Energy Efficiency Potential and Goals Study for 2015 … Efficiency Potential and Goals Study for 2015 and Beyond . ... Amul Sathe, Semih Oztreves, Carishma Menon, ... Share of 2026

©2016 Navigant Consulting, Inc.

Energy Efficiency Potential and Goals Study for 2015 and Beyond Additional Achievable Energy Efficiency Load Shape Analysis

Prepared for:

California Public Utilities Commission

Submitted by: Navigant Consulting, Inc. 1 Market Street Spear Tower, Suite 1200 San Francisco, CA 94105 415-356-7100 navigant.com Reference No.: 174655 January 29, 2016

Memorandum

1 Market Street Spear Tower #1200 San Francisco, CA 94105 415.356.7100 main navigant.com

To: Mike Jaske, Chris Kavalec, CEC From: Greg Wikler, Amul Sathe, Semih Oztreves, Carishma Menon, Navigant Date: January 29, 2016 Re: AAEE Load Shape Analysis to Support CEC Demand Forecast

Background/Purpose

The California Energy Commission (CEC) demand forecast in the 2015 Integrated Energy Policy Report (IEPR) includes estimated impacts of energy efficiency through 2026. The demand forecast leverages data from the CPUC Potential and Goals (PG) model to develop the forecast of Additional Achievable Energy Efficiency (AAEE) savings. To date, AAEE results have been produced for annual electricity savings (kWh), peak demand savings (kW) and annual gas savings for each IOU service territory. The demand forecast informs procurement and transmission planning for the CPUC, CEC, and CAISO. As adoption of distributed energy resources such as solar photovoltaics and electric vehicles are expected to increase in California over the next decade, the hourly load profile of California’s net demand is expected to change. To better inform how the hourly load profile will change and to better inform the long term demand forecast, CEC sought to develop 8760 hourly load profiles for AAEE savings. Through the CPUC PG study, Navigant was tasked with developing 8760 load savings profiles for AAEE to support the 2015 IEPR forecast. CEC staff applied the 8760 load shapes to the AAEE energy savings results (GWh) to estimate demand savings (MW) in each hour of the year. Navigant supported CEC staff in interpreting the hourly profile of demand savings and comparing it to the PG model results. It is ultimately up to CEC staff to choose what approach to use in determining peak demand savings for the purposes of IEPR. This document summarizes Navigant’s scope, methodology, and results in this effort.

Memorandum to California Energy Commission January 29, 2016

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1. METHODOLOGY Based on the scope of work for this effort, Navigant completed five tasks to develop normalized 8760 load shapes for AAEE savings:

• Task 1 – Select High Impact (Named) End Uses • Task 2 – Source Load Shape Data • Task 3 – Map Load Shape Profiles to PG Study Measures • Task 4 – Shift Normalized Load Shape Data to a Representative Year • Task 5 – Aggregate Measure Load Shapes to End Use Load Shapes

1.1 Task 1: Select High Impact (Named) End Uses Navigant worked with the CEC to prioritize sectors and end uses for this analysis. Navigant first quantified cumulative AAEE savings from 2016 to 2026 by sector and end use. These savings included those from IOU rebate programs as well as Codes and Standards. Per the scope of the AAEE study, the Codes and Standards savings only included those from the list in Appendix A. Navigant then identified high impact end uses to develop a preliminary list of named end uses based on the following criteria:

1. End use savings were more than 10 percent of sector savings. 2. Where end use category did not meet the above criterion, end use savings were more than 3

percent of total savings. Table 1 shows the final named end use list, which covers approximately 87 percent of total cumulative AAEE savings from 2016 to 2026. The remaining AAEE savings were lumped together into a “residual” end use category. A preliminary version of this list included Commercial plug loads (AppPlug) and Commercial refrigeration (ComRefrig). However, Navigant excluded these end uses from the final named end use list as relevant load shape data was not available. Navigant also added Industrial HVAC to the final named end use list as load shape data was readily available (even though it does not meet the above two selection criteria).

