### LBNL

- 6578E



# Incorporating Energy Efficiency in to
Western Interconnection Transmission Planning

Galen Barbose 1 

# , Alan Sanstad 1 

# , Charles Goldman 1



# , Stuart McMenamin 2 

# , and Andy Sukenik 2

1 

### Lawrence Berkeley National Laboratory, 2 

### Itron



# Environmental Energy Technologies Division

February 2014

The work described in this report was funded by the National Electricity Delivery Division of the U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability under Lawrence Berkeley National Laboratory Contract No. DE

- AC02

- 05CH11231. 

# E RNEST 

# O RLANDO 

# L AWRENCE


# B ERKELEY 

# N ATIONAL 

# L ABORATORY

### Disclaimer

This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents

that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or The Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency

t hereof, or The Regents of the University of California.

Ernest Orlando Lawrence Berkeley National Laboratory is an equal opportunity employer.

### LBN L

- 6578E



# Incorporating Energy Efficiency in to Western Interconnection Transmission Planning

Prepared for the Office of Electricity Delivery and Energy Reliability National Electricity Delivery Division U.S. Department of Energy

Principal Authors

Galen Barbose, Alan H. Sanstad, Charles A. Goldman

Ernest Orlando Lawrence Berkeley National Laboratory 1 Cyclotron Road, MS 90R4000 Berkeley CA 94720

- 8136

February 201 4

The work described in this report was funded by the National Electricity Delivery Division of the U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability under Lawrence Berkeley National Laboratory Contract No. DE

- AC02

- 05CH11231.

### Acknowledgements

This work was supported by the National Electricity Delivery Division of the U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability under Contract No. DE

- AC02

- 05CH11231. We would particularly like to thank Larry Mansueti of the U.S . Department of Energy for his support of this work. T he authors would like to thank the many individuals who provided input and feedback on earlier iterations of the analysi s, including Doug Larson, Steve Ellenbecker, and Tom Carr with the Western Interstate Energy Board, Dian Grueneich and Michael Wheeler (formerly with the California Public Utilities Commission), Rebecca Smith (Nevada Public Utilities Commission), Lisa Schwartz (formerly with the Regulatory Assistance Project and now the Oregon Department of Energy), the many members of the State/Provincial Steering Committee (SPSC), participants in the SPSC’s DSM Work Group, and WECC staff. We also extend our gratitude to the many utility and state energy agency staff who provided critical data and information required for this analysis. Of course, any omissions or inaccuracies

are our own.

ii

### Table of Contents

Table of Contents

................................ ................................ ................................ ........................... iii

List of Figures

................................ ................................ ................................ ................................ . v

List of Tables

................................ ................................ ................................ ................................ . vi

Acronyms and Abbreviations

................................ ................................ ................................ ....... vii

1. Introduction

................................ ................................ ................................ ............................... 1

2. WECC 10

- Year Common Case

................................ ................................ ................................ 4

2.1 Customer

- Funded Energy Efficiency Programs

................................ ............................. 5

2.2 Federal Appliance, Lighting, and Equipment Standards

................................ ................ 6

2.3 Expected Energy Efficiency Savings

................................ ................................ ............ 10

2.4 Adjustments to LRS Load Forecasts

................................ ................................ ............. 12

2.5 WECC Common Case Load Forecasts

................................ ................................ ......... 13

3. SPSC 10

- Year High DSM Case

................................ ................................ .............................. 15

3.1 Data Sources and Assumptions

................................ ................................ ..................... 16

3.2 High DSM Case Savings Projections

................................ ................................ ........... 19

3.3 High DSM Case Load Forecasts

................................ ................................ ................... 20

4. SPSC 20

- Year High DSM Case

................................ ................................ .............................. 23

4.1 The SAE Load Forecasting Framework and Base Case Assumptions

......................... 24

4.2 Accounting for Energy Efficiency Impacts within the SAE Framework

..................... 27

4.3 SAE Reference Ca se

................................ ................................ ................................ ..... 30

4.4 High DSM Case Approach and Assumptions

................................ ............................... 34

4.5 High DSM Case Energy Efficiency Savings

................................ ................................ 36

4.6 High DSM Case Load Forecasts

................................ ................................ ................... 38

5. Recommendations: Potential Improvements to Data, Methodology, and Process f or Future TEPPC Study Cycles

................................ ................................ ................................ .............. 40

References

................................ ................................ ................................ ................................ ..... 46

Appendix A. WECC 10

- Year Common Case: Additional Details on the Energy Efficiency Assumptions and Adjustments

................................ ................................ ................................ 48

A.1 Summary Tables on Efficiency Adjustments for WECC 10

- Year Common Case

...... 49

A.2 BA

- Specific Energy Efficiency Assumptions and Adjustments: Narrative Summaries

54

Appendix B. SPSC 10

- Year High DSM Case: State

- by

- State Analyses

................................ ...... 68

Appendix C. SPSC 20

- Year High DSM Case: Additional Methodological Details, Assumptions, and Analysis

................................ ................................ ................................ .......................... 111

C.1 Weather and economic data and analyses for the SAE Base Case Forecast

.............. 111

iii

### C.2. End

- Use Categories and Efficiency Units

................................ ................................ .. 111

C.3. Data Sources for End

- Use Assumptions in the SAE Base Case

................................ . 113

C.4. EE Adjustments for SAE Re ference Case

................................ ................................ .. 116

C.5. SAE Reference Case Load Forecasts

................................ ................................ .......... 123

C.6. Data Sources and Assumptions for Average Stock Efficiencies in the High DSM Case

125

C.7 Calculating Efficiency Gains in the High DSM Case

................................ ................. 130

C.8 High DSM Case Incremental Energy Sav ings

................................ ............................ 133

C.9 SPSC High DSM Case Load Forecasts

................................ ................................ ...... 136

iv

### List of Figures

Figure 1. WECC Balancing Authorities (circa 2011)

................................ ................................ ..... 3 Figure 2. Illustration of Energy Efficiency Adjustment for the WECC Common Case Load

Forecasts

................................ ................................ ................................ ................................ ... 4 Figure 3. Projected Cumulative Savings from Federal Standards

................................ .................. 7 Figure 4. Expected Energy Efficiency Savings for the 10

- Year Common Case: Annual Energy 11 Figure 5. Expected Energy Efficiency Savings for the 10

- Year Common Case: Non

- Coincident

Peak

................................ ................................ ................................ ................................ ......... 11 Figure 6. Adjustments to LRS Load Forecasts (Annual Energy)

................................ ................. 12 Figure 7. Adjustments to LRS Load Forecasts (Non

- Coincident Peak)

................................ ....... 13 Figure 8. WECC 10

- Year Common Case Load Forecast Growth Rate s (Annual Energy)

.......... 14 Figure 9. WECC 10

- Year Common Case Load Forecast Growth Rates (Non

- Coincident Peak) 14 Figure 10. Types of Energy Efficiency Potential

................................ ................................ .......... 15 Figure 11. Illustration of Energy Efficiency Adjustment for the SPSC 10

- Year High DSM Case

................................ ................................ ................................ ................................ ................. 16 Figure 12. Energy Efficiency Savings for the 10

- Year High DSM Case: Annual Energy

........... 20 Figure 13. Energy Efficiency Savings for the 10

- Year High DSM Case: Non

- Coincident Peak 20 Figure 14. SPSC 10

- Y ear High DSM Case Load Forecast Growth Rates (Annual Energy)

....... 21 Figure 15. SPSC 10

- Year High DSM Case Load Forecast Growth Rates (Non

- Coincident Peak)

................................ ................................ ................................ ................................ ................. 22 Figure 16. Application of the SAE Load Forecasting Model for the SPSC High DSM Study Case

................................ ................................ ................................ ................................ ................. 23 Figure 17. SAE Econometric Framework

................................ ................................ ..................... 25 Figure 18. Data Inputs and Outputs for the SAE Model

................................ ............................... 26 Figure 19. Adjusting SAE Load Forecasts Using th e DSM Trend Method

................................ . 29 Figure 20. SAE Reference Case DSM Adjustments

................................ ................................ ..... 31 Figure 21. SAE Reference Case Load Forecast Growth Rates (2010

- 2032)

................................ 32 Figure 22. Comparison of SAE Reference Case and WECC Reference Case Load Forecasts

.... 33 Figu re 23. SAE Reference Case Load Forecasts by Customer Segment

................................ ...... 33 Figure 24. SAE Reference Case Load Forecasts (Residential & Commer cial) by End

- Use Group

................................ ................................ ................................ ................................ ................. 34 Figure 25. SAE High DSM Case Incremental Savings Relative to SAE Reference Case

........... 37 Figure 26. SAE High DSM Case Incremental Savings by Customer Sector

................................ 37 Figure 27. SAE High DSM Case Incremental Savings by End

- Use Group

................................ . 38 Figure 28. Growth Rates for SPSC High DSM Case and WECC Reference Case (Annual Energy)

................................ ................................ ................................ ................................ .... 39 Figure 2 9. Growth Rates for SPSC High DSM Case and WECC Reference Case (Peak Demand)

................................ ................................ ................................ ................................ ................. 39

v

### List of Tables

Table 1. Adjustments to 2011 LRS Load Forecasts for Customer

- Funded Efficiency Programs

. 6 Table 2. Federal Standards Expected to be Ad opted over 2009

- 2013

................................ ............ 8 Table 3. Approaches Used to Adjust LRS Forecasts to Account for the Impact of Federal

Standards

................................ ................................ ................................ ................................ . 10 Table 4. Projected Savings in 2021 from Federal Standard Updates Issued from 2009

- 2013

..... 10 Table 5. Energy Efficiency Potential Studies Used for the SPSC 10

- Year High DSM Case

....... 16

Table 6. End Uses Included in SAE Model

................................ ................................ .................. 26

Table A

- 1. Embedded and Expected Energy Efficiency Savings (Annual GWh, 2021)

........... 49

Table A

- 2. Embedded and Expected Energy Efficiency Savings (Non

- Coincident Peak MW, 2021)

................................ ................................ ................................ ................................ ....... 50

Table A

- 3. Adjustments to the LRS Load Forecasts (2021)

................................ ....................... 51

Table A

- 4. Comparison of LRS and WECC Common Case Forecasts (Annual GWh)

............. 5 2

Table A

- 5. Comparison of LRS and WECC Common Case Forecasts (Non

- Coincident Peak MW)

................................ ................................ ................................ ................................ ........ 53

vi

### Ac ronyms and Abbreviations

ACEEE

– American Council for an Energy

- Efficient Economy

AEO

– Annual Energy Outlook

AESO

– Alberta Electric System Operator

APS

– Arizona Public Service Company

ARRA

– American Recovery and Reinvestment Act

ASAP

– Appliance Standards Awareness Project

AVA

– Avista Corporation

BA

– b alancing authority

BCTC

– British Columbia Transmission Corporation

BPA

– Bonneville Power Administration

CFE

– Comision Federal de Electricidad

CHPD

– Public Utility District No. 1 of C helan County

DOE

– Department of Energy

DOPD

– Public Utility District No. 1 of Douglas County

DG

– distributed generation

DR

– demand response

DSM

– demand

- s ide m anagement

EE

– energy efficiency

EIA

– Energy Information Administration

EISA

- Energy Indepe ndence and Security Act

EPE

– El Paso Electric Company
FAR_EAST

– Idaho Power Company, Far East load zone

FERC

– Federal Energy Regulatory Commission

GCPD

– Public Utility District No. 2 of Grant County

IID

– Imperial Irrigation District

IPCO

– Idaho Powe r Company

IRP

– Integrated Resource Plan

LBNL

– Lawrence Berkeley National Laboratory

LDWP

– Los Angeles Department of Water and Power

LRS

– Loads and Resources Subcommittee
MAGIC_VLY

– Idaho Power Company, Magic Valley load zone

NEMS

– National Energy Modeling System

NERC

- North American Electric Reliability Corporation

NEVP

– Nevada Power Company

NPCC

– Northwest Power and Conservation Council

NWMT

– NorthWestern Energy
PACE _ ID

– PacifiCorp East

– Idaho

PACE

_ UT

– PacifiCorp East

– Utah

PACE

_ WY

– Pac ifiCorp East

– Wyoming

PACW

– PacifiCorp West

PGE

– Pacific Gas & Electric
PGE_BAY

– Pacific Gas & Electric, San Francisco Bay load zone
PGE_VLY

– Pacific Gas & Electric, Central Valley load zone

PGN

– Portland General Electric

vii

### PNM

– Public Service Compa ny of New Mexico

PSC

– Public Service Company of Colorado

PSE

– Puget Sound Energy

RTEP

- Regional Transmission Expansion Planning

SCE

– Southern California Edison

SCL

– Seattle City Light

SDGE

– San Diego Gas & Electric

SMUD

– Sacramento Municipal Utilit y District

SPP

– Sierra Pacific Power Company

SPSC

– State

- Provincial Steering Committee

SRP

– Salt River Project

TEP

– Tucson Electric Power Company

TEPPC

– Transmission Expansion Planning and Policy Committee

TIDC

– Turlock Irrigation District

TPWR

– Tacoma Power
TREAS_VLY

– Idaho Power Company, Treasure Valley load zone

WACM

– We stern Area Power Administration, Colorado

- Missouri Region

WALC

– Western Area Power Administration

, Lower Colorado Region

WAUW

– Western Area Power Administration

, Upper Great Plains West

WECC

– Western Electricity Coordinating Council

WGA

– Western Governors’ Association

viii

## 1. Introduction

The Western Electricity Coordinating Council (WECC) conducts transmission planning studies through its Transmission Expansion Planning and Policy Committee (TEPPC). In recent years, WECC’s transmission planning process has been substantially expanded and enhanced with funding from the U. S. Department of E nergy provided under the American Recovery and Reinvestment Act (ARRA) of 2009. This expanded effort, designated the Regional Transmission Expansion Planning (RTEP) project, entails the development of biennial 10

- and

20

- year transmission plans that serve to identify future transmission expansion needs and options for meeting those needs. The analysis conducted for each plan evaluates numerous stakeholder

driven “study cases” (i.e., scenarios) using production cost modeling and capacity expansion modeling tools. These study cases are selected through WECC’s annual study request process, whereby stakeholder groups can recommend specific study cases for analysis during the annual study cycle. State regulators and energy agencies provide input to WECC’s tran smission planning analyses via (among other channels) the State

- Provincial Steering Committee (SPSC), an entity formed by the Western Governors’ Association (WGA) , which participates in the annual study request process

.

Lawrence Berkeley National Labor atory (LBNL) provide s technical assistance to the SPSC and

WECC with the development of demand

- side management ( DSM )

- related assumptions and modeling inputs for WECC’s transmission planning analyses

. In this capacity, LBNL’s work to

date has largely revol ved around the implementation of specific SPSC study requests for both the

10

- year and 20

- year plans . I n particular, these study requests have included (a) “reference case s ”

that incorporate the expected impacts of current DSM

- related policies and plans and (b) “High

DSM” study cases that entail higher levels of DSM impacts than anticipated in the r eference

c ase. This activity has occurred under the auspices of the SPSC DSM Work Group

. P articipants in that group

, including state regulatory and energy a gency staff, utilities, and regional DSM

experts

, have vetted and provided input on key assumptions and methodologies. Critical review and input has also been provided by the TEPPC DSM Task Force, the TEPPC Data Work Group, and other key participant group s within the TEPPC process.

This report documents the energy efficiency

- related analyses developed by LBNL and its

contractors have conducted within the 2011 and 2012 TEPPC study cycles

. This includes three distinct study cases: the 10

- Year Reference Ca se (termed the WECC 10

- Year Common Case),

the 10

- Year SPSC High DSM /DG Case, and the 20

- Year SPSC High DSM /DG Case

. Each of those study cases included assumptions and analys e s for three types of DSM resources: energy efficiency, demand response (DR), and distributed generation (DG). This report focuses specifically on energy efficiency ; the DR and DG components of the study cases are addressed in

separate reports (Satchwell et al., 2013; Olson and Schlag, 2013)

. For each study case, model inputs were developed for each of the 39 individual load zones used within WECC’s modeling tools; these load zones correspond roughly to the set of balancing authorities (BAs) shown in Figure 1

. 1

1 The TEPPC load zones and the BA regions differ in several respects. First, s everal BAs consist of a number of

smaller TEPPC load zones ; in particular, the CISO BA consists of four load zones (PG&E_BAY, PG&E_VLY, SCE, and SDGE), and the PACE BA consists of three load zones (PACE_ID, PACE_UT, and PACE_WY). Second,



### 1

### This report is targeted primari ly for participants in WECC’s transmission planning process, and is intended to serve as a reference document to inform future transmission planning efforts within the Western Interconnection. In addition, the methods described herein for modeling

energy efficiency impacts within WECC’s recent transmission planning analyses may also have broader application: e.g., for regional transmission planning organizations engaged in FERC Order 1000 compliance activities, for individual utilities conducting integrate d resource planning, and for interconnection

- wide transmission planning efforts in other regions

.

The report is organized as follows. In C hapter 2, we describe the energy efficiency assumptions and analysis for the WECC 10

- Y ear Common Case , which was dev eloped during TEPPC’s 2011 study cycle and formed the basis for WECC’s first 10

- Year Transmission Plan . Chapter 3 presents the corresponding information for the SP S C 10

- Y ear High DSM Case, also developed within the 2011 study cycle . Chapter 4 moves to the 20

- year planning horizon, and describes in

detail the analysis and assumptions employed in developing the SPSC 20

- Y ear High DSM C ase during TEPPC’s 2012 study cycle. Finally, C hapter 5 contains our recommendations identifying potential data, modeling, and process improvements for future TEPP C study cycles.

five BAs are generation

- only (GWA, Dynegy, Gila River, Griffith, and Harquahla), and thus have no cor responding

TEPPC load zone.



### 2Source: WECC 

### Figure 1 . WECC Balancing Authorities (circa 201 1 )



### 3

## 2. WECC 10

- Year Common Case

The 10

- year study cases within TEPCC’s 2011 and 2012 study cycles were focused on the horizon year 2022. The reference point for these study cases was a single study case, termed the WECC Common Case, which is intended to reflect expected (i.e., 1

- in

- 2) l oads, generation, and

transmission over the 10

- year study horizon. This chapter describes the process used to develop the Common Case load forecasts, focusing on a set of adjustments made to the load forecasts in order to capture the expected impact of en ergy efficiency programs and policies implemented over the study period.

The load forecasts for the WECC Common Case were developed by starting with the load

forecasts that BAs submitted in response to WECC’s 2011 LRS data request ( henceforth referred

to as the LRS load forecasts ), which cover the period 2011

- 2021. The SPSC DSM Work Group

determined

– with extensive input from BA load forecasting staff and review by the TEPPC Data Work Group

– the extent to which the LRS load forecasts fully capture the expected impact of existing energy efficiency policies and program plans implemented over the forecast period. To

the extent that the forecasts were determined to not fully capture the expected those energy efficiency pol icy impact s , they were adjusted downward accordingly , yielding the WECC Common Case load forecast for the year 2021

. Th is basic process is illustrated schematically in Figure 2

. The 2021 forecasts were then extrapolated to 2022 , the horizon year for WECC’s 10

- Year study during its 2011 study cycle, using the average annual growth rate over the 2010

- 2021 period.

Figure 2

. Illustration of Ene rgy Efficiency Adjustment for the WECC Common Case Load

Forecasts



### In developing projections of expected energy efficiency savings and the associated adjustments

to the LRS load forecasts , two classes of energy efficiency policies were of primary interest: (1)

customer

- funded energy efficiency programs 2 

### and (2) federal minimum efficiency standards for appliance s , lighting, and other end

- use equipment (henceforth referred to simply as “federal

2 Customer

- funded energy efficiency programs , also referred to as ratepayer

- funded energy efficiency programs

, refer to the class of energy efficiency programs that are funded by utility ratepayers and administered either by the utility or by an independent program administrator. 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021Energy or Peak Demand

LRS Load Forecast Embedded Savings from EE Policies & Programs (2011

- 2021)

Total ExpectedSavings

from EE Policies & Programs (2011

- 2021)

WECC Common Case Load Forecast



### 4

### standards”)

. The DSM Work Group focused on these two classes of policies on the grounds that they are likely to be the most significant source of policy

- driven energy efficiency savings over the forecast period , and thereby ignored o ther energy efficiency policies and programs , such as state appliance efficiency standa rds, state building codes, and ARRA

- funded efficiency

programs. This narrow focus is one among several “conservatisms” built into the analysis.

In addition, the analysis focused on specific timeframes. For customer

- funded efficiency programs, the analys is focused on the impact of programs implemented over the 2011

- 2021 period; it was assumed that the LRS load forecasts adequately captured residual savings from historical programs implemented prior to 2011, and therefore no further accounting was

conducte d for historical programs . For federal standards, the DSM Work Group focused specifically on standards adopted and updates to existing standards scheduled to occur through January 2013. This is another conservatism in the methodology, as DOE is required to continue updating standards regularly after that date. 3



### 2.1 Customer

- Funded Energy Efficiency Programs

Customer

- funded energy efficiency programs have been implemented in the United States for

more than three decades and, in many states and regions, have accelerated rapidly in recent years

as a result of new or increased policy support. The WECC Common Case load forecasts are intended to fully reflect the expected savings from customer

- funded programs implemented over the LRS load forecast period (2011

- 20 21), given current policies and utility resource plans.

