system load data from the recent ten-year period provides the best basis for predicting what to expect during an EEM’s remaining useful life; following that rationale, data prior to the most recent ten years was excluded from ADM’s analysis. In both service territories, the highest system peak occurred in 2007, and system peaks have declined moderately since.

The hourly load data for the recent ten-year period was thoroughly reviewed and except for “spring ahead” hours (when clock times change from Standard Time to Daylight Savings Time), it was determined that the data was consistent and appropriate. The data for “spring ahead” hours are inconsistent, with values given as follows: (1) the value from the preceding hour is used and is an acceptable means of handling the data; and (2) a zero, which is an inaccurate value that would pull down the average. For this analysis, zero values were converted to blanks, and therefore not included in the averaging calculation. Overall this is a minor issue that did not impact ADM’s final analysis of system-level critical peak hours.

ADM determined that system load characteristics vary by season. To accommodate the seasonal variations, the hour of peak system load was determined for each month. ADM concluded that a one-hour peak demand period per month is appropriate.

The final determination of the appropriate peak demand hour per month per territory is provided above; see the table in the preceding section of this appendix. The designated peak demand hour per month per territory was utilized for M&V analyses of energy efficiency programs implemented in 2011, 2012, 2013, and 2014. Subject to ADM’s periodic re-checking of system load data, it is expected that the designated peak demand hour per month per territory will continue to be utilized for subsequent program years.

This M&V methodology update occurred for the following reason. Compared to the three-hour critical peak demand window used for M&V analyses of 2010 programs, the updated critical peak demand definition (i.e., one hour per month per territory) provides a more accurate determination of energy efficiency programs’ contributions to reducing system peak demand. In other words, the one-hour peak kW reduction will align with the actual hour of system peak.


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NV Energy’s hourly system load data demonstrated well-defined peaks during summer and winter months. However, certain transition months – such as May in Northern Nevada – have a nearly identical double peak. It is obvious that specific weather conditions during any given year cause one or the other of the two peaks to predominate. In the final analysis, transition months have far less peak demand than summer months, so a transition month peak hour is essentially insignificant to the determination of the system peak hour, which will typically occur in July and occasionally occur in August (but never in May).

ADM also analyzed hourly system load by various day types. The day type that exhibited highest average demand was selected as the appropriate day type for final determination of peak hour. The day types investigated were (1) All Days, (2) Weekdays, (3) Non-Holiday Weekdays (i.e., Workdays) and (4) Weekend & Holidays. A curve for each month was developed by day type. All days for a given day type were averaged for a given month by hour of the day to develop an average 24 hour load curve. For the north and south the summer peak typically occurs during hour 17, which is the hour that ends at 17:00 (5:00 PM). The greatest summer peak demand is the highest peak demand experienced by both companies.

The analysis determined that of the four day types, Workdays averaged the highest system demand for most hours of the day. Generally, the peak hour calculated from the average Workday curve was identified as the peak hour for the month for the given territory. The peak hours for two transition months in each territory were adjusted to maintain a more consistent set of peak hours. Adjustments were made for May and June for Sierra and April and November for NPC. The selection of the peak hour for these months were based on differences of less than 1 percent in the average demand in MW between the mathematical peak hour and the assigned peak hour.

To validate these decisions ADM also analyzed all-time record peak days and an average of the day from each month that the peak occurred. The second method thus included ten days in the calculation of the average. The results from these analyses supported the average Workday results. Analysis files have not been included in this report due to the large size of spreadsheets.


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APPENDIX D: DETERMINING ENERGY SAVINGS PER MONTH BY RATE CLASS

This chapter provides a detailed description of ADM’s analytical steps for determining the energy (kWh) savings per month by rate class values that are provided in the M&V reports for program year 2014.16

D.1. APPORTIONMENT OF ANNUAL ENERGY SAVINGS BY RATE CLASS

NV Energy’s DSM programs generally include populations of customers from more than one rate class. NV Energy tracks the rate class for each identifiable customer participating in DSM programs. However, participant information is not known for certain DSM programs, such as the Residential Energy Efficient Lighting program or other “upstream” or “midstream” programs where incentives are provided through contractual arrangements with manufacturers or distributors of the rebated products. For DSM programs for which participant information is not known, ADM collected participant information at the point of sale or conducted customer surveys to identify the proportions of participants that belong to various rate classes.

D.2. APPORTIONMENT OF ANNUAL ENERGY SAVINGS BY MONTH

ADM developed a methodology that utilizes energy savings curves to calculate the portion of annual energy savings that occurs during each month of the year. An energy savings curve describes the temporal nature of energy savings. For example, on any given day the energy savings achieved by an LED exit sign are approximately 1/365 of the verified annual energy savings for that LED exit sign. On the other hand, an efficient air conditioner may not save any energy during the month of January but may achieve 35 percent of its annual energy savings in the month of July alone. ADM constructed appropriate energy savings curves from metered data collected during M&V of NV Energy DSM programs (or other programs if appropriate), customer billing data, calibrated DOE2 simulations and engineering calculations. The energy savings curves were coupled with project implementation dates on a record-by-record basis to produce accurate determinations of the energy savings achieved for each month of the year.

D.3. HIGH LEVEL SUMMARY OF ADM’S CALCULATION METHODOLOGY

ADM developed a methodology that utilizes energy savings curves to calculate the portion of annual energy savings that occurs during each month of the year. An energy savings curve

Monthly energy (kWh) savings for each program were calculated by applying an appropriate hourly or daily energy savings curve to each program participant’s ex post verified energy savings, then aggregating kWh savings for each month. The energy savings curve distributes a participant’s

16 The Public Utilities Commission of Nevada (PUCN) requires NV Energy to report energy (kWh) savings per month and per rate class for each Demand Side Management (DSM) program.


84


energy savings over time. Its shape is therefore dependent on not only the measure installed (i.e., lighting vs. HVAC), but also on the building type and sometimes its location.

The overall process by which ADM calculated monthly kWh savings was to (1) download from DSM Central all program tracking data, i.e., ex ante expected kWh savings, measure type, measure completion date, rate class, etc., (2) calculate ex post values per participant, (3) assign an energy savings curve to each participant’s ex post savings to distribute ex post energy savings by rate class over each of the 8,760 hours in a year, and (4) aggregate ex post verified savings for the purpose of presenting savings by month and by rate class.

ADM also calculated first-year kWh savings for each program by combining measure startup date (from DSM Central) with the aforementioned process. A detailed description of the steps involved in tabulating first-year kWh savings is provided in section D.5.1 below.

D.4. ENERGY SAVINGS CURVES

D.4.1. DEFINITION The phrase ‘energy savings curve’ is used to describe the temporal dependence of energy savings. The curves are typically hourly (1 × 8760 array), daily (1 × 365 array), or monthly (1 × 12 array). The energy savings curves are often normalized such the sum of all array elements is unity. When normalized, each element describes the fraction of annual savings that is expected to occur in each hour, day, or month.

D.4.2. NOMENCLATURE Note that if the term ‘load shape’ is encountered in the spreadsheets that are used to tally monthly energy savings by program and rate class, one should take it to be the same as ‘energy savings curve’ as described herein. The reason for the usage of the term ‘load shape’ is twofold:


An energy savings curve for a measure may or may not be synchronous with the load curve of the base case technology against which savings are determined. There are energy efficiency measures (EEMs) for which the normalized savings curve is synchronous and proportional to the normalized load shape or curve of the base case technology. Examples of such EEMs include CFLs versus incandescent lights if it is assumed that (1) there are null or negligible interactive effects and (2) pre- and post-retrofit


85


usage schedules are identical. If the savings curve for an EEM is synchronous with the base case technology load shape, then the two curves have identical shapes. For other EEMs, the energy savings curve is asynchronous with the load curve of the base case technology. Examples of EEMs with asynchronous savings curves include economizers, occupancy sensors, and control systems. For such measures, the shape of the energy savings curve is different from the shape of the base case technology.

As part of our evaluation effort ADM determines for each EEM whether to use normalized energy savings curves that are either synchronous or asynchronous with the normalized load shape of the base case technology.

D.5. TABULATING MONTHLY ENERGY (KWH) SAVINGS PER RATE CLASS


D.5.1. FIRST-YEAR kWh SAVINGS ‘First-year’ kWh savings are savings that occur during the same calendar year in which a conservation program was implemented. For NV Energy, a program year is the same as a calendar year. Thus ‘first-year’ kWh savings for a measure installed during the 2017 program year are equal to that measure’s kWh savings during the 2017 calendar year.


For each rate class, for each day of 2017, identify all measures that have been implemented (or ‘installed’ or ‘started up’) by the subject day.

For each rate class, for each day of 2017, for all measures that that have been installed by the subject day, multiply the ex post verified ‘typical-year’ annualized kWh savings17 for each measure type by that measure’s daily kWh bin. In other words, multiply the measure-level annual kWh by the measure-level daily bin from the appropriate energy savings curve.

17 ‘Typical-year’ annualized kWh savings is 365 consecutive days of energy savings – usually a full calendar year other than Leap Year – attributed to an energy efficiency measure(s) for which ex post verified kWh savings will occur during a multi-year measure life. For example, an NV Energy conservation measure installed during the 2017 program year (i.e., during the 2017 calendar year) will normally provide kWh savings starting on its date of installation. ‘First-year’ savings is the savings that occurs during the 2017 calendar year. ‘Full-year’ savings is the savings occurring during subsequent calendar years. If the calendar year immediately following the program year is a Leap Year (i.e., 366 days instead of 365), we regard the second calendar year after the program year to be the best year to reference for ‘typical-year’ annualized kWh savings.


86





D.5.2. ‘TYPICAL-YEAR’ ENERGY (KWH) SAVINGS ‘Typical-year’ energy (kWh) savings represents 365 consecutive days of energy savings attributed to a measure(s) or program for which ex post verified savings will occur across a multi-year measure life.18





For each rate class, ‘typical-year’ kWh savings is the program-level monthly kWh savings for that rate class summed across all 365 days of any non-Leap Year after the 2017 calendar year.

18 The distinction between ‘typical year’ and ‘full year’ is that a ‘typical year’ is a 365-day year. A Leap Year is not a ‘typical year’ – instead, a Leap Year is a ‘full year’ that has 366 days. In M&V reports, the kWh savings tables (which show monthly savings per rate class) usually indicate titles such as “Full Year 2018”, “Full Year 2019”, and “Full Year 2020 (Leap Year)”. 19 When tallying kWh savings per month per rate class, the use of hourly bins or daily bins is equally correct and accurate. ADM typically uses daily bins (which are created from hourly bins) in our kW guru™ Excel files simply because a workstation processor can complete the billions of computations in a large kW guru™ file relatively faster when the number of computations is based on 365 daily bins instead of 8760 hourly bins per calendar year. Hourly bins in kW guru™ files (i.e., the 8760 hourly bins per ‘typical year’) exist for the following two purposes: 1) they are summed across the 24 hours of each day to create the aforementioned daily bins; and 2) they provide the hourly resolution that enables us to analyze and report critical peak demand (kW) savings per month per rate class for any specified kW-reporting period.


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https://curve.19


For any given program, ‘full-year’ kWh savings for a Leap Year will be marginally higher than ‘full-year’ kWh savings for a ‘typical year’ or non-Leap Year. Thus, we always use a non-Leap Year when we quantify ‘typical-year’ kWh savings.