Table 1: Named End Use List

Sector Use Category Share of 2026 Cumulative Market Potential

Sector Savings Total Savings

Agricultural MachDr 80.4% 2.7% Agricultural ProcRefrig 18.3% 0.6% Commercial HVAC 7.6% 4.2% Commercial Lighting 51.5% 28.5% Commercial WholeBlg 22.9% 12.7%

Industrial HVAC 7.9% 0.6% Industrial Lighting 39.1% 3.2% Industrial MachDr 48.4% 4.0%

Mining OilGasExtract 100.0% 0.4% Residential AppPlug 33.2% 10.3% Residential HVAC 13.5% 4.2%


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Sector Use Category Share of 2026 Cumulative Market Potential

Sector Savings Total Savings Residential Lighting 32.8% 10.2% Residential WholeBlg 11.1% 3.4%

Street Lighting Streetlight 100.0% 1.7% Total 86.6%

1.2 Task 2: Source Load Shape Data Navigant performed an extensive load shape data search to compile representative 8760 load profiles for measures in the named end use categories. Where possible, Navigant sourced California-specific load shapes. Where California-specific data was not available, Navigant leveraged additional secondary resources to fill gaps using load shapes from other states. This was done only if the measure in question was not weather-sensitive. Navigant compiled a total of 126 load shapes, each representative of a unique sector-measure type combination. Navigant normalized these load shapes as applicable. Table B1 in Appendix B summarizes these load shapes by sector, measure type and source. Navigant initially proposed to develop a single statewide load shape for each named end use category. However, the final deliverable is at the IOU level as the majority of sourced load shapes were available by IOU and weather sensitive measures can have different load shapes for different IOUs.

1.2.1 Residential Measures California’s Database for Energy Efficient Resources (DEER) contains 12 normalized residential load shapes by IOU. Of these, 7 were used in this analysis. These load shapes represent approximately 50 percent of measures that make up AAEE savings in the residential sector. For remaining measures such as exterior lighting and home electronics, Navigant sourced load shape data from OpenEI1, a public database containing hourly residential load profiles by end use and climate zone. Navigant studied relevant end use profiles across a number of climate zones in California, and concluded that these profiles did not vary by climate zone as the end uses were not weather-sensitive. Thus, Navigant simply sourced a common load profile for these measures. For variable speed pool pump measures, Navigant leveraged pool pump load data from a Southwestern utility. For whole building measures, Navigant sourced each IOU’s publicly available 8760 residential load data.

1.2.2 Commercial Measures California’s DEER database contains 7 normalized non-residential load shapes by IOU. These load shapes represent all measures that make up AAEE savings in the commercial sector except whole building measures. For most whole building measures, Navigant once again sourced each IOU’s publicly available 8760 commercial load data. Where more than one load shape was available for the commercial sector, Navigant chose load data for customers with medium demand.

1.2.3 Agricultural, Industrial, Mining and Street Lighting (AIMS) Measures For Agricultural measures, Navigant sourced PG&E and SCE’s publicly available 8760 agricultural load data. Pumping-specific load shapes were sourced for Machine Drive (MachDr) measures. In the industrial 1 8760 hourly load profile data for residential customers at the end-use level available at: http://en.openei.org/datasets/dataset/commercial-and-residential-hourly-load-profiles-for-all-tmy3-locations-in-the-united-states

http://en.openei.org/datasets/dataset/commercial-and-residential-hourly-load-profiles-for-all-tmy3-locations-in-the-united-states




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sector, HVAC was the only end use that was weather-sensitive. Thus, Navigant leveraged DEER’s non-residential HVAC load shapes for Industrial HVAC measures. For Lighting and Machine Drive measures, Navigant used industrial lighting and manufacturing load data from a Northeastern and Canadian utility respectively. For Mining measures, Navigant sourced PG&E’s publicly available 8760 load data for oil and gas extraction customers. Finally, for Street Lighting measures, Navigant sourced PG&E and SCE’s publicly available 8760 street lighting load data.

1.3 Task 3: Map Load Shape Profiles to PG Study Measures The mapping of load shape profiles to PG Study measures was a key step to producing meaningful and realistic load shapes for CEC’s load forecasting. Navigant followed a two-step approach to determine the most appropriate load shape profile for each measure. First, Navigant created a list of all the available load shapes that are applicable to this study and held a working session with key staff from the PG Study who played key roles in creating and characterizing the measures of the PG Study to determine the most appropriate mapping of load shape profiles to PG Study measures including IOU rebate program measures and those from Codes and Standards. Then, Navigant cross checked its experts’ mapping to IOU compliance filings, which contain IOU mapping of load shape profiles to their measures for the past years. Based on its comparison, Navigant remapped some of the measures where the staff wasn’t sure of the mapping and finalized the mapping.