As part of the 2011 LRS data request, BAs were asked to provide projections of the energy efficiency program savings incorporated into their LRS load forecast. 4 

### LBNL reviewed these energy efficiency savings projections and, with input from SPSC DSM Work Group participants

and other state and regional energy efficiency experts, assessed the consistency of these savings projections with applicable statutory and regulator y policies, recent utility integrated resource plans, and utility DSM program plans.

LBNL then communicated with load forecasting staff at many of the individual balancing

authorities in order to : (a) clarify any ambiguities in the savings projections submitted through the LRS request , (b) discuss any apparent discrepancies between the submitted savings projection and what would be expected under current policies and program plans

, and (c)

confirm whether the 2011 LRS load forecasts fully account for t he expected energy efficiency program savings. If the savings projection provided by a BA differed significantly from the expected amount, or if the load forecast did not fully account for the BA’s savings projection,

3 The decision to focus only on scheduled updates through January 2013 was justified on the grounds that standards adopted at later dates would likely have minimal impacts on loads in 2022, given the lag between the conclusion of rulemakings and the date that standards go into effect, and given the pace of equipment stock turnover. If, however, adjustments were being made over a longer time horizon, such as for WECC’s 20

- Year study, some consideration of continued updates to federal standard over time may be warranted in order to ensure that the forecasts reflect reasonable assumptions about the likely impacts of federal stan dards.

4 This was a new element in the LRS data request for 2011. Responses to t his question, however, were not mandatory, and therefore not all BAs provided this data. Moreover, many BAs evidently did not interpret the question as intended, and therefore the submitted savings projections were of widely varying quality, requiring a

s ignificant amount of follow

- up with BA staff.



### 5

### then the LRS load forecast was adjust ed downward accordingly. As shown in Table 1 , such adjustments were made for 11 BAs. Further details describing the underlying basis of the projected savings for each BA, as well as the savings projections themselves, are summarized in Appendix A.

Table 1

. Adjustments to 2011 LRS Load Forecasts for Customer

- Funded Efficiency Programs

AVA The LRS load f orecast only accounts for savings from existing efficiency programs, but not for any of

the planned new programs within Avista’s most recent IRP . As such, the forecast was adjusted

downward based on the IRP savings projection for new efficiency programs.
CISO The LRS load f orecast accounts on ly for "committed" energy efficiency savings, and therefore excludes "uncommitted" savings associated with IOU programs implemented after 2012, savings from POU programs implemented after 2010, and savings from other future changes to codes/standards.

Th e forecast was therefore adjusted downward based on the sum of: (a) the incremental uncommitted savings assumed by the CPUC within the IOUs' long

- term procurement proceeding and (b) the estimated savings from EE programs implemented by POUs within the CISO footprint, based on those utilities' most recent long

- term EE savings targets.

IPC The LRS load f orecast accounts for only existing programs in Idaho Power’s 2011 IRP, but not for the projected savings from planned new programs identified within the IRP

. T he forecast wa s therefore adjusted downward based on the IRP savings projection for new efficiency programs.
NWMT The LRS load f orecast accounts for a continuation of energy efficiency programs at NorthWestern Energy ’s historical rate of savings. The load forecast was therefore adjusted downward based on the

difference between the utility’s planned savings level , as projected within its 2009 Electric Default Supply Procurement Plan

, and the utility’s historical rate of savings

. PACE &

PACW The LRS load f orecast assumes a level of savings based on the target from PacifiCorp’s 2008 IRP

U pdate rather than the updated efficiency targets in the utility’s 2011 IRP

. Therefore, the PACE and

PACW load forecast s were adjusted downward by an amount equal to t he difference between the savings targets in the 2011 IRP and the 2008 IRP Update.

PSCO The LRS load f orecast assumes a level of savings based on an earlier set of long

- term savings targets

esta blished under Docket. 08

- 0560. The forecast was therefore adjusted downward slightly in order to account for the higher level of savings required under the updated savings goals adopted in March 2011 (Decision No. C11

- 0442).

PNM The LRS load f orecast does not include any impacts from future energy efficiency programs, and was therefore adjusted downward based on the savings required of PNM to comply with New Mexico’s energy efficiency resource standard.

SRP The LRS load f orecast roughly accounts for the level of savings required to m eet SRP’s Sustainable Portfolio Plan savings targets through 2017 , but does not include any savings from programs implemented in subsequent years

. T he load forecast was therefore adjusted downward to account for the expected savings needed to meet the Sustainable Portfolio Plan savings targets in 2018

- 2021.

TEP The LRS load f orecast partially accounts for the effects of planned customer

- funded energy efficiency programs over the forecast period, but not at the level necessary to meet the Arizona Energy Efficiency

Standard. As such, the load forecast was adjusted downward based on the additional amount of savings required of Tucson Electric and Unisource to comply with standard.

WACM The load f orecast that Colorado Springs Utilities (CSU) provided to W APA, which is then rolled up into the LRS load forecast for the WACM balancing authority , does not account for the impacts of any future energy efficiency programs. As such, the WACM forecast was adjusted downward based on the energy efficiency savings pro jection provided by CSU.



### 2.2 Federal Appliance, Lighting, and Equipment Standards

The U.S. federal government establish es minimum efficiency standards for a wide variety of consumer appliances, lighting technologies, an d other end

- use equipment. For most of these products

, DOE is responsible for setting the s tandard level and is required to conduct periodic rulemakings to evaluate potential updates to the standard. For a number of other products,

6

### Congress has instead established the initial minimum effici ency standard directly through legislation. A notable example is the federal standard for general service lamps enacted through the 2007 Energy Independence and Security Act (EISA), which mandates a phase

- out of

traditional incandescent light bulbs.

Figure 3 shows the projected total U.S. savings over time from federal standards adopted or updated through January 2013 , based on analysis conducted by the American Coun cil for an Energy Efficient Economy (ACEEE) and the Appliance Standards Awareness Project (ASAP)

. 5 

### The projected savings are separated into two components:

• S avings from standards established prior to 2009 (some of which don’t go into effect until some number of years afterwards, such as the EISA lighting standard, which was phased

- in starting in 2012)

• S avings from standards adopted or updated over the 2009

- 201 3 timeframe (listed in

Table 2 )

, including a number of updates that had not yet occurred at the time of th is analysis, but that DOE had committed to updating by Janua ry 2013

Source: Derived from estimates reported in ACEEE/ASAP report, “KaBOOM: The Power of Appliance Standards Opportunities for New Federal Appliance and Equipment Standards” (July 2009). 

### Figure 3

. Projected Cumulative Saving s from Federal Standards



### As indicated in Figure 3 , savings from the set of standards established prior to 2009

— which represent the bulk of the total projected savings

— accumulate over the 2010

- 2020 period at the

same rate as over the 2000

- 2010 period. This growth in savings occurs as a result of stock turn

over, as old inefficient equipment is replaced with more

- efficient models that comply with the

ex isting federal standard. Additional savings from updates issued during 2009

- 2013 also

accumulate over the 2010

- 2020 period, and represent an acceleration in the rate of savings from federal standards relative to the historical rate of growth. As describe d below, the fact that the

2009

- 2013 updates represent an acceleration in savings is central to the DSM Work Group’s process of adjusting the LRS forecasts to reflect the expected impact of federal standards.

5 Note that the ACEEE/ASAP study was conducted in 2009, and therefore the savings projections for standards adopted between then and January 2013 were based on assumptions about the standard level that DOE woul d adopt. 

### 0% 2% 4% 6% 8% 10% 12% 14%

2000 2010 2020 

### Savings from 2009

- 2013 Standards
Savings from Pre

- 2009 Standards
Extrapolated Historical Savings% of Total U.S. ElectricityConsumption



### 7

### Table 2 . Federal Stand ards Expected to be Adopted over 2009

- 2013

Product Planned Final Rule Date * Compliance DateResidential Battery chargers Jul

- 11 2014

Central AC and heat pumps Jun

- 11 2014

Clothes dryers Jun

- 11 2014

Clothes washers Dec

- 11 2015

External power suppliers Jul

- 11 2014

Furnace fans Jan

- 13 2016

Microwave ovens Mar

- 11 2014

Refrigerators Dec

- 10 2013

Room AC Jun

- 11 2014

Water heaters Mar

- 10 2013Commercial Bever a ge vending machines Aug

- 09 2012

Commercial clothes washers Jan

- 10 2013

Fluorescent ballasts Jun

- 11 2014

Fluorescent lamps Jun

- 09 2012

Incandescent reflector lamps Jun

- 09 2012

BR/exempted reflector lamps Jan

- 10 2013

Liquid

- immersed transformers Jan

- 13 2016

Low volt. dry

- type transformers Jan

- 13 2016

Metal halide lamp fixtures Jan

- 12 2015

Reach

- in refrigerators & freezers Jan

- 13 2016

Small electric motors Feb

- 10 2013

Walk

- in coolers & freezers Jan

- 12 2015

* Planned final rule date, as of the time that the analysis for the WECC Common Case was being conducted



### LBNL made adjustments to the LRS load forecasts to account for the expected impact of all federal standards adopted and all updates to existing standards scheduled to occur through January 2013. To so, LBNL sought information from the load fore casting staff of individual balancing authorities regarding the manner in which their forecasts model the impact of federal standards. Based on the information received, one of two potential standardized methods was used for most BAs in order to adjust the LRS forecasts to account for the expected impact of

federal standards (see Table 3 for a summary, and see Appendix A for BA

- specific details ) :

Method 1 . Many BAs indicated that their load forecasts do not explicitly model the impact of federal standards. The default assumption in these cases is that, by virtue of the underlying econometric models, these load forecasts implicitly extrapolate into the future the historical rate of savings from federal standards, and that they therefore fully capture the savings from pre

2009 federal standards, but do not capture any of the expected savings from 2009

- 2013

standards . These load forecasts were therefore a djusted downward based on the projected savings from the 2009

- 2013 updates (i.e., the olive

- colored “wedge” in Figure 3 ) . For each BA, the expected savings from 2009

- 2013 standards was estimated from the state

- level projections

8

### presented in Table 4 , by pro

- rating the state

- level savings based on the portion of th e statewide

load w ithin the BA; the LRS load forecast was then adjusted downward by that amount.

Method 2 . In other instances, BAs reported that their load forecasts were based on end

- use models or included statistical adjustments that are able to capture the impact of spe cified federal standards. In practice, these load forecasts generally capture the impact of all federal standards adopted as of the date that the forecast was prepared, but do not model the impact of scheduled updates to federal standards. For these BAs , the load forecasts were assumed to capture the both

the impact of pre

- 2009 standards (as in Method 1) as well as the impact of standards adopted between 2009 and year

- end 2010 (i.e., a portion of the olive

- colored “wedge” in Figure 3 )

. These load forecasts were therefore adjusted downward based on the expected impact of only

those standards that had not yet been adopted at the time that the forecasts were prepared bu t for which DOE has scheduled an update by January 2013. For each BA, the expected savings from prospective standards scheduled for adoption by January 2013 was estimated from the state

- level projections for “Prospective” standards presented in Table 4 , by pro

- rating the state

- level savings based on the portion of the statewide load within the BA; the load forecast was then adjusted downward by this amount.

For three other BAs (CISO, PACE, and PACW), neither Method 1 nor Method 2 was applied

. T he adjustments to the LRS load forecasts for these BAs addressed multiple energy efficiency policies simultaneously

, including federal standards , and therefore no separate adjus tment for federal standards was required. Specifically, i n the case of CISO, the LRS forecast was adjusted

downward based on the Energy Commission’s estimate of the “incremental uncommitted” savings associated with achievement of the state’s savings goals for the IOUs, as discussed previously in Section 2.1. The incremental uncommitted savings was assumed to largely capture the impact of the 2009

- 2013 updates to federal standards, and therefore no se parate adjustment was applied to the CISO forecast for federal standards

. Similarly, for PACE and PACW, the adjustment made to the LRS forecast was based on the savings projection in PacifiCorp’s IRP, and that savings projection (according to PacifiCorp s taff) was inclusive of savings from future

federal standards.

For the remaining BAs, no adjustments to the LRS forecasts were made for federal standards, though the reasons for this treatment vary. For BPA and most of the northwestern public utility district BAs (CHPD, DOPD, GCPD, and TPWR), the LRS l oad forecasts were determined to be

net of the NPCC’s conservation targets, and those targets were assumed to largely capture the savings from recent and future federal standards updates; therefore, no ad justment to the LRS

forecasts was made in these cases. For the three non

- U.S. BAs (AESO, BCTC, and CFE), no adjustment was made because these regions are not directly subject to U.S. federal efficiency standards (though provincial standards exist in BC 6



### , and spillover in efficiency impacts from U.S. standards could occur across borders into all three regions). Finally, for WALC and WAUW, no adjustments were made simply due to a lack of information about how the LRS load forecasts were prepared

.

6 Information was sought from BCTC to determine whether the load forecast submitted to WECC reflects the expected impact of provincial efficiency standards, but a response was not received within the required timeframe for this analysis.



### 9

### Table 3

. Approaches Used to Adjust LRS Forecasts to Account for the Impact of Federal Standards

Adjustment Approach Balancing Authorities

Method 1 APS, EPE, IID, IPC, NWMT, PGE, PNM, PSE, SCL, SMUD, SRP, TEP, TID

Method 2 AVA, LADWP, NEVP, PSCO, SPP

Federal standards impact included in

broader adjustment CISO, PACE, PACW

No adjustment BPA, CHPD, DOPD, GCPD, TPWR : Impact of federal standards captured in LRS forecast

AESO, BCTC, CFE : U.S. federal standards not applicable

WALC, WAUW : Insufficient information



### Table 4

. Projected Savings in 2021 from Federal Standard Updates Issued from 2009

- 2013

State Energy Savings (GWh) Peak Demand Savings (MW)

Already Adopted ( 2009

- 2010 ) Prospective ( 2011

- Jan.

2013) Total Already Adopted ( 2009

- 2010 ) Prospective ( 2011

- Jan.

2013) Total

AZ 967 1,395 2,362 304 475 778

CA 4,377 6,183 10,560 966 1,472 2,438

CO 688 1,051 1,739 126 191 317

ID 206 314 520 40 60 100

MT 141 221 362 26 39 65

NV 365 592 956 104 159 263

NM 286 421 707 62 95 158

OR 515 764 1,279 94 143 237

UT 305 466 771 63 96 159

WA 878 1,315 2,193 158 242 400

WY 79 129 208 14 24 38

TOTAL 8,806 12,851 21,657 1,956 2,998 4,954 Sources: DOE Technical Support Documentation accompanying adopted standards (for “ Already Adopted ” standards); ACEEE/ASAP "KaBOOM: The Power of Appliance Standards Opportunities for New Federal Appliance and Eq uipment Standards" (for “P rospective ” standard s with final rules scheduled by January 2013).



### 2.3 Expected Energy Efficiency Savings

The 10

- Year Common Case load forecasts are intended to reflect the expected impact of

customer

- funded energy efficiency programs and federal standards. The projected savings from

the se two sets of policies are summarized in Figure 4 (annual energy) and Figure 5 (annual peak demand) , focus ing specifically on the savings from customer

- funded energy efficiency programs implemented over the 2011

- 2021 and the savings from new federal standards or updates to existing standards issued from January 2009 to January 2013. S avings are expressed in terms of the percentage reduction in 2021 loads for each BA and for WECC as a whole

. Note that for a number of BAs, the underlying policies or data sources do not d istinguish between the savings

from customer

- funded programs and from federal standards

, in which case the figures present only the combined impact (the green bars). Refer to Appendix A for further details, including energy and peak savings projections ex pressed in absolute GWh and MW terms , and details on the underlying policies and data sources used to develop the projections for each BA

.

10

### As shown, the set of energy efficiency policies considered for the 10

- Year Common Case are expected to reduce WECC

- w ide annual energy requirements by roughly 10% in 2021 , and

aggregate non

- coincident peak demand by 12%. Naturally, the impacts vary considerably across individual BAs, reflecting differing degrees of underlying policy support, as well as differences

in cu stomer mix , climate, end

- uses, and other factors.

Figure 4

. Expected Energy Efficiency Savings for the 10

- Year Common Case: Annual Energy

Figure 5

. Expected Energy Efficiency Savings for the 10

- Year Common Case: Non

- Coincident Peak 0% 2% 4% 6% 8% 10% 12% 14% 16% 18%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC TotalPercent of 2021 Annual Energy 

### Customer

- Funded EE Programs (2011

- 2021) Federal Standards Adopted 2009

- 2013 Combination of Policy Mechanisms

California Canada Northwest Southwest

0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC TotalPercent of 2021 Annual Peak MW 

### Customer

- Funded EE Programs (2011

- 2021) Federal Standards Adopted 2009

- 2013 Combination of Policy Mechanisms

California Canada Northwest Southwest



### 11

### 2.4 Adjustments to LRS Load Forecasts

To the extent that the LRS load forecasts submitted by the BAs were determined to not fully capture the expected impacts of current energy efficiency policies and program plans, they were adjusted downward accordingly. These adjustments are summarized in Figure 6 (annual energy)

and Figure 7 (peak demand), in terms of the percentage reduction to the annual energy and peak

demand in 2021

. See Appendix A for the corresponding adjustments expressed in absolute GWh

and MW terms on a monthly basis. As noted previously , adjustments were defined for the year 2021, as that was the terminal year of the LRS load forecasts; once the adjustments were made, the forecasts were then extrapolated out to 2022, the horizon year of WECC’s 10

- Year study.

As shown, no adjustments wer e made for ten BAs ( i.e., the WECC Common Case load forecast s for those BAs are simply equal to the original LRS load forecast s submitted to WECC ) . The remaining 22 BA load forecasts were all adjusted downward , at a minimum, to account for the expected acceleration in savings from federal standards. The magnitude of this individual adjustment ranges from roughly 1

- 3% of annual energy and 1

- 4% of non

- coincident peak

demand , depending on the extent to which the LRS load foreca st accounts for updates to federal

standards scheduled to occur through January 2013

. In addition, the forecasts for 11 BAs were adjusted downward to account for the expected impact of customer

- funded efficiency programs. Those adjustments were quite siz eable in the case of several BAs where existing policies mandate substantial efficiency savings that are not fully captured within the LRS forecast s

.

In aggregate, WECC

- wide load in 2021 was adjusted downward by 3. 2 % for annual energy and by 5. 0 % for pe ak demand. More than half of the overall WECC

- wide adjustment consists of the adjustment to the CISO forecast, which, as noted previously in Table 1 , did not account for a large portion of expected savings from efficiency programs and policies over the forecast period.

Figure 6

. Adjustments to LRS Load Fo recasts (Annual Energy) - 8% - 7% - 6% - 5% - 4% - 3% - 2% - 1% 0%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC TotalAdjustment to 2021 Annual Energy



### Customer

- Funded EE Programs Federal Standards Adopted 2009

- 2013 Combination of Policy Mechanisms California Canada Northwest Southwest



### 12

### Figure 7

. Adjustments to LRS Load Fo recasts ( Non

- Coincident Peak)



### 2.5 WECC Common Case Load Forecasts

The WECC Common Case load forecasts are based on the LRS load forecasts submitted by each BA, adjusted downward in many cases to reflect the expected impact of current energy efficiency polici es and program plans. Figure 8 and Figure 9 present the WECC Common Case

load forecasts for each BA in terms of its compound annual growth rate (CAGR) over the 2010

- 202 2 period. The figures also show the impact of the energy efficiency adjustments identified in Section 2.4, in terms of the associated reduction in CAGR for each BA. The total height of the

stacked bars in Figure 8 and Figure 9 therefore ind icate the CAGR of the original LRS load

forecasts.

Across the entire WECC footprint, load growth in the Common Case occurs at a CAGR of 1.4% per year in terms of annual energy, and 1.3% in terms of aggregate non

- coincident peak demand. The energy efficie ncy adjustments reduced forecasted WECC

- wide growth rate by 0.3% per year

for annual energy and by 0.5% per year for aggregate non

- coincident peak. As to be expected, growth rates vary considerably across BAs, ranging from 0.3% to 4.0% per year for annual energy and from

- 0.2% to 4.0% per year for non

- coincident peak demand. V ariation in growth rates across BAs largely reflects differences in the original LRS load forecasts submitted by the BAs. However, the energy efficiency adjustments made by the DSM Work Group also differed

across BAs , and therefore variation in the Common Case growth rates across BAs also reflects

differences in the s ize of th e energy efficiency adjustments. Of particular note, given its size, the growth rate for the CISO balancing authority was reduced from 1.5% to 0.8% per year for annual energy, and from 1.5% to 0.3% per year for peak demand. - 14% - 12% - 10%

- 8% - 6% - 4% - 2% 0%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC TotalAdjustment to 2021 Annual Peak MW



### Customer

- Funded EE Programs Federal Standards Adopted 2009

- 2013 Combination of Policy Mechanisms California Canada Northwest Southwest



### 13

### Figure 8

. WECC 10

- Year Common Case Load Forecast Growth Rates (Annual Energy)

Figure 9

. WECC 10

- Year Common Case Load Forecast Growth Rates ( Non

- Coincident Peak) 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC Total2010-2022 CAGR (Annual Energy) 

### Reduction from Original Forecast

WECC Common Case

California Canada Northwest Southwest

- 0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC Total2010-2022 CAGR (Peak Demand) 

### Reduction from Original Forecast

WECC Common Case

California Canada Northwest Southwest



### 14

## 3. SPSC 10

- Year High DSM Case

Pursuant to SPSC ’s 2011 study request, the 10

- Year High DSM /DR Case is intended to reflect a

future with higher levels energy efficiency savings than in the Common Case (as well as higher levels of demand response and distributed generation, which are not discussed within this report) . The SPSC DSM Work Group defined the efficiency benchmark for the High DSM /DR

Case

– hereafter referred to simply as the “High DSM Case”

– to be all cost

- effective energy efficiency available over the course of the 10

- year forecast period.