Following is an example of the determination of daily kWh savings generated by a program. Let’s consider a hypothetical program that targets two energy efficiency (EE) measures: residential lighting and residential cooling. For this hypothetical program, Table D-1 below provides a simple comparison of the measures’ respective:

‘typical-year’ energy savings; daily bin value in its energy savings curve for a specific day – February 1st – of any given

year after the EE measures were installed; 20


In Table D-1 below, the assumption is that 1,000,000 kWh of annual energy savings (‘typical-year’ savings as reported in M&V reports) were achieved through distribution of LEDs and 500,000 kWh of annual (typical-year) energy savings were achieved through implementation of high efficiency air conditioning (AC) measures. Energy (kWh) savings on February 1st are obtained by multiplying ‘typical-year’ kWh savings by the entries corresponding to February 1st

in the respective normalized energy savings curves. In this example, the daily bin for space cooling is zero because no space cooling is expected to occur on February 1st.

Table D-1. Sample calculation of energy savings achieved for a given rate class on February 1 for a hypothetical program targeting residential lighting and space cooling.





Feb. 1 daily bin value in each EE measure’s energy savings curve: 0.0030 0.0000

Feb. 1 energy (kWh) savings in a typical year: 3,000 0


20 The daily bin value for February 1 represents the February 1 daily fraction of ‘typical-year’ annual energy (kWh) savings.


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D.5.2. LEAP YEAR SAVINGS To account for the extra day in February in Leap Years, one of the following methods is used. Either method produces accurate and very similar ex post verified energy savings determinations for Leap Years.




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DSM-16

Page 297 of 359

Schools Program NV Energy – Northern Nevada (SPPC)

Program Year 2017


Prepared for:

Prepared by:


916-363-8383

Page 298 of 359

TABLE OF CONTENTS Section Title ....................................................................................................... Page 1. Executive Summary .....................................................................................1

2. Program Background ...................................................................................2

3. M&V Methodology .....................................................................................4

4. Energy Impact Findings .............................................................................17

5. Key Findings and Recommendations ........................................................20

Appendix A: Site-Level Analyses ...................................................................................22

Appendix B: Savings per Month per Rate Class .............................................................49

Appendix C: Calculation Methodolody for Critical Peak Demand (kW) Savings .........51

Appendix D: Determining Energy Savings per Month per Rate Class ...........................55

i

Page 299 of 359

1. EXECUTIVE SUMMARY

This measurement and verification (“M&V”) report provides measured and verified energy impacts achieved by the Schools Program that NV Energy offered in the northern Nevada service territory during 2017.

The main features of the approach used for the impact evaluation for this program are as follows:

M&V data was collected through review of program data and on-site inspections. Based on data provided by NV Energy, a sample design was developed for on-site data collection for the impact evaluation. The sample size provides measured and verified ex post program savings for northern Nevada with 6.9 percent precision at the 90 percent confidence level.

On-site visits were utilized to verify measure installation; to determine measure operating parameters, and to collect data for determining ex post verified energy (kWh) and critical peak demand (kW) savings. Facility staff were interviewed to determine the operating hours of the installed system and to locate any additional benefits or shortcomings associated with the installed system. For some M&V sites, lighting equipment, HVAC equipment, or other measures were monitored to obtain accurate information on hours of operation.

Ex post verified energy impacts for the 2017 Schools Program in northern Nevada are shown in Table 1-1. The realization rate for the program in northern Nevada is 113 percent. Ex post verified energy impacts totaled: 3,253,549 annual savings; and 1,055,983 kWh “first-year” savings during the 2017 calendar year. Summer critical peak demand savings are 471 kW.

Table 1-1. Summary of Annual kWh Savings, Northern Nevada Schools Program


Annual Ex Ante kWh Savings


Realization Rate

1,055,983 2,876,298 3,253,549 113%


1

2. PROGRAM BACKGROUND

This chapter provides an overview of the structure of the Schools Program. In addition, a summary of the evaluation approach is provided in this chapter.

2.1 DESCRIPTION OF THE SCHOOLS PROGRAM

The Schools Program was designed to facilitate energy efficiency and peak demand reduction in the schools served by NV Energy. The program’s approach was to identify existing barriers to energy reduction in the schools and institutions participating in the program and to recommend and implement solutions to overcome those barriers. Services included selected combinations of:

Benchmarking school energy performance;

Workshops to develop master plans;

Training and technical assistance to identify efficiency improvements in existing buildings;

Dissemination of best practices from highly energy efficient school districts; and,

Designing and building high energy efficient and LEED schools through both new construction and retrofit projects.

During 2017, a total of 43 projects were implemented in northern Nevada with total ex ante expected savings of 2,876,298 kWh.

Table 2-1 shows the distribution of ex ante expected savings by measure type. For the Schools Program in northern Nevada, approximately 75 percent of ex ante expected savings are associated with lighting measures.

Table 2-1. Ex Ante Expected Savings per Measure, Northern Nevada Schools

Measure Category Project Count

Total Ex Ante kWh Savings

Percent of Total Ex Ante kWh Savings

Faucet Aerators HVAC Lighting Retrofit New Construction

8 4

28 3

10,510 442,361

2,169,153 254,274

0.4% 15% 75% 9%

Total 43 2,876,298 100%

2.2 OVERVIEW OF EVALUATION APPROACH

The overall objective of the impact evaluation of the Schools Program in northern Nevada was to determine the energy savings (kWh) and summer critical peak demand (kW) reductions resulting from program activities during 2017.


2

Schools Program – 2017 NV Energy, Northern Nevada M&V Report March 2018

The approach for the impact evaluation had the following main features.

Available documentation (e.g., audit reports, savings calculation work papers, network reports, etc.) was reviewed for a sample of sites, with particular attention given to the calculation procedures and documentation of savings calculations.

On-site data collection was conducted for a sample of 12 sites to verify measure installation and capture the data necessary for determining savings and demand reductions.

A comprehensive lighting operation study was conducted using monitoring data from 16 schools in northern and southern Nevada. Annual hours of operation determined for northern Nevada are described in detail in section 3.1.4 of this M&V report. Those hours of operation for northern Nevada were used in both ex ante expected, and ex post verified savings calculations.

Annual kWh savings were determined using the following techniques:

o Analysis of lighting savings was accomplished using an engineering model that utilizes information on operating parameters collected on-site and, if appropriate, industry standards.

o For HVAC measures, the original analyses used to calculate the ex ante expected savings were reviewed and the operational settings and inputs of the calculations were verified. For custom measures or relatively more complex measures, simulations with the DOE-2 energy analysis model were used to determine energy use and savings from the installed measures.


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3. M&V METHODOLOGY

This chapter addresses the calculation of annual kWh savings and summer critical peak demand kW savings resulting from measures installed in customer facilities under the Schools Program in northern Nevada during 2017.

3.1. METHODOLOGY FOR CALCULATING GROSS SAVINGS

The methodology used for calculating gross savings is described in this section.

3.1.1 Sampling Plan

Data used to calculate the gross savings achieved through the Schools Program in northern Nevada were collected for a sample of sites at which projects were completed during 2017. Data provided by NV Energy through DSM Central showed that during 2017, a total of 43 projects were completed at school sites in northern Nevada, providing ex ante expected savings of 2,876,298 kWh annually.

Inspection of ex ante data on kWh savings for individual sites provided by the implementation contractor indicated that the distribution of savings was positively skewed, with a relatively small number of sites accounting for a high percentage of the projected savings. A sample design for selecting projects using the Dalenius-Hodges stratified random sampling method was used that took such skewness into account and allowed savings to be determined with 10 percent relative precision (or better) at the 90 percent confidence level. Sampling for this program was performed at the project level. Projects were categorized into sampling strata as shown in Table 3-1. The actual precision achieved for the sample is 7.7 percent. For sites included in the M&V sample, each of the associated projects implemented at the site was included in the M&V impact analysis.

M&V sampling and data collection were relatively concurrent with program implementation. ADM used a near real-time process whereby a portion of the sample was selected periodically as projects in the program were completed. The timing of sample selection was contingent upon the timing of the completion of projects during the program year.

Table 3-1 shows the strata boundaries, total ex post verified energy savings, the coefficient of variation, and the number of sample sites for the northern Nevada Schools Program sample for each stratum.


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Table 3-1. Population Statistics by Sample Stratum for Northern Nevada Schools Program

Sampling Criteria Sampling Strata

Total Strata 1 Strata 2 Strata 3 Strata 4

Strata Boundaries (Ex Post kWh) 243-15,373 15,998-32,807 38171-109,613 119,313-695,754 243-695,754

Number of Projects 19 10 8 6 43

Total Ex Post kWh Savings 117,758 251,263 674,844 2,218,310 3,262,310 Average Ex Post kWh Savings 6,198 25,126 84,355 369,718 75,865

Standard Dev of Ex Post kWh savings 5,569 5,480 26,841 236,661 148,207

Coefficient of Variation 0.90 0.22 0.32 0.64

Sample n 2 2 2 6 12

Statistical Precision of kWh Savings Estimation at 90% Level of Confidence +/-7.7%

3.1.2 Review of Documentation After the sample was selected, NV Energy’s program implementation contractor provided documentation pertaining to those projects. The first step in the evaluation effort was to review this documentation and other program materials that were relevant to the evaluation effort.

For each project, the available documentation (e.g., audit reports, savings calculation work papers, etc.) for each rebated measure was reviewed, with particular attention given to the calculation procedures and documentation of savings calculations. Documentation reviewed for all projects in the sample included program forms, databases, reports, billing system data, weather data, and any other potentially useful data. Each project was reviewed to determine whether the following types of information had been provided:

Documentation for the equipment changed, including (1) descriptions, (2) schematics, (3) performance data, and (4) other supporting information;

Documentation for the new equipment installed, including (1) descriptions, (2) schematics, (3) performance data, and (4) other supporting information; and,

Information about the savings calculation methodology, including (1) what methodology was used, (2) specifications of assumptions and sources for these specifications, and (3) correctness of calculations.

If there was uncertainty regarding a project or apparently incomplete project documentation, ADM staff contacted the implementation contractor to obtain further information to ensure the development of an appropriate site-specific M&V plan.


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3.1.3 On-Site Data Collection Procedures On-site visits were used to collect data that were used in calculating savings impacts. Visits to the sampled sites were used to collect primary data on the facilities participating in the program.

When sites were selected for the M&V sample, ADM notified NV Energy program management and concurrently provided the Schools program implementation team a list of projects for which ADM would attempt to schedule M&V activities. This notification also served as a request to the implementation contractor for any documentation relating to the projects, including school name, site address or other premise identification, as well as the respective contact information for the school representative with whom ADM needed to schedule M&V activities.

During an on-site visit, the field staff accomplished three major tasks:

First, ADM field staff verified the implementation status of all measures for which customers received incentives. ADM verified that the energy efficiency measures were indeed installed, that they were installed correctly and that they still functioned properly.

Second, ADM field staff collected the physical data needed to analyze the energy savings that have been realized from the installed improvements and measures. Data was collected using a form that was prepared specifically for the project in question after an in-house review of the project file.