1.4 Task 4: Shift Normalized Load Shape Data to a Representative Year In order to develop end use load shapes, the weekdays and weekends of different load shapes from different data sources had to match each other. Therefore, Navigant needed to shift these load shape profiles to a representative year. Navigant selected 2013 as the representative year since most of the sources already had load shape profiles from 2013 and it was also not a leap-year. Navigant did not need to shift load shapes sourced from PG&E and SCE as these were from 2013. Similarly Navigant did not need to shift the DEER load shapes as these were representative of 1991, a year in which weekdays and weekends aligned exactly with 2013’s. SDG&E load shape data was only available for the most recent year, so Navigant shifted this load data to 2013. Navigant also had to shift load shapes sourced from OpenEI, as these were developed for a common year in which January 1 is a Sunday. Load shapes sourced from the Northeastern and Canadian utilities were also from 2013, so Navigant once again did not make any adjustments. The load shape from the Southwestern utility was from 2014, so Navigant shifted it to 2013. All the load shapes provided in the deliverable are for 2013. CEC can apply these load shapes to savings in any given year by shifting the load shapes as needed.

1.5 Task 5: Aggregate Measure Load Shapes to Named End Use Load Shapes

Navigant developed an aggregate load shape for each named end use category identified in Table 1, for each IOU, by weighting the individual, normalized load shapes mapped to the measures within each sector by 2026 cumulative IOU rebate program as well as Codes and Standards savings This weighted averaging resulted in 42 unique load shapes for the named end use category. For the residential and commercial residual load shapes, Navigant duplicated the residential and commercial whole building load shapes from the named end use category. For the industrial and agricultural residual load shapes, Navigant simply sourced and normalized each IOU’s publicly available 8760 large commercial/industrial and agricultural load data respectively. This resulted in 12 unique load shapes for the residual category. In all, a total of 54 load shapes are being provided to the CEC.


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2. RESULTS – 8760 NORMALIZED LOADSHAPES

2.1 Summary of Loadshapes An excel spreadsheet that accompanies this memo contains a repository of the 54 load shapes, which together represent three IOUs, six sectors, eight named end uses and four residuals. The spreadsheet includes a load shape viewer tool that the CEC can use to dynamically view each load shape by IOU and sector-end use combination any given time period during the representative year. The following paragraphs discuss the final 8760 load shapes for PG&E’s named end uses by sector to provide some insight into the outcome of this analysis. All the trends discussed in this section for PG&E also apply to SCE and SDG&E. Graphs similar to those shown in this section for PG&E can be found for SCE and SDG&E in Appendix C. Figure 1 shows the named end use 8760 load shapes for the residential sector. In general, Navigant observed limited variance between weekdays and weekends for all the named end use load shapes in the residential sector. Navigant found that savings from HVAC measures have the highest impact on demand changes throughout the year, whereas lighting and Plug Loads (AppPlug) stay generally flat. This observation is especially important as it proves that the same amount of annual energy savings for two different end uses have significantly different grid impacts. Comparing HVAC to Plug Loads and lighting in this case reveals that the percent of annual energy savings attributable to the peak demand reduction would be significantly higher for HVAC. This is assuming that the system peak occurs during the summer.

Figure 1: PG&E 8760 Load Shapes for Named End-uses in the Residential Sector

Navigant plotted a similar graph for PG&E’s commercial sector, as shown in Figure 2. In general, Navigant observed some variance between weekdays and weekends for all the named end use load shapes in the commercial sector. Similar to the residential sector, the HVAC load shape significantly spikes during the


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summer. However, in commercial sector the effect of this spike during summer months seems to be relatively lower compared to residential sector. This observation shows that the same amount of annual energy savings from the same end use, such as HVAC, in two different sectors have different grid impacts. Comparing residential to commercial HVAC in this case reveals that the percent of annual energy savings attributable to the peak demand reduction would be significantly higher for residential HVAC, once again assuming that the system peak occurs during the summer.

Figure 2: PG&E 8760 Load Shapes for Named End-uses in the Commercial Sector

Navigant plotted the named end use load shapes for AIMS sectors, as shown in Figure 3. This figure shows more seasonally variable load shapes compared to the Residential and Commercial sectors. In general, savings for most AIMS end uses are incurred during the summer months. Exceptions to this are Industrial Lighting and Machine Drive profiles, which are generally flat, and the street lighting profile, which shows no seasonal difference.