This cost

- effectiveness criterion corresponds to what is commonly referred to as the “economic potential” in efficiency potenti al analysis, as illustrated in Figure 10

. The economic potential is a

subset of the “ technical potential, ” which represents the energy efficiency savings that would

occur by upgrading all technologies (e.g., light bulbs, motors, windows, etc.) to the most

efficient model available, irrespective of cost

. The economic potential is thus that portion of the technical potential that is determined to be cost

- effective, given the cost of each individual efficiency measure and its benefits (e.g., avoided fuel and generation capacity costs). The “program achievabl e potential” , on the other hand, is a subset of the economic potential, and represents the savings that would be achieved through a specific program or set of programs, given specified incentive levels and other program design features. The SPSC 10

- year H igh DSM Case is intended as a “bounding analysis” indicating the savings that might be achieved through very aggressive energy efficiency efforts across WECC, but does not stipulate the particular mix of policies and programs (codes, standards, customer

- fu nded DSM, etc.) that could achieve these savings. Thus, economic potential was deemed to represent the most appropriate benchmark to define the High DSM Case energy efficiency savings.

Figure 10

. Types of Energy E fficiency P otential



### The High DSM Case load forecast for each BA was constructed by building off the analysis that was conducted for the WECC Common Case, as illustrated in Figure 11

. As described in the previous chapter, the analysis for the WECC Common Case involved developing adjustments to

the LRS load forecast for each BA . Those adjustments were intended to reflect the energy efficiency savings projected to occur under curr ent policies and program plans, to the extent that those savings were not already captured within the LRS forecast. For the High DSM Case, an additional set of adjustments were then applied to the Common Case forecasts, to reflect the

difference between t he estimated economic potential and the energy efficiency savings in the

Common Case. As in the analysis for the Common Case, these adjustments were applied to the load forecast for the year 2021 (the terminal year in the underlying LRS load forecasts), a nd the resulting High DSM load forecasts were then extrapolated to 2022, the horizon year for WECC’s

10

- Year study.

15

### Figure 11

. Illustration of Energy Efficiency Adjustment for the SPSC 10

- Year High DSM Case



### 3.1 Data Sources and Assumptions

The central analytical component to the development of the High DSM load forecasts was to estimate the economic potential (i.e., all cost

- effective energy efficiency savings) for each BA. To the extent possible, this analysis relied upon the most recent energy efficiency potential studies conducted for utilities and regions within WECC (as of the time that the analysis for the High DSM study case was conducted, circa October 2011). The set of energy efficiency potential studies utilized for t his effort are listed in Table 5 . In the case of states or BAs for

which no recent potential st udies existed

, economic potential estimates were estimated by extrapol ating the results from potential studies in similar or nearby regions.

Table 5

. Energy E fficiency P otential S tudies U sed for the SPSC 10

- Year High DSM Case
Region Utilities Studies

Mountain PSCo KEMA. 2010. Colorado DSM Market Potential Assessment: Final Report. Prepared for Xcel Energy.

Tri

- State Nexant. 2010. System Wide Electric Energy Efficiency Potential Study

Colorado Springs Summit Blue Consulting. 2010. Colorado Springs Utilities Demand

- Side Management Potential Study and Plans.

Alberta Canadian Manufacturers and Exporters Association. 2010. Improving Energy Efficiency for Alberta’s Industrial and Manufacturing Sectors.

Pacific

Northwest N/A (region

- wide) Northwest Power and Conservation Council. 2010. 6 th Powe r Plan

. BC Hydro Marbek & Associates. 2007. BC Hydro 2007 Conservation Potential

Review

. Idaho Power Nexant. 2009. Idaho Power Demand Side Management Potential Study.

Northwestern Nexant. 2010. NorthWestern Energy Assessment of Energy Efficiency Potentials (2010

- 2029)

.

Pacific Power Quantec. 2007. PacifiCorp Assessment of Long

- Term, System

- Wide Potential for Demand

- Side and Other Supplemental Resource

Cadmus. 2011. Assessment of Long

- Term System

- wide Potential for

Demand

- Side and Other Supplemental Resources

. 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021Energy or Peak Demand

WECC Common Case All Cost
Effective EE

High DSM/DG Case Balancing Authority Load
Forecast Submitted to WECC Embedded Savings from EE Policies & Programs (2011

- 2021) Expected EE Savings from

Current
Policies & Programs (2011

- 2021)



### 16Southwest Rocky Mountain Power See Pacific Power

Arizona Public Service ICF. 2007. Arizona Public Service Energy Efficiency Potential Study.

Public Service New

Mexico Itron. 2006. Public Service New Mexico Electric Energy Efficiency Potential Study

. Salt River Project Cadmus. 2010. Salt River Project 2012

- 2017 Energy Efficiency Plan, Final Report.

California Investor

- owned utilities California Energy Commission. 2010. Incremental Impact s of Energy Efficiency Policy Initiatives Relative to the 2009 Integrated Energy Policy Report Adopted Demand Forecast

. Itron. 2008. California Energy Efficiency Potential Study

.



### Applying the results of recent energy efficiency studies to develop savings projections for each BA for the SPSC High DSM Case typically required a range of additional assumptions and analytical steps (e.g., adjusting the potential estimates based on the d ifference between the baseline for the potential study and the baseline for the Common Case savings projection, grossing up potential estimates from the customer

- meter to the bus

- bar, estimating peak demand savings from energy savings, extrapolating effici ency potential estimates from an individual utility to a larger region, etc.). These additional elements of the analysis are detailed in Appendix B, and are summarized below for each state/province (with the exception of the Pacific Northwest states, whic h are summarized together):

• Alberta : Economic potential estimates from a recent Alberta industrial/ manufacturing

sector potential study from the Canadian Manufact urers and Exporters (CME) , and other potential studies for neighboring utilities/states, were applied to AE SO’s LRS load

forecast for 2020 . Oil

- sands industry potential was estimated indirectly based upon petroleum refining sector potential estimate from the CME study

.

• Arizona : Economic potential estimates from a recent study conducted for S RP by

Cadmus were used. Results were grossed up to the bus

- bar to account for T&D losses.

Peak demand savings, which were not included in SRP study , were estimated based on stipulated peak

- energy ratios using peak estimates from recent potential studies fo r neighboring states. SRP results were extrapolated to other Arizona BAs , on a sectoral basis, based upon 2008 retail sales for each customer sector

.

• British Columbia : Economic potential estimation for BCTC were based on t he results of a 2007 BC Hydro po tential study. Results were grossed up to the bus

- bar level to account

for T&D losses. Estimates were extrapolated from BC Hydro service territory to the entire balancing authority in proportion to retail sales.

• California : The savings projection for CAI SO was based on the California Energy

Commission ’s 2010 “incremental uncommitted savings” report. Economic potential

results for investor

- owned utilities grossed up to account for T&D losses, and extrapolated to the remainder of CAISO BA. S avings project ion for the other California balancing authorities were constructed by extrapolating the potential estimate for the IOUs’, in proportion to each balancing authority’s net energy for load . P eak demand savings for each non

- CAISO balancing authority were cal culated based on the peak

- to

energy savings ratio implied by the Common Case savings assumption for the same

17

### balancing authority (in part to maintain greater consistency with the underlying load shapes inherent in each balancing authority’s Common Case for ecast).

• Colorado : High DSM assumptions for the PSCO balancing authority were based on the 2010 potential study for PSCo, the utility, by K EMA . That study’s “net economic potential” estimates (excluding naturally occurring efficiency) were used. Avoided T&D

losses were added to the net economic potential estimate , and PSCo service territory savings potential estimates were extrapolated to the entire PSCO balancing authority. High DSM scenario savings estimates for the WACM balancing authority were based on the results of two recent energy efficiency studies conducted for utilities in the region: A 2009 study for Colorado Springs Utilities and a 2010 study for Tri

- State Generation and Transmission Cooperative. The e conomic potential estimate for Colorado Springs was adjusted to account f or naturally

- occurring savings, and the potential estimates from both

studies were grossed up to the bus

- bar to account for avoided T&D losses. The results

were then extrapolated to the entire Colorado portion of the WACM balancing authority.

• Nevada : No recent efficiency potential studies for Nevada utilities were available, so the savings projections for the 10

- year High DSM case were developed by extrapolating the results of two recent economic potential studies for other utilities in the desert southwest:

a 2010 potential study for Salt River Project and a 2007 potential study for Rocky

Mountain Power (RMP)

. T he potential study results were extrapolated to the two Nevada balancing authorities in Nevada, on a sector

- by

- sector basis, in proportion to the 2008 retail sales in each balancing authority. Because the SRP and RMP potential studies did

no t analyze peak demand savings, peak savings associated with a chieving the economic potential were estimated by applying a stipulated peak

- to

- energy savings ratio for each sector, based on findings from other potential studies conducted f or utilities in the

Southwest.

• P acific Northwest States : 10

- year High DSM Case efficiency savings projections were developed for each state, and these state

- level savings then allocated to the balancing

authorities in the PNW. The savings projections were based on the conservation poten tial assessment conducted by the Northwest Power and Conservation Council (NPCC) for its

6 th 

### Power Plan. At the request of the SPSC DSM Work G roup, NPCC staff provided an estimate of the “total economic potential” in 2020, for the NPCC planning area as a whole. This estimate assumed greater achievement of potential, and fewer constraints on efficiency program implementation, than the regional conservation target in the 6 th 

### Plan,

and therefore the efficiency projection for the SPSC High DSM Case were great er than the conservation target in the 6 th 

### Plan

. The ratio of this total economic potential to the NPCC’s conservation potential for the entire NPCC planning area was applied to each NPCC state (Idaho, Oregon, Washington, and Western Montana) to yield sta te

- specific

estimates of the total economic potential. The se state

- level potential estimates were then allocated to individual balancing authorities in proportion to retail sales.

• Utah : The s avings projection for the PACE balancing authority was based in part on the Cadmus 2011 energy efficiency potential study for PacifiCorp, which estimated technical potential in the year 2030 for the Utah portion of PacifiCorp’s service territory. These

18

### results were applied to estimate the technical potential for the year 2021 . An e conomic potential estimate was then derived from the technical potential estimate, and finally the

results were extrapolated to the entire Utah portion of the PACE balancing authority

.

• Wyoming : As with Utah, the savings projection for the PACE balancing authority was based in part on the Cadmus 2011 energy efficiency potential study for PacifiCorp, which estimated technical potential in the year 2030 for the Wyoming portion of PacifiCorp’s service territory. These results were appli ed to estimate the technical potential for the year 2021. An economic potential estimate was then derived from the technical potential estimate, and finally the results were extrapolated to the entire Utah portion of the PACE balancing authority. High DS M scenario savings estimate for the WACM balancing authority were based on a 2010 energy efficiency potential study for Tri

- State Generation and Transmission Cooperative , conducted by Nexant. For the High DSM scenario

, it was

assumed that the full economi c potential is achieved in the Wyoming portion of the WACM balancing authority region. Avoided T&D losses were then added to the economic potential estimates, and the potential study results were extrapolated to the entire Wyoming portion of the WACM balan cing authority.

3.2 High DSM Case Savings Projections

The 10

- Year High DSM Case savings projections are intended to reflect the achievement of all

cost

- effective efficiency potential available over the forecast period , and are based on recent energy efficien cy potential studies conducted for western utilities

. The se savings projections are summarized in Figure 12 (annual energy) and Figure 13 (annual peak demand)

, along with the corresponding savings projections for the WECC Common Case, for comparison . Savings are expressed in terms of the percentage reduction in 2021 loads for each BA and for WECC as a

whole. Refer to Table B

- 1 and Table B

- 2 in Appendix B for further details, including energy and peak demand savings expressed in absolute GWh and MW terms.

As shown, the energy efficiency savings projections for the 10

- Year High DSM Case reduce

WECC

- wide annual energy requirements by roughly 18 % in 2021, and aggregate non

- coincident peak demand by 21 %. These impacts are almost double those of the efficiency savings in the Common Case (a 10 % reduction in annual energy and a 12% reduction in peak demand).

Naturally, the High DSM Case savings projections vary considerably across individual BAs, typically ranging from a 15

- 25% reduction in annual energy and a similar range for peak demand

reductions. These variations reflect differences in such things as customer mix, climate, end

uses, and other factors. For example, because energy efficiency p otential for the industrial sector is typically lower than for residential and commercial customers, those BAs that have relatively large amounts of industrial load (such as NWMT) have relatively low energy efficiency savings in the High DSM Case. There a re also sizable differences across BAs in terms of the incremental savings in the High DSM Case relative to Common Case, which partially reflects differences in the aggressiveness of energy efficiency policies assumed within the Common Case. For example, for CISO, where energy efficiency savings in the Common Case are relatively large, the High DSM Case savings equate to only an additional 1.5% reduction in

19

### annual energy in 2021, whereas for AESO, which has negligible efficiency policies in the

Common Case , the High DSM Case equates to an additional 19% reduction in load.

Figure 12

. Energy Efficiency Savings for the 10

- Year High DSM Case: Annual Energy

Figure 13

. Energy Efficiency Savings for the 10

- Year High DSM Case: Non

- Coincident Peak



### 3.3 High DSM Case Load Forecasts

As described at the beginning of this chapter, t he SPSC 10

- Year High DSM Case load forecasts

are built off the WECC Common Case forecasts, adjust ed downward to reflect the additional savings in the High DSM Case beyond what is captured within the Common Case forecasts (i.e., 0% 5% 10% 15% 20% 25% 30%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC TotalPercent of 2021 Annual Energy 

### High DSM/DG Case

WECC Common Case

California Canada Northwest Southwest

0% 5% 10% 15% 20% 25% 30%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC TotalPercent of 2021 Peak Demand 

### High DSM/DG Case

WECC Common Case

California Canada Northwest Southwest



### 20

### beyond the expected savings from current energy efficiency policies and program plans )

. Figure

14 and Figure 15 present the SPSC 10

- Year High DSM Case load forecast for each BA in terms

of its CAGR ove r the 2010

- 2022 period , compared to the corresponding CAGR for the Common

Case

. Refer to Table B

- 1 and Table B

- 2 in Appendix B for further details, including the

forecasted values fo r energy and peak demand savings in 2021, expressed in absolute GWh and

MW terms.

In effect, the energy efficiency savings assumed in the High DSM Case largely flatten load growth for WECC as a whole. Within the High DSM Case, WECC

- wide load grows at a r ate of 0.5% per year in terms of annual energy and 0.3% per year in terms of aggregate non

- coincident peak demand . This compares to growth rates of 1.4% and 1.3% per year in the Common Case

, for annual energy and peak demand, respectively

. The additional savings achieved in the High

DSM Case thus serve to shave reduce WECC

- wide growth rates by about 1% per year. As in the
Common Case, growth rates in the High DSM Case vary considerably across BAs, ranging from

- 1.1% to 2.3% per year for annual energy and from

- 1

. 3 % to 2

. 5 % per year for non

- coincident peak demand (excluding CFE, for which no savings projections were made for the High DSM Case)

. This v ariation is partly a reflection of the underlying variation in growth rates for the Common Case, as well a s reflecting differences in the size of the energy efficiency potential estimated for the High DSM Case.

Figure 14

. SPSC 10

- Year High DSM Case Load Forecast Growth Rates (Annual Energy) - 1.5% - 1.0% - 0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC Total2010-2021 CAGR (Annual Energy)



### WECC Common Case

High DSM/DG Case

California Canada Northwest Southwest



### 21

### Figure 15

. SPSC 10

- Year High DSM Case Load Forecast Growth Rates (Non

- Coincident Peak) - 1.5% - 1.0% - 0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0%CISOIIDLADWPSMUDTIDAESOBCTCAVABPACHPDDOPDGCPDIPCNWMTPACWPGEPSESCLTPWRWAUWAPSCFEEPENEVPPACEPNMPSCOSPPSRPTEPWACMWALCWECC Total2010-2021 CAGR (Annual Peak) 

### WECC Common Case

High DSM/DG Case

California Canada Northwest Southwest



### 22

## 4. SPSC 20

- Year High DSM Case

The basic principle for SPSC’s 20

- year High DSM study case was to develop a load forecast that reflects significantly higher energy efficiency levels than in the WECC Reference Case. For this

purpose, LBNL and its contractor, Itron, created a set of forec asts using Itron’s S tatistically

A djusted E nd

- U se (SAE) load forecasting framework and modeling tool, which allows the specification of end

- use efficiency levels for 30 individual residential and commercial end

- use categories.

This approach differs consi derably from that used for the 10

- year High DSM analysis, and was intended to offer a greater degree of internal consistency and transparency. In particular, unlike

the 10

- year High DSM study case, which relied on a disparate set of energy efficiency pote ntial studies with varying methodologies and scopes, the 20

- year High DSM study case is based on a common methodological framework and scope of end

- use measures across all regions. In addition, whereas the 10

- year High DSM study case required the applicat ion of energy efficiency potential study results to load forecasts developed independently by the various BAs

– leading to possible double

- counting or under

- counting of energy efficiency savings

– the 20

- year High DSM study case was built upon an initial s et of load forecasts developed using the Itron SAE framework, allowing for more

- explicit and internally consistent accounting of energy efficiency impacts.

Figure 16

. Application of the SAE Load Forecasting Model for the SPSC High DSM Study Case



### The analysis for the 20

- year High DSM study case proceeded in the following steps, which are illustrated schematically in Figure 16 . First, we u sed the SAE model to develop an initial set of load forecasts, termed the “SAE Base Case” forecasts, which reflect regional assumptions about

end

- use efficiency trends based primarily upon those of the U. S. Energy Information Administration (EIA ) ’s Nation al Energy Modeling System (NEMS). T hese Base Case forecasts

were then the starting point for developing the “SAE Reference Case” forecasts, which incorporated state

- specific assumptions about future DSM program savings under current policies and program pl ans. The SAE Base Case forecasts were also the starting point for developing the “SAE High DSM Case” forecasts, which are based on stipulated increases in end

use effici ency, relative to the Base Case . Finally, the SPSC 20

- year High DSM Case forecast

was calculated by, in effect, calibrating the SAE High DSM Case to the WECC Reference Case.Energy or Peak Demand

SAE Load Forecasts(3) SAEHigh DSMCase(2) SAE Reference Case(1) SAE Base Case

WECC Study Cases(4) SPSCHigh DSMCaseWECC Reference Case X% Calculated X%

Reduction



### 23

### This step was necessitated by the fact that the 2032 WECC Reference Case was developed independently from the SAE load forecast effort , by simply extrapolating the 2022 Common

Case forecasts out an additional 10 years . The calibration of the SAE High DSM forecast simply entailed taking the percentage load reduction from the SAE Reference Case to the SAE High

DSM Case

– identified generically in Fig ure 16 as an “X% Reduction”

– and then applying that percentage reduction to the WECC Reference Case forecast.

This chapter is organized as follows. In the next section, we describe the I tron SAE framework and the key input assumptions for the SAE Base Case. In S ection 4.2 , we discuss conceptually how DSM impacts are captured within the SAE load forecasting framework. In Section 4.3 , we then summarize the methodology for and present the numerical results of the SAE Reference

Case load forecast. In S ection 4.4 we describe the approach to the SAE High DSM Case, including the underlying assumptions, data sources for the projected efficiency improvements, key calculations, and numerical results. Sections 4.5 and 4.6 present the details of the SAE High DSM incremental energy savings (relative to the SAE R eference Case) and the SPSC High DSM WECC load forecasts, respectively. Further details about the SAE load forecasting model and

about the assumptions employed in developing the SAE load forecasts are contained within Appendix C, and are referenced through out the relevant sections of this chapter.

4.1 The SAE Load Forecasting Framework and Base Case Assumptions

As its name suggests, the statistically

- adjusted end

- use approach is a hybrid framework, combining end

- use technology detail with statistical estimati on. It was developed to extend the standard utility econometric load forecasting methodologies in order to incorporate information on DSM programs to account for the effects of these programs on loads, including the potential effects of new or changed prog rams. Previous applications of the Itron SAE tool have included modeling the effects of federal policies and regulations

– such as those for energy

- efficient lighting

– at the utility service territory level, and multi

- utility, multi

- jurisdictional, long

- r un forecasting of hourly loads as well as peak demands.

The core of the SAE framework is an econometric model of the form shown in Figure 17 . Here,
energy consumpt ion ( Energy ) is represented as a function of a set of indices ( XCool

, XHeat , and XOther) constructed from detailed end

- use and building stock data, as shown in the boxes above

the formula. 7 

### For this analysis, models of this functional form were estimated for monthly energy use and monthly peak demand within each BA.

7 The other terms in the regression formula

, e m , and a , are the error term and the y

- intercept , respectively

.