Third, ADM field staff interviewed the contact personnel at the facility to obtain additional information on the installed system to complement the data collected from other sources.

At some sites, monitoring was conducted to gather more information on the operating hours of the installed measures. Monitoring was conducted at sites where it was deemed that the monitored data would be useful for further refinement and higher accuracy of savings calculations; if, project documentation allowed for detailed calculations monitoring was not implemented.

3.1.4 Procedures for Calculating kWh Savings from Measures Installed Through Schools Projects

ADM uses measure-specific methodologies to analyze and determine gross savings per project. The project-specific analyses and savings calculations can be found in Appendix A. Table 3-2 below provides a brief summary of typical M&V methodologies for determining energy savings.


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Table 3-2. Typical M&V Methodologies for Determining Energy Savings

Type of Measure Method to Determine Savings

Lighting Monitoring or project documentation data to determine pre-and post-installation measure wattages and fixture hours-of-use.

HVAC (including packaged units, chillers, cooling towers, controls/EMS)

Equivalent full load hour method to calculate savings resulting from HVAC measures. Equivalent full load hours were derived using eQuest models using corresponding weather files.1

The methodologies outlined in Table 3-2 guided the implementation contractor’s calculations of ex ante estimated energy savings, as well as the M&V analysis of ex post verified energy savings. After determining ex post verified energy savings, ADM calculated program-level kWh savings by applying a ratio procedure in which the achieved savings for the M&V sample sites were imputed to the whole population of program participants.

Energy savings realization rates were calculated for each project for which on-site data collection and engineering analysis/building simulations were conducted.2 Sites with relatively high or low realization rates were further analyzed to determine the reasons for the discrepancy between ex ante expected and ex post verified energy savings. Information on how realization rates were determined is included in the site-level M&V analyses presented in Appendix A.

The following discussion describes procedures used for calculating savings from various measure types. Site-specific information on savings calculations is contained in Appendix A.

Analyzing Savings from Lighting Measures: Lighting measures that were examined included retrofits of existing fixtures, and lamps and/or ballasts with energy efficient fixtures. These types of measures reduce demand, while not affecting operating hours. Lighting control strategies that might include the addition of energy conserving control technologies such as motion sensors or daylighting controls were also examined. These measures typically involve a reduction in hours of operation and/or a lower electric current passing through the fixtures.

Annual lighting hours of operation were determined by a comprehensive lighting study performed by ADM. Over 80 lighting loggers were deployed in 16 schools in both Nevada territories to determine annual operation hours by territory, type of school (elementary, middle, and high) and

1 A calculation of pre and post energy consumption using full load hours which represent the equivalent time a unit will spend operating at peak capacity during a typical year

2 The realization rate is the ratio of ex post verified energy (kWh) savings to ex ante expected energy (kWh) savings. At the project level: ex post verified energy (kWh) savings is the achieved savings that has been measured and verified for each M&V site; ex ante expected energy (kWh) savings was provided by the implementation team (through the project application procedure, and as recorded in the tracking system for the program) previous to the M&V activities that led to the determination of the achieved savings.


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by space type (used for gym retrofits). From this study, ADM determined the following blended annual lighting hours of operation for northern Nevada whole-school retrofits:


The following blended hours are representative of lighting operation for gym retrofits:


Table 3-3 presents lighting operating hours specific to various space types in the northern territory:

Table 3-3. Annual Lighting Operating Hours by Space Type, SPPC

Space Type Classroom Hallway Restroom Library

Work Room/ Storage

Office Lab Music Room/

Theater Elementary 2,576 2,335 2,667 3,089 568 2,456 3,044 2,622 Middle 2,076 5,162 3,930 2,382 568 2,893 2,225 2,800 High 1,985 4,643 3,658 3,944 568 3,743 2,080 4,222

Lighting hours for lights with an occupancy sensor were reduced by 30 percent, based on engineering calculations that are consistent with the most recent industry literature.3

Analyzing the savings from lighting measures requires data for retrofitted fixtures on (1) wattages before and after retrofit and (2) hours of operation before and after the retrofit.

ADM uses per-fixture baseline demand, retrofit demand, and appropriate post-retrofit operating hours to calculate annual energy savings for sampled fixtures of each usage type. Annual energy savings for each sample fixture is calculated using the following formula:

Where: kWhpre = amount of kWh used by preexisting fixtures kWhpost = amount of kWh used by post-installation fixtures

Savings from lighting measures in conditioned spaces are adjusted by the region-specific, building type-specific heating cooling interaction factors in order to calculate total savings attributable to lighting measures, inclusive of impacts on HVAC operation.

3 Study referenced for lighting occupancy sensor hours reduction of 30 percent: Levine, M., Geller, H., Koomey, J., Nadel S., Price, L., “Electricity Energy Use Efficiency: Experience with Technologies, Markets and Policies” ACEEE, 1992. Lighting control savings fraction of 30 percent is consistent with current programs offered by National Grid, Northeast Utilities, Long Island Power Authority, NYSERDA, and Energy Efficiency Vermont.


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Analyzing Savings from HVAC Measures: Savings for HVAC measures installed at a facility are derived by using equivalent full load hours developed through DOE-2 simulations. DOE-2 is an industry standard building energy analysis and load simulation program. DOE-2 combines a description of the building layout, constructions, usage, and conditioning systems with hourly weather data to perform an hourly simulation of building energy use. The DOE-2 simulations allow calculation of the primary and secondary effects of lighting measures on energy use. Each simulation calculates HVAC energy and demand usage to be expected under different assumptions about equipment and/or construction conditions.

For the analysis of HVAC measures, the data collected through on-site visits and monitoring are utilized. Using these data, ADM calculates energy savings for the energy efficient equipment and measures installed in each of the participant facilities by developing independent assessments of the savings through engineering calculations. By using an equivalent full load hour method for the analysis, the energy use associated with the end use affected by the measure(s) being analyzed can be quantified. After having made these calculations, the energy use that would have been observed without the measure(s) is determined. The difference between what would have been observed and the end use energy of the implemented measure is the energy savings for the HVAC measure.

Heating and Cooling Interaction Factor: Installing energy efficient lighting in air-conditioned spaces saves electricity in two ways: first by reducing lighting electrical loads; and second by introducing less heat in conditioned spaces, hence incrementally decreasing space cooling loads. The relatively low heat output from energy efficient lighting also incrementally increases space heating loads. Space heating and cooling impacts of energy efficient lighting are described using a ratio that is known as the heating and cooling interaction factor (HCIF). HCIF was included in the calculations to determine energy savings (kWh) associated with the facility’s HVAC equipment. The HCIF was incorporated into the energy impact analysis for each M&V sample site; please note that all of the site-level analyses are provided in Appendix A.

The HCIF value represents the ratio of a) total post-retrofit kWh savings and b) lighting-only kWh savings. For example, if HCIF = 1.01, then post-retrofit kWh usage for heating and cooling decreased overall, thus improving by 1 percent the overall kWh savings provided by the energy efficient lighting measure. Conversely, if HCIF = 0.99, then post-retrofit kWh usage for heating and cooling increased overall, thus reducing by 1 percent the overall kWh savings provided by the energy efficient lighting measure. HCIF for this program was developed in tandem with the energy savings curves; the development of both methods is explained in section 3.1.5 below.

3.1.5 Determining Energy Savings Curves

ADM utilized measure-specific Energy Savings Curves (“Curves”) to determine program-level


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energy (kWh) savings per month per rate class and program-level critical peak demand (kW) savings per month per rate class.

The Curves were derived from Curves published through the California End Use Survey (CEUS).4 To adapt CEUS Curves to northern Nevada schools, ADM modified the CEUS Curves as follows:

Curves were aligned with the Washoe County School District instructional calendar.

A Heating and Cooling Interactive Factor (HCIF) specific to northern Nevada was incorporated into the Curve for interior lighting measures. This modification improves the shape and accuracy of the interior lighting Curve but does not change annual energy (kWh) savings given that the Curve was normalized before being employed to disaggregate kWh savings into 8,760 hourly bins per rate class. A detailed explanation of the HCIF derivation is provided below (in this same section, immediately following Figure 2).

Assessing the Applicability of CEUS Curves: Appropriate Energy Savings Curves are essential to the M&V task of allocating kWh and critical peak kW savings to hourly and monthly bins for each rate class that participated in the 2017 program. ADM explored using an Energy Savings Curve that was specific to the 2017 Schools Program: ADM had conducted a monitoring study in Nevada schools in 2011; therefore, we examined the feasibility of using the monitored lighting and HVAC schedules to create appropriate Energy Savings Curves.5 However, after examining the logger data from the 2011 study, we found that the monitored lighting and HVAC schedules closely matched the respective CEUS Curves for schools. ADM determined that the CEUS curves, after being modified to align with the actual instructional calendar for Washoe County Schools, would provide the most accurate Energy Savings Curves for this program.

4 The California Commercial End-Use Survey (CEUS) is a comprehensive study of energy loads or end-uses in the commercial building sector. Itron performed the Survey under contract to the California Energy Commission (CEC); Pacific Gas & Electric, San Diego Gas and Electric, Southern California Edison, Southern California Gas Company and the Sacramento Municipal Utility District participated in the Survey. The Survey captured detailed building systems data, building geometry, electricity and gas usage, thermal shell characteristics, equipment inventories, operating schedules and other commercial building characteristics. A stratified random sample of 2,800 commercial facilities was targeted and a sample of 2,790 was actually completed. Commercial premises were weighted and aggregated to building segment results. Available study results include: floor stocks; fuel types and weights; electricity and natural gas consumption; energy-use indices (EUIs); energy intensities; and 16-day hourly end-use load profiles for twelve categories of commercial (i.e., non-residential) building types, including schools. For an in-depth explanation of how CEUS conducted the monitoring and calculations, please refer to: http://www.energy.ca.gov/2006publications/CEC-400-2006-005/CEC-400-2006-005.PDF. 5 The 2011 monitoring study is described in Section 3.1.4 above.


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http://www.energy.ca.gov/2006publications/CEC-400-2006-005/CEC-400-2006-005.PDF


Alignment of CEUS Curve with Washoe County School District Calendar: Most schools in the 2017 Schools Program follow a traditional nine-month schedule. CEUS curves for lighting and HVAC measures were based predominantly on traditional nine-month schedules; for example, the CEUS curve for interior lighting depicts monthly energy usage declines in June and reaches a nadir in July, then increases during August and September until it reaches a peak in October. The CEUS lighting curve is the dark line shown in Figure 2.

Given that the CEUS data indicates that all schools were out-of-session during at least part of July, and the majority of schools were out-of-session during all of July, ADM used CEUS data for July to “model” all out-of-session weekdays for Washoe County’s summer vacation weeks. In other words, ADM modified the CEUS curves to incorporate the actual summer schedule for Washoe County Schools. Because the CEUS lighting schedules during July weekdays closely match ADM’s 2011 lighting study data for summertime non-instructional days, ADM created each Energy Savings Curve for the PY2017 Schools Program by transposing weekday savings from the corresponding July CEUS curve onto all of Washoe County’s out-of-session summer days. Therefore, compared to the corresponding CEUS curves, ADM’s PY2017 Schools Program Curves have lower summertime kWh savings.