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Figure 3: PG&E 8760 Load Shapes for Named End-uses in the AIMS Sectors

2.2 Important Notes Navigant notes the following caveats to these loadshapes analysis:

• Several load shapes sources from IOU published data needed to be cleaned as several data anomalies were observed:

o The pumping load shape mapped to PG&E’s Agricultural Machine Drive measures showed a linear increase in meter readings during the month of January. Navigant assumed this was the result of faulty meters or logging software, and replaced this data with data from February. Navigant ensured that days between the months aligned, and renormalized the load shape.

o Meter readings for PG&E’s residential and commercial load dropped to zero on March 10th at 4AM. Navigant assumed this was the result of either an outage or logging software failure, and replaced this data with data from 3AM on the same day before renormalizing the load shape.

o PG&E’s 8760 load data for oil and gas extraction customers shows an abnormal spike in December. Navigant did not attempt to correct for this abnormality as the spike does not occur during peak summer months.

o SCE’s 8760 residential and commercial load data show abnormal spikes in May. Navigant assumed that this was the result of a heatwave, and did not attempt to correct for this abnormality as the spikes do not occur during peak summer months.

• The Industrial Machine Drive load shape was sourced from a Canadian utility. Navigant recognizes

that holidays in the United States and Canada are different, but did not correct for this as the load shape is generally flat, which should not compromise the approximation of peak demand savings for this end use.


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• For some measures (such as equipment controls) the actual load shape of savings can be different than that of the end-use or sector consumption. Such load shapes were unavailable for this analysis. Thus the load shape for the end use or sector are used as an approximation.


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3. PEAK DEMAND SAVINGS RESULTS COMPARISON This section provides a comparison of the demand savings results calculated by using the 8760 approach to the demand savings results from the draft AAEE analysis released on October 19th, 2015. The 8760 demand savings are calculated using six different “scenarios” while the draft AAEE analysis released in October 2015 was based on the PG Model. The goal of this section is to highlight pros and cons of each approach and support the CEC to determine the most appropriate approach and results to inform the 2015 IPER demand forecast. The six scenarios show demand savings resulting from energy efficiency in the year 2026 using 2013, 2014, and 2015 peak demand dates for CAISO system load and for individual IOU system loads. Table 2 provides the total demand savings coincident with the CAISO system peak and also the demand savings coincident with each of the IOU system peak alongside with the demand savings from the PG Model. Tables in Appendix D show the demand savings results disaggregated by sector for each of the IOUs. Table 3 shows percent difference between the PG Model results and the 8760 results for different scenarios. Table 4 shows the peak demand days for CAISO system load and for individual IOU system loads. Navigant calculated the demand savings for each scenario by taking the average of demand savings from a total of nine hours that consist of hours between 2pm-5pm during the peak demand day and the day before and the day after the peak demand day not including weekends or holidays. This approach is consistent with the approach DEER uses to calculate peak demand savings2.

Table 2: Peak Demand Savings Comparison by IOU for Six Scenarios (MW in year 2026)

Service Territory

PG Model Results

CAISO coincident

peak period

based on 2013

Individual IOU

coincident peak

period based on

2013

CAISO coincident

peak period

based on 2014

Individual IOU

coincident peak

period based on

2014

CAISO coincident

peak period

based on 2015

Individual IOU

coincident peak

period based on

2015

PG&E 1,811 1,563 1,580 1,634 1,700 1,518 1,892 SCE 2,128 1,732 2,073 1,800 1,804 1,790 1,813

SDG&E 451 341 417 392 392 409 406

Total 4,390 3,636 4,070 3,826 3,896 3,717 4,112

2 To determine the electric demand impacts of measures, DEER uses the average kWh reduction over a 9-hour window. The nine-hour window is from 2p.m. to 5 p.m. over a three-day “heat wave” that is determined for each climate zone. The three-day demand periods for the new (2009) weather data is chosen based on these criteria:

• occurs between June 1st and September 30th, • does not include weekdays or holidays, • has the highest value for

o average temperature over the three-day period, o the average temperature from noon to 6 p.m. over the three-day period, o the peak temperature over the three-day period.

