APPENDIX B: PORTFOLIO OPTIMIZATION MODEL1. Portfolio Optimization Model Methodology The 2017 IRP uses a Portfolio Optimizer tool developed in-house to solve for the least cost portfolio

Snohomish County PUD – 2017 Integrated Resource Plan Appendix B| B-1

APPENDIX B: PORTFOLIO OPTIMIZATION

MODEL

PUBLIC UTILITY DISTRICT #1 OF SNOHOMISH COUNTY

Prepared by

Generation, Power, Rates, and Transmission Management Division


PORTFOLIO OPTIMIZATION MODEL

Appendix B provides further information on the Portfolio Optimization model used in the IRP,

as well as the substantive assumptions used to inform inputs that the model uses. The content is

organized as follows:

1. Methodology

2. Model Inputs:

a. Supply-Side Assumptions

b. Demand Response Resource Assumptions

1. Portfolio Optimization Model Methodology

The 2017 IRP uses a Portfolio Optimizer tool developed in-house to solve for the least cost

portfolio that satisfies PUD-defined Planning Standards in a given scenario. The Optimizer

requires as inputs, the outputs of the Probabilistic Load Resource Balance Model1. Specifically,

the Probabilistic Load Resource Balance Model outputs that describe the performance of existing

resources within the existing portfolio, customer load, and the resulting possible Load Resource

Balance positions at different likelihoods and points in time across the 2018-2037 study period.

The Portfolio Optimizer model uses those inputs and combines it with information about

potential demand-side and supply-side resources that could be added to a portfolio and then

calculates the following attributes of possible new portfolio combinations:

The ability to meet PUD Planning standards for resource adequacy on an energy and capacity

basis;

The ability to meet Renewable Portfolio Standard (RPS) compliance obligations under the

target compliance methodology;

The incremental carbon dioxide air emissions associated with generation from new supply-

side portfolio additions; and

1 Described in detail in Appendix A


The cost of adding new resources, the value of any resulting surplus energy, and the Net

Present Value of the resulting portfolio.

The purpose of this section is to outline the Portfolio Optimizer tool functions, the assumptions it

incorporates, and its role in the analytical framework that resulted in the PUD’s Long Term

Resource Strategy. As described in other portions of this IRP document, the general analytical

process for determining the PUD’s Long Term Resource Strategy, is described by Figure 1.

Figure B-1

Application of Planning Standards in Portfolio Optimization.

The Portfolio Optimizer meets planning standards in two ways: it imports data from the

Probabilistic Load Resource Balance specific to the Planning Standards, and it creates rules that

each portfolio must satisfy in order to be considered a “valid” portfolio that ultimately gets

ranked by Net Present Value to determine the lowest Net Present Value (or least cost) Portfolio.

In some scenarios, the existing portfolio of resources are sufficient to meet some Planning

Standards in some time periods without any additional resources for that time period. An

example of this is Renewable Portfolio Standard compliance, which is satisfied in the first


several years of most scenarios by the renewable generation attributes of resources in the existing

portfolio.

In other time periods, additional resources are needed to augment the existing resources in the

portfolio in order to meet Planning Standards. For example, in all scenarios, there is a need for

some form of winter peaking capacity resource beyond what the portfolio of existing resources

contains. The Optimizer model requires a potential portfolio to add enough resources to satisfy

all Planning Standards in all time periods, or be deemed an invalid portfolio. Figure B-2 shows

how each of the planning standards are incorporated into the Portfolio Optimizer.

Figure B-2

Planning Standard Probabilistic Model Data Inputs Optimization Constraints

(Model Rules)

Annual Resource Adequacy Annual LRB @P50 ≥ 0aMW after

any needed portfolio additions.

No forecast market purchases

allowed to meet standard

Annual LRB@P50 of the existing

portfolio before new additions for

all years 2018-2037

Rule will mark as “invalid” any

portfolios with Annual LRB’s less

than 0 aMW in any year, after

portfolio additions.

Monthly HLH Resource

Adequacy Monthly HLH LRB@P5 ≥ 0aMW

after any needed portfolio

additions,

Forecast Market Purchases to

Address Monthly HLH LRB

Deficit ≤100aMW

Monthly HLH LRB@P5 of the

existing portfolio before new

additions for 4 indicator months

per year (Dec, March, April,

August) for all years (2018-2037)


portfolios with Monthly HLH

LRB’s less than -100 aMW in any

month and any year, after portfolio

additions (forecast market

purchases allowed to cover LRB

deficits between 0aMW to -

100aMW).