For the interior lighting curve, Figure 1 depicts the difference in kWh savings when comparing an in-session weekday (e.g., the lighter line, representing September 10) versus out-of-session weekday (e.g., the darker line, representing July 15).

0.00%

0.01%

0.02%

0.03%

0.04%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Percen

tAnn

ualSavings

CEUS Out of Session vs In Session (Hour on Weekday)

15 Jul 10 Sep

Figure 1. CEUS Interior Lighting Savings Curve, out of session vs. in session weekday

Figure 2 below compares the original CEUS interior lighting curve to the one ADM modified to better represent the Washoe County School District Calendar; the differences are not dramatic, but the ADM curve does show lower lighting energy savings during June and August.


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Figure 2. Comparison of original CEUS curve to one ADM modified to reflect the Washoe County School District Calendar

Incorporating HCIF Factors into the Savings Curve: The heating and cooling interactive factor (HCIF) was incorporated into the energy impact analysis for each M&V sample site; all of the site-level analyses are provided in Appendix A. The overall savings curve also reflects secondary heating and cooling impacts in a manner that is consistent with the site-specific HCIF calculations. The methodology employed is described by ASHRAE6, which calculates HVAC impacts by estimating cooling and heating equipment efficiencies and applying specific thermal fractions for cooling and heating.7

kWh impactAC = kWh savingsLighting x Thermal FractionAC ÷ MCOPElectric AC

Equation 1

kWh impactHeating = kWh savingsLighting x Thermal FractionHeat ÷ MCOPElectricHeat

Equation 3 Where:

kWh savingsLighting = Verified annual energy savings from lighting retrofit. Thermal FractionAC = Fraction of the lighting impact that results in avoided cooling. ADM uses bin calculators to determine this fraction.8

Thermal FractionHeat = Fraction of the lighting impact that results in an increased need to heat the affected space. ADM uses bin calculators to determine this fraction.

6 Calculating Lighting and HVAC Interactions”, Robert Rundquist, PE, Karl F. Johnson and Donald J. Aumann, PE, ASHRAE Journal, November 1993. 7 Thermal fraction is the ratio of annual lighting energy contributing to cooling (or heating) load. 8 Bin calculators determine over each hour of the year the fraction of avoided or increased thermal load due to the

efficient lighting measure.


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MCOPElectricAC = Marginal coefficient of performance, A/C system. Only the electric portion of cooling energy usage informs MCOP.

MCOPElectricHeat = Marginal coefficient of performance, heating system. Only the electric portion of heating energy usage informs MCOP9

All variables related to heating and air conditioning are modified for climate conditions in northern Nevada. In Figure 3 below, the modified CEUS lighting shape depicted by gray dashes (“- - - - Interior Lighting”) is normalized to unity after the interactive effects are incorporated.

In summary, for the PY2017 Schools Program, a total of three Curves were utilized to calculate critical peak demand (kW) savings. The three Curves (interior lighting, exterior lighting, and HVAC) were derived by modifying CEUS curves to fit the school district calendar. The modified Curves are appropriate for Washoe County schools. Figure 3 provides an example of the modified Curves; the Figure 3 depiction represents average daily savings curves for interior lighting, exterior lighting, and HVAC in a Washoe County school.

0.00%

0.02%

0.04%

0.06%

0.08%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Percen

tofA

nnua

lSavings

Daily Savings Curves

Interior Lighting Exterior Lighting HVAC

Figure 3. Average Daily Savings Curves, Washoe County

9 For example, MCOPElectricHeat may have approximate values near unity for electric resistive heating, near 2 for electric heat pumps, and 15 for forced air gas furnaces/boilers. The 1/15 in the latter case represents the electric energy usage of the air handlers, but is cast in the form of COP.


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Figure 4 below shows the final program-level annual savings Curve.

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

10%

1 2 3 4 5 6 7 8 9 10 11 12

Percen

tAnn

ualSavings

Month

Schools Program Annual Savings Curve

Figure 4. Program-Level Annual Savings Curve, SPPC Schools Program

Table 3-4 lists the energy savings curves and the source of those curves which were employed to allocate kWh and critical peak kW savings per month and per rate class for this program.

Table 3-4. Energy Savings Curves Specific to this 2017 Program

Energy Savings Curve Source / Modification Applicability

Program-level curve for PY2017 Schools Program

CEUS interior lighting, exterior lighting, and cooling savings curves, modified to represent the Washoe County School District calendar

Northern Nevada schools on the Washoe County School District calendar.

3.1.6 Calculating First-Year kWh Savings

First-year kWh savings were calculated by determining the percentage of the year remaining when each measure was installed. We used measure startup data from NV Energy’s DSM Central database to calculate the number of days left in the calendar year as of the measure startup date. For each measure, the number of days remaining in the year was then used along with the normalized energy savings curve described above to determine the share of annualized kWh savings realized during the 2017 calendar year. First-year kWh savings was summed by month across each customer rate class in the program population to determine first-year kWh savings per month per rate class. The first-year kWh savings table is provided in Table B-1 in Appendix B.

3.1.7 Calculation of Critical Peak Demand (kW) Savings

The critical peak demand period per month for Sierra Pacific Power is defined as the hour in each month when system load is most likely to reach a critical peak. The critical peak demand hour per

M&V Methodology 14 Page 313 of 359


month is shown below in Table 3-5. The summer critical peak demand period is defined as the hour ending at 17:00 hours or 5:00 pm in the month of July. In other words, based on ADM’s analysis of historical data, system load is most likely to reach an annual maximum level on any given July day during the hour ending at 17:00 hours or 5:00 pm.

Table 3-5. Critical Peak Demand Period per Month, SPPC Month Hour (SPPC Ending at: January 19 19:00

February 19 19:00 March 20 20:00 April 21 21:00 May 17 17:00 June 17 17:00 July 17 17:00

August 17 17:00 September 17 17:00

October 20 20:00 November 19 19:00 December 19 19:00

Critical peak demand (kW) savings are calculated by month and by rate class utilizing ex post verified program savings determinations and appropriate measure-level, 8760-hour energy savings curves. For each 2017 participant in this program, ex post annualized energy savings per measure is allocated to the participant’s rate class, and to the specific energy savings curve for that measure. The result is a two-dimensional matrix providing per-rate-class savings per hour for all 8,760 hours of the typical calendar year. The results are then inspected to identify the maximum critical peak demand (kW) savings per month, i.e., the question we are answering is: “For each month of a typical year, what is the maximum kW savings during the hour specified in Table 3-5?”

Summer critical peak demand savings is defined as the maximum kW reduction that could be expected during any day in July during the hour ending at 5:00 pm. For this program, verified summer critical peak demand reduction was 471 kW. Complete ex post critical peak demand (kW) savings by month and by rate class are provided in Appendix B. For more information on how ADM calculates summer critical peak demand savings, please see Appendix C.

3.1.8 Determination of Effective Useful Life (EUL) and Lifetime Savings

ADM analyzed various data to determine the Effective Useful Life (EUL) for each category of measures installed by NV Energy’s 2017 Schools Programs. ADM subsequently employed its EUL determinations to calculate lifetime energy (kWh) savings per measure category and at the program level. To determine EUL values for the Lighting Retrofits, and HVAC Retrofits measure


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categories, ADM utilized the California Database for Energy Efficient Resources (“DEER”).10

Note the following measure-specific issues related to the EUL values provided in this M&V report:

For the HVAC Retrofits category, all measures installed by the 2017 Schools Program have the same EUL value in DEER, i.e., 15 years.

For the various Lighting Retrofit measures installed by the 2017 Schools Program, DEER provides EUL values such as approximately four years for qualified CFLs; 12 years for CFL fixtures, which in ADM’s judgment is also appropriate for LED fixtures, given that DEER does not provide an EUL value for LED fixtures; 15 years for fluorescent fixtures and exterior lighting; and 16 years for LED exit lights. ADM used the appropriate EUL values from DEER to calculate the respective weighted average EUL values for Lighting Retrofits measure category, which are 13.4 years for SPPC.

To determine program-level EUL values, ADM calculated weighted averages across all measure categories; EUL calculations and lifetime energy (kWh) savings per measure category are provided in the following chapter. ADM concluded that the weighted EUL is 15.0 years.

10 http://www.deeresources.com/deer0911planning/downloads/EUL_Summary_10-1-08.xls


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http://www.deeresources.com/deer0911planning/downloads/EUL_Summary_10-1-08.xls

https://DEER).10

4. ENERGY IMPACT FINDINGS

This chapter presents the results and findings from the field data collection and energy savings analysis.

4.1 RESULTS OF EX POST VERIFIED SAVINGS CALCULATIONS

To calculate annual kWh savings and summer critical peak kW reductions for the northern Nevada Schools Program, data were collected and analyzed for a sample of 33 sites. The data were analyzed using the methods described in Section 3.1 to calculate project energy savings and peak kW reductions and to determine the realization rates for the program in northern Nevada. The results of that analysis are reported in this section. Project-level analyses are presented in Appendix A.

4.1.1 Ex Post Verified kWh Savings

The ex post verified kWh savings of the northern Nevada Schools Program during 2017 are summarized by sampling stratum in Table 4-1. Overall, the achieved ex post verified savings of 3,253,549 kWh was equal to approximately 113 percent of ex ante savings.

Table 4-1 Summary of Annual Ex Ante kWh Savings by Stratum

Stratum Ex Ante kWh Savings

Ex Post kWh Savings

Realization Rate

Stratum 1 7,904 7,901 100% Stratum 2 42,420 38,898 92% Stratum 3 134,030 134,008 100% Stratum 4 1,822,774 2,218,310 122%

Total 2,007,128 2,399,117 120%

Table 4-2 provides site-level ex ante expected and ex post verified savings for the northern Nevada Schools Program.


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Table 4-2. Ex Ante Expected and Ex Post Annual Verified kWh Savings for Sampled Projects

Facility Name Ex Ante kWh Savings

Ex Post kWh Savings

Project Realization Rate

Reynolds Building 1,657 1,656 100%

TMCC - Applied Technology Center Campus, Edison Building 6,247 6,245 100%

University of Nevada, Reno, Thompson Hall 15,998 12,483 78%

Pioneer High School 26,422 26,415 100%

Truckee Meadows Community College, Dandini Campus 38,171 38,171 100%

Mark Twain Elementary School 95,859 95,837 100%

Empire Elementary School 119,311 119,313 100%

Eagle Valley Middle School 180,537 180,517 100%

E. L. Wiegand Fitness Center 224,274 365,425 163%

Carson Middle School 243,904 243,884 100%

University of Nevada, Reno 358,939 613,417 171%

Carson High School 695,809 695,754 100%

Subtotal for M&V Sample 2,007,128 2,399,117 120%

Not Sampled 869,170 854,432 98%

Total 2,876,298 3,253,549 113%

First-year savings are calculated using the methodology presented in Section 3.1.5. First-year energy savings for Schools Program projects implemented in northern Nevada in 2017 totaled 1,055,983 kWh. First year and annual ex post verified kWh savings of the northern Nevada Schools Program during 2017 are summarized in Table 4-3.

Table 4-3. First Year and Annual kWh Savings, Northern Nevada Schools Program



1,055,983 3,253,549


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See Appendix B for tables presenting the kWh savings for the Schools Program in NV Energy’s northern Nevada service territory broken out by month by rate class for the years 2017 through 2020.