Source: Codes and Standards Update for the 2013-14 Cycle. DEER, 2014: http://deeresources.com/files/DEER2013codeUpdate/download/DEER2014UpdateDocumentation_2-12-2014.pdf


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Table 3: Peak Demand Savings Comparison to PG Model (% Difference in 2026)

Service Territory

CAISO coincident

peak period

based on 2013

Individual IOU

coincident peak period

based on 2013

CAISO coincident

peak period

based on 2014

Individual IOU


based on 2014

CAISO coincident

peak period

based on 2015

Individual IOU


based on 2015

PG&E -14% -13% -10% -6% -16% 4% SCE -19% -3% -15% -15% -16% -15%

SDG&E -24% -8% -13% -13% -9% -10%

Total -17% -7% -13% -11% -15% -6%

Table 4: Histrocial System Peak Dates for CAISO and by IOU

2013 2014 2015

CAISO June 28 September 15 September 10 PG&E July 3 July 25 August 17 SCE September 5 September 15 September 8

SDG&E August 30 September 16 September 9

As seen in Table 3, the 8760 approach consistently results in lower peak demand savings compared to PG Model savings regardless of the scenario. Navigant investigated the drivers for this discrepancy in the results and found three possible reasons:

• DEER calculates the demand savings by climate zone based on each climate zone’s own peak

period determined by local weather data , which when rolled up to sector or IOU level overestimates the total demand savings as they are necessarily non-coincident with each other.

• C&S savings in AAEE are from those C&S that are not yet evaluated, many do not have CASE studies. Often the CEC and DOE analysis focuses on energy savings rather than demand saving so most demand savings from C&S in AAEE are estimated. These estimates are likely using coincident peak to energy ratios but the sources are unknown. CEC staff has observed in the past that the PG model has higher peak to energy ratios for C&S compared to IOU programs.

o Around 85% of the savings in the residential sector in 2026 are from C&S, while this number is 20% for commercial. This means residential demand savings are driven by C&S while commercial demand savings are driven by IOU program savings.

o The combined factors of C&S demand savings being overestimated and C&S accounting for the majority of residential savings explains the discrepancy in the residential sector between the PG Model estimates and the 8760 approach. Commercial sector savings are much better aligned since they are dominated by program savings to begin with.

• The whole building and residual loadshapes are based on 2013 actual demand data as discussed in the previous sections (see Table B1), and it is known that 2013 was an unusual year with a June system peak. This potentially impacted the distribution of kWh savings across the year given the methodology used for the 8760 approach, which likely resulted in lower demand savings estimates for these end uses for the June 1st to September 30th time period. However,


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these loadshapes only applied to approximately 20% of demand savings, so the final impact may not a major driver.

Figure 5 illustrates the demand savings by sector and the total demand savings for PG&E. The trends discussed in this section for PG&E also apply to SCE and SDG&E. Charts similar to those shown in this section for PG&E can be found for SCE and SDG&E in Appendix E. Demand savings named as “8760” show the hourly savings assumptions for hours between 2pm-5pm not including weekends and holidays and from July 1st to September 30th, which is consistent with the DEER approach except the CZ aspect. The horizontal flat lines show the peak results from the PG Model as a comparison. The shaded vertical areas illustrates the peak demand periods that were used to calculate the peak demand savings for each scenario. In aggregate, the two approaches result in relatively comparable demand savings. At multiple hours during the peak times in the summer months, the maximum demand savings resulting from the 8760 approach are well aligned with the PG Model demand savings estimates. However, during other peak hours the 8760 approach results fall short of the PG Model. For PG&E, using PG&E’s system peak period to estimate peak savings results in estimate closer to the PG model than using the CAISO system peak period. This is expected as the PG Model bases demand savings for the hottest time of the year in the selected service territory, not necessarily the entire state.

Figure 4: PG&E Hourly Demand Savings Comparison by Sector3

The two approaches show different estimates by sector but in total they are well in the range. The PG Model approach provides little flexibility and less transparency in the demand savings assumptions and the addition of savings from different times of the year should not be used in the demand forecast for

3 CAISO 2013 peak demand time (June 28) is not shown on the chart as it falls outside of the time range shown on the chart.


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capacity planning purposes. The 8760 approach provides lots of flexibility and high transparency in the assumptions while likely not favoring C&S savings over program savings in terms of demand savings.