Monthly Peak Week HLH

Resource Adequacy Monthly

Peak Week LRB@P5≥ 0aMW

after any needed portfolio

additions,

Forecast Market Purchases to

Address Monthly Peak Week

HLH LRB Deficit ≤200aMW

Monthly Peak Week LRB@P5 of

the existing portfolio before new

additions for the peak week

period of 4 indicator months per

year (Dec, March, April, August)

for all years (2018-2037)


portfolios with Monthly Peak

Week HLH LRB’s less than -200

aMW in any month and any year,

after portfolio additions, (forecast

market purchases allowed to cover

LRB deficits between 0aMW to -

200aMW).

RPS Compliant Portfolio

“Valid” portfolio’s will meet RPS

compliance obligations under the

Target methodology by some

combination of procuring

renewable resources with REC

generating attributes or

purchasing unbundled RECs, to

augment RECs produced by the

existing portfolio.

Existing annual portfolio

production @P50 is imported

from the Probabilistic LRB model

for eligible resources. Annual

Load @P50 is also imported from

the LRB model to set the annual

RPS target in MWh for each year

2018-2037.

Rule will not mark as “invalid” any

portfolios with insufficient RECs

(either through portfolio resources

or unbundled REC purchases) to

meet the RPS target in any given

year 2018-2037.


How the Portfolio Optimizer Works.

Broadly speaking, the portfolio optimizer works from a starting point of the Load Resource

Balance of the existing portfolio at a given performance likelihood (ex: Monthly HLH LRB @

P5), that then adds and evaluates different combinations of possible resources to satisfy Planning

Standards. The Optimizer calculates the incremental cost of each resulting portfolio, the

incremental carbon emissions associated with each portfolio, and the optimal combination of

renewable resources and unbundled RECs to meet RPS requirements.

For example, a given scenario may have an annual energy need of 15aMW in the years 2032-

2037 that needs be fulfilled in order to satisfy the planning standard that the Annual Load

Resource balance be equal to or greater than 0aMW. When the optimization runs, the Optimizer

will cycle through all of the possible resources from the resource menu, including both supply

and demand side resources, and evaluate them at different possible delivery dates (a 15aMW

biomass delivered in the year 2027 for example). As the optimizer evaluates different resources

at different delivery dates, it determines whether the planning standard has been met, and what

the NPV of the resulting portfolio will be. In the given example, it would continue to optimize by

trying different combinations of resources until it has found the most cost-effective way to meet

the 2032-2037 annual energy need of 15aMW. In the actual model, all planning standards and

portfolio needs are simultaneously considered across the 2018-2037 study period.

Figure 2 shows an example of how the NPV of the portfolio is improved over the course of

optimization by finding incrementally better combinations to address portfolio needs. In the case

of the pictured optimization, the original NPV of the portfolio was $3.15 billion, but was reduced

to $2.37 billion after the optimizer had cycled through almost 50,000 iterations of possible

portfolio combinations.


Figure B-3

An internal algorithm helps the Optimizer efficiently select portfolio components based on

previous iterations and their success in helping a portfolio either lower portfolio costs or satisfy a

component of planning standard requirements. Each Optimization typically requires tens of

thousands of iterations to solve, and in most cases more than 100,000 iterations are required. The

optimizer algorithm helps this process to be completed in a timely fashion – most scenarios can

be solved with an optimal portfolio in around 2 hours. Staff considers an optimization complete

when after an optimization run has found a solution, a subsequent run of the same optimization

cannot find a solution with a lower NPV.

Model assumptions that affect calculated Portfolio Resource Adequacy

As the Portfolio Optimizer runs, one of its primary functions is to determine whether a potential

portfolio satisfies resource adequacy planning standards. These planning standards for annual

energy, Monthly HLH capacity, and Monthly Peak Week HLH capacity represent the PUD’s

maximum willingness to be short the resources needed to serve demand, and be exposed to


wholesale market prices to serve demand over the study period. These planning standards are

given in Figure B-2. The determination of portfolio adequacy is based in large part on

assumptions of existing resource generation, and assumptions of generation of potential resource

additions.