4.1.2 Ex Post Verified kW Savings

The ex post verified summer critical peak demand reduction for the Schools Program in NV Energy’s northern Nevada service territory is 471 kW.

The complete ex post determination for critical peak demand savings (kW savings) per month and per rate class are provided in Appendix B. Further information on ADM’s methodology for calculating critical peak demand savings can be found in Appendix C.

4.1.3 Effective Useful Life (EUL) and Lifetime Energy (kWh) Savings

As described in section 3.1.8 in the previous chapter, ADM determined the Effective Useful Life (EUL) for each category of measures installed by NV Energy’s 2017 Schools Programs. ADM subsequently employed EUL values to calculate lifetime energy (kWh) savings per measure category and at the program level.11 As shown in Table 4-4 below, ADM determined program-level lifetime savings of 48,900,960 kWh. Program-level EUL was then taken to be lifetime kWh divided by annual kWh, or 48,900,960 divided by 3,253,549, i.e., 15.0 years. ADM thus determined weighted average EUL of 15.0 years for the 2017 program.

Table 4-4. EUL and Lifetime Energy Savings, SPPC Schools Program, PY2017

Measure Category Ex Post Verified

Annual kWh Savings

EUL (years)

Ex Post Verified Lifetime kWh Savings

Faucet Aerator 2,158,640 13.4 28,861,350 HVAC 691,137 20.0 13,822,745 Lighting 393,266 15.5 6,076,403 New Construction 10,506 13.4 140,462

Total Energy (kWh) Savings; Weighted Average EUL 3,253,549 15.0 48,900,960

11 To determine measure level EUL for new construction sites, ex post annual savings were categorized into either lighting or HVAC savings.


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https://level.11

5. KEY FINDINGS AND RECOMMENDATIONS

This chapter presents key findings and recommendations.

5.1 KEY FINDINGS

At the program level, ex post verified energy (kWh) savings determined by ADM did not vary significantly from ex ante estimated energy (kWh) savings reported by NV Energy’s implementation contractor (“IC”). Typical variances between ex post verified energy savings and ex ante were the result of wattage differences between site plans and actual installation. As ADM, has noted in previous evaluations of this program, the implementation team (i.e., CLEAResult and NV Energy program management) has generally worked very effectively to achieve a good understanding of M&V approaches and algorithms for the various measures implemented by the program.

5.2 RECOMMENDATIONS

This section presents ADM’s recommendations for the evaluation of the 2017 Schools Program.


As has occurred in previous years, the Schools Program implementation team should seek – and ADM should provide at the earliest possible date in 2017 – M&V review and feedback related to the ex ante estimated energy (kWh) savings values per measure for program year 2017 (“PY2017”).

5.2.2 Recommendation 2 For PY2017, the SPPC program continues to include “higher education”. This participant category may require relatively significant M&V efforts during 2017. For these PY2017 participants, the Schools Program implementation team should endeavor to keep ADM informed relative to types of facilities and measures that may be new to the program and for which applications are anticipated. In order to minimize M&V impacts on program participants, ADM should be included in pre-project discussions with participants for sites that have significant sources of variability and may require monitoring. At the date of this M&V report, these recommended coordination activities are underway for PY2017.

5.2.3 Recommendation 3 Fixture counts for each individual space, room, or section of hallway are necessary for effective M&V work in the buildings found in Schools, colleges and universities. In past reports, ADM had suggested providing more specific per space counts because secondary inspections had to be conducted to verify quantities. During PY2017, appropriate per-space counts were provided and no sites needed a secondary inspection. For PY2018, ADM recommends that per-space fixture counts continue to be provided in contractor’s project documentation.


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5.2.4 Recommendation 4 During PY2017, several projects had hours of operation that varied significantly from the deemed numbers hours used to calculate ex ante savings. These projects typically included dedicated office buildings, warehouses or exterior lighting. We recommend that for these atypical building types, hours of operation should be obtained on a per-site basis.

5.2.5 Recommendation 5 During PY2017, several retrocommissioning sites became a part of the sample. For retrocommissioning sites with setpoint changes, ADM has concerns about the longevity of these setpoints. ADM recommends that we check the setpoints for these sites midway through PY2018 to verify the persistence of savings.

5.2.6 Recommendation 6 The Schools Program will add several large whole school and new construction projects in PY2018. As such, it is extremely important to understand the program population as early in the year as possible. To date, projects have been included in the program population for sampling purposes upon completion. In PY2017, this resulted in several sites being added in December. For the upcoming program year, earlier notice of both the number and estimated savings of program sites will be critical for the M&V effort. This could be achieved through access to a database of upcoming projects, or other regular update methods. This data would be used to determine sample strata boundaries and to update sample size through the program year.


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APPENDIX A: SITE-LEVEL ANALYSES

Site Truckee Meadows Community College - Dandini Campus Address 7000 Dandini Boulevard, Sun Valley, NV 89502 Project Number SPPC-2017Schools_1220603

Executive Summary

Under project SPPC-2017Schools_1220603, Truckee Meadows Community College - Dandini Campus. received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (33) HPS fixtures to (33) LED fixtures; and (16) T8 fixtures to (16) LED fixtures. (6) metal halide fixtures to (6) T8 fixtures.


During the M&V visit, ADM staff verified equipment installation and determined the lighting operating schedule.









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Hours Expected

kWh Savings

Realized kWh



High Pressure Sodium to LED 9 9 138 36 4,308 3,955 3,955 1.00 100% 11.61 45,900




Metal Halide to T8 6 6 91 33 7,120 2,704 2,704 1.09 100% 7.02 18,989 T8 Linear Fluorescent to LED 2 2 58 24 7,120 528 528 1.09 100% 7.02

9.55

3,710

364,380 Total 38,171 38,171 100%

Results




Site TMCC - Applied Technology Center Campus, Edison Building Address 475 Edison Way, Reno, NV, 89501 Project Number SPPC-2017Schools_1220604

Executive Summary

Under project SPPC-2017Schools_1220604, TMCC - Applied Technology Center Campus, Edison Building received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (12) T8 fixtures to (12) LED fixtures.


During the M&V visit, ADM staff verified equipment installation and determined the lighting operating schedule.











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Hours Expected

kWh Savings

Realized kWh



T8 to LEDs 12 12 112 45 7,120 6,247 6,245 1.09 100% 7.02 43,858

Total 6,247 6,245 100% 7.02 43,858

Results



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M&V Report Schools Program – 2017 NV Energy, Northern Nevada

March 2018


Wiegand Fitness Center 1644 N. Virginia Street, Reno, NV 89557 SPPC-2017Schools_1220605

Executive Summary

Under project SPPC-2017Schools_1220605, Wiegand Fitness Center received incentives from NV Energy for both a lighting and HVAC project implemented at their facility. The realization rate for this project is 163 percent.

Project Description

The customer installed a lighting power density of 0.46 W/ft².

Additionally, the customer installed a water-cooled chiller with efficiencies higher than required by code. The customer installed a 300-ton York YMC-S1055AAS chiller with a full load efficiency of 0.547 kW/ton, and a part load efficiency of 0.339 kW/ton. The efficiency requirements from the IECC 2012 code are 0.576 kW/ton and 0.549 kW/ton for full and part load performance, respectively.


During the M&V visit, ADM staff verified equipment installation and determined the lighting operating schedule. The baseline lighting operating hours were verified through an interview with facility staff. The post-implementation lighting hours were verified through loggers placed in the sites electrical panel.

Lighting density energy savings are calculated as:



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ft.2TotalBuilding = Square Footage of the Facility

(W/ft2)LPDBase = Baseline Lighting Power Density as Established by Local Code Requirement

(W/ft2)LPDInstalled = Verified Lighting Power Density

t = Annual lighting operating hours

HCIF = HVAC interactive factor

The table shown below presents expected and realized energy savings for the lighting power density installed under the project.

Lighting Power Density Savings Calculations

Location Square

Feet W/Ft²

Hours Expected

kWh Savings

Realized kWh


Rate EUL Lifetime Savings

Code As Built

Wiegand Fitness 110,224 1.00 0.46 4,765 194,681 313,051 1.10 161% 14.69 4,598,719

*Rated life is a weighted average of all post retrofit fixtures in their respective area

ADM staff also verified the installation of the new chiller during the site visit. In order to calculate the savings due to the chiller, ADM used the Equivalent Full Load Hour method. ADM used the following equation to calculate the HVAC energy savings:



EFLH = Equivalent full load hours (831 hours for a University)

kW/ton B = Baseline equipment part load efficiency

kW/ton A = As-Built equipment part load efficiency

The savings associated with the new chiller are shown in the table below:

High Efficiency Chiller Savings Calculations

Number of Units Tons Equipment

Type


Eff Type


Eff Type EFLH CAF

Expected Total kWh

Realized Total kWh


Savings

1 300 Water cooled

centrifugal 0.549 kW/ton 0.339 kW/ton 831 1.000 29,593 52,374 177% 20 1,047,480



Summary of Savings Calculations

Measure Expected Total kWh

Realized Total kWh

Realization Rate

Lighting 194,681 313,051 161%

Chiller 29,593 52,374 177%

Total 224,274 365,425 163%

Results

The project-level realization rate is 163 percent. For the lighting project, the ex post savings do not match the ex ante savings because the hours of use recorded by the loggers were higher than the hours claimed. If these hours had matched the realization rate would be 100 percent.

For the chiller project, the ex post savings are higher mainly due to different EFLH being used in the ex ante calculations. The ex ante calculator, provided with the project documentation, shows that the savings were calculated using much lower EFLH for a 9-month elementary school instead of a university.


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March 2018


UNR Thompson Building 1664 N. Virginia Street, Reno, NV 89557 SPPC-2017Schools_1245167

Executive Summary

Under project SPPC-2017Schools_1245167, UNR Thompson Building received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 78 percent.

Project Description

The customer installed a lighting power density of 0.88 W/ft².


During the M&V visit, ADM staff verified equipment installation and determined the lighting operating schedule. The baseline lighting operating hours were verified through an interview with facility staff. The post-implementation lighting hours were verified through light on-off loggers.

Lighting density energy savings are calculated as:

Where: kWhsavings = Annual energy savings ft.2TotalBuilding = Square Footage of the Facility

(W/ft2)LPDBase = Baseline Lighting Power Density as Established by Local Code Requirement

(W/ft2)LPDInstalled = Verified Lighting Power Density

t = Annual lighting operating hours

HCIF = HVAC interactive factor

The table shown below presents expected and realized energy savings for the lighting power density installed under the project.


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Lighting Power Density Savings Calculations

Location Square

Feet W/Ft²

Hours Expected

kWh Savings

Realized kWh



Code As Built

Thompson Building 18,362 1.20 0.88 2,104 15,998 12,483 1.00 78% 15.00 187,247

Total 15,998 12,483 78% 15.00 187,247

*Rated life is a weighted average of all post retrofit fixtures in their respective area

Results

The project-level realization rate is 78 percent. For the lighting project, the ex post savings are less than the ex ante because the observed weighted hours, 2,104, were lower than the 3,315 submitted. If the hours that were logged matched the whole building hours the realization rate would be 100 percent.