### 24Source: Itron


### Figure 17

. SAE Econometric Framework



### Developing the econometric models required a w ide variety of data inputs, as illustrated in Figure 18 . Load data, which were provided by WECC, consisted of historical hourly load data for each BA, extending over the period 1998

- 2010; for the purpose of estimating the model, these data were transformed into monthly energy and peak loads. The economic and demographic data, obtained from Moody’s Analytics (a subscription service), included both

historical and forec asted data for the number of households, population, income, employment, and gross state product. These economic and demographic data were provided for each U.S. state in the Western Interconnection and for the three primary urban areas (Vancouver, Calgar y, and Edmonton) within the two Canadian provinces in WECC. Historical weather data were obtained from DTN (a subscription service), consisting of daily average dry bulb temperatures for 100 weather stations dispersed throughout WECC, for the period 1991

- 2011. From those data, average weather conditions (cooling degree days, heating degree days, and peak producing weather) were computed for each BA and month, and were used as the basis for the load

forecasts. See Section C.1 in Appendix C for further info rmation about the economic and

weather data. Res XHeat +

Com XHeat Res XCool +

Com XCool Residential Heating Equipment Stock

Saturation & Efficiency Thermal Efficiency Square Footage Trends Usage Trends Commercial Heating Equipment Stock

Fuel Share & Intensity Floor Space Trends Usage Trends

Cooling Degree Days Heating Degree Days Residential Cooling Equipment Stock

Saturation & Efficiency Thermal Efficiency Square Footage Trends Usage Trends Commercial Cooling Equipment Stock

Fuel Share & Intensity Floor Space Trends
Usage Trends Residential Other Equipment Stock

Saturation & Efficiency Usage Trends Commercial Other Equipment Stock

Fuel Share & Intensity Usage Trends
Industrial

Economic Trends Energy Intensities

Res XOther +

Com XOther +

Ind XOther



### 25Source: Adapted from Itron


### Figure 18

. Data Inputs and Outputs for the SAE Model



### The final

– and critical

– set of inputs for the SAE model is the building stock and end

- use data.

The end

- use data include historical data and forecasts for the saturation and average stock efficiency levels of 30 separate residential and commercial end

- uses, as listed in Table 6 . These

definitions of end

- use categories, as well as the units used to characterize efficiency, are taken with some variations directly from those used in the Energy Information Administration (EIA)’s National Energy Modeling System (NEMS).

Table 6

. End Uses Included in SAE Model

Residential Commercial

Furnace Cooking Heating Heat Pump Refrigerator Cooling Ground Source Heat Pump Second Refrigerator Ventilation Secondary Heat Freezer Waste Heat

Furnace Fan Dish Washer Cooking

Central Air

- Conditioning Clothes Washer Refrigeration Heat Pump Cooling Dryer Outside Lighting Ground Source Heat Pump Cooling Television Inside Lighting

Room Air

- Conditioning Lighting Office Equipment

Waste Heat Miscellaneous Miscellaneous

Source: Adapted from Itron.



### As a default, end

- use saturation and average efficiency values used for each BA were based on

the EIA’s 2012 Annual Energy Outlook ( AEO ) reference case, which includes historical data and

forecasts for each end

- use, specified at the U.S. Census region level. Where possible, however,

state

- or utility

- specific data were used in lieu of the EIA Census region data and projections.

These alt ernative data sources included: the U.S. Census, FERC, Northwest Power and Conservation Council, the California Energy Commission, National Research Council Canada, and a variety of utility

- specific energy efficiency potential studies. Details on which da ta
sources were used for each end

- use and BA can be found within Section C.3 in Appendix C. As

26

### discussed later in Section 4.4, the SAE High DSM load forecasts were developed by modifying these stock efficiency assumptions.

4.2 Accounting for Energy Efficiency Impacts within the SAE Framework

Energy efficiency policy and program impacts are embedded in several aspects of the SAE load forecasting model. First and most obviously, efficiency improvements are captured in the stock efficiency data and pr ojections used to develop the SAE end

- use indices (i.e., the right

- hand side of the regression equation in Figure 17 ). In addition, when the SAE mode l is estimated from historical load data, the effects of historical DSM programs embedded in such data will be reflected in the resulting model. To the extent that historical energy efficiency gains are greater (or less) than what is reflected in the hist orical stock efficiency data used to estimate the model, any load forecasts developed with the model will reflect a continuation of those historically higher (or lower) gains in energy efficiency, relative to the stock efficiency projection used to develop the load forecast.

Within the remainder of this section, we provide further details on the degree to which the SAE Base Case load forecasts reflect the expected impacts of energy efficiency policies and programs over the forecast period, and describe th e methods that can be used to adjust the load forecasts to reflect higher or lower energy efficiency impacts.

4.2.1 Energy Efficiency Impacts in the EIA

- AEO Reference Case Stock Efficiency Forecasts

As described in Section 4.1, the SAE Base Case forecast relie s on end

- use stock efficiency projections from EIA’s 2012 AEO Reference Case. Those stock efficiency projections partially capture future energy efficiency policy impacts 8 

### , and thus by extension, so do the SAE Base

Case load forecasts. The details of how d ifferent efficiency policies and programs are represented in the AEO Reference Case, however, vary as a consequence of the internal structure

of NEMS.

Federal appliance standards are represented explicitly by assigning appropriate values to model

paramet ers governing available end

- use technology characteristics. Specifically, the NEMS residential module’s internal selection procedure for new appliance purchases involves life

- cycle cost minimizing choices from a “menu” consisting of appliances providing a given end

- use energy service (such as refrigeration or clothes washing) but having different efficiencies and purchase costs. Thus, a federal appliance standard can be represented by removing from this

menu all units with efficiencies below the standard le vel. Within the 2012 AEO Reference Case, all federal appliance standards that had been established as of the date that the forecast was

8 EIA is required by the U. S. Congress to be “policy

- neutral” and therefore, in its Reference Case projection, attempts to incorporate the effects of federal, state and local policies a nd programs that are already in place at the time of the projection, or that have been both enacted and have had any required enabling legislation and/or funding appropriations put into effect. Energy and environment al policies, programs, and regulations (including but not limited to those affecting efficiency specifically) that may be under consideration or development, but have not been enacted, enabled and funded, are not represented in the EIA Reference Case. Howe ver, EIA regularly produces “side

cases” of the AEO that analyze the prospective effects of such measures, in addition to side cases that reflect different assumptions on the values of key exogenous inputs such as energy prices and rates of economic growth

.



### 27

### prepared (circa June 2012) are reflected within the end

- use stock efficiency projections, but no future updates to exist ing standards are included.

By contrast, NEMS accounts for state and local DSM programs (as well as other state and local energy efficiency policies and programs) only indirectly , through the calibration to historical equipment shipments data. 9 

### By virtue of that calibration, the AEO Reference Case stock efficiency projections implicitly reflect a continuation of DSM program activity at roughly the rate that has historically occurred (nationally). Thus, to the extent that future DSM program activity within a given BA region is similar to historical national trends, the stock efficiency assumptions used within the SAE Base Case will capture the impact of that future DSM program activity reasonably well. If, however, the expected future DSM program activity w ithin a given BA is more aggressive than historical national trends (as could be expected for most BAs within WECC), then the stock efficiency assumptions used within the SAE Base Case for that BA will

under

- state future DSM program impacts.

4.2.2 DSM Adjustmen ts within the SAE Framework

In general, load forecasts developed using the SAE model capture future DSM program impacts through two mechanisms: (a) the initial stock efficiency projections embedded in the X variables

of the model and (b) the relationship between actual the historical load data and the end

- use driven X variables. For the SAE Base Case load forecasts in particular, the use of EIA regional stock efficiency projections for many end

- uses and BAs ensures that the load forecasts will

reflect a c ontinuation of historical trends in national DSM program savings impacts . In addition,

the calibration of the model to historical load data for each BA will then ensure that the load forecasts also reflect a continuation of any historical difference between the rate of DSM savings for that particular BA and the national average.

In many cases

– such as in the analysis conducted for the SPSC High DSM study case

– it is necessary to develop load forecasts that reflect some higher or lower degree of DSM program activity than is embedded in the regional SAE inputs

. The SAE approach allows for several alternative approaches, depending on data availability and quality: 10



### (1) Add

- Back : Under this approach, the historical load data are modified by removing the impact of past DSM programs, and the model is econometrically estimated from those

9 This is in part because NEMS is disaggregated only to the U. S. Census region level, so that the model cannot explicitly capture details of state and local programs or policies, and in part because of general limitations on data and empirical research that would enable the effects of state and local policies and programs to be reasonably well represented across the U. S. The calibration of the NEMS residential and commercial modules for the AEO Reference Case is accomplished by setting a small number o f key parameters so that the model’s projections of residential and commercial energy use are consistent with historical trends in energy use and appliance shipment data. In both modules, the most important such parameters are the “hurdle rates” that gover n end

- use technology adoption by life

- cycle cost minimization; the calibration adjusts these hurdle rates to a level consistent with historical energy use and appliance shipment data. In addition, in the residential module calibration, both costs and

absol ute bounds are imposed upon certain end

- use technology replacement and fuel switching decisions; in the commercial module, direct switching costs are not imposed but other limitations are placed on the pace and extent to which new technologies

– including higher

- efficiency types

– can penetrate the market. These details are discussed at greater length in Auffhammer and Sanstad (2011).

10 These methods are described in greater detail in McMenamin and Quan (2010).



### 28

### hypothetical historical loads. The load forecast generated with the model therefore represents projected loads without future DSM programs. That forecast is then adjusted downward ex post based on the projected cumulative savings from both historical and future DSM programs. This method is most appropriate when there is a relatively short history of DSM program activity, and program data allow for accurate estimation of the ir past and future effects at the requisite level of granularity.

(2) DSM Variable : DSM program savings are incorporated explicitly as an independent variable in the regression model (i.e., among the set of “Other” variable on the right

- hand side of the equation in Figure 17 ). The data requirements for this method are similar to

the Add

- Back method, but further require that historical DSM program impacts of been large enough to be able to detect a statistically significant effect on historical loads

.

(3) DSM Trend : With this approach, t he econometric model is estimated on actual historical data without any modification . Forecasts from the estimated model assume continuation of programs at the average historical activity level. If future DSM program s are expected

to either accelerate or decelerate relative to the estimated historical trends, an ex post adjustment is made to the initial load forecast

. The size of the adjustment reflects the expected difference between cumulative DSM impacts expected to occur with existing programs and cumulative impacts expected to occur with accelerated or decelerated

programs . This method , which is illustrated in Figure 19, is appropriate when a relatively long history of DSM effects in present in the data, and when t he forecast horizon is itself relatively long.

Source: McMenamin and Quan (2010).


### Figure 19

. Adjusting SAE Load Forecasts Using the DSM Trend Method



### The SAE Reference Case and SAE High DSM load forecasts were both developed by starting with the SAE Base Case forecasts and then making a set of adjustments to reflect higher levels of assumed energy efficiency impacts. For the SAE Reference Case forecasts, the DSM Trend With this Method, the blue line represents the Base Case with

continued DSM efforts.

The green line represents

acceleration due to more aggressive DSM activity.



### 29

### method (i.e., the third of t hree general approaches outlined above) was generally used . This technique was the simplest and most appropriate given the data available for most of the WECC

BAs. Further details on the EE adjustments for the SAE Reference Case are given in the next

secti on. For the SAE High DSM Case load forecasts, an entirely different technique from the

three general approaches described above was used. This approach, which is described in great

detail in Section 4.4 , involved a form of ex post adjustment s applied at the individual end

- use level , based on stipulated increases in average stock efficiency

.

4.3 SAE Reference Case

The SAE forecasting framework was used to develop a set of reference case load forecasts intended to reflect the expected impact of current policies and program plans over the 20

- year forecast period . As mentioned above, this required making adjustments t o the SAE Base Case

load forecast for many BAs, in order to capture the effects of higher EE program

- induced savings relative to historical trends. Important to note is that the SAE Reference Case forecasts are wholly distinct from the official WECC Refere nce Case forecasts used in the 20

- year study. 11 

### The SAE Reference Case forecasts were not directly used or modeled within WECC’s 20

- year study, but instead, provide the baseline from which to measure the incremental impacts of the High DSM Case (as illustr ated in the earlier schematic, Figure 16 ).

4.3.1 Reference Case EE Adjustments

For BAs outside of California, the process for developing the reference case EE adjustment s involved the following series of steps (consistent with the DSM Trend method described in the previous section):

(1) The historical average annual incremental savings from EE programs was calculated for the period 1998

- 2010 (i.e., the historical period over which the SAE model was estimated). EIA Form

- 861 was the default data source used for this step, but historical savings data obtained directly from utilities, state PUCs, or regional energy efficiency organizations was used instead, if available.

(2) A proj ection of the expected EE program savings under current policies and program plans was developed for the 2011

- 2032 period, largely extending the analysis and assumptions utilized for the 10

- Year WECC Common Case (see Section 2.1).

(3) The SAE Base Case foreca st for 2032 was then adjusted downward based on the difference between the expected cumulative savings from EE programs implemented over

the 2011

- 2032 period (step 2) and the cumulative savings that would occur under a simple extrapolation of historical tr ends (step 1). The EE adjustment for each BA is

further allocated across the end

- use level energy and peak demand forecasts, yielding the SAE Reference Case load forecast disaggregated by end

- use.

Further details about both the adjustments for non

- California BAs, including the assumptions about historical and expected EE program savings, are provided in Section C.4 of Appendix C

.

11 WECC developed its own 20

- year reference c ase by extrapolating the 10

- year WECC Common Case forecasts

.



### 30

### F or the California BAs, the SAE Reference Case load forecasts were, instead, developed by directly modifying the initial end

- use efficiency assumptions

. Specifically, within the SAE modeling framework, projections of end

- use saturation and stock efficiencies were translated into projections of Energy Intensity (EI), which is typically denominated in un its of kWh per household per year (for residential end uses) or kWh per square foot per year (for commercial end uses). In the case of the California BAs, it was possible to directly specify EI projections

that reflect achievement of the state’s most rece nt long

- term energy efficiency goals, using the results of a recent energy efficiency potential study conducted for the CPUC (Navigant 2012)

. These EI projections were then used (in lieu of the EI projections used for the SAE Base Case forecasts) to gener ate a new set of load forecasts for the California BAs. Using this approach, the expected impact of future EE program s was captured via the SAE model inputs , rather than

as an ex post adjustment to the Base Case forecasts

.

Figure 20

. SAE Reference Case DSM Adjustments



### As shown in Figure 20 , DSM adjustments for the SAE Reference Case forecasts were made for 22 of the 39 load zones, where the size of the adjustment (expressed as a percentage of the SAE

Base Case forecast for 2032 ) ranged from 0

- 12% of annual energy consumption and 0

- 11% of annual peak demand. See Section C.4 of Appendix C for data on these DSM adjustments expressed in absolute GWh and MW terms. For the remaining load zones, where no DSM adjustments were made rel ative to the Base Case, the expected cumulative DSM program savings under current policies and program plans was generally deemed to be similar to what

would occur under a continuation of historical trends.

4.3.2 SAE Reference Case Load Forecast s

The SAE Refer ence Case load forecasts, produced by applying BA

- specific DSM adjustments to the SAE Base Case forecasts, are depicted in Figure 21 in terms of the CAGR over the 201 0

- 2032 period , for both annual energy and non

- coincident peak demand. See Section C.5 of Appendix C for forecast data expressed in terms of GWh and MW. Across the entire WECC footprint, load growth in the SAE Reference Case occurs at a CAGR of 1.4% per y ear ( annual - 12% - 10%

- 8% - 6% - 4% - 2% 0%AESOAPSAVABCTCBPACFECHPDDOPDEPEFAR_EASTGCPDIIDLDWPMAGIC_VLYNEVPNWMTPACE_IDPACE_UTPACE_WYPACWPGE_BAYPGE_VLYPGNPNMPSCPSESCESCLSDGESMUDSPPSRPTEPTIDCTPWRTREAS_VLYWACMWALCWAUWWECC

Annual Energy

Non

- Coincident PeakPercentage Reduction from SAE Base Case



### 31

### energy ) and 1 .2 % per year ( aggregate non

- coincident peak demand ) . As to be expected, growth rates vary considerably across BAs, ranging from

- 0.3% to 2.7 % per year for annual energy and

from

- 0. 8% to 2.6 % per year for non

- coincident peak deman d. 12 

### Variation in growth rates across

BAs reflects differences in the underlying load growth drivers (e.g., population and economic growth) as well as differences in the intensity of expected DSM program activity over the forecast period.

Figure 21

. SAE Reference Case Load Forecast Growth Rates (2010

- 2032)



### As noted previously, the SAE Reference Case forecasts are not used directly within WECC’s 20

- year study, but rather serve as the reference point from which to measure t he incremental savings in the SAE High DSM load forecasts. Instead, WECC’s 20

- year study relies on a separate set of

reference case forecasts

– termed the WECC Reference Case

– that were derived by extrapolating

the WECC 10

- Year Common Case load forecasts . As a cross

- check on both forecasts, it is instructive to compare the 20

- year WECC Reference Case forecasts with the SAE Reference

Case forecasts. As shown in Figure 22 , which focuses specifically on projected growth rates in annual energy consumption, the SAE Reference Case and WECC Reference Case forecasts are generally quite consistent, with relatively small differences for most BAs. Of particular note, the proje cted growth across WECC, as a whole, is identical between the two forecasts (1.4% per

year , as shown on the far right

- hand side of the x

- axis ).

12 A peculiarity in the forecasted growth rates for the PACE_ID load zone is apparent, where annual energy is forecasted to grow at a rate of 1.3% per year, whereas peak demand is forecasted to decline at

- 0.8% per year. Although further investigations woul d be needed to identify the precise cause, we suspect that it may be due to the

way the PACE load forecast was disaggregated into its three constituent load zones (which similarly might explain why, for PACE_UT, the forecasted growth in peak demand is sign ificantly higher than growth in energy)

. Given the manner in which the SAE Reference Case load forecasts are used (i.e., only as a benchmark against which to measure the incremental impacts of energy efficiency savings the High DSM case), this potential i ssue has limited material consequence for WECC’s modeling of the SPSC High DSM study case . Nevertheless, any future iterations of this analysis should examine and seek to resolve this peculiarity

. - 1.0% - 0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%AESOAPSAVABCTCBPACFECHPDDOPDEPEFAR_EASTGCPDIIDLDWPMAGIC_VLYNEVPNWMTPACE_IDPACE_UTPACE_WYPACWPGE_BAYPGE_VLYPGNPNMPSCPSESCESCLSDGESMUDSPPSRPTEPTIDCTPWRTREAS_VLYWACMWALCWAUWWECC2010-2032 CAGR 

### Annual Energy

Non

- Coincident Peak



### 32

### Figure 22

. Comparison of SAE Reference Case and WECC Reference Case Load Forecasts



### One important feature of the SAE forecasting framework is that it allows load forecasts to be disaggregated by customer segment and end

- use. Indeed it is this feature that lends the SAE framework to modeling the impact of energy efficiency programs and po licies. Figure 23 decomposes the SAE Reference Case load forecast into the three constituent customer segments,

for WECC as a whole as well as for each of three main g eographical regions within WECC (the Northwest, the Southwest, and California). 13 

### As shown, residential and commercial loads represent the bulk of annual electricity consumption within WECC, with industrial load constituting the remaining 22% of total WECC energy . The same pattern is similar across the three regions, but is most pronounced in California (where industrial load constitutes just 14% of annual electricity demand) and least pronounced in the Northwest (where industrial load constitutes 28% of the total load).

Figure 23

. SAE Reference Case Load Forecasts by Customer Segment

13 For our purposes here, the Pacific Northwest includes al l BAs in Oregon, Washington, Idaho, Montana, British Columbia, and Alberta; and the Southwest includes all BAs in all remaining states other than California. - 1.0% - 0.5% 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%AESOAPSAVABCTCBPACFECHPDDOPDEPEFAR_EASTGCPDIIDLDWPMAGIC_VLYNEVPNWMTPACE_IDPACE_UTPACE_WYPACWPGE_BAYPGE_VLYPGNPNMPSCPSESCESCLSDGESMUDSPPSRPTEPTIDCTPWRTREAS_VLYWACMWALCWAUWWECC2010-2032 CAGR Annual Energy

Non

- Coincident Peak

36% 39% 36% 35% 41% 47%

36% 42% 22% 14%
28% 23%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

WECC California Northwest Southwest 

### SAE Reference Case: Annual Energy (2032)

Industrial

Commercial

Residential



### 33

### Residential and commercial loads within the SAE forecasts are further disaggregated into 30 separate end

- uses (no end

- use detail exists for industrial load). To illustrate the relative significance of the various end

- uses and to provide context for the end

- use level adjustments applied in the High DSM case

, Figure 24 decomposes the SAE Reference Case forecast of annual energy for residential and commercial loads into ten end

- use “groups” (where, for simplicity, the 30 separate end

- uses in the SAE model have been collapsed into a smaller number of categories). Clearly there are many diff erent end

- uses that constitute a significant portion of load. Within WECC overall, the three largest end

- use groups are Commercial Miscellaneous, Residential Miscellaneous & TV, and Commercial Lighting, together representing just over 50%

of all residenti al and commercial load. These three groups are also the largest or among the largest within each of the three regions. As to be expected, the relative significance of heating and cooling

- related end

- use groups vary across regions depending on climate; fo r example, Residential Cooling represents 10% of electricity usage in the Southwest but only 2% in the

Northwest

. In contrast, Residential Space & Water Heating represents 16% of usage in the Northwest but only 8% in the Southwest and 2% in California. Thi s reflects the relatively high saturation of electricity for these end

- uses as well as colder winter weather in the Northwest region. See Section C.5 of Appendix C for additional numerical details.