Existing resource generation is based upon simulations done in the Probabilistic Load Resource

Balance Model for each scenario. This model produces estimates for load, resource generation,

and the resulting portfolio position as a variety of likelihood’s and time periods. Embedded in

that model are the following assumptions:

1. All contracted resources with contracts set to expire over the study period, will expire as

specified in the contract without extension. The associated resource generation does not

contribute to the PUD’s portfolio after that time in the portfolio model. Over the study

period, three wind contracts and two smaller bio-fueled projects are set to expire.

2. The PUD’s BPA contract represents a significant proportion of its resource portfolio, and

the current terms of the PUD’s agreement are set to expire in 2028. While it is possible

that terms of that agreement could change post-2028, the IRP models that in the 2028-

2037 period, the BPA contract functions as if under the existing terms of the agreement.

There was not sufficient information available to assume any specific deviations from the

terms of the current agreement.

3. With the exception of the Climate Change scenario, which includes anticipated changes

in local and regional hydrology due to climate change, existing resource generation is

based on probabilistic simulations of their historic generation under current operating

parameters. As a result, an underlying assumption of the existing portfolio load resource

balance used by the Portfolio optimizer is that resources perform within the bounds of

their historical production and operating constraints of those resources don’t change

dramatically.


2. Supply-Side Resource Assumptions

The Portfolio Optimization Model considers supply-side resources and demand-side resources in

an integrated portfolio approach to arrive at candidate portfolios for each scenario. The purpose

of this section is to outline how supply-side resources were modelled for consideration in the

Portfolio Optimization Model.

The Portfolio Optimization Model’s purpose is to find the most cost effective resource

combination to meet portfolio needs in a scenario, such that all Planning Standards are satisfied.

Planning standards measure energy, capacity and Renewable Portfolio Standard compliance and

as a result these attributes need to be assigned to potential resources, along with their costs of

acquisition.

Annual Energy Attributes

Annual Energy attributes for supply-side resources were given by the equation:

Annual Energy (MW)= Resource Nameplate (MW)*Capacity Factor@P50

Generation(%)*Annual Dispatch(%)

Figure B-4 lists supply side resources options, their nameplate, capacity factor, annual dispatch

and annual energy attributes. Note that some storage supply-side resources that transfer energy

from one time period to another have no net annual energy attributes (marked with an * below).


Figure B-4

Annual Energy Attributes of Supply Side Resources

Resource Nameplate

(MW)

Effective

Capacity

Factor

Assumed

Dispatch

Rate or

Equivalent

Avg

Annual

Energy

Simple Cycle Combustion Turbine (SCCT) 239 100% 10% 23.9

25 MW Short Term Capacity Contract (5 year) 25 100% 10% 2.5

Dual Fuel Reciprocating Engine 50 100% 10% 5.0

Pumped Storage Hydro Low 100 N/A 18% -

Pumped Storage Hydro High 100 N/A 18% -

Landfill Gas 10 85% 100% 8.5

Biomass 15 85% 100% 12.8

Geothermal (traditional) 25 90% 100% 22.5

Energy Storage – Battery 25 N/A 100% -

Long Distance Wind (Montana) 50 44% 100% 21.8

Run of River Hydro (small hydro) 30 45% 100% 13.6

WA/OR Wind* 50 35% 100% 17.5

Customer Owned DG 15 11% 100% 1.7

Utility Scale Solar (U/S Solar E Wash) 25 27% 100% 6.8

Utility Scale Solar (U/S Solar W Wash) 5 13% 100% 0.6

Monthly Capacity Attributes

Monthly capacity attributes during Monthly On-Peak hours for supply-side resources were given

by the equation:

HLH Capacity (MW)= Resource Nameplate (MW)*Capacity Factor@P5


The Monthly Capacity equation differs from the Annual Energy equation only by the assumed

generation during the Monthly HLH hours. In order to match the Planning Standard conditions

of a PUD Load Resource Balance of P5 (the Load Resource Balance that would be exceeded 19

out of 20 years), Monthly HLH generation is measured as would be expected in at P5 generation

conditions for the resource. Figure B-5 lists supply side resources options and their nameplate,

capacity factor, annual dispatch and December HLH capacity attributes.