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4. RECOMMENDED NEXT STEPS Navigant identified larger discrepancies in few specific end-uses. The 8760 approach consistently underestimates the residential HVAC and Whole Building savings compared to PG Model approach. On the other hand, it consistently overestimates the commercial ComRefrig and Lighting, and the industrial HVAC end uses. Sector and end-use level analysis will likely uncover useful insights for further refinement of the demand savings forecast. This will require time and budget that was outside of this scope of work. Despite these discrepancies, Navigant suggests using the 8760 approach for IEPR as the results of the 8760 approach are more rigorous than the PG Model when it comes to peak demand savings. CEC staff should consider which year is most appropriate to define the peak period and if CAISO peak or IOU peak should be used. Regardless of what is picked, Navigant generally observes that the 8760 approach will produce a more conservative peak demand reduction than the PG Model.


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APPENDIX A: LIST OF CODES AND STANDARDS INCLUDED IN AAEE

• Fed Appliance: Commercial Refrigeration (Cycle 2)

• Fed Appliance: External Power Supplies

• Fed Appliance: General Service Fluorescent Lamps

• Fed Appliance: Metal Halide Lamp Fixtures

• Fed Appliance: Residential Central AC and Heat Pumps

• Fed Appliance: Residential Clothes Dryers

• Fed Appliance: Residential Clothes Washers (Front Loading)

• Fed Appliance: Residential Electric Storage Water Heaters

• Fed Appliance: Residential Gas Instant Water Heaters

• Fed Appliance: Residential Oil Fired Water Heaters

• Fed Appliance: Small Electric Motors

• Fed Appliance: Walk-in Coolers & Freezers

• Future T-20: Air Filter Labeling

• Future T-20: Computers - Tier 1 | Desktops

• Future T-20: Computers - Tier 1 | Notebooks

• Future T-20: Dimming Ballasts

• Future T-20: Electronic Displays

• Future T-20: Game Consoles (Tier 1)

• Future T-20: Game Consoles (Tier 2)

• Future T-20: Pool Pumps & Spas

• Future T-20: Set Top Boxes (Tier 1)

• Future T-20: Small Diameter Directional Lamps

• Future T-20: Small Network Equipment

• Future T-20: Water Meters

• 2016 T-24 - Multi-family NC

• 2016 T-24 - Nonres NC

• 2016 T-24 - Single family NC

• 2019 T-24 - Multi-family NC

• 2019 T-24 - Nonres NC

• 2019 T-24 - Single family NC

• 2022 T-24 - Nonres NC


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APPENDIX B: SUMMARY OF SOURCED LOAD SHAPE DATA FOR NAMED END USE CATEGORY

Table B1: Load Shape Data Sources by Sector and Measure Type

Sector Measures/End Uses Source Year of Data

Residential

Clothes Washers

DEER 2011(Res)

Res_ClothesDishWasher

1991

Dish Washers Res_ClothesDishWasher

HVAC

HVAC_Eff_AC HVAC_Duct_Sealing HVAC_Refrig_Charge

RefgFrzr_HighEff RefgFrzr_Recyc-Conditioned

Indoor Lighting Indoor_CFL_Ltg

Outdoor Lighting OpenEI

Appl:InteriorEquipment:Electricity Common Year

1 Home Electronics General:ExteriorLights:Electri

city Variable Speed

Pool Pump Southwestern utility 2014

Whole Building California IOU load data for residential customers

E1 (PG&E) DOM - S/M (SCE)

2013

Residential (SDG&E) 2014 – 2015

Residual Aggregated Residential Whole Building load shape developed

for this analysis

PG&E_Residential_WholeBlg SCE_Residential_WholeBlg

SDG&E_Residential_WholeBlg

2013

Commercial

HVAC DEER 2011(Non-Res)

HVAC_Chillers HVAC_Refrig_Charge

HVAC_Split-Package_AC HVAC_Duct_Sealing

HVAC_Split-Package_HP 1991

Lighting Indoor_CFL_Ltg

Indoor_Non-CFL_Ltg

Whole Building California IOU load data for commercial customers

A10 (PG&E) GS-2 (SCE)

2013

Med Com/Ind (SDG&E) 2014 – 2015

Residual 2013


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Sector Measures/End Uses Source Year of Data