Figure 24

. SAE Reference Case Load Forecasts (Residential & Commercial) by End

- Use Group



### 4.4 High DSM Case Approach and Assumptions

4.4.1 Overview and Rationale

T he High DSM study case focuses on a single year

, 2032, which is the end of the planning

horizon

. This case stipulates average stoc k efficiencies for each end

- use in that year, and then adjusts the SAE Base C ase load forecast for each end

- use based on the efficiency improvements

relative to the average stock efficiencies from the Base Case forecasts

. The average stock

efficiencies st ipulated for the High DSM case are intended to represent the most efficient equipment presently commercially available, i.e., circa 2012. That is, for the High DSM Case we assume that the average stock efficiency for each end

- use increases to the upper bo und 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

WECC California Northwest Southwest Residential Appliances

Residential Cooling

Residential Lighting

Residential Miscellaneous & TV

Residential Space & Water Heating

Commercial HVAC

Commercial Lighting

Commercial Miscellaneous

Commercial Office

Commercial Refrigeration 

### SAE Reference Case: Annual Energy (2032), Residential & Commercial Loads



### 34

### of technology in today’s markets

. 14 

### This assumption was selected on the basis of three criteria: namely, to develop efficiency assumptions for the High DSM Case that are aggressive (thus providing a “stress test” for the transmission planning exercise), that are grounded in verifiable data, and that are easy to communicate to and be understood by a diverse stakeholder audience. 15



### T he approach to developing the High DSM load forecasts is akin to assuming achievement of the full “technical potential” for energy efficiency, based on current commercially available technologies. 16 

### While the mechanisms driving these efficiency increases are not specified, we implicitl y presume that utility DSM programs

, as well as codes and standards and other potential

market forces or policy interventions

, may contribute. Because the SAE Base Case forecast already captures some future energy efficiency savings (as discussed in Section 4.2 ), the load reductions from the SAE Base Case to the SAE High DSM Case therefore do not represent the totality of all energy efficiency impacts over the forecast period, but rather represent only the

incremental energy efficiency savings to those alrea dy captured within the SAE Base Case

forecast.

For the residential and commercial sectors, the basic calculations to create the SAE High DSM

case were as follows:

(1) For each end

- use, specify the most efficient model currently commercially available

. (2) Calc ulate the percentage efficiency gain associated with moving from the average stock efficiency in the SAE Base Case load forecast for 2032 to the most efficient model currently commercially available (3) Apply the associated percentage energy savings to the SAE Base Case end

- use loads to

calculate the SAE High DSM Case load for that end

- use (4) Sum across end

- uses to calculate the total load for each BA

We provide further details on these steps below, following a summary description of information sources for, and assumptions regarding, equipment efficiencies.

14 While we are using presently

- available “best” technologies to define our 2032 high efficiency be nchmarks, it is important to note that our approach is consistent with the emergence of even higher

- efficiency units than the current

best

- on

- market becoming available and being adopted over the next two decades. Because we are focusing on stock

averages , the posited increases in the latter implicitly assume a corresponding increase in the high ends of efficiency ranges. Inasmuch as the current best

- on

- market is assumed to become the average in 2032, these high ends would be achieved with technologies that are either currently

- existing but not yet commercially available, emerging, or as yet to be conceived and developed.

15 A more complex approach might, instead, specify particular policy and programmatic assumptions for the High DSM case, and then translate those assumptions into stock efficiency projections for each end use and BA

. Alternatively, one might attempt to develop economic potential estimates for each region, and translate those estimates into specific stock efficiency projections. Either approa ch would have required a substantially greater number of assumptions and judgments, as well as a significant expansion to the analytical and data requirements.

16 Our analysis is analogous to a standard technical potential scenario in that it posits a larg e increase in efficiency levels relative to a baseline without addressing costs or cost

- effectiveness explicitly. However, the SAE framework differs from the highly

- detailed stock accounting

- based methodologies that are typically applied to efficiency

pote ntial studies. It is, among other differences, at a considerably higher level of aggregation with respect to end

- use details, and does not represent the specifics of building types. In addition, our approach to High DSM incremental energy savings does not account for interactions among end

- uses that are typically dealt with in detailed, “bottom

up” efficiency potential studies.



### 35

### F or industrial load, a simpler procedure was used

. In contrast to the engineering

- economics philosophy of the NEMS residential and commercial modules, the NEMS industrial module is based on a much simpler econometric forecasting structure; in particular, this module does not represent technologies, technol ogy types, or end

- use detail specifically. For industrial load forecasting, the Itron SAE framework reflects the NEMS structure. Thus, our approach to defining 2032 High DSM industrial efficiency targets also differed from that taken for residential

and c ommercial. We simply stipulated a percentage reduction of 10% from the SAE B ase C ase industrial load. This simple stipulated adjustment was based on a review of recent energy efficiency potential studies; further details are provided in Section C.6 of App endix C

.

4.4.2 Stock efficiency data sources and assumptions

The 30 residential and commercial end

- use s within the SAE load forecasting model largely correspond to those used within EIA’s NEMS. For each end

- use , NEMS requires information about the range of ef ficiency levels currently available and projected to be available in the future. To develop stock efficiency assumptions for the High DSM Case, we relied primarily upon studies conducted for the EIA by Navigant Consulting on present and future end

- use tec hnology

characteristics (Navigant, 2007; Navigant, 2008; Navigant & SAIC, 2011a; Navigant & SAIC, 2011b) . For most residential and commercial end

- uses, th e se studies identify the present

- day ( e.g., year 2010) commercially

- available high efficiency opt ion, and these are the default values used to define average stock efficiency levels for the High DSM case. 17



### Some of the end uses included within the SAE model, however, have not been included in recent Navigant analyses. In a number of other cases, the Nav igant 2010 high efficiency option is lower than the projected 2032 stock efficiency in the base case. And in several cases, the SAE definitions of categories

– i.e., the technologies within them

– differ from their counterparts in

NEMS. In these instances , where possible we used Navigant’s projected high efficiency option for a future year (rather than for 2010). In other cases, however, it was necessary to develop the High DSM case stock efficiency assumption from other sources, including LBNL’s recent

“ MaxTech” report and technical support documents from the U. S. DOE appliance standards

program (Desroches & Garbesi

, 2011)

.

The specific data sources used for each end

- use and balancing authority are identified in Section C.6 of Appendix C , along with d etails of any required additional calculations.

4.5 High DSM Case Energy Efficiency Savings

The SAE High DSM Case load forecasts can be compared to the SAE Reference Case forecasts in order to illustrate the incremental savings in the High DSM Case relative to what is expected to occur under the current set of energy efficiency policies and program plans. As shown in Figure 25

, WECC

- wide annual energy consumption in the High DSM Case is 21.6% lower than

17 Current high

- efficiency levels on the market are steadily increasing for a number of end uses, and at any given time, estimates a s to what this level is for a given end use may vary. Thus, we are not interpreting the Navigant estimates as being definitive. Rather, they are reasonably comprehensive and in addition were constructed in such a way as to conform with the structure of t he NEMS model, upon which the SAE framework is based. Both these aspects motivated the use of the Navigant studies as our primary source.



### 36

### in the Reference Case, and aggregate non

- coincident peak demand is 22.1% lower. Incremental savings vary across the BAs, in most cases ranging from 15

- 25% of the Reference Case load forecast, reflecting differences in en d

- use characteristics across BAs and regions

.

Figure 25 . SAE High DSM Case Incremental Savings Relative to SAE Reference Case



### T he residential and the commercial sectors together represent the overwhelming majority of

incrementa l savings relative to the Reference Case

, as shown in Figure 26 , which decomposes the savings by customer sector

, for WECC as a whole as well as for each of three geog raphical regions. Compared to the residential and commercial sectors, the savings for the industrial sector was relatively low in the High DSM Case (i.e., a 10% reduction relative to the Reference Case), and as a result, the industrial sector represents a disproportionately small part of the overall incremental savings. Between t he residential and commercial sectors

, each represent s similar proportions of the total incremental savings, with savings in California and the Southwest skewed slightly towards the commercial sector and savings in the Northwest skewed slightly

towards the residential sector.

Figure 26

. SA E High DSM Case Incremental Savings by Customer Sector

- 30.0% - 25.0% - 20.0% - 15.0% - 10.0%

- 5.0% 0.0%AESOAPSAVABCTCBPACFECHPDDOPDEPEFAR_EASTGCPDIIDLDWPMAGIC_VLYNEVPNWMTPACE_IDPACE_UTPACE_WYPACWPGE_BAYPGE_VLYPGNPNMPSCPSESCESCLSDGESMUDSPPSRPTEPTIDCTPWRTREAS_VLYWACMWALCWAUWWECC 

### High DSM Case Incremental Savings: Percentage Reduction from SAE Reference Case (2032) Annual Energy

Non

- Coincident Peak

44% 42% 48% 43% 47% 52% 41% 49% 9% 6% 11% 8%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

WECC California Northwest Southwest 

### SAE High DSM Case Incremental Savings: Annual Energy (2032)

Industrial

Commercial

Residential



### 37

### The residential and commercial sector savings can be further decomposed by end

- use , as shown

in Figure 27 , whic h presents the distribution in residential and commercial sector savings across

the 10 end

- use groups introduced previously in Section 4.3.2

. In general, the contr ibution from any individual end

- use is a function of both its share of total load in the Reference Case (see Figure 24 ) and the specified efficiency improvement betwe en the Reference Case and High DSM

Case (see Section C.8 of Appendix C ) . The net effect of these underlying drivers, as shown in Figure 27 , is that the savings are somewhat evenly distributed across end

- uses, with six of the ten

end

- use groups each constituting 10

- 20% of the WECC

- wide incremental savings. Commercial

HVAC is lar gest source of incremental energy savings for WECC as a whole (20% of the total), and either the largest or among the largest for each of the three regions (ranging from 15

- 26%). The significance of most other end

- uses varies regionally, reflecting differ ences in regional

climate and end

- use saturation trends. F or example, as to be expected, Residential Cooling is a major contributor to total incremental for BAs in the Southwest, while Residential Space & Water Heating is a major contributor in the Northw est.

Figure 27

. SAE High DSM Case Incremental Savings by End

- Use Group



### 4.6 High DSM Case Load Forecasts

S ection 4.4.1 described the process used to develop the SAE High DSM load forecasts. As noted at the beginning of this chapter, the SPSC High DSM Case load forecast for each BA was then derived by taking the percentage reduction in load between the SAE Reference Case and SAE High DSM Case, and then applying that percentage load reduction to the WECC Reference Case

forecast. This additional step served, in effect, to calibrate the High DSM load forecasts to the WECC Reference Case, so that meaningful comparisons could be made between the results of WECC’s transmission planning model for the two study cases.

Figure 28 and Figure 29 show the SPSC High DSM load forecast growth rates (2010

- 2032) for annual energy and non

- coincident peak, respectively

, by BA and for WECC as a whole. For comparison, the figures also show the corresponding growth rates for the WECC Reference Case. Further information on the High DSM forecasts, including detailed tables, is provided in 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

WECC California Northwest Southwest Residential Appliances

Residential Cooling

Residential Lighting

Residential Miscellaneous & TV

Residential Space & Water Heating

Commercial HVAC

Commercial Lighting

Commercial Miscellaneous

Commercial Office

Commercial Refrigeration 

### SAE High DSM Case Incremental Savings: Annual Energy (2032), Res. & Comm. Loads



### 38

### Sect ion C.9 of Appendix C

. As shown in the figures, average annual growth rates are substantially lower in the SPSC High DSM Case than in the WECC Reference Case. WECC

wide, growth in annual energy is nearly flat in the High DSM Case (an annual growth rate o f 0.3%/yr), compared to a 1.4% growth rate in the Reference Case. The aggregate non

- coincident peak demand across WECC (i.e., the sum of all BAs’ individual peak demands) actually declines slightly over time in the High DSM Case, with a negative growth ra te of

- 0.1%/yr, compared to a positive growth rate of 1.0%/yr in the WECC Reference Case.

As to be expected, growth rates in the High DSM Case vary considerably across BAs, mirroring the variability in growth rates within the WECC Reference Case. In term s of annual energy, average annual growth rates range from

- 1.1% to 2.9%/yr, with almost half of all BAs registering negative growth (i.e., declining loads) over the 2010

- 2032 period. Growth rates for peak demand range from

- 2.1% to 3.4%/yr across the BAs , with negative growth for almost two

- thirds of BAs.

Figure 28

. Growth Rates for SPSC High DSM Case and WECC Reference Case (Annual Energy)

Figure 29

. Growth Rates for SPSC High DSM Case and WECC Ref erence Case (Peak Demand) - 2.0% - 1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0%AESOAPSAVABCTCBPACFECHPDDOPDEPEFAR_EASTGCPDIIDLDWPMAGIC_VLYNEVPNWMTPACE_IDPACE_UTPACE_WYPACWPGE_BAYPGE_VLYPGNPNMPSCPSESCESCLSDGESMUDSPPSRPTEPTIDCTPWRTREAS_VLYWACMWALCWAUWWECC 

### WECC Reference Case SPSC High DSM Case2010-2032 CAGR (Annual Energy)

- 2.0% - 1.0% 0.0% 1.0% 2.0% 3.0% 4.0% 5.0%AESOAPSAVABCTCBPACFECHPDDOPDEPEFAR_EASTGCPDIIDLDWPMAGIC_VLYNEVPNWMTPACE_IDPACE_UTPACE_WYPACWPGE_BAYPGE_VLYPGNPNMPSCPSESCESCLSDGESMUDSPPSRPTEPTIDCTPWRTREAS_VLYWACMWALCWAUWWECC 

### WECC Reference Case SPSC High DSM Case2010-2032 CAGR (Non-Coincident Peak)



### 39

## 5. Recommendations : Potential Improvements to Data, Methodology, and

Process for Future TEPPC Study Cycles

Energy efficiency has become an increasingly prominent element within individual utility resource plans throughout the Western Interconnection, with many utilities planning to offset much of their (otherwise) forecasted load growth through customer energy efficiency programs. Energy efficiency has accordingly also become an integral element to the TEPPC studies, with efforts both to ensure that current efficiency policies are adequately captured within the Common Case load forecasts, and to examine alterna te study cases with varying assumptions about future energy efficiency trends.

Historically, WECC has had limited needs for data on energy efficiency historical or planned energy efficiency impacts

. Important strides in the course of the past TEPPC study cycles have

been made to improve data collection and analysis related to energy efficiency impacts on BA load forecasts. Further opportunities exists , however, to streamline those processes, and to improve their consistency and rigor.

We offer a number of recommendations for WECC (and WIEB SPSC) to consider that may improve energy efficiency data collection and analysis for future TEPPC study cycles. In general, these recommendations are targeted to TEPPC participants in general, thoug h in some instances, they may be oriented more narrowly towards WECC staff and TEPPC leadership.

(1) R equest that BAs submit secondary load forecasts with no future energy efficiency

programs 18



### If LRS load forecasts will continue to be used as the foundation for the TEPPC study case load forecasts, TEPPC stakeholders must be able to determine the impacts embedded in the forecast s from ratepayer

- funded efficiency programs implemented over the forecast period (i.e., the red

shaded area in Figure 2 ). 19 

### As past study cycles have shown, tremendous variability exists among BAs in terms of how, and the extent to which, planned energy efficiency program savings

are incorporated into the LRS load forecasts (see Table 1, for example) . Several efforts have been made during previous TEPPC study cycles to improve data collection related to energy efficiency program impacts embedded in the LRS forecast

– including a supp lementary survey sent to LSEs during the 2010 study cycle, and several voluntary questions added to the LRS data request form in 2012. Although both efforts were valuable , neither obviated the need for follow

up communications by LBNL staff with individua l BA load forecasting staff.

The challenges to collecting consistent data on embedded energy efficiency impa cts are several

fold. First, some of the required energy efficiency terminology and concepts may be unfamiliar or prone to misinterpretation by tr ansmission and/or resource planners , more broadly

– for example, the distinction s between “new” vs. “existing” efficiency programs or between

18 Although distributed generation (DG) is not the subject of the present report, the same recommendation could be

mad e regarding DG impacts embedded within the LRS load forecasts.

19 This information is needed for two discrete purposes: first, to ensure that the Common Case forecasts reflect the expected impact of current energy efficiency policies and utility plans; and second, to allow for the development of alternate study cases with varying assumptions about future energy efficiency trends and policy impacts.



### 40

### “incremental” and “cumulative” savings. Second, there is tremendous variability across BAs in

terms of the underl ying load forecasting methodology and the accounting of energy efficiency program impacts. T he way in which questions about embedded energy efficiency savings are formulated must therefore often be tailored to the specific load forecasting technique s used

. Consequently, much of the individual follow

- up with BAs has revolved around reframing the questions into terms that are more applicable for the particular BA’s forecasting approach.

In order to circumvent these challenges, we suggest that WECC request that BAs submit a secondary load forecast with no future ratepayer

- funded energy efficiency programs

. 20 

### The delta between this forecast and the primary LRS load forecast would therefore represent the embedded energy efficiency savings in the LRS forecast. T his approach should be relatively easy to

communicate to BAs in a standardized manner, regardless of their particular load forecasting

method , and without requiring ambiguous terminology . Some BAs may of course indicate that they are un able to provide such a secondary forecast; however, that challenge is no different than the one currently faced, where some BAs are unable to provide information about the amount of energy efficiency savings that is embedded in the ir LRS forecast. We beli eve that r eframing the question in the manner suggested above would likely increase the response rate and consistency across the data provided, relative to the current approach.

Ideally, the secondary load forecasts would be requested as part of WECC’s an nual LRS data collection process , in order to minimize administrative burdens and duplication

. If this suggested alternative load forecast approach proves untenable, then we would recommend that TEPPC develop and implement a separate, parallel data collec tion instrument and pro cess, similar to the approach used in the 2010 TEPPC study cycle

. A separate, stand

- alone instrument

would also allow for broader or more in

- depth data collection than is feasible through the LRS process, and thus might actually be preferable if additional information about demand

- side

resources was determined to be needed.

(2) R etain capabilities within the TEPPC process to track and quantify the expected impact of energy efficiency programs and policies

Future energy efficiency trends will be driven to a significant degree by various policies and programmatic efforts at the federal, state, and utility levels. Developing TEPPC study cases requires an independent capability to track these policies and estimate their impacts ( not u nlike the need to track and quantify the impacts of state RPS policies or EPA air pollutant regulations on future generation build

- out)

. This capability is needed both to ensure that the 10

- year Common Case and 20

- year Reference Case forecasts are consist ent with current policies and program plans and to inform alternate study cases with varying energy efficiency policy assumptions.

This function has thus far been served primarily by the SPSC DSM Work Group and to some extent by the TEPPC DSM Task Force a nd Data Work Group. As the organizations surrounding the TEPPC studies continue to evolve, we recommend that WECC staff and TEPPC leadership

20 For practical reasons, we suggest focusing on ratepayer

- funded energy efficiency programs. Although other types of energy efficiency policies (e.g., codes and standards) can also yield significant impacts on loads, BAs are less likely to be able to preci sely estimate the embedded impact of those policies within their forecasts.



### 41

### actively consider where this function ought to reside over the long

- term (e.g., as part of SPSC,
SPSG, DWG, WECC s taff, etc.) and how best to staff it

. Wherever it resides, this function

should include at least the following activities:

• Reviewing and compiling data from utility IRPs and utility DSM or EE program plans on planned energy efficiency program savings ;

• T racking state energy efficiency resource standards (EERS) and developing projections of the associated energy efficiency savings ;

• Tracking and compiling data on actual utility energy efficiency program savings from recent historical years, to provide bench marks for projections ; and

• Engaging regional energy efficiency experts and utility energy efficiency program staff to review and inform assumptions about current policy and program impacts

.

(3) Consider alternative load forecast shaping methods, in order ca pture the impact of end

use efficiency trends on hourly load profile s

The TEPPC studies require hourly load forecasts , which have been created by applying historical hourly load shapes for each BA to forecasted monthly energy and peak demand. When alternative energy efficiency study cases are developed, adjustments to the monthly energy and peak load forecasts are made , and the same historical hourly load profiles are then applied to the adjusted monthly energy and peak loads . This approach to creating hourly load forecasts precludes any ability to model changes in hourly load shapes that may occur as a result of changes in end

- use efficiency (or saturation ) over time, either in the Common Case or in

alternative study cases, such as the High DSM study case. For example, a dramatic improvement in the efficiency of space cooling end

- uses (e.g., residential central air conditioning) could result in a significant flattening of summer

- month load shape in some load zo nes, while widespread fuel switching from electric to gas space heating could significantly flatten winter

- month load shapes in other regions. The ability to adequately capture such changes to hourly load shapes may become even more important in order to reliably model and plan for the integration needs associated increasing amounts of variable generation.

We recommend that WECC staff and TEPPC participants consider an alternative approach to developing hourly load forecasts that would partially 21 

### account for changes to hourly load shapes

over time associated with changes in end

- use energy efficiency levels

. Under this approach, the first step would involve applying historical hourly load profiles to monthly energy and peak load

forecasts that assume no fu ture energy efficiency programs . These are the “secondary” load forecasts that, under recommendation (1), we suggest be requested directly from B A s

. H owever, if not obtained directly from the BAs, they could be derived by “adding back” the energy

efficien cy program savings that are embedded in the LRS forecasts. This step would yield hourly load forecasts for each BA that reflect an assumption of no future energy efficiency program savings. Those hourly load forecasts would then be decremented in each ho ur using hourly energy efficiency savings profiles developed for each BA that reflect the specific amount and composition of end

- use energy efficiency savings assumed in the given study case.