Figure B-5

Monthly Capacity Attributes of Supply Side Resources for December On Peak (HLH)

Resource Nameplate

(MW)

Effective

Capacity

Factor

Assumed

Dispatch

Rate or

Equivalent

Dec

HLH

Capacity




Pumped Storage Hydro Low 100 95% 100% 95.0

Pumped Storage Hydro High 100 95% 100% 95.0

Landfill Gas 10 85% 100% 8.5

Biomass 15 85% 100% 12.8


Energy Storage – Battery 25 50% 100% 12.5



WA/OR Wind* 50 12% 100% 5.9




Peak Week Capacity Attributes

Monthly Peak Week capacity attributes during Monthly Peak Week hours for supply-side

resources were given by the equation:

Peak Week HLH Capacity (MW)= Resource Nameplate (MW)*Capacity Factor@P5


The Monthly Peak Week Capacity equation differs from the Monthly Capacity equation only by

the assumed generation during Peak Week hours. In order to match the Planning Standard

conditions of a PUD Load Resource Balance of P5 (the Load Resource Balance that would be

exceeded 19 out of 20 years), Peak Week HLH generation is measured as would be expected in


at P5 generation conditions for the resource during the Peak Week time period. Figure B-6 lists

Supply Side Resources evaluated, their nameplate, capacity factor, annual dispatch and

December Peak Week (on peak) capacity attributes.

Figure B-6

Monthly Peak Week Capacity Attributes of Supply Side Resources

December Peak Week (Mon-Fri, 6x16)

Resource Nameplate

(MW)

Effective

Capacity

Factor

Assumed

Dispatch

Rate or

Equivalent

Dec

Peak

Week

Capacity




Pumped Storage Hydro Low 100 97% 100% 97.0

Pumped Storage Hydro High 100 97% 100% 97.0

Landfill Gas 10 97% 100% 9.7

Biomass 15 97% 100% 14.6


Energy Storage – Battery 25 50% 100% 12.5



WA/OR Wind* 50 3% 100% 1.7




Renewable Portfolio Standard Compliance Attributes

Renewable Portfolio attributes for supply-side resources were measured in Renewable Energy

Credit MW and given by the equation:

REC (MW)= Resource Nameplate (MW)*Annual Capacity Factor@P50 Generation(%)*Annual

Dispatch(%)*REC Multiplier(0 if Ineligible, 1 if Eligible for 100% of MWhs, 2 if Eligible for

MWh*2)


Figure B-7 lists supply side resource options, their Annual Energy, REC multiplier, and REC

MW attributes:

Figure B-7

Annual Renewable Energy Credit Attributes of Supply Side Resources

Resource

Avg

Annual

Energy

REC

Multiplier

Annual

REC MW

Simple Cycle Combustion Turbine (SCCT)

23.9 -

-

25 MW Short Term Capacity Contract (5 year)

2.5 -

-

Dual Fuel Reciprocating Engine

5.0 -

-

Pumped Storage Hydro Low - -

-

Pumped Storage Hydro High - -

-

Landfill Gas

8.5 1.0

8.5

Biomass

12.8 1.0

12.8

Geothermal (traditional)

22.5 1.0

22.5

Energy Storage – Battery - -

-

Long Distance Wind (Montana)

21.8 1.0

21.8

Run of River Hydro (small hydro)

13.6 -

-

WA/OR Wind*

17.5 1.0

17.5

Customer Owned DG

1.7 2.0

3.4

Utility Scale Solar (U/S Solar E Wash)

6.8 1.0

6.8

Utility Scale Solar (U/S Solar W Wash)

0.6 2.0

1.3

The following describes the way environmental attributes or renewable energy credits (RECs),

associated with a renewable resource, were modeled and made available in the 2017 IRP analysis

and satisfy the PUD’s annual EIA renewables compliance requirement.


The environmental attributes or RECs associated with energy produced by a Washington state

eligible renewable resource can be purchased separately from the energy itself. The assumption

for unbundled RECs is that the seller of the REC owns or contracts for the renewable resource

and may have RECs surplus to their own compliance need or are trying to maximize the revenue

from the energy and REC streams for their project portfolio.

Today, the Northwest has a reasonably liquid bilateral market for unbundled RECs, with REC

prices forecast for the 2018 through 2022 period near $5 per REC as shown in the table below:

2018 2019 2020 2021 2022

Anticipated

Average REC Cost $2.00 $ 3.00 $ 5.00 $ 5.00 $ 5.00

Staff anticipate that as renewables compliance requirements in Washington state increase from

9% to 15% of load in 2020, and both California and Oregon experience increases in their annual

renewables requirements, the availability of surplus RECs in the region will diminish, increasing

the cost of unbundled RECs beyond the short-term REC market’s price range today. Because the

long term market for RECs in the 2020’s through 2037 is less liquid and price discovery is

limited, the PUD chose to be more conservative and not forecast future compliance RECs prices

that reflect near term prices across the 20 year IRP study period.