University of Nevada, Reno (UNR) 1664 N. Virginia Street, Reno 89557 SPPC-2017Schools

Executive Summary

This project involved upgrading the chilled water plant pumps and chillers. The kWh realization rate for this project is 171 percent.

Project Description

Cooling at the University of Nevada, Reno was originally provided by multiple cooling plants operating as individual primary loops. This project involves combining the Jot Travis, Mona Mack, Harry Reid, Davidson, Ansari, Chemistry, and Laxalt chilled water plant loops to a single campus wide variable chilled water plant loop. Chilled water loops Jot Travis, Mona Mack, and Harry Reid were demolished, all un-needed pumps were removed, and a new chiller plant was added. The new chiller plant consists of two chillers, two cooling towers, two chilled water pumps with VFDs, and two condenser water pumps. This project has two associated energy efficiency measures:

EEM 1: Pump Upgrade: Removal of 35 constant speed motors and pumps totaling 436.33 HP and replacing them with 7 premium efficiency motors with variable frequency drives totaling 265 HP.

EEM 2: Chiller Upgrade: Three building cooling plants (Jot Travis, Mona Mack, and Harry Reid) were demolished and these buildings were added to the campus loop. The Laxalt Loop and Chemistry loop were combined, so that all buildings are now on a single campus loop. Two new chillers were added to serve the new campus loop.


During the M&V process, ADM staff performed a desk review of the project. For the estimated savings associated with EEM 1 (removal of constant speed pumps) savings occur due to a reduction in total campus pumping energy. For the estimated savings associated with EEM 2 (demolition of the three building cooling plants), savings will occur due to an improvement in chiller plant efficiency.

Savings for the removal of the constant speed pumps was estimated using standard engineering equations and affinity laws. The baseline pump power was assumed to be a constant power consumption whenever the campus loop was active. The baseline pumps are constant speed; therefore, this assumption is reasonable. Baseline pump kW was calculated using nameplate horsepower, the average post installation motor efficiency, and a load factor calculated using the post implementation trending data. Baseline and as-built pump operating schedules were assumed to be consistent, therefore pump operation was determined using post implementation trend data.

Pump and chiller trending data was gathered in the post retrofit period from 5/28/2017 through 10/31/2017. Trending data for the Chemistry, Davidson 1&2, Laxalt, and Modular Chiller Plant 1&2 chilled water plant loops included pump VFD percent, pump supply and return temperatures, chiller amps, and outside air temperature. Savings for the removal and consolidation of the chilled water pumps were estimated by calculating the difference between the energy use of a baseline


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configured chilled water plant and as-built configured chilled water plant. For each loop pump load was calculated using the rated pump flow and the following equation:

Then, average hourly pump load and outside air temperature were regressed to create a system level pump load curve seen below:

For each loop, pump kW was calculated using VFD percent, one-time measurements taken at full load and affinity laws. Then, average hourly pump kW and outside air temperature were regressed to create a system level pump curve seen below:

Using the above curves and TMY3 weather data from the Reno Tahoe International Airport as-built pump kW was calculated.


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Savings for the removal and consolidation of the chillers were estimated by calculating the difference between the energy use of a baseline configured chillers and as-built configured chillers. Chilled water loop tons were calculated using the post trend data used to estimate the pumps operation. Peak load of each baseline chiller was known and assumed to linearly increase from 0 tons at 54.56oF to peak load tons at 94oF. The balance point of 54.56oF was calculated using the post implementation trend data. Baseline Ton vs kW curves for each chiller were obtained from manufacturer design specification sheets or from similar chillers and used to calculate the chiller power using the calculated load profile. The calculated chiller power is the baseline chilled water plant energy use. As-built energy use was calculated using the chiller amp trend data. Calculated chiller kW and outside air temperature were regressed to create a system level curve which can be seen below:

Baseline energy use was calculated using the calculated baseline chiller kW and as-built energy use was calculated using the as-built system curve and TMY3 weather data from the Reno Tahoe International Airport. Savings for the chillers were the difference between the calculated baseline and as-built chiller plant energy use.

Results



(kWh/yr.)

Ex Post Savings

(kWh/yr.)


Lifetime Savings (kWh/yr)

Chilled Water Plant 358,939 613,417 171% 20 12,268,349 Total 358,939 613,417 171% 20 12,268,349

The project-level kWh realization rate is 171 percent.

The high realization rate is due to high ex post chiller savings and differences in regression approaches for the pump and chiller data. For the pumps, the ex ante used a linear regression with estimated max and min pump speeds at respective temperatures. The ex post analysis used the


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trended data to regress pump kW and load against outside air temperatures. For the chillers the ex ante used a regression equation calculated in the programming language R. The ex ante regression could not be provided, thus no appreciable differences between chiller the regression coefficients could be inferred.


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March 2018


Western Nevada College Reynolds Building 2201 W College Pkwy, Carson City, NV 89703 SPPC-2017Schools_1236229

Executive Summary

Under project SPPC-2017Schools_1236229, Western Nevada College Reynolds Building. received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100%.

Project Description

The customer retrofitted:

(2) T8 fixtures to (1) LED fixture (28) T8 fixtures to (7) LED fixtures (3) T8 Fixtures to (1) LED fixture (4) 96” T8 fixtures to (6) LED fixtures







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Measure Quantity (Fixtures) Wattage Pre

Hour s

Post Hours

Expected kWh

Savings

Realized kWh



Old New Old New

T8 to LED 2 1 116 32 443 310 101 101 1.09 100% 15 1,520

T8 to LED 28 7 112 64 443 310 1,365 1,364 1.09 100% 15 20,462

T8 to LED 3 1 89 64 443 310 108 107 1.09 99% 15 1,611

T8 to LED 4 6 110 64 443 310 83 83 1.09 100% 15 1,241

Total 1,657 1,656 100% 15 24,833

Results




March 2018


Empire Elementary School 1260 Monte Rosa Dr., Carson City, NV 89703 SPPC-2017Schools_1239111

Executive Summary

Under project SPPC-2017Schools_1239111, Empire Elementary School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (30) CFL fixtures to (30) LED fixtures; and (39) HPS fixtures to (39) LED fixtures (9) Incandescent fixtures to (9) LED fixtures (872) T8 fixtures to (872) LED fixtures (1) U-Tube fixture to (1) LED fixture.






37











Measure Expected

kWh Savings

Realized kWh

Savings


Savings

Total 119,311 119,313 100% 14.64 1,747,280

Results



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March 2018


Eagle Valley Middle School 4151 E. 5th St., Carson City, NV 89701 SPPC-2017Schools_1239112

Executive Summary

Under project SPPC-2017Schools_1239112, Eagle Valley Middle School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (70) CFL fixtures to (70) LED fixtures; and (2) HPS fixtures to (2) LED fixtures (10) incandescent fixtures to (10) LED fixtures (54) T5 fixtures to (54) LED fixtures (1306) T8 fixtures to (1306) LED fixtures







39










Measure Expected

kWh Savings

Realized kWh

Savings


Savings

Total 180,537 180,517 100% 13.26 2,393,499

Results



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March 2018


Mark Twain Elementary School 2111 Carriage Crest Drive, Carson City, NV 89706 SPPC-2017Schools_1241147

Executive Summary

Under project SPPC-2017Schools_1241147, Mark Twain Elementary School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (67) CFL fixtures to (67) LED fixtures; and (27) LED fixtures to (27) LED fixtures (10) Metal Halide (10) LED fixtures (37) incandescent fixtures to (37) LED fixtures (52) T5 fixtures to (52) LED fixtures. (644) T8 fixtures to (644) LED fixtures.








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Measure Expected

kWh Savings

Realized kWh

Savings


Savings

Total 95,859 95,837 100% 14.28 1,368,900

Results



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March 2018


Carson Middle School 1140 W. King St., Carson City, NV 89703 SPPC-2017Schools_1241148

Executive Summary

Under project SPPC-2017Schools_1241148, Carson Middle School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (47) CFL fixtures to (47) LED fixtures (22) HPS fixtures to (22) LED fixtures (47) LED fixtures to (47) LED fixtures (11) mercury vapor fixtures to (11) LED fixtures (1) metal halide fixture to (1) LED fixture (27) incandescent fixtures to (27) LED fixtures (94) T5 fixtures to (94) LED fixtures (1584) T8 fixtures to (1584) LED fixtures (3) u-tube fixtures to (3) LED fixtures.





43












Measure Expected

kWh Savings

Realized kWh



Total 243,904 243,884 1.10 100% 14.00 3,414,272

Results



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March 2018


Pioneer High School 275 East Park St., Carson City, NV 89706 SPPC-2017Schools_1241155

Executive Summary

Under project SPPC-2017Schools_1241155, Pioneer High School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (59) CFL fixtures to (59) LED fixtures (19) HPS fixtures to (19) LED fixtures (2) LED fixtures to (2) LED fixtures (25) incandescent fixtures to (26) LED fixtures (123) T8 fixtures to (123) LED fixtures.


During the M&V visit, ADM staff verified equipment installation and determined the lighting operating schedule. The baseline lighting operating hours were verified through an interview with facility staff.






45









Measure Expected

kWh Savings

Realized kWh

Savings

82403Reali zation Rate EUL Lifetime

Savings

Total 26,422 26,415 100% 12.4 327,440

Results



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March 2018


Carson High School 1111 N. Saliman Rd., Carson City, NV 89701 SPPC-2017Schools_1241156

Executive Summary

Under project SPPC-2017Schools_1241156, Carson High School received incentives from NV Energy for a lighting project implemented at their facility. The realization rate for this project is 100 percent.

Project Description

The customer retrofitted: (3,495) T8 fixtures to (3,495) LED fixtures. (138) T5 fixtures to (138) LED fixtures (62) Metal Halide fixtures to (62) LED fixtures (42) HPS fixtures to (42) LED fixtures (105) Incandescent fixtures to (105) LED fixtures (185) Various other fixtures to (185) LED fixtures






47











Measure Expected

kWh Savings

Realized kWh



Total 695,809 695,754 1.10 100% 13.10 9,117,586

Results



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APPENDIX B: SAVINGS PER MONTH PER RATE CLASS

2017: Energy Savings (kWh) per Month per Rate Class (First Year) Rate Tariff Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

GS-1 - - - - - - - 276 7,445 11,662 20,979 26,269 66,630

GS-2 - 253 598 535 559 448 381 661 33,240 37,750 35,494 33,730 143,649

GS-2 TOU - - - - - - - - 26,292 65,209 59,762 55,867 207,130

GS-3 - 27,241 33,886 30,933 35,756 30,715 26,708 35,260 37,281 37,425 32,157 28,425 355,788

IS-1 - 1,547 3,654 3,271 3,415 2,739 2,326 2,933 3,330 3,578 3,279 3,023 33,094

OGS-1 - - - - - - - - 10,408 11,183 10,248 9,449 41,288

OGS-2 - - - - - - - 160 34,366 65,310 57,252 51,316 208,405

Total - 29,042 38,138 34,740 39,730 33,902 29,415 39,289 152,360 232,117 219,171 208,079 1,055,983