21 The approach recommended here would only account for hourly load shape impacts associated with ratepayer

funded energy efficiency programs, but not other energy efficiency polici es (such as federal equipment standards). Furthermore, it would not account for hourly load shape impacts associated with changes in equipment saturation

.



### 42

### Implementing this alternative approach would require additional data collection (or otherwise additional assumptions and estimations):

• A break

- down of energy efficiency program savings into end

- use categories for each BA

. For the Common Case, this information could potentially be requested from BAs, in conj unction w ith the load forecasts, though the level of granularity and category definitions will undoubtedly vary considerably across BAs and utilities. For High DSM load forecasts that rely on energy efficiency potential studies, like the 10

- year High DSM study cas e described in Section 3, the potential studies themselves will generally contain

information about the end

- use composition of the potential estimate

– though, again, the granularity and categories will often differ across studies. Thus, a relatively simp le end

use categorization would likely be required, or generic assumptions would need to be employed in order to disaggregate the savings projections into more

- refined end

- use categories.

• End

- use hourly load profiles by region

. H ourly load shapes by end

- use and region could likely be purchased from companies that conduct end

- use load forecasting or energy efficiency potential studies, if not publically available. These end

- use load shapes would then be applied to the end

- use level savings projections in order to create the hourly energy efficiency savings profiles for each BA that reflect the specific mix of end

- uses in that BA’s projected energy efficiency program portfolio. Therefore, a consistent set of

end

- use categories would be needed for both the savings projections and the load profiles. Special attention would be required for BAs that span distinct climate zones, in order to ensure that appropriate load profiles were applied to each end

- use.

Given the level of effort that might potentially be required in order to adopt the hourly load forecasting approach recommended above, we suggest that WECC staff and TEPPC participants first conduct a scoping analysis to evaluate the potential impact of a more

- refined approach on load shapes and TEPPC mo deling results (e.g., by selecting several representative load zones and comparing hourly load shapes using the current approach and using a more

- refined approach),

and to further assess data availability and quality.

(4) Consider commissioning a WECC

- wide en ergy efficiency potential study or tool

As described in Section 3, the 10

- year High DSM case was based on a large number of energy efficiency potential studies conducted for utilities throughout the Western Interconnection. Naturally, the scope, assumpti ons, and methods of those studies varied considerably; and extrapolations were required for BAs without a recent potential study.

TEPPC participants may wish to consider the value of commissioning a single WECC

- wide energy efficiency potential that coul d be used to inform future High DSM study cases. The advantage of such a study is that would provide a consistent (and potentially more transparent) basis for estimating economic potential and achievable potential across the interconnection. However, such an endeavor could also prove contentious, as different stakeholders may disagree over appropriate assumptions and study design , and inconsistencies would inevitably arise

43

### between the study results for particular regions and the results from separate poten tial studies for those regions. One potential alternative might be to commission the development of a n energy efficiency potential spreadsheet tool

– like the one developed by the Brattle Group to estimate DR potential

– that would enable TEPPC participan ts to create alternative energy efficiency potential estimates, and that could potentially be updated over time. Such a tool might be used to develop energy efficiency potential estimates only for those regions without recent studies or, if

used to develo p potential estimates for all regions, could be calibrated to recent potential studies based on more sophisticated techniques and data.

(5) If WECC adopts the SAE load forecasting tool, we recommend leveraging the techniques described in this report, as wel l as consideration of several additional data

collection activities and analytical refinements

WECC has considered procuring Itron’s SAE load forecasting software for use in develop ing

load forecasts for TEPPC stud y case s. The framework is a powerful tool for integrating end

- use detail with other information in load forecasting, and i ts applicability to long

- term forecasting for transmission planning is demonstrated in this report. We strongly encourage WECC staff to leverage some of the specific tech niques and data sources described within this report. In particular, we recommend the use of the “DSM adjustment method” to develop reference case forecasts, and that High DSM load forecasts be developed using the same kind of stock efficiency

- based adjus tments as described at length in Section 4.

To support and enhance its usefulness for this purpose going forward, we also offer a number of

additional recommend ations and potential areas for i mprovement :

(a) Request data from BAs and utilities on end

- use saturation and stock efficiency

The SAE model requires historical data and projections for stock efficiency and saturation of 30

residential and commercial end

- uses. As a default, the EIA Annual Energy Outlook is used to populate these data inputs. Those data, however, are disaggregated into only two census regions

in the Western Interconnection. The analysis reported in this study included a concentrated effort to identify and assemble BA

- specific data on en d

- use stock efficiency and saturation, drawing on a variety of public sources (see Section 4.1).

As WECC progresses in its use of the SAE forecasting system, we recommend that WECC staff reach out to BA load forecasting staff for additional BA

- specific data on end

- use efficiency or saturation levels. Many individual utilities may have developed more accurate end

- use saturation and stock efficiency assumptions for their own load forecasting efforts, based on customer surveys or other non

- public research. In fact, many utilities use the SAE platform for their load forecasts, and maintain these saturation and stock efficiency assumptions in exactly the

same format as WECC would need. Although utilities are generally reluctant to share this

information wit h outside parties, they may be more willing to provide this information to WECC staff directly, particularly if provided under some protection of confidentiality.

As a first step, we would suggest that WECC identify which utilities use the SAE forecasti ng system, and reach out to those entities to assess whether they would be willing to provide the

44

### requisite end

- use data inputs. For other utilities/BAs, we recommend that WECC staff pre

populate the end

- use data inputs using the default EIA data and publ ic data sources obtained for

the 20

- year High DSM study, and then send those input sheets to utility/BA load forecasting staff

for review.

(b) Refine peak demand savings impacts for specific end

- uses

As described in Section 4, High DSM load forecasts were de veloped with the SAE model by first stipulating increases in average stock efficiency for each end

- use. Those stock efficiency improvements were translated into an equivalent percentage reduction in energy use for the corresponding end

- use, and those savi ngs percentages were then applied to the Base Case energy

forecast for that end

- use in order to derive the High DSM case energy forecast. The same end

use savings percentages were then applied to the peak demand forecast for each end

- use in order

to deriv e the High DSM case peak load forecast. For most end

- uses, this approach is reasonable. However, f or some end

- uses, the percentage savings in energy and in peak demand could differ significantly from one another. For example, f or HVAC end

- uses, energy s avings may arise partially through the introduction of variable speed drives that reduce energy consumption under

part

- load conditions. For future applications of the SAE model to develop High DSM load forecasts, we recommend refining the peak demand savi ngs estimates to better capture the hourly savings profile specific to those end

- uses where the stipulated stock efficiency improvement would not simply reduce load proportionally across all hours.

(c) Consider options for modeling b uilding shell improvements and behavioral efficiency in future High DSM studies

The 20

- year High DSM study case presented in this report considered only changes to average stock efficiencies as a source of future energy efficiency savings. Many efficiency programs and policies (e .g., building codes) also target improvements in building shell characteristics, as well as changes to consumer behavior, and those kinds of measures could potentially be included in future applications of the SAE model for High DSM load forecasts. Buildi ng shell efficiency measures, in particular, could be readily modeled within the SAE framework , as building thermal efficiency is among the required data inputs. For behavioral efficiency programs , a considerable amount of judgment would likely be require d in order to translate those program impacts into a form that could be readily input into the SAE model.

(d) Refine methods for extrapolating industrial efficiency potential estimates

As discussed in Section 4.4.1, the SAE forecasting model does not contai n end

- use level detail

for the industrial sector. T he 20

- year High DSM load forecasts were developed by reviewing recent energy efficiency potential studies, and based on those studies, stipulating a 10% reduction in industrial load relative to the Base Case for each BA. For future applications of the

SAE model to c reate High DSM forecasts, we recommend that energy efficiency potential

estimates for specific industrial sub

- sectors be compiled from recent energy potential studies. Those potential estimates can then be extrapolated to other regions based on the specif ic industrial mix of each BA (or state), drawing on economic data published by the Department of Commerce’s Bureau of Economic Analysis

.

45

## References

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Commercial Sec tors,” background paper, Resources for the Future and National Energy Policy Institute, Washington, DC, January.

Comstock, William. 2012. Personal communication, US Energy Information Administration.

CME (Canadian Manufacturing and Exporters). 2010. Improving Energy Efficiency for Alberta’s Industrial and Manufacturing Sectors.

Desroches, L.

- B., and K. Garbesi (Principal Authors). 2011. Max Tech and Beyond

– Maximizing Appliance and Equipment Efficiency by Design. Lawrence Berkeley National Laborator y Report

#LBNL

- 4998E, July 20.

Franco, V

. , J

. Lutz, A

. Lekov, and L

. Gu. 2008. Furnace Blower Electricity: National and Regional Savings Potential . Florida Solar Energy Center publication FSEC

- CR

- 1774

- 08.

McMenamin, Stuart, and Mark Quan, 2010, “Incorpor ating DSM into the Load Forecast.” White paper, Itron, Inc.

Navigant Consulting, Inc. 2007. “EIA

– Technology Forecast Updates

– Residential and Commercial Building Technologies

– Reference Case Second Edition (Revised).” Presented to the Energy Informat ion Administration September 21, 2007; Reference No. 20070831.1.

Navigant Consulting, Inc. 2008. “EIA

– Technology Forecast Updates

– Residential and Commercial Building Technologies

– Advanced Case: Residential and commercial lighting,

commercial refri geration, and commercial ventilation technologies.” Presented to the Energy Information Administration September 2008.

Navigant Consulting, Inc. 2009. Energy Savings Potential and RD&D Opportunities for Commercial Building Appliances

– Final Report. Su bmitted to U. S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Building Technologies Program, December

21.

Navigant Consulting, Inc. with SAIC. 2011a. “FINAL DRAFT EIA

– Technology Forecast Updates

– Residential and Commercial Bu ilding Technologies

– Reference Case.” Presented to the U. S. Energy Information Administration, September 22.

Navigant Consulting, Inc. with SAIC. 2011b. “SECOND DRAFT EIA

– Technology Forecast Updates

– Residential and Commercial Building Technologies

– Advanced Case.” Presented to the U. S. Energy Information Administration, September 22.

46

### Navigant Consulting, Inc. 2012. Analysis to Update Energy Efficiency Potential, Goals, and Targets for 2013 and Beyond, Track 1

– Statewide Investor Owned Utility Energy Efficiency Potential Study . Prepared for the California Public Utilities Commission, May 8

Olson, A. and N. Schlag. 2012. “Estimating DG Potential for the 2032 High DG/DSM Case,” Presentation to the SPSC DSM Work Group, September 25.

Satchwell, A. , G. Barbose, C. Goldman, R. Hledik, and A. Faruqui. 2013. Incorporating Demand Response into Western Interconnection Transmission Planning . Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL

- 6381E

.

U. S. Department of Energy (USDOE). 2009a. “Energy Use Determination.” Chapter 6 of Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment: Residential Dishwashers, Dehumidifiers, and Cooking
Products, and Commercial Clothes Washers. Offi ce of Energy Efficiency and Renewable Energy, Building Technologies Program, Appliances and Commercial Equipment Standards, Washington, DC: March.

U. S. Department of Energy (USDOE). 2009b. “Energy Conservation Program: Energy

Conservation Standards for C ertain Consumer Products (Dishwashers, Dehumidifiers, Microwave Ovens, and Electric and Gas Kitchen Ranges and Ovens) and for Certain Commercial and Industrial Equipment (Commercial Clothes Washers): Final Rule. Federal Register Vol. 74, No. 66, April 9, p p. 16040

- 16096.

U. S. Energy Information Administration (USEIA). 2011. Commercial Demand Module of the National Energy Modeling System: Model Documentation 2011 . Office of Energy Analysis Report DOE/EIA

- M066(2011), November.

47

## Appe ndix A

. WECC 10

- Year Common Case : Additional Details on the Energy Efficiency Assumptions and Adjustments

This appendix provides further details on the assumptions employed to develop the WECC

Common Case

. Presented first are a series of summary tables containing additional details on the results presented within Sections 2.3 through 2.5 :

• Table A

- 1 and Table A

- 2 summarize the DSM Work Group’s projections for the amount of savings expected to occur as a result of current policies and program plans , as well as estimates for the amount of savings embedded within LRS load forec asts

. As described and explained further within Section 2, the analysis focused principally on two classes of energy efficiency policies: customer

- funded energy efficiency programs and (2) federal minimum efficiency standards. For customer

- funded efficie ncy programs, the analysis focused on the impact of programs implemented over the 2011

- 2021 period , and

f

or federal standards, the Work Group focused specifically on standards adopted and updates to existing standards scheduled to occur through January 2013.

• 

### Table A

- 3 summarizes the adjustments to the LRS load forecast for each BA. These adjustments are equal to the difference between the expected savings and embedded savings shown in Table A

- 1 and Table A

- 2

.

• 

### Table A

- 4 and T able A

- 5 summarize the WECC Common Case load forecasts for 2021 and corresponding growth rates for each BA, compared to the LRS load forecast. The

difference between the forecasts for e ach BA reflect s the adjustments in Table A

- 3

.

Following the summary tables are a series of narrative summaries describing the energy efficiency assumptions and adjustments for each BA . These narrative summaries provide an overview of the specific policies and data sources used to construct the WECC Common Case, and document the findings from communications with BA load forecasti ng staff.

48

### A.1 Summary Tables on Efficiency Adjustments for WECC 10

- Year Common Case

Table A

- 1 . Embedded and Expected Energy Efficiency Savings (Annual GWh , 2021 )

Region Balancing Authority Embedded Savings in LRS Forecast Expected Savings for Common Case

Customer

Funded Federal

Standards Total Customer

Funded Federal

Standards Total

California CISO n/a n/a 21,890 n/a n/a 39,884

IID 269 0 269 269 143 412

LADWP 2,519 421 2,940 2,519 1,016 3,535

SMUD 1,649 0 1,649 1,649 601 2,251

TID 185 0 185 185 95 279

Canada AESO 1,107 0 1,107 1,107 0 1,107

BCTC n/a n/a 11,076 n/a n/a 11,076

Northwest AVA 939 118 1,056 1,235 295 1,530

BPA n/a n/a 6,504 n/a n/a 6,504

CHPD n/a n/a 135 n/a n/a 135

DOPD n/a n/a 117 n/a n/a 117

GCPD n/a n/a 314 n/a n/a 314

IPC 1,455 165 1,620 1,690 334 2,025

NWMT 501 0 501 578 272 851

PACW n/a n/a 1,769 n/a n/a 2,061

PGE 3,289 0 3,289 3,289 526 3,816

PSE 4,081 0 4,081 4,081 586 4,667

SCL 1,021 0 1,021 1,021 237 1,258

TPWR n/a n/a 588 n/a n/a 588

WAUW n/a n/a n/a 9 19 28

Southwest APS 5,940 0 5,940 5,940 979 6,919

CFE 0 0 0 0 0 0

EPE 561 0 561 561 353 914

NEVP 1,011 247 1,258 1,011 648 1,659

PACE n/a n/a 2,544 n/a n/a 3,283

PNM 40 0 40 833 585 1,419

PSCO 4,415 483 4,898 5,604 1,221 6,826

SPP 486 118 604 486 308 795

SRP 1,949 0 1,949 3,368 867 4,235

TEP 1,381 0 1,381 2,277 372 2,649

WACM 249 0 249 457 606 1,063

WALC 83 186 269 83 186 269

WECC Total 33,129 1,737 79,804 38,253 10,250 112,466

Notes: Values represent the reduction in 2021 demand from customer

- funded programs implemented over the 2011

- 2021 period and from new or updated federal standards issued from 2009

- 2013. “ n/a ” is used if the data sources or underlying policies, themselves, do not distinguish between the savings from these two types of policies, in which

case only the combined total savings is identified.



### 49

### Table A

- 2 . Embedded and Expected Energy Efficiency Savings (Non

- Coincident Peak MW , 2021 )

Region Balancing Authority Embedded Savings in LRS Forecast Expected Savings for Common Case

Customer

Funded Federal

Standards Total Customer

Funded Federal

Standards Total

California CISO n/a n/a 4,517 n/a n/a 11,469

IID 60 0 60 60 33 92

LADWP 515 93 608 515 235 750

SMUD 262 0 262 262 139 401

TID 44 0 44 44 22 65

Canada AESO 154 0 154 154 0 154

BCTC n/a n/a 2,000 n/a n/a 2,000

Northwest AVA 93 22 115 123 55 178

BPA n/a n/a 880 n/a n/a 880

CHPD n/a n/a 26 n/a n/a 26

DOPD n/a n/a 47 n/a n/a 47

GCPD n/a n/a 127 n/a n/a 127

IPC 236 36 272 274 64 338

NWMT 81 0 81 94 49 142

PACW n/a n/a 297 n/a n/a 346

PGE 552 0 552 552 98 650

PSE 836 0 836 836 107 943

SCL 182 0 182 182 43 225

TPWR n/a n/a 114 n/a n/a 114

WAUW n/a n/a 5 9 19 5

Southwest APS 855 0 855 855 323 1,178

CFE 0 0 0 0 0 0

EPE 124 0 124 124 79 203

NEVP 223 70 294 223 178 402

PACE n/a n/a 492 n/a n/a 616

PNM 6 0 6 136 131 267

PSCO 832 89 920 1,056 223 1,278

SPP 139 33 173 139 85 224

SRP 413 0 413 714 286 999

TEP 215 0 215 354 123 477

WACM 47 0 47 95 111 206

WALC 17 57 74 17 57 74

WECC Total 5,885 400 14,790 6,816 2,457 24,874

Notes: Values represent the reduction in 2021 demand from customer

- funded programs implemented over the 2011

- 2021 period and from new or updated federal standards issued from 2009

- 2013. “ n/a ” is used if the data sources or underlying policies, themselves, do not distinguish between the savings from these two types of policies, in which

case only the combine d total savings is identified

.



### 50

### Table A

- 3

. Adjustments to the LRS Load Forecasts (2021)

Region Balancing Authority Annual Energy Non

- Coincident Peak

GWh % Reduction

to LRS MW % Reduction

to LRS

California CISO

- 17,994

- 6.7%

- 6,952

- 12.4%

IID

- 143

- 3.2%

- 33

- 2.7%

LADWP

- 595

- 1.9%

- 142

- 1.7%

SMUD

- 601

- 3.2%

- 139

- 3.1%

TID

- 95

- 3.2%

- 22

- 3.2%

Canada AESO 0 0.0% 0 0.0%

BCTC 0 0.0% 0 0.0%

Northwest AVA

- 474

- 3.2%

- 63

- 2.3%

BPA 0 0.0% 0 0.0%

CHPD 0 0.0% 0 0.0%

DOPD 0 0.0% 0 0.0%

GCPD 0 0.0% 0 0.0%

IPC

- 404

- 2.0%

- 66

- 1.4%

NWMT

- 350

- 3.0%

- 61

- 3.2%

PACW

- 292

- 1.3%

- 49

- 1.1%

PGE

- 526

- 2.2%

- 98

- 2.3%

PSE

- 586

- 2.2%

- 107

- 2.0%

SCL

- 237

- 2.2%

- 43

- 2.2%

TPWR 0 0.0% 0 0.0%

WAUW 0 0.0% 0 0.0%

Southwest APS

- 979

- 2.3%

- 323

- 3.3%

CFE 0 0.0% 0 0.0%

EPE

- 353

- 3.2%

- 79

- 3.5%

NEVP

- 401

- 1.5%

- 108

- 1.6%

PACE

- 739

- 1.3%

- 124

- 1.1%

PNM

- 1,379

- 7.6%

- 260

- 8.2%

PSCO

- 1,928

- 4.0%

- 358

- 4.4%

SPP

- 191

- 1.5%

- 51

- 2.4%

SRP

- 2,286

- 6.1%

- 586

- 7.4%

TEP

- 1,268

- 7.9%

- 262

- 7.8%

WACM

- 813

- 2.7%

- 159

- 3.3%

WALC 0 0.0% 0 0.0%

WECC Total

- 32,633

- 3.2%

- 10,084

- 5.0%



### 51

### Table A

- 4 . Comparison of LRS and WECC Common Case Forecasts (Annual GWh)