REC prices were instead modeled to address the question - if REC prices over the 20 year IRP

study period were modeled at their theoretical maximum, would the PUD have a preference to

purchase unbundled RECs or to invest in a renewable resource (which includes energy, capacity

and REC characteristics) as a future generating asset? The addition of unbundled RECs at the

maximum REC price was found to be a more optimal solution in the scenarios and sensitivities

examined, except in the High Growth scenario; the High Growth scenario had annual energy

portfolio needs. The High Growth scenario met the renewables requirement with a combination

of renewable generating assets and unbundled REC contracts.


Maximum unbundled REC prices were modeled at the cost to develop a new Northwest wind

project, where project development costs were reduced by the value of the project’s energy sold

at forecast market prices over a 25 year asset life.2 The remaining value was allocated to the

environmental attributes or RECs associated with the project’s annual production. The Business

As Usual (BAU) case was found to produce the highest unbundled REC prices, and as a result

was used as the REC price for all scenarios. A sample set of years showing the REC price

calculation components and REC prices used are given in the table below.

2018 2020 2025 2028 2032 2037

Nominal Wind Cost per

MWh for a New WA

Wind Project Delivered in

the Calendar Year listed $70.75 $ 74.33 $ 84.09 $ 90.56 $ 99.96 $113.10

Nominal Energy Value $ 27.59 $ 34.55 $ 43.62 $ 47.18 $ 52.82 $ 59.24

Nominal Fundamental

REC Price $ 43.15 $ 39.77 $ 40.48 $ 43.39 $ 47.14 $ 53.85

The volume of unbundled RECs assumed to be available from the market were modelled as

100,000 MWh increments available under a 25 year contract term, with a modeling limitation

that a maximum of 1,000,000 MWh of unbundled RECs could be accumulated during any

calendar year.

In every scenario but the High Case, it was more cost effective to meet annual renewables

requirement using the target methodology with a strategy of at least 95% procured unbundled

RECs. Figure B-8 shows a comparison of unbundled REC purchases and new renewable

resource generation, expressed in the 20-year average annual aMW of REC generation. The

smaller renewable resources contributing to the Business As Usual w/ CA Carbon and Climate

Change w/ Low Societal Cost of Carbon Cases were small, local solar installations, and customer

owned distributed generation resources respectively3. The Long Term Resource Strategy

2 New wind development costs did not assume extension of the Federal production tax credit beyond 2020. 3 These resources were likely selected as a result of the modeling construct that unbundled REC contracts could only

be purchased in 100,000 MWh contract increments, as each resource contributes to addressing a remaining need

after a REC contract add, and the PUD has no annual energy need for the energy they provide in these scenarios.


(“Climate Change with Low Societal Cost of Carbon”) anticipates meeting about 95% of its RPS

compliance needs with unbundled RECs over the 2018-2037 study period.

Figure B-8

Comparison of Renewables Requirement Portfolio Additions by Scenario

As a result of the more conservative approach to the fundamental price of RECs in the IRP

analysis, the cost of procuring RECs is a large proportion of the overall cost of meeting future

needs. The cost of procuring RECs at the fundamental REC price in the Long Term Resource

Strategy represented 50% of the total portfolio costs, but was still more cost effective than

procuring an equivalent amount of bundled energy and RECs through new renewable resource

additions, since the majority of the resource’s output would have been surplus to the PUD’s

annual energy needs, and not sufficient to address the seasonal capacity needs. Figure B-9 shows

optimized portfolio cost and revenue components at three different REC price levels.

Low LoadGrowth

Case

Business AsUsual w/CA

Carbon

Business AsUsual w/No

Carbon

ClimateChange w/

Low SocietalCost ofCarbon

High LoadGrowth

Case

20-Year New Renewable ResourceGeneration RECs

(in average aMW)- 1 - 3 68

20-Year Unbundled REC Purchases(in average aMW)

68 72 78 68 22

- 10 20 30 40 50 60 70 80 90

1002

0-Y

ear

Ave

rage

aM

W p

urc

has

ed


Conservation and capacity resource choices did not change in these optimizations, but the cost of

Procuring RECs and the overall Portfolio NPV were significantly reduced.