GS-1 30,025 34,966 50,026 52,747 98,016 95,186 95,213 139,417 119,333 75,257 41,346 26,241 857,770

GS-2 39,681 35,343 39,671 37,539 37,930 29,592 26,504 33,025 36,054 41,056 35,433 34,070 425,898

GS-2 TOU 65,570 58,366 65,463 61,923 62,545 48,905 43,879 54,544 59,538 67,770 58,598 56,423 703,523

GS-3 33,569 30,630 35,344 33,925 38,039 31,095 28,579 37,514 38,225 38,511 31,123 28,697 405,251

IS-1 3,556 3,168 3,555 3,364 3,399 2,652 2,375 2,960 3,231 3,680 3,176 3,053 38,171

OGS-1 11,116 9,901 11,114 10,516 10,626 8,290 7,425 9,252 10,100 11,502 9,926 9,544 119,313

OGS-2 60,525 54,787 62,653 59,863 65,003 52,418 47,823 61,876 64,273 67,162 55,425 51,816 703,623

Total 244,044 227,160 267,826 259,876 315,558 268,138 251,798 338,588 330,754 304,936 235,027 209,844 3,253,549

Appendix B

Page 348 of 359

49


2019: Energy Savings (kWh) per Month per Rate Class (Full-Year) Rate Tariff Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

GS-1 30,287 36,052 48,322 54,539 101,001 91,878 96,549 138,037 122,428 71,856 40,514 26,309 857,770

GS-2 39,778 35,410 38,616 38,773 37,844 28,632 27,172 32,546 37,221 41,120 34,408 34,378 425,898

GS-2 TOU 65,728 58,476 63,741 63,938 62,403 47,337 44,969 53,761 61,443 67,874 56,923 56,928 703,523

GS-3 33,669 30,775 34,349 35,071 38,218 30,045 29,230 37,012 39,425 38,279 30,227 28,951 405,251

IS-1 3,565 3,174 3,461 3,475 3,392 2,566 2,435 2,917 3,336 3,685 3,084 3,081 38,171

OGS-1 11,144 9,920 10,818 10,862 10,602 8,021 7,612 9,118 10,427 11,520 9,639 9,631 119,313

OGS-2 60,694 54,995 60,920 61,867 65,175 50,667 48,945 61,030 66,308 66,916 53,826 52,279 703,623

Total 244,864 228,802 260,227 268,525 318,634 259,146 256,912 334,421 340,588 301,251 228,620 211,557 3,253,549

2020: Energy Savings (kWh) per Month per Rate Class (Full Year and Leap Year) Rate Tariff Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total

GS-1 30,287 38,184 48,322 54,539 101,001 91,878 96,549 138,037 122,428 71,856 40,514 26,309 859,902

GS-2 39,778 37,019 38,616 38,773 37,844 28,632 27,172 32,546 37,221 41,120 34,408 34,378 427,507

GS-2 TOU 65,728 61,127 63,741 63,938 62,403 47,337 44,969 53,761 61,443 67,874 56,923 56,928 706,174

GS-3 33,669 32,222 34,349 35,071 38,218 30,045 29,230 37,012 39,425 38,279 30,227 28,951 406,698

IS-1 3,565 3,318 3,461 3,475 3,392 2,566 2,435 2,917 3,336 3,685 3,084 3,081 38,315

OGS-1 11,144 9,920 10,818 10,862 10,602 8,021 7,612 9,118 10,427 11,520 9,639 9,631 119,313

OGS-2 60,694 54,995 60,920 61,867 65,175 50,667 48,945 61,030 66,308 66,916 53,826 52,279 703,623

Total 244,864 236,785 260,227 268,525 318,634 259,146 256,912 334,421 340,588 301,251 228,620 211,557 3,261,531

Critical Peak Demand Reduction (kW) per Month per Rate Class Rate Tariff Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

GS-1 34.5 34.9 36.8 48.1 271.0 230.1 246.3 340.8 341.9 64.3 38.7 31.8

GS-2 32.6 32.6 26.8 20.0 50.0 47.9 35.9 36.8 53.9 22.4 32.3 32.6

GS-2 TOU 54.8 54.8 45.5 34.5 81.8 78.4 58.9 60.3 88.1 38.4 54.2 54.8

GS-3 27.3 27.3 22.4 17.7 62.8 57.6 49.4 58.0 71.8 21.0 27.3 27.0

IS-1 2.9 2.9 2.4 1.8 4.5 4.3 3.2 3.3 4.8 2.0 2.9 2.9

OGS-1 9.0 9.0 7.3 4.7 14.0 5.6 10.1 10.3 6.1 4.8 9.0 9.0

OGS-2 48.6 48.8 39.3 25.9 87.8 35.1 67.3 73.3 45.3 26.6 48.5 48.5

Total 209.7 210.3 180.4 152.7 571.9 459.0 471.0 582.9 611.9 179.5 212.9 206.6

Appendix B

Page 349 of 359

50

APPENDIX C: CALCULATION METHODOLODY FOR CRITICAL PEAK DEMAND (KW) SAVINGS

C.1. OVERVIEW OF CALCULATION METHODOLOGY FOR KW SAVINGS

This section provides a description of analytical steps employed to determine critical peak demand savings per month per rate class for NV Energy’s 2017 DSM programs. For the 2017 M&V reports, demand (kW) reduction per month per rate class is determined using essentially the same methodology that is used to disaggregate annual energy (kWh) savings into monthly kWh savings per rate class. Please see the following Appendix D for a more detailed description of the methodology for determining energy (kWh) savings per month per rate class.

M&V reports for 2017 DSM programs do not provide critical peak demand (kW) savings for the 2017 calendar year. To do so would provide an incomplete, potentially misleading picture of critical peak kW savings, because each monthly kW reduction value would represent only a fraction of the total population of measures that are installed during the program year. Instead, M&V reports for 2017 DSM programs provide monthly critical peak kW savings values for 2018– 20120 and for subsequent years for the life of the measures installed – which are representative of the whole population of measures installed by each program during the 2017 calendar year. This approach for reporting “typical” (or “full year”) coincident peak kW reduction is the preferred approach for impact evaluations. For this program, Table B-5 in the preceding section provides the full-year, or 2017 calendar-year, values for critical peak kW savings per month and per rate class.

C.2. ANALYTICAL STEPS AT THE MEASURE LEVEL

At the measure level, for every record (i.e., individual measure) in NV Energy’s DSM Central, ADM assigns an appropriate normalized 8,760 energy savings curve. A normalized energy savings curve is comprised of 8,760 hourly fractions summing to exactly 1 (unity).12 For each measure, ADM determines ex post annual kWh savings, which is then multiplied by each of the 8,760 hourly fractions to disaggregate the annual kWh into 8,760 hourly kW bins.

C.3. ANALYTICAL STEPS AT THE PROGRAM LEVEL

To determine program-level demand (kW) reduction for a given hourly kW bin, ADM sums the hourly kW bin across all measures in the program. For example, the program-level kW reduction for the hour ending at 5PM on the 200th day of the year is the sum of kW for all measures in the

12 ADM has developed a library of normalized energy savings curves that are appropriate for Northern and Southern Nevada. Many of the residential energy savings curves were derived from NV Energy’s program-specific data, while others were derived from data provided in the 2008 California Database of Energy Efficiency Resources (2008 DEER).


51

https://unity).12


program during that hour on that day.

To determine monthly critical peak demand (kW) reduction for the program, ADM inspects program-level kW reduction during the one-hour critical peak demand period that is defined for each month of the year. The following table provides the monthly critical peak demand periods for NPC and Sierra, which were determined from ADM’s analysis of peak system load data provided by NV Energy.

Table C-1. Critical Peak Demand Period per Month, NV Energy

Month Critical Peak Period, NPC

Hour Ending at:

Critical Peak Period, Sierra

Hour Ending at:

January 19 19:00 19 19:00

February 19 19:00 19 19:00

March 20 20:00 20 20:00

April 20 20:00 21 21:00

May 17 17:00 17 17:00

June 17 17:00 17 17:00

July 17 17:00 17 17:00

August 17 17:00 17 17:00

September 17 17:00 17 17:00

October 19 19:00 20 20:00

November 19 19:00 19 19:00

December 19 19:00 19 19:00

For example, the critical peak demand period for July is the hour from 16:00:01 or 4:00:01 PM to 17:00:00 or 5:00:00 PM. To determine July’s program-level critical peak kW savings, ADM inspects average hourly kW reduction during 4:00:01 to 5:00:00 PM for every day in July: the highest value represents July’s critical peak kW savings. The same procedure is followed for all months of the year. Summer critical peak demand savings is defined as July’s critical peak kW savings; the rationale for doing so is that historical data reveals that during any given year, NV Energy’s peak system demand in either territory will typically occur during a July day between 4:00:01 to 5:00:00 PM.

To determine the monthly kW reduction per rate class, each program-level monthly critical peak kW savings value is disaggregated into rate class bins by correlating monthly kW savings for a given measure to the measure’s assigned customer rate class as listed in NV Energy’s DSM Central.


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Calculations for energy (kWh) savings – and for demand (kW) reduction – per month per rate class require complex algorithms that are executed in massive Excel files, which are also known as kW guru™ files.

C.4. ANALYSIS OF SYSTEM-LEVEL CRITICAL PEAK DEMAND PERIODS

ADM analyzed NV Energy’s system-level critical peak hours to determine a consistent reference for peak demand impacts of M&V evaluation of all NV Energy programs. ADM’s analysis encompassed Sierra Pacific Power Company (“Sierra”) in the north and Nevada Power Company (“NPC”) in the south.

Hourly system load data from 1985 through 2011 for Sierra and from 1999 through 2011 for NPC was provided by NV Energy. In analyzing the hourly load data, it was determined that the system peaks for Sierra in 1985 were only half of what they have been in the more recent ten-year period. The percentage change in daily system peaks between summer and winter were smaller in the 80’s and 90’s than in the more recent ten-year period. Therefore, ADM concluded that the use of system load data from the recent ten-year period provides the best basis for predicting what to expect during an EEM’s remaining useful life; following that rationale, data prior to the most recent ten years was excluded from ADM’s analysis. In both service territories, the highest system peak occurred in 2007, and system peaks have declined moderately since.

The hourly load data for the recent ten-year period was thoroughly reviewed and except for “spring ahead” hours (when clock times change from Standard Time to Daylight Savings Time), it was determined that the data was consistent and appropriate. The data for “spring ahead” hours are inconsistent, with values given as follows: (1) the value from the preceding hour is used and is an acceptable means of handling the data; and (2) a zero, which is an inaccurate value that would pull down the average. For this analysis, zero values were converted to blanks, and therefore not included in the averaging calculation. Overall this is a minor issue that did not impact ADM’s final analysis of system-level critical peak hours.

ADM determined that system load characteristics vary by season. To accommodate the seasonal variations, the hour of peak system load was determined for each month. ADM concluded that a one-hour peak demand period per month is appropriate.

The final determination of the appropriate peak demand hour per month per territory is provided above; see the table in the preceding section of this appendix. The designated peak demand hour per month per territory was utilized for M&V analyses of energy efficiency programs implemented in 2011, 2012, 2013, and 2014. Subject to ADM’s periodic re-checking of system load data, it is expected that the designated peak demand hour per month per territory will continue to be utilized for subsequent program years.