Aggregated Commercial Whole Building load shape developed

for this analysis

PG&E_Commercial_WholeBlg

SCE_Commercial_WholeBlg SDG&E_Commercial_WholeB

lg

2014 – 2015

Industrial

HVAC DEER 2011(Non-Res) HVAC_Split-Package_AC 1991 Lighting Northeastern utility

2013 Machine Drives Canadian utility

Residual California IOU load data for industrial customers

E19 (PG&E) GS-2 (SCE)

2013

Lrg Com/Ind (SDG&E) 2014 - 2015

Agricultural

Machine Drives PG&E and SCE load data for

agricultural customers

AG1B per hp (PG&E) PA1 (SCE) 2

2013

Refrigeration AG1B per kW (PG&E)

PA2 (SCE) 2 2013

Residual California IOU load data for agricultural customers

AG1B per hp (PG&E) PA1 (SCE)

2013

Agriculture (SDG&E) 2014 - 2015

Mining Oil and Gas Extraction

PG&E load data oil and gas extraction customers

AG5B 3 LS1 (PG&E)

2013

Street Lighting Street Lighting PG&E and SCE load data for street lights St-Ltng (SCE) 2 2013

1 January 1 is a Sunday 2 SCE’s load shape was used for SDG&E 3 PG&E’s load shape was used for SCE and SDG&E


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APPENDIX C: SCE AND SDG&E NAMED END USE LOAD PROFILES BY SECTOR

Figure C1: SCE 8760 Load Shapes for Named End-uses in Residential Sector

Figure C2: SCE 8760 Load Shapes for Named End-uses in the Commercial Sector


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Figure C3: SCE 8760 Load Shapes for Named End-uses in the AIMS Sectors

Figure C4: SDG&E 8760 Load Shapes for Named End-uses in the Residential Sector


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Figure C5: SDG&E 8760 Load Shapes for Named End-uses in the Commercial Sector

Figure C6: SDG&E 8760 Load Shapes for Named End-uses in the AIMS Sectors


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APPENDIX D: PEAK DEMAND SAVINGS BY SECTOR AND BY UTILITY

Table D1: PG&E Demand Savings Comparison by Sector for Six Scenarios

Sector PG Model Results

CAISO coincident

peak period based on

2013

Individual IOU


based on 2013

CAISO coincident


2014

Individual IOU


based on 2014

CAISO coincident


2015

Individual IOU


based on 2015

Residential 982 485 492 500 616 384 681 Commercial 753 892 892 947 878 967 1,005

Industrial 46 81 89 88 91 93 95 Mining 2 3 3 2 3 3 3

Agricultural 28 102 103 95 111 70 107 Street Lighting 0 1 1 1 1 1 1

Totals 1,811 1,563 1,580 1,634 1,700 1,518 1,892

Table D2: SCE Demand Savings Comparison by Sector for Six Scenarios


CAISO coincident


2013

Individual IOU


based on 2013

CAISO coincident


2014

Individual IOU


based on 2014

CAISO coincident


2015

Individual IOU


based on 2015

Residential 1,046 543 732 522 516 508 532 Commercial 1,014 1,060 1,203 1,146 1,156 1,146 1,144



Totals 2,128 1,732 2,073 1,800 1,804 1,790 1,813


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Table D3: SDG&E Demand Savings Comparison by Sector for Six Scenarios


CAISO coincident

peak period

based on 2013

Individual IOU

coincident peak

period based on

2013

CAISO coincident

peak period

based on 2014

Individual IOU

coincident peak

period based on

2014

CAISO coincident

peak period

based on 2015

Individual IOU

coincident peak

period based on

2015 Residential 239 116 155 142 142 139 136 Commercial 204 206 243 231 231 250 251



Totals 451 341 417 392 392 409 406


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APPENDIX E: HOURLY DEMAND SAVINGS COMPARISON BY SECTOR AND BY UTILITY

Figure E1: SCE Hourly Demand Savings Comparison by Sector4



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Figure E2: SDG&E Hourly Demand Savings Comparison by Sector5



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Figure E2: PG&E Hourly Demand Savings Comparison by Sector6


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Energy Efficiency Potential and Goals Study for 2015 … Efficiency Potential and Goals Study for 2015 and Beyond . ... Amul Sathe, Semih Oztreves, Carishma Menon, ... Share of 2026