Figure B-9

Comparison of Climate Change with Low Societal Cost of Carbon

at Various REC Price Levels

Fundamental

Price per REC

Average Price of

$15/REC

Average Price of

$10/REC

Cost for New Conservation $ 147,987,664 $ 147,987,664 $ 147,987,664

Cost of New Capacity

Additions $ 259,431,989 $ 259,431,989 $ 259,431,989

Cost of New DG Additions $ 34,781,279 $ 34,781,279 $ 34,781,279

Cost of Procured RECs $ 453,201,140 $ 139,842,011 $ 93,228,007

Total Portfolio Costs $ 895,402,072 $ 582,042,943 $ 535,428,939

Less Portfolio Revenues $ 473,243,503 $ 473,243,503 $ 473,243,503

Portfolio NPV $ 422,158,569 $ 108,799,440 $ 62,185,437

Supply Side Resource Costs

Supply side resources were priced based upon peer review and in-house modeling of

development and operations costs for different resource types. Annual supply side resource cost

components include:

Debt service costs associated with developing the project

Any associated fuel costs needed to generate electricity (this includes consideration of

carbon pricing in fuel costs in scenarios with carbon pricing for fossil fueled resources)

Any transmission costs needed to bring the resource’s electricity to the PUD busbar

Any anticipated variable integration charges associated with adding the resource

Any Operation and Maintenance (O&M) costs associated with operation of the resource


Different resources have different lifespans and are available at different times over the course of

the IRP study period of 2018-2037. For instance, a new Wind project is assumed to have a useful

life of 25 years, while a new Pumped Storage Hydro project is assumed to have a useful life of

50 years. Both hypothetical projects could also be delivered in many possible years over the

course of the study period. In order for resources to be considered on their overall cost

effectiveness within the portfolio, projects were adjusted by delivery date, and end effects were

added to compare resource costs over the same period of time.

To value resources delivered in different time periods, individual resource cost estimates were

developed for each year the resource was available. Fuel forecasts (including carbon pricing

forecasts), an assumed cost escalation rate of 2.5% and a discount rate of 5% were used to

inform all resource cost estimates.

To compare resources with different lifespans, end effects were added to resources with lifespans

shorter than the longest possible resource life in the supply side resources studied. The potential

resource with the longest useful life in the Supply Side Resource Option list was a Pumped

Storage Hydro project delivered in the year 2037. With a useful life of 50 years estimated, a

Pumped Storage Hydro project delivered in 2037 would last until the year 2087. For resources

with useful lives shorter than this (including potential Pumped Storage Hydro Projects delivered

before the year 2037), an equivalent amount of annual energy to the resource being considered

was replaced by market purchases for each year after the resources useful life expired, until

2087. For example, a project delivered in 2027 with a useful life of 20 years, and an annual

energy output of 10aMW, would have end effects equal to the sum of 10aMW*Market

Price*8760 hours of every year from 2047 to 2087. In this way, projects of differing lives can be

considered on the same basis, and the PUD is less at risk of selecting a supply side resource

using only information about its potential shorter-term contributions to the utility.

Figure B-10 shows the resource cost components, and end effect cost components of a 5MW

local solar project delivered at different times across the study period.


Figure B-10

2018 2027 2037

Resource Costs $15,630,864 $12,661,687 $9,950,324

End Effects $2,953,268 $1,991,844 $1,183,006

Total Costs $18,584,132 $14,653,530 $11,133,330

3. Demand Response Resource Assumptions

The 2017 IRP included consideration of five demand response programs that could be included

in candidate portfolios using the integrated portfolio approach. These programs are described in

Figure B-11 below. This section describes how the Demand Response programs were treated in

the Portfolio Optimization Model for inclusion in candidate portfolios.

Figure B-11

Summary of Demand Response Program Options

Program Description

Residential Direct Load

Control - Space and Water

Heat

Curtails enrolled resident’s space and water heaters for time-

limited event calls to reduce utility’s Peak Loads


Control - Water Heat

Curtails enrolled resident’s water heaters for time-limited event

calls to reduce utility’s Peak Loads


Control - WiFi Thermostat

Curtails enrolled resident’s space heaters for time-limited event

calls to reduce utility’s Peak Loads

Residential Smart Water

Heaters

Allows the utility to program tech-enabled Water Heaters to

primarily heat water during off-peak hours for use in on-peak

hours. Not event limited on a monthly or annual basis.