Region Balancing Authority 2010 LRS Load Forecast WECC Common Case

2021 CAGR 2021 CAGR Reduction

in CAGR

from LRS

California CISO 228,435 267,850 1.5% 249,856 0.8% 0.6%

IID 3,558 4,403 2.0% 4,260 1.7% 0.3%

LADWP 28,278 31,384 1.0% 30,789 0.8% 0.2%

SMUD 17,162 18,576 0.7% 17,975 0.4% 0.3%

TID 2,450 2,924 1.6% 2,829 1.3% 0.3%

Canada AESO 71,722 110,704 4.0% 110,704 4.0% 0.0%

BCTC 58,457 66,892 1.2% 66,892 1.2% 0.0%

Northwest AVA 12,238 14,939 1.8% 14,465 1.5% 0.3%

BPA 51,876 53,450 0.3% 53,450 0.3% 0.0%

CHPD 3,151 4,037 2.3% 4,037 2.3% 0.0%

DOPD 1,346 1,931 3.3% 1,931 3.3% 0.0%

GCPD 4,064 5,103 2.1% 5,103 2.1% 0.0%

IPC 16,359 19,944 1.8% 19,540 1.6% 0.2%

NWMT 10,525 11,618 0.9% 11,268 0.6% 0.3%

PACW 20,654 23,041 1.0% 22,749 0.9% 0.1%

PGE 20,103 23,593 1.5% 23,067 1.3% 0.2%

PSE 24,536 26,926 0.8% 26,340 0.6% 0.2%

SCL 9,899 10,899 0.9% 10,662 0.7% 0.2%

TPWR 4,863 5,443 1.0% 5,443 1.0% 0.0%

WAUW 634 821 2.4% 821 2.4% 0.0%

Southwest APS 30,900 42,423 2.9% 41,444 2.7% 0.2%

CFE 10,649 14,606 2.9% 14,606 2.9% 0.0%

EPE 8,049 11,033 2.9% 10,679 2.6% 0.3%

NEVP 24,502 27,066 0.9% 26,665 0.8% 0.1%

PACE 46,410 56,226 1.8% 55,487 1.6% 0.1%

PNM 14,920 18,267 1.9% 16,887 1.1% 0.7%

PSCO 41,865 48,480 1.3% 46,552 1.0% 0.4%

SPP 11,345 12,885 1.2% 12,694 1.0% 0.1%

SRP 30,590 37,569 1.9% 35,283 1.3% 0.6%

TEP 14,356 16,126 1.1% 14,857 0.3% 0.7%

WACM 23,381 30,644 2.5% 29,831 2.2% 0.3%

WALC 6,766 7,549 1.0% 7,549 1.0% 0.0%

WECC Total 854,043 1,027,348 1.7% 994,715 1.40% 0.30%



### 52

### Table A

- 5 . Comparison of LRS and WECC Common Case Forecasts (Non

- Coincident Peak MW)

Region Balancing Authority 2010 LRS Load Forecast WECC Common Case

2021 CAGR 2021 CAGR Reduction

in CAGR

from LRS

California CISO 47,535 56,276 1.5% 49,324 0.3% 1.2%

IID 1,004 1,209 1.7% 1,176 1.4% 0.3%

LADWP 6,785 8,186 1.7% 8,044 1.6% 0.2%

SMUD 4,436 4,496 0.1% 4,357

- 0.2% 0.3%

TID 588 689 1.5% 667 1.2% 0.3%

Canada AESO 10,196 15,437 3.8% 15,437 3.8% 0.0%

BCTC 10,716 12,077 1.1% 12,077 1.1% 0.0%

Northwest AVA 2,179 2,738 2.1% 2,675 1.9% 0.2%

BPA 9,826 10,376 0.5% 10,376 0.5% 0.0%

CHPD 603 717 1.6% 717 1.6% 0.0%

DOPD 361 415 1.3% 415 1.3% 0.0%

GCPD 689 846 1.9% 846 1.9% 0.0%

IPC 3,372 4,854 3.4% 4,788 3.2% 0.1%

NWMT 1,704 1,881 0.9% 1,820 0.6% 0.3%

PACW 3,779 4,287 1.2% 4,238 1.0% 0.1%

PGE 3,627 4,257 1.5% 4,159 1.3% 0.2%

PSE 4,810 5,410 1.1% 5,303 0.9% 0.2%

SCL 1,845 1,941 0.5% 1,898 0.3% 0.2%

TPWR 989 1,035 0.4% 1,035 0.4% 0.0%

WAUW 116 147 2.2% 147 2.2% 0.0%

Southwest APS 7,046 9,796 3.0% 9,473 2.7% 0.3%

CFE 2,174 3,353 4.0% 3,353 4.0% 0.0%

EPE 1,616 2,270 3.1% 2,191 2.8% 0.3%

NEVP 6,034 6,767 1.0% 6,659 0.9% 0.1%

PACE 7,948 11,139 3.1% 11,015 3.0% 0.1%

PNM 2,636 3,166 1.7% 2,905 0.9% 0.8%

PSCO 7,608 8,179 0.7% 7,821 0.3% 0.4%

SPP 1,925 2,153 1.0% 2,102 0.8% 0.2%

SRP 6,513 7,960 1.8% 7,373 1.1% 0.7%

TEP 3,088 3,358 0.8% 3,096 0.0% 0.7%

WACM 3,630 4,764 2.5% 4,605 2.2% 0.3%

WALC 1,421 1,585 1.0% 1,585 1.0% 0.0%

WECC Total 166,799 201,762 1.7% 191,678 1.27% 0.5%



### 53

### A.2 BA

- Specific Energy Efficiency Assumptions and Adjustments : Narrative Summaries

AESO

Based on guidance from provincial representatives for last year's study, it was determined that savings current ly being achieved through customer

- funded energy efficiency programs are relatively negligible and that there were no policies in place that would require a significant

ramp

- up in program activity. It was therefore assumed that savings from customer

- funded programs would accumulate at a rate of 0.1% of retail sales per year over the forecast period, and that this level of savings was fully captured (albeit implicitly) by the LRS load forecast . This is the generic assumption used by the DSM Wor k Group throug hout its analysis for utilities with little or no history of administering significant energy efficiency program portfolios, and where no specific data were available regarding planned energy efficiency program activity.

APS

Customer

- Funded EE Progra ms : APS staff confirmed that the load forecast submitted to WECC is net of planned savings from customer

- funded energy efficiency programs implemented over

the 2011

- 2021 period, and that the level of savings assumed would meet or exceed the amount required to comply with Arizona's Energy Efficiency Rule. Therefore, no adjustment was made

for customer

- funded energy efficiency program savings.

Federal Standards : APS staff also indicated that the load forecast does explicitly model federal

standa rds for residential refrigeration and A/C, but not other end

- uses (lighting) or customer segments (commercial or industrial), and that only existing standards are modeled. It is therefore unlikely that the load forecast submitted to WECC in March 2011 cap tures the impact of federal

standards updated over the 2009

- 2013 period, and therefore the standardized “Method 1” adjustment was made to the load forecast in order to capture the impact of those standards, as

described in Section 2.2

.

AVA

Custom er

- Funded EE Programs : Avista staff indicated that the AVA load forecast submitted to WECC is net of only the expected impact of pre

- existing programs, but does not account for the incremental impact of planned new programs specified in the 2011 IRP. Avis ta staff provided LBNL with information that was used to estimate the 2011 IRP savings targets, segmented into savings associated with existing programs and savings associated with new programs. The forecast was then adjusted downward based on the planned savings from new programs. It is assumed that savings from NEEA regional market transformation programs implemented over the forecast period are captured within the initial load forecast, under the presumption that NEEA program savings continue to accumul ate at more

- or

- less their historical rate.

Federal Standards : The expected savings from federal standards adopted over the 2009

- 2013 timeframe was estimated by pro

- rating the statewide savings for each state in which AVA overlaps, in proportio n to the portion of statewide load within the balancing authority. Avista

staff confirmed that the AVA load forecast submitted to WECC does account for all historical

54

### changes to federal standards, but does not account for any planned updates. Savings ass ociated with federal standards already adopted since January 2009 were therefore assumed to be

embedded w ithin the load forecast, and the load forecast was adjusted based on the standard “Method 2” adjustment described in Section 2 .2

.

BPA

BPA Staff confirmed that the load forecast submitted to WECC this year is net of the balancing authority's pro

- rated share of the 6th plan conservation targets (in contrast to last year's forecast, which accounted for conservation savings only at the historica l rate). Therefore, no adjustment to the BPA forecast is required for the WECC Common Case. Note that the 6th plan conservation targets are not specific to any individual policy mechanism, and therefore the projections of embedded and expected savin gs in the table below are not disaggregated into

customer

- funded EE programs and federal standards. It is also assumed that savings from regional NEEA market transformation programs are included within the Counci l's conservation

targets.

BCTC

BC Hydro Retail Load : BC Hydro provided to WECC a projection of the total expected savings

from customer

- funded energy efficiency programs and from codes/standards. LBNL is currently awaiting confirmation from BC Hydro that this savings amount has already been ne tted off of the load forecast submitted to WECC, and on what basis the savings projections were derived. In the interim, the savings projection provided to WECC is assumed to be fully captured in the load forecast and is assumed to represent the level of savings expected based on current policies and program plans (as was the case in last year's BC Hydro load forecast). Note that the savings projection provided to WECC aggregates the impact of customer

- funded programs and provincial codes/standards; there fore the table below reports only the combined amount. Note also that the savings projection provided WECC represents savings at the customer

- meter; these values were therefore grossed up to the bus

- bar by adding an assumed 7% T&D losses.

For tisBC Retail Load : The BCTC balancing authority also includes the retail load of FortisBC. The expected savings from FortisBC customer

- funded EE programs is based on the DSM savings projection in the utility's 2012 Long

- Term Demand Side Management Plan. Pending further information from BC Hydro, it is assumed that the balancing authority load forecast submitted to WECC fully captures the expected impact of FortisBC's programs.

CHPD

CHPD Staff confirmed that the load forecast submitted to WECC is "net" of planned energy efficiency savings and that the savings projection that Chelan provided to WECC represents the planned energy efficiency savings levels netted off the forecast. Those savings levels are based

on a pro

- rata share of the regional conservation goals found in the Council's 5th Power Plan,

which Chelan used for its la test I937 conservation goals. Thus, no adjustments were made to the load forecast submitted to WECC. Note that the Council's conservation targets are not specific

to an y individual policy mechani sm; therefore, the projections of embedded an d expected

55

### savings in the table below are not disaggregated into customer

- funded EE programs and federal standards. It is also assumed that savings from regional NEEA market transfor mation programs are included within the Council's conservation targets.

CISO

The CISO load forecast submitted to WECC was prepared by the CEC and is a modified version of the CEC's December 2009 Demand Forecast (the 2009 IEPR forecast). That forecast accounts only for "committed" energy efficiency savings, and therefore excludes " uncommitted" savings associated with IOU programs implemented after 2012, savings from POU programs implemented after 2010, and savings from other future changes to codes/standards. The forecast was therefore adjusted downward based on the sum of: (a) th e uncommitted savings projection developed by the CPUC for the IOUs' long

- term procurement proceeding and (b) the estimated cumulative savings from municipal utility customer

- funded EE programs within the CISO footprint, based on those utilities' most rece nt long

- term EE savings targets. Note that the committed and uncommitted savings estimates developed by the CEC span a somewhat broader set of policies than just customer

- funded programs and federal standards; therefore only total embedded and expected sa vings projections are shown in the table below , rather than as separate projections for customer

- funded programs and federal standards. Note also that further discussions with CEC staff may result in changes to the assumed level of "committed" savings;

ho wever, any changes to that assumption will affect the embedded and expected savings projections by an equal amount, and therefore will not result in any change to the proposed

WECC Common Case load forecast.

DOPD

The expected savings are equal to Douglas PUD’s pro

- rated share of the Council's 6th plan

conservation targets. DOPD staff confirmed that the load forecast submitted to WECC is "net" of the savings target identified within the utility’s last IRP plus expected savings from the state’s

low income weatherization program, and we assume that the total assumed savings are roughly consistent with the utility’s pro

- rated share of the Council's 6th plan targets. Note that the Council's conservation targets are not specific to any individual policy mechan i sm; therefore, the projections of embedded an d expected savings in the table below are not disaggregated into

customer

- funded EE programs and federal standards. It is also assumed that savings from regional NEEA market transformation programs are included within the Council's conservation

targets.

EPE

Customer

- Funded EE Programs : EP E staff confirmed that the load forecast submitted to WECC is net of energy efficiency and DG requirements, and that energy efficiency savings projection provided to WECC is based on compliance with New Mexico's and Texas' energy efficiency

resource standa rds. Therefore, no adjustment was made for customer

- funded energy efficiency program savings.

56

### Federal Standards : EPE staff also confirmed that the load forecast submitted to WECC does not model any changes to federal lighting/appliance/equipm ent standards. Therefore the standardized “Method 1” adjustment was made to the load forecast in order to capture the impact of those standards, as described in Section 2.2

.

IID

Customer

- Funded Energy Efficiency Programs : Like all Californi a municipal utilities, IID is required to establish 10

- year savings targets for customer

- funded EE programs. The most recent set of targets were reported to the CEC in a March 2011 compliance filing. IID staff confirmed

that the load forecast submitted t o WECC does not explicitly consider the impact of customer

funded energy efficiency programs, although it does implicitly assume a continuation of savings at historical levels. Insufficient data is available to assess IID's historical savings or how that level of savings compares to its current long

- term goals; therefore, for simplicity, the load forecast is assumed to fully capture IID's long term savings goals.

Federal Standards : IID staff confirmed that the load forecast submitted to WECC d oes not explicitly consider the impact of federal standards. Therefore the standardized “Method 1” adjustment was made to the load forecast in order to capture the impact of those standards, as

described in Section 2.2

.

IPC

Customer

- Funded EE Programs : IPC staff confirmed that the load forecast submitted to WECC

is net of only the existing, committed programs in the 2011 IRP, but that it does not account for the savings from the new planned programs identified in the IRP. Therefore, the IPC l oad forecast provided to WECC in 2011 is adjusted downward based on the projected savings from new programs identified in Idaho Power's 2011 IRP. Savings from NEEA regional market transformation programs implemented over the forecast period are assumed to be captured in the load forecast, under the presumption that NEEA program savings will continue to accumulate at

more

- or

- less their historical rate; therefore, the embedded and expected savings projections for

customer

- funded energy efficiency programs in clude the projected savings from NEEA programs, in addition to the IRP savings projections (which exclude NEEA program savings).

Federal Standards : Idaho Power staff indicated that the company's current load forecasting

model does include stat istical end

- use adjustments for residential load, and captures all historical changes to federal standards for residential end

- uses as well as future updates to federal

standards for residential end

- uses, based on consensus forecasts of future implementati on of standard levels from the American Council for an Energy

- Efficient Economy, and the

Association of Home Appliance Manufacturers. However, IPC staff confirmed that the load forecast does not model any changes to federal standards for commercial custom ers, although the company is planning to do so in the future. For the purpose of the WECC Common Case forecast, it was assumed that the IPC load forecast originally submitted to WECC accounts for

all 2009

- 2013 federal standards targeting residential end u ses, but does not account for any of the

2009

- 2013 standards targeting commercial end uses. The forecast was therefore adjusted

57

### downward based on the projected impact of those 2009

- 2013 federal standards targeting

commercial end

- uses.

GCPD

Th e expected savings are equal to GCPD's pro

- rated share of the Council's 6th plan targets. Pending confirmation from GCPD staff, it is assumed that the load forecast submitted to WECC is already "net" of this level of savings. Note that the Council's cons ervation targets are not specific to any individual policy mechan ism; therefore, the projections of embedded an d expected savings in the table below are not disaggregated into customer

- funded EE programs

and federal standards. It is also assumed that sa vings from regional NEEA market transformation programs are included within the Cou ncil's conservation targets.

LDWP

Customer

- Funded EE Programs : The LADWP balancing authority consists of three utilities (LADWP, Burbank, and Glendale), each of which is required to establish 10

- year energy efficiency savings targets. LADWP staff confirmed that the load forecast submitted to WECC is "net" of savings from planned customer

- funded energy efficiency programs, based on each utility’s' most recently approved s et of targets. (Note, though, that the current targets for LADWP terminate in 2016, and thus the utility used the 2010 IRP savings projections for years thereafter, which taper off significantly after 2016). Given that the utilities' savings targets are fully captured by the load forecast submitted to WECC, no adjustments were made for customer

funded energy efficiency programs.

Federal Standards : LADWP staff indicated that the load forecast submitted to WECC does contain an adjustment to ac count for the impact of the Huffman lighting standards, but not for any future standards. The forecast therefore likely captures some or all of the savings associated with the lighting standard update adopted by DOE in 2009. For simplicity, we assume th at savings associated with all federal standards already adopted since January 2009 are embedded within the load forecast (as the lighting standard represents the bulk of the total expected savings from federal standards updates since January 2009), and ap ply the standard “Method 2” adjustment described in Section 2.2 in order to account for the expected savings from prospective standards scheduled for adoption by January 2013.

NEVP

Customer

- Funded EE Programs : NV Energy staff confirmed that the NEVP load forecast submitted to WECC is net of planned energy efficiency savings, based on the savings targets assumed within the company’s 2010 Energy Supply Plan, as provided to WECC. Note that these savings targets are net of measure decay, and are therefore significantly lower than what one would derive by simply extrapolating the most recent 2011

- 2013 DSM Plan annual savings acquisition rates out over the entire forecast period. Because the load forecast submitted to WECC fully captures NV Energy ’s currently planned savings targets, no adjustment was made to

the forecast.

58

### Federal Standards : The expected savings from federal standards adopted over the 2009

- 2013 timeframe was estimated by pro

- rating the statewide savings, in proportion to the porti on of statewide load within the balancing authority. NV Energy staff confirmed that the NEVP load forecast does account for historical changes to federal standards, but does not account for any planned updates. Savings associated with federal standards a lready adopted since January 2009 were therefore assumed to be embedded within the load forecast, and the standard “Method 2” adjustment described in Section 2.2 was applied in order to account for the expected savings from prospective standards scheduled for adoption by January 2013.

NWMT

Customer

- Funded EE Programs : NorthWestern Energy staff indicated that the load forecast submitted to WECC is net of energy efficiency program savings, based only on the historical rate of savings. NorthWestern’s 2009 Electric Default Supply Procurement Plan indicates that, from

2006

- 2009, the utility achieved average annual incremental savings of 5.2 aMW per year. In contrast, the Plan proposes to acquire savings at an average annual rate of 6.0 aMW over the

forecast period. Therefore, the load forecast submitted to WECC was adjusted downward based on the difference between the planned savings level and the savings level that was achieved over

the 2006

- 2009 period.

Federal Standards : NorthWestern Energy staff could not confirm whether the load forecast submitted to WECC explicitly considers the impact of federal standards. Given the manner in which the forecast accounts for savings from customer

- funded programs (i.e., accounting for future savings only at the histo rical rate), it was similarly assumed that the forecast does not account for any increase in the rate of savings accumulation from federal standards, and the standardized “Method 1” adjustment was made to the load forecast in order to capture the impact

of those standards, as described in Section 2.2

.

PACE

Customer

- Funded EE Programs (PacifiCorp) : PacifiCorp staff confirmed that the PACE load forecast submitted to WECC accounted for the planned energy efficiency program savings from

the 2008 IR P Update (issued in 2010). The savings targets in the more

- recent 2011 IRP (issued March 31, 2011) are somewhat higher than the targets in the 2008 IRP Update. Therefore, to develop the WECC Common Case forecast, the original PACE forecast was decremente d by an amount equal to the difference between the savings targets in the 2011 IRP and the 2008 IRP Update.

Customer

- Funded EE Programs (Other Utilities in PACE) : In addition to PacifiCorp, the PACE balancing authority also includes all of th e municipal utilities and cooperatives in Utah, as well

as Montana

- Dakota Utilities in Wyoming (representing, in aggregate, about 15% of the balancing authority load). The DSM Work Group’s projection of expected savings from MDU's programs are based on th e most recent 3

- year DSM plan, and assume that the average annual incremental savings in that plan continue over the entirety of the 2011

- 2021 forecast period. The expected savings from Utah municipal utilities and cooperatives are based on the assumption that savings accumulate at a rate of 0.1% of retail sales per year. This is the generic conservative

59

### assumption used by the DSM Work Group throughout its analysis for small utilities with little or no history of administering significant energy efficienc y program portfolios and/or where no specific data were available regarding planned energy efficiency program activity. No information was provided to WECC or to LBNL regarding the extent to which the load forecast submitted to WECC accounted for expected savings from customer

- funded programs by MDU or by the Utah municipal utilities and cooperatives. For simplicity, given the relatively small portion of the overall PACE load represented by these utilities, it was assumed that the original

load forecast s ubmitted to WECC fully captures the expected savings from those utilities'

programs.

Federal Standards : The expected savings from federal standards adopted over the 2009

- 2013 timeframe was estimated by pro

- rating the statewide savings, for eac h state in which PACE overlaps, in proportion to the portion of statewide load within the balancing authority. PacifiCorp staff confirmed that the PACE load forecast does account for all historical changes to federal standards, but does not account for an y planned updates. Savings associated with federal standards already adopted since January 2009 was therefore assumed to be embedded within the load forecast, and the standard “Method 2” adjustment described in Section 2.2 was applied in

order to account for the expected savings from prospective standards scheduled for adoption by January 2013.

PACW

Customer

- Funded EE Programs : PacifiCorp staff confirmed that the PACW load forecast submitted to WECC accounted for the planned energy efficiency program savings from the 2008 IRP Update (issued in 2010). The savings targets in the more

- recent 2011 IRP (issued March 31, 2011) are somewhat higher than the targets in the 2008 IRP Update. Therefore, to develop the WECC Common Case forecast, the orig inal PACW forecast was decremented by an amount equal to the difference between the savings targets in the 2011 IRP and the 2008 IRP Update. PacifiCorp staff confirmed that the EE targets for Oregon contained within the 2011 IRP were provided to PacifiCor p by the Oregon Energy Trust, and are intended to reflect all achievable

cost

- effective efficiency, based on the Trust's current planning assumptions.

Federal Standards : The expected savings from federal standards adopted over the 2009

- 2013

timefram e was estimated by pro

- rating the statewide savings, for each state in which PACW overlaps, in proportion to the portion of statewide load within the balancing authority. PacifiCorp staff confirmed that the PACW load forecast does account for all historic al changes to federal standards, but does not account for any planned updates. Savings associated with federal standards already adopted since January 2009 were therefore assumed to be embedded within the load forecast, and the standard “Method 2” adjustm ent described in Section 2.2 was applied in order to account for the expected savings from prospective standards scheduled for adoption by January 2013.