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This M&V methodology update occurred for the following reason. Compared to the three-hour critical peak demand window used for M&V analyses of 2010 programs, the updated critical peak demand definition (i.e., one hour per month per territory) provides a more accurate determination of energy efficiency programs’ contributions to reducing system peak demand. In other words, the one-hour peak kW reduction will align with the actual hour of system peak.

NV Energy’s hourly system load data demonstrated well-defined peaks during summer and winter months. However, certain transition months – such as May in Northern Nevada – have a nearly identical double peak. It is obvious that specific weather conditions during any given year cause one or the other of the two peaks to predominate. In the final analysis, transition months have far less peak demand than summer months, so a transition month peak hour is essentially insignificant to the determination of the system peak hour, which will typically occur in July and occasionally occur in August (but never in May).

ADM also analyzed hourly system load by various day types. The day type that exhibited highest average demand was selected as the appropriate day type for final determination of peak hour. The day types investigated were (1) All Days, (2) Weekdays, (3) Non-Holiday Weekdays (i.e., Workdays) and (4) Weekend & Holidays. A curve for each month was developed by day type. All days for a given day type were averaged for a given month by hour of the day to develop an average 24-hour load curve. For the north and south the summer peak typically occurs during hour 17, which is the hour that ends at 17:00 (5:00 PM). The greatest summer peak demand is the highest peak demand experienced by both companies.

The analysis determined that of the four days types, Workdays averaged the highest system demand for most hours of the day. Generally, the peak hour calculated from the average Workday curve was identified as the peak hour for the month for the given territory. The peak hours for two transition months in each territory were adjusted to maintain a more consistent set of peak hours. Adjustments were made for May and June for Sierra and April and November for NPC. The selection of the peak hour for these months were based on differences of less than 1 percent in the average demand in MW between the mathematical peak hour and the assigned peak hour.

To validate these decisions ADM also analyzed all-time record peak days and an average of the day from each month that the peak occurred. The second method thus included ten days in the calculation of the average. The results from these analyses supported the average Workday results. Analysis files have not been included in this report due to the large size of spreadsheets.


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APPENDIX D: DETERMINING ENERGY SAVINGS PER MONTH PER RATE CLASS


D.1. APPORTIONMENT OF ANNUAL ENERGY SAVINGS BY RATE CLASS

NV Energy’s DSM programs generally include populations of customers from more than one rate class. NV Energy tracks the rate class for each identifiable customer participating in DSM programs. However, participant information is not known for certain DSM programs, such as the Residential Energy Efficient Lighting program or other “upstream” or “midstream” programs where incentives are provided through contractual arrangements with manufacturers or distributors of the rebated products. For DSM programs for which participant information is not known, ADM collected participant information at the point of sale or conducted customer surveys to identify the proportions of participants that belong to various rate classes.

D.2. APPORTIONMENT OF ANNUAL ENERGY SAVINGS BY MONTH

ADM developed a methodology that utilizes energy savings curves to calculate the portion of annual energy savings that occurs during each month of the year. An energy savings curve describes the temporal nature of energy savings. For example, on any given day the energy savings achieved by an LED exit sign are approximately 1/365 of the verified annual energy savings for that LED exit sign. On the other hand, an efficient air conditioner may not save any energy during the month of January but may achieve 35 percent of its annual energy savings in the month of July alone. ADM constructed appropriate energy savings curves from metered data collected during M&V of NV Energy DSM programs (or other programs if appropriate), customer billing data, calibrated DOE2 simulations and engineering calculations. The energy savings curves were coupled with project implementation dates on a record-by-record basis to produce accurate determinations of the energy savings achieved for each month of the year.

D.3. HIGH LEVEL SUMMARY OF ADM’S CALCULATION METHODOLOGY

ADM developed a methodology that utilizes energy savings curves to calculate the portion of annual energy savings that occurs during each month of the year. An energy savings curve

Monthly energy (kWh) savings for each program were calculated by applying an appropriate hourly or daily energy savings curve to each program participant’s ex post verified energy savings, then aggregating kWh savings for each month. The energy savings curve distributes a participant’s

13 The Public Utilities Commission of Nevada (PUCN) requires NV Energy to report energy (kWh) savings per month and per rate class for each Demand Side Management (DSM) program.


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energy savings over time. Its shape is therefore dependent on not only the measure installed (i.e., lighting vs. HVAC), but also on the building type and sometimes its location.

The overall process by which ADM calculated monthly kWh savings was to (1) download from DSM Central all program tracking data, i.e., ex ante expected kWh savings, measure type, measure completion date, rate class, etc., (2) calculate ex post values per participant, (3) assign an energy savings curve to each participant’s ex post savings to distribute ex post energy savings by rate class over each of the 8,760 hours in a year, and (4) aggregate ex post verified savings for the purpose of presenting savings by month and by rate class.

ADM also calculated first-year kWh savings for each program by combining measure startup date (from DSM Central) with the aforementioned process. A detailed description of the steps involved in tabulating first-year kWh savings is provided in section C.5 below.

D.4. ENERGY SAVINGS CURVES

D.4.1. DEFINITION The phrase ‘energy savings curve’ is used to describe the temporal dependence of energy savings. The curves are typically hourly (1 × 8760 array), daily (1 × 365 array), or monthly (1 × 12 array). The energy savings curves are often normalized such the sum of all array elements is unity. When normalized, each element describes the fraction of annual savings that is expected to occur in a given hour, day, or month.

D.4.2. NOMENCLATURE Note that if the term ‘load shape’ is encountered in the spreadsheets that are used to tally monthly energy savings by program and rate class, one should take it to be the same as ‘energy savings curve’ as described herein. The reason for the usage of the term ‘load shape’ is twofold:


An energy savings curve for a measure may or may not be synchronous with the load curve of the base case technology against which savings are determined. There are energy efficiency measures (EEMs) for which the normalized savings curve is synchronous and proportional to the normalized load shape or curve of the base case technology. Examples of such EEMs include CFLs versus incandescent lights if it is assumed that (1) there are null or negligible interactive effects and (2) pre- and post-retrofit


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usage schedules are identical. If the savings curve for an EEM is synchronous with the base case technology load shape, then the two curves have identical shapes. For other EEMs, the energy savings curve is asynchronous with the load curve of the base case technology. Examples of EEMs with asynchronous savings curves include economizers, occupancy sensors, and control systems. For such measures, the shape of the energy savings curve is different from the shape of the base case technology.

As part of our evaluation effort ADM determines for each EEM whether to use normalized energy savings curves that are either synchronous or asynchronous with the normalized load shape of the base case technology.

D.5. TABULATING MONTHLY ENERGY (KWH) SAVINGS PER RATE CLASS


D.5.1. FIRST-YEAR kWh SAVINGS ‘First-year’ kWh savings are savings that occur during the same calendar year in which a conservation program was implemented. For NV Energy, a program year is the same as a calendar year. Thus ‘first-year’ kWh savings for a measure installed during the 2017 program year are equal to that measure’s kWh savings during the 2017 calendar year.


For each rate class, for each day of 2017, identify all measures that have been implemented (or ‘installed’ or ‘started up’) by the subject day.

For each rate class, for each day of 2017, for all measures that that have been installed by the subject day, multiply the ex post verified ‘typical-year’ annualized kWh savings14 for each measure type by that measure’s daily kWh bin. In other words, multiply the measure-level annual kWh by the measure-level daily bin from the appropriate energy savings curve.

14 ‘Typical-year’ annualized kWh savings is 365 consecutive days of energy savings – usually a full calendar year other than Leap Year – attributed to an energy efficiency measure(s) for which ex post verified kWh savings will occur during a multi-year measure life. For example, an NV Energy conservation measure installed during the 2017 program year (i.e., during the 2017 calendar year) will normally provide kWh savings starting on its date of installation. ‘First-year’ savings is the savings that occurs during the 2017 calendar year. ‘Full-year’ savings is the savings occurring during subsequent calendar years.


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D.5.2. ‘TYPICAL-YEAR’ ENERGY (KWH) SAVINGS ‘Typical-year’ energy (kWh) savings represents 365 consecutive days of energy savings attributed to a measure(s) or program for which ex post verified savings will occur across a multi-year measure life.15





For each rate class, ‘typical-year’ kWh savings is the program-level monthly kWh savings for that rate class summed across all 365 days of any non-Leap Year subsequent to the 2017 calendar year.

15 The distinction between ‘typical year’ and ‘full year’ is that a ‘typical year’ is a 365-day year. A Leap Year is not a ‘typical year’ – instead, a Leap Year is a ‘full year’ that has 366 days. In M&V reports, the kWh savings tables (which show monthly savings per rate class) usually indicate titles such as “First Year 2017”, “Full Year 2018”, “Full Year 2019”, “Full Year 2020 (Leap Year)”. 16 When tallying kWh savings per month per rate class, the use of hourly bins or daily bins is equally correct and accurate. ADM typically uses daily bins (which are created from hourly bins) in our kW guru™ Excel files simply because a workstation processor can complete the billions of computations in a large kW guru™ file relatively faster when the number of computations is based on 365 daily bins instead of 8760 hourly bins per calendar year. Hourly bins in kW guru™ files (i.e., the 8760 hourly bins per ‘typical year’) exist for the following two purposes: 1) they are summed across the 24 hours of each day to create the aforementioned daily bins; and 2) they provide the hourly resolution that enables us to analyze and report critical peak demand (kW) savings per month per rate class for any specified kW-reporting period.


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https://curve.16


For any given program, ‘full-year’ kWh savings for a Leap Year will be marginally higher than ‘full-year’ kWh savings for a ‘typical year’ or non-Leap Year. Thus, we always use a non-Leap Year when we quantify ‘typical-year’ kWh savings.

Following is an example of the determination of daily kWh savings generated by a program. Let’s consider a hypothetical program that targets two energy efficiency (EE) measures: residential lighting and residential cooling. For this hypothetical program, Table D-1 below provides a simple comparison of the measures’ respective:

‘typical-year’ energy savings; daily bin value in its energy savings curve for a specific day – February 1st – of any given

year after the EE measures were installed; 17


In Table D-1 below, the assumption is that 1,000,000 kWh of annual energy savings (‘typical-year’ savings as reported in M&V reports) were achieved through distribution of LEDs and 500,000 kWh of annual (typical-year) energy savings were achieved through implementation of high efficiency air conditioning (AC) measures. Energy (kWh) savings on February 1st are obtained by multiplying ‘typical-year’ kWh savings by the entries corresponding to February 1st

in the respective normalized energy savings curves. In this example, the daily bin for space cooling is zero because no space cooling is expected to occur on February 1st.

Table D-1. Sample calculation of energy savings achieved for a given rate class on February 1 for a hypothetical program targeting residential lighting and space cooling.


EE Measure = “Indoor

Lighting”

EE Measure = “Space

Cooling” ‘Typical-year’ energy savings (annual kWh): 1,000,000 500,000 Feb. 1 daily bin value in each EE measure’s energy savings curve: 0.0030 0.0000 Feb. 1 energy (kWh) savings in a typical year: 3,000 0


D.5.2. LEAP YEAR SAVINGS To account for the extra day in February in Leap Years, one of the following methods is used. Either method produces accurate and very similar ex post verified energy savings determinations for Leap Years.

17 The daily bin value for February 1 represents the February 1 daily fraction of ‘typical-year’ annual energy (kWh) savings.


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