Commercial & Industrial

Curtailment

Curtails enrolled all or a portion of Commercial and Industrial

customer loads during time limited event calls to reduce utility’s

Peak Loads

Demand Response programs are different than both supply-side and demand side resources in a

number of important ways. From the PUD’s perspective, currently available Demand Response

programs provide benefit by temporarily shifting load from one time period to another time

period. This is useful because the difference between on-peak hour load and off-peak hour load

is substantial, and shifting on-peak load to off-peak hours is one approach that could contribute

to meeting on-peak needs during capacity constrained periods. However, unlike conservation and

supply-side resources, demand response programs are often limited in the amount of total times


they can be used, and how clustered those uses can be. For example, while an Industrial

customer may enroll in a voluntary curtailment program to curtail its load for a total of 120 hours

in a year, it is less likely that customer would be willing to curtail all 120 hours from Monday-

Friday in one specific week, or 500 hours over the course of a year. For this reason, Demand

Response programs required a tailored modelling methodology to be considered in the Portfolio

Optimization Model.

Another characteristic of Demand Response programs is that they don’t reduce overall demand

like conservation – instead they primarily shift demand from one time period to another.

The Demand Response programs in the Demand Response Potential Assessment included

information on the assumed MW capacity of each program for a peak hour in every year of the

2018-2037 study period, and the duration of the call for that particular Demand Response

program type. Those details for the year 2037 are provided in Figure B-12 below. Note that the

Smart Water Heater Program is not call limited and can provide load shifting from on-peak

periods to off-peak periods at all times due to its program design.

Figure B-12

Comparison of Demand Response Program Event limitations

Program Peak Hours/Year

Limit

Avg Event Length

(Hours)

2037 MW

Achievable

DLC - WiFi T-Stat 50 4 20

DLC - Air & H20

Heat 50 4 133

DLC - H20 Heat 50 4 125

C&I Curtail 50 4 10

Smart H20 Heat N/A N/A 34

The 2017 IRP considered the Planning Standards applicable to the Peak Week on-peak hours

during a Month, and the On-Peak Hours during the Month. Both of these time horizons (80 hours

in a peak week, and at least 372 on-peak hours in a month) are broader than the duration of an

average event length for any of the demand response programs considered (typically four). As a


result, some modeling assumptions were needed to measure the potential impact of an available

demand response program across the planning standard time periods.

The most significant modeling assumption made was that all Demand Response program “call

events” could occur within each annual December Planning Standard period. This assumption

supposes that all 50 annual program hours could occur during a December Peak Week, and

would therefore also occur during the December On-Peak hour period. While this assumption

may not be realistic, it allowed the PUD to see if generic demand response programs could be

cost-effective under favorable conditions, in order to provide the PUD enough information to

determine if it should explore specific deliveries of potential demand response programs.

Demand Response Potential Calculation for December Peak Week

The formula that provided the potential achievable aMW for the December Peak Week period

for each demand response program was given by the following equation:

Annual Achievable MW* (Peak Hours per Year Limit/Peak Hours in Week[80])

The results of the calculation for each program is provided in Figure 3 below. The values shown

represent the contributions to December Peak Week portfolio needs provided by each demand

response program in the Portfolio Optimization Model.


Figure B-13

Achievable Potential of Demand Response Programs for

December Peak Week by Year

Demand Response Potential Calculation for December On-Peak Hours

The formula that provided the potential achievable aMW for the December On-Peak period for

each demand response program was given by the following equation.

Available MW*( Available Hours/Peak Hours in Dec HLH[~400])

The results of the calculation for each program is provided in Figure B-14 below. The values

shown represent the contributions to December On-Peak portfolio needs provided by each

demand response program in the Portfolio Optimization Model.

0

50

100

150

200

250

aMW

DLC - Wifi T-Stat DLC - Air & H20 Heat DLC - H20 Heat C&I Curtail Smart H20 Heat


Figure B-14

Achievable Potential of Demand Response Programs during

December On-Peak Hours (HLH) by Year

0

10

20

30

40

50

60

70

80

aMW

DLC - Wifi T-Stat DLC - Air & H20 Heat DLC - H20 Heat

C&I Curtail Smart H20 Heat

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APPENDIX B: PORTFOLIO OPTIMIZATION MODEL1. Portfolio Optimization Model Methodology The 2017 IRP uses a Portfolio Optimizer tool developed in-house to solve for the least cost portfolio