60

### PGE

Customer

- Funded EE Programs : PGE staff confirmed that the PGE load forecast is net of the savings from all planned energy efficiency programs, and is based on a savings projection provided to PGE by the Energy Trust. Therefore, no adjustment to the initial forecast was made

for customer

- funded energy efficiency program savings. The savings projecti on provided to PGE extends only through 2017, and PGE staff indicated that for the 2018

- 2021 period, they assumed that savings would continue at the same rate as prior to 2017.

Federal Standards : PGE staff also confirmed that the PGE load for ecast does not model any changes to federal lighting/appliance/equipment standards. Therefore, the standardized “Method 1” adjustment was made to the load forecast in order to capture the impact of standards adopted

over the 2009

- 2013 period, as described in Section 2.2

.

PNM

Customer

- Funded EE Programs (PNM utility) : PNM Staff confirmed that the load forecast submitted to WECC does not capture the expected impact of PNM customer

- funded energy efficiency programs

-- that is, it is a "pre

- energy efficiency" forecast. PNM provided WECC with its forecast of planned savings from customer

- funded energy efficiency programs, which complies with New Mexico's energy efficiency resource standard. The load forecast that PNM provided to WECC was therefore reduced by an amount equal to PNM's projected energy efficiency savings.

Customer

- Funded EE Programs (other utilities in the PNM balancing authority) : The PNM balancing authority includes several municipal utilities and a number of cooperative s served by

Tri

- State G&T; together, these utilities constitute roughly 26% of the total PNM balancing authority load. Information was not readily available regarding the planned energy efficiency savings by these entities or the amount of savings already embedded in the overall balancing authority forecast. Lacking such information, the DSM Work Group assumes that customer

funded energy efficiency programs implemented by these entities will yield annual incremental savings equal to 0.1% of retail sales p er year (a relatively modest level, though not atypical for small public utilities), and that this level of savings is captured within the load forecast by virtue of the econometric methods used to develop the forecast.

Federal Standards : PNM staf f also confirmed that the PNM load forecast does not model any changes to federal lighting/appliance/equipment standards. Therefore, the standardized “Method 1” adjustment was made to the load forecast in order to capture the impact of standards adopted

o ver the 2009

- 2013 period, as described in Section 2.2

.

PSCO

Customer

- Funded EE Programs (Xcel) : Xcel staff indicated that the load forecast provided to WECC is net of the previous set of long

- term savings goals established under Docket. 08

- 0560. The forecast was therefore adjusted downward slightly in order to account for the higher level of

savi ngs required under the updated goals adopted in March 2011 (Decision No. C11

- 0442).

61

### Customer

- Funded EE Programs (other utilities in PSCO balancing authority) : The PSCO balancing authority also includes a number of other utilities, including Black Hills, t he municipal utilities served by Platte River (Fort Collins and several smaller utilities), a number of cooperatives served by Tri

- State, as well as cooperatives served by the Arkansas River Power Authority and Municipal Energy Agency of Nebraska. Accord ing to PSCO staff, those utilities provide their own load forecasts to PSCO, and did not provide any information about whether those forecasts are "net" of planned energy efficiency savings. For simplicity (given the relatively small size of those other u tilities compared to PSCO and the time and resources required for independent validation), we assume that the load forecasts for those smaller entities do fully account for the expected impact of their customer

- funded energy efficiency programs.

For Black Hills, the expected savings is based on the long

- term savings targets approved by the

commission in Docket No. 08A

- 518E. For Platte River Power Authority (PRPA), the expected savings is based on the savings projection provided by PRPA to WECC, which is s omewhat below the saving level currently being achieved by Fort Collins. For the Tri

- State, ARPA, and MEAN cooperatives within the PSCO balancing authority, expected savings are based on a stipulated annual incremental savings of 0.1% of retail sales per year; this is the generic assumption used by the DSM Work Group throughout its analysis, for utilities with little or no history of administering significant energy efficiency program portfolios, and where no specific data were available regarding planned energy efficiency program activity.

Federal Standards : PSCO staff confirmed that the utility's load forecast does account for all historical changes to federal standards, but does not account for any planned updates. Savings

associated with federal stand ards already adopted since January 2009 were therefore assumed to be embedded within the load forecast, and the standard “Method 2” adjustment described in

Section 2.2 was applied in order to account for the expected savings from prospective standards

sche duled for adoption by January 2013.

PSE

Customer

- Funded EE Programs : PSE staff confirmed that the load forecast submitted to WECC is net of planned energy efficiency savings, though they have not yet confirmed whether the amount of savings assumed is co nsistent with the savings projections in their 2011 IRP. Pending confirmation from PSE, it is assumed that the load forecast provided to WECC fully accounts for the level of savings identified in the utility’s 2011 IRP. Therefore, no adjustment to the fo recast is currently made for customer

- funded EE programs.

Federal Standards : PSE staff confirmed that the load forecast submitted to WECC does not explicitly consider the impact of recent or scheduled updates to federal standards through 2013 (although th e utility did explicitly model the impact of the EISA lighting standard established in 2007). Therefore, the standardized “Method 1” adjustment was made to the load forecast in order to capture the expected impact of standards adopted over the 2009

- 2013 p eriod, as

described in Section 2.2

.

62

### SCL

Customer

- Funded EE Programs : The savings projection that SCL provided to WECC is consistent with the conservation targets identified in Seattle City Light's 2010 IRP. Seattle City Light staff has not yet confirmed that the load forecast submitted to WECC is already net of this level of savings. Pending confirmation from the utility, it is assumed that the forecast is net of this expected savings level, and therefore no adjustments were made to the forecast for cus tomer

funded programs.

Federal Standards : Seattle City Light staff have not yet indicated whether the load forecast submitted to WECC explicitly considers the impact of federal standards. Pending confirmation from the utility, the standardize d “Method 1” adjustment was made to the load forecast in order to capture the expected impact of standards adopted over the 2009

- 2013 period, as described in

Section 2.2

SMUD

Customer

- Funded EE Programs (SMUD) : SMUD staff confirmed that the load forecast submitted to WECC is net of assumed savings from SMUD customer

- funded energy efficiency programs over the 2011

- 2021 period. However, the assumed energy efficiency savings through 2017 are equal to about 70% of the long

- term targets established by the SMUD Board (which currently extend only through 2017), and in years after 2017, the assumed savings in SMUD's

load forecast are about 40% of the SMUD Board

- approved target for 2017. SMUD staff

indicated that the a ssumed savings reflect near

- term EE program forecasts through 2014 and the Itron potential study estimates for years after 2014, rather than the SMUD Board

- approved goals, because of the uncertainty surrounding both the unmanaged forecast and EE program

ef

fectiveness. In deference to the judgment of SMUD's planning team, the SPSC DSM Work Group’s projection of expected savings from SMUD customer

- funded EE program savings is equal to the level assumed by SMUD when developing their load forecast, and no adju stment to
the forecast was made.

Customer

- Funded EE Programs (other utilities in the SMUD balancing authority) : The SMUD balancing authority includes several other municipal utilities (Modesto, Redding, or Roseville); like all municipal utilities in Calif ornia, these three utilities are also required to establish long

term savings goals and to report those targets to the CEC. SMUD staff could not confirm whether the forecast also accounted for the expected impacts from planned energy efficiency programs b y those utilities. For simplicity, given the relatively small size of those utilities compared to SMUD, it was assumed that the long

- term savings targets for those utilities were also fully captured by the balancing authority load forecast.

F ederal Standards : SMUD staff also confirmed that the load forecast submitted to WECC does not model any changes to federal lighting/appliance/equipment standards. Therefore, the standardized “Method 1” adjustment was made to the load forecast in order to capture the expected impact of standards adopted over the 2009

- 2013 period, as described in Section 2.2

. SMUD staff, however, did indicate that the company is testing Itron's statistically

- adjusted end

63

### use model for future load forecasting, and this model would allow federal standards to be explicitly modeled in future years.

SPP

Customer

- Funded EE Programs : NV Energy staff confirmed that the SPP load forecast submitted to WECC is net of planned energy efficiency savings, based on the savings targets ass umed within the company’s 2010 Integrated Resource Plan, as provided to WECC. Note that these savings targets are net of measure decay, and are therefore significantly lower than what one would derive by simply extrapolating the most recent 2011

- 2013 DSM Plan annual savings acquisition rates out over the entire forecast period. Because the load forecast submitted to WECC fully captures NV Energy’s currently planned savings targets, no adjustment was made to

the forecast.

Federal Standards : T he expected savings from federal standards adopted over the 2009

- 2013 timeframe was estimated by pro

- rating the statewide savings, in proportion to the portion of statewide load within the balancing authority. NV Energy staff confirmed that the SPPC load forecast does account for historical changes to federal standards, but does not account for any planned updates. Savings associated with federal standards already adopted since January 2009 were therefore assumed to be embedded within the load forecast, a nd the standard “Method 2” adjustment described in Section 2.2 was applied in order to account for the expected savings from prospective standards scheduled for adoption by January 2013.

SRP

Customer

- Funded EE Programs : SRP staff confirmed that the load forecast submitted to WECC is net of the planned savings levels as of last summer, which extend only through 2017. The expected savings from customer

- funded programs are, instead, based on the recently adopted Sustainable Portfolio Plan target levels. A ccording to SRP staff, the planned savings levels in the forecast are roughly in line with the utility's current expectations for the savings levels needed to meet the Sustainable Portfolio Plan savings targets through 2017 (due, in part, to the

recent dow nward revision to their load forecast, which in turn reduces the amount of savings required to meet the percentage targets under the Sustainable Portfolio Plan). Thus, the load forecast was adjusted only to account for the expected savings needed to meet the Sustainable Portfolio Plan savings targets in the years 2018

- 2021. In calculating the cumulative embedded and expected savings, it was assumed that 48% of the annual incremental savings from SRP's EE program portfolio is associated with the M

- Power pr ogram, and that the savings from that program do not persist beyond the first year. The 48% assumption is based on SRP's forecast of EE program savings for FY11.

Federal Standards : SRP staff have not yet confirmed whether the load forecast su bmitted to WECC explicitly considers the impact of federal standards. Pending further information, the standardized “Method 1” adjustment was made to the load forecast in order to capture the expected impact of standards adopted over the 2009

- 2013 period, as described in Section 2.2

.

64

### TEP

Customer

- Funded EE Programs : TEP/UNS staff indicated that the load forecast submitted to WECC partially accounts for the effects of planned customer

- funded energy efficiency programs over the forecast period, but not at the level necessary to meet the Arizona Energy Efficiency Standard. The program savings forecast that TEP originally submitted to WECC represents the cumulative savings required to comply with the Arizona Energy Efficiency Rule, of which only a portion is captured within the load forecast. TEP staff provided a separate table to LBNL identifying the level of savings that was captured by the original forecast. To construct the WECC Common Case forecast, TEP's original load forecast was therefore reduced by an amount equal to the difference between the level of savings required to meet the EE standard (as indicated by TEP) and the amount already incorporated into the forecast.

Federal Standards : TEP staff confirmed that the load forecast submitted to WECC does not model any changes to federal lighting/appliance/equipment standards. Therefore, the standardized “Method 1” adjustment was made to the load forecast in order to capture the expected impact of standards adopted over the 2009

- 2013 period, as described in Section 2.2

.

TID

Customer

- Funded Energy Efficiency Programs : Like all California municipal utilities, TID is required to establish 10

- year savings targets for customer

- funded EE programs. The most recent set of targets were reported to the CEC in a March 2011 compliance filing. The SPSC DSM Work Group was unable to confirm with TID staff whether their forecast is net of planned energy efficiency savings, and if so, by how much. Given the lack of information available, the

s ame assumptions were employed for TID as for IID. Namely, the forecast was assumed to fully capture planned energy efficiency savings, based on the utility's most

- recent 10

- yea r savings

targets.
Federal Standards : The SPSC DSM Work Group was unable to confirm with TID staff whether/how the forecast accounts for savings from federal standards. Given the lack of information available, it was assumed that the forecast does not explicitly model the impact of federal standards; and therefore, the standardized “Method 1” adjustment was made to the load forecast in order to capture the expected impact of standards adopted over the 2009

- 2013 period,

as described in Section 2.2

.

TPWR

Tacoma Power staff indicat ed that the forecast prepared by the uti lity is net of planned conservation, and that the planned energy efficiency savings are based on the Council's conservation targets , which the utility then incorporates into its IRP . Thus, no adjustments were

made to the load forecast submitted to WECC. Note that the Council's conservation targets are not specific to any individual policy mechan ism; therefore, the projection of embedded and expected savings in the table below are not disaggregated into customer

- funded EE programs

and federal standards. It is also assumed that savings from regional NEEA market transformation programs are included within the Co uncil's conservation targets.

65

### WACM

Customer

- Funded EE Programs (Colorado Springs Utilities) : The WACM balancing authority consists of a large number of municipal utilities and cooperatives located in Colorado and Wyoming, the largest of which being Colorado Springs Utilities (CSU), and most others are served by Tri

- State or the Municipal Energy Agency of Nebraska. CSU staff provided LBNL with the utility’s current projection of savings from customer

- funded EE programs, and indicated that the load forecast that CSU provided to WACM does not account for the impacts of DSM programs. As such, the W ACM forecast was adjusted downward based on the savings projection provided by CSU.

Customer

- Funded EE Programs (all other utilities in WACM) : All other utilities in the balancing authority are assumed to achieve savings of 0.1% per year (or 1% cumulati vely over the forecast period); this is the generic conservative assumption used by the DSM Work Group for utilities with little or no history of administering significant energy efficiency program portfolios, and where no specific data were available rega rding planned energy efficiency program activity. WAPA staff indicated that the forecast was developed by extrapolating prior years' load, without explicitly accounting for the impacts of energy efficiency programs. As such, the savings from

all utilitie s other than CSU were assumed to be fully captured within the WACM load forecast, by virtue of the fact that no significant increase in program savings from those utilities is expected.

Federal Standards : Given the manner in which the load forecast was constructed, it was assumed that the forecast does not capture the expected impact of federal standards adopted over the

2009

- 2013 timeframe. Therefore, the standardized “Method 1” adjustment was made to the load forecast in order to capture the expected impact of standards adopted over the 2009

- 2013 period,

as described in Section 2.2

.

WALC

The WALC balancing authority consists of a large number of municipal utilities and cooperatives located in Arizona and New Mexico. The expected savings from custo mer

- funded EE programs was estimated based on an assumed annual incremental savings of 0.1% of load per year; this is the generic conservative assumption used by the DSM Work Group for utilities with little or no history of administering significant energy efficiency program portfolios, and where no specific data were readily available regarding planned energy efficiency program activity. The expected savings from 2009

- 2013 federal standards over the forecast period was estimated

based on the standard meth od of pro

- rating expected statewide savings based on the proportion of 2021 statewide load within the balancing authority, for each of the states with which WALC overlaps. Western Area Power Administration (WAPA) prepares the load forecast for the region, and WAPA staff indicated that the WALC load forecast submitted to WECC was constructed simply by applying a stipulated 1% growth rate to the previous year's actual peak load, as a 1% load growth is the minimum required by WECC's L&R program that avoids

tr iggering a load growth alarm. Given the simplified method of developing the load forecast, it is practically infeasible to specify the degree to which the forecast accounts for the expected savings from current energy efficiency policies and program plans , and therefore no adjustment

66

### was made to the forecast originally submitted to WECC.

WAUW

The WAUW balancing authority consists of a relatively small amount of load in eastern Montana. The expected savings from customer

- funded EE programs wa s estimated based on an assumed annual incremental savings of 0.1% of load per year; this is the generic conservative assumption used by the DSM Work Group for utilities with little or no history of administering significant energy efficiency program portf olios, and where no specific data were readily available regarding planned energy efficiency program activity. The expected savings from

2009

- 2013 federal standards over the forecast period was estimated based on the standard method of pro

- rating expected statewide savings based on the proportion of 2021 statewide load within the balancing authority. Western Area Power Administration (WAPA) prepares the load forecast for the region, but did not provide any information regarding the degree to which the

loa d forecast captures the expected savings from customer

- funded EE programs or federal standards. Given the lack of information available, and given the arguably negligible size of the WAUW load, no adjustment was made to the original load forecast submitte d to WECC.

67

## Appendix B

. SPSC 10

- Year High DSM Case: State

- by

- State Analyses

As described in Chapter 3 , the SPSC DSM Work Group decided that 10

- year High DSM case

would be based on achievement of all cost

- effective energy efficiency savings (i.e., the “economic potential”) in each balancing authority. T his appendix provides details on the development of the efficiency savings projection s for each state and BA. Table B

- 1 and Table

B

- 2 first summarizes the energy and peak demand savings proje ctions and High DSM Case forecasts across all states and provinces. The remainder of the appendix consists of a series of state or province

- specific sections providing details on the development of the High DSM Case efficiency projections and load forecast for each BA in the respective state/province

. ( Note that

all BAs in Pacific Northwest states are addressed with in a single section.)

68

### Table B

- 1

. High DSM Load Forecasts for 2021: Annual Electricity Consumption

Balancing Authority Cumulative EE Savings (GWh ) Annual Energy in

2021 (GWh) Cumulative Savings ( % of 2021 Load ) CAGR (2010

- 2021)

Common

Case High

DSM Common

Case High

DSM Common

Case High

DSM Common

Case High

DSM

CISO 39,884 44,288 249,856 245,452 13.8% 15.3% 0.8% 0.7%

IID 412 804 4,260 3,868 8.8% 17.2% 1.7% 0.8%

LADWP 3,535 5,403 30,789 28,920 10.3% 15.7% 0.8% 0.2%

SMUD 2,251 3,467 17,975 16,758 11.1% 17.1% 0.4%

- 0.2%

TID 279 507 2,829 2,602 9.0% 16.3% 1.3% 0.5%

AESO 1,107 22,141 110,704 89,670 1.0% 19.8% 4.0% 2.1%

BCTC 11,076 17,931 66,892 60,037 14.2% 23.0% 1.2% 0.2%

AVA 1,530 3,453 14,465 12,542 9.6% 21.6% 1.5% 0.2%

BPA 6,504 13,370 53,450 46,585 10.8% 22.3% 0.3%

- 1.0%

CHPD 135 987 4,037 3,185 3.2% 23.7% 2.3% 0.1%

DOPD 117 520 1,931 1,528 5.7% 25.4% 3.3% 1.2%

GCPD 314 1,258 5,103 4,160 5.8% 23.2% 2.1% 0.2%

IPC 2,025 4,097 19,540 17,467 9.4% 19.0% 1.6% 0.6%

NWMT 851 1,173 11,268 10,946 7.0% 9.7% 0.6% 0.4%

PACW 2,061 4,951 22,749 19,859 8.3% 20.0% 0.9%

- 0.4%

PGE 3,816 5,600 23,067 21,283 14.2% 20.8% 1.3% 0.5%

PSE 4,667 6,407 26,340 24,600 15.1% 20.7% 0.6% 0.0%

SCL 1,258 2,644 10,662 9,275 10.6% 22.2% 0.7%

- 0.6%

TPWR 588 1,316 5,443 4,715 9.7% 21.8% 1.0%

- 0.3%

WAUW 28 65 821 784 3.3% 7.6% 2.4% 1.9%

APS 6,919 11,022 41,444 37,341 14.3% 22.8% 2.7% 1.7%

CFE 0 0 14,606 14,606 0.0% 0.0% 2.9% 2.9%

EPE 914 1,239 10,679 10,354 7.9% 10.7% 2.6% 2.3%

NEVP 1,659 6,509 26,665 21,815 5.9% 23.0% 0.8%

- 1.1%

PACE 3,283 9,226 55,487 49,544 5.6% 15.7% 1.6% 0.6%

PNM 1,419 1,899 16,887 16,407 7.8% 10.4% 1.1% 0.9%

PSCO 6,826 8,542 46,552 44,836 12.8% 16.0% 1.0% 0.6%

SPP 795 2,297 12,694 11,191 5.9% 17.0% 1.0%

- 0.1%

SRP 4,235 10,011 35,283 29,506 10.7% 25.3% 1.3%

- 0.3%

TEP 2,649 4,221 14,857 13,286 15.1% 24.1% 0.3%

- 0.7%

WACM 1,063 6,467 29,831 24,426 3.4% 20.9% 2.2% 0.4%

WALC 269 1,709 7,549 6,109 3.4% 21.9% 1.0%

- 0.9%

WECC 112,466 203,525 994,715 903,656 10.2% 18.4% 1.4% 0.5%



### 69

### Table B

- 2

. High DSM Load Forecasts for 2021: Annual Non

- Coincident Peak Demand

Balancing Authority Cumulative EE Savings (MW ) Annual Energy in

2021 (MW) Cumulative Savings ( % of 2021 Load ) CAGR (2010

- 2021)

Common

Case High

DSM Common

Case High

DSM Common

Case High

DSM Common

Case High

DSM

CISO 11,469 12,736 49,324 48,057 18.9% 20.9% 0.3% 0.1%

IID 92 180 1,176 1,088 7.3% 14.2% 1.4% 0.7%

LADWP 750 1,146 8,044 7,648 8.5% 13.0% 1.6% 1.1%

SMUD 401 617 4,357 4,140 8.4% 13.0%

- 0.2%

- 0.6%

TID 65 119 667 614 8.9% 16.2% 1.2% 0.4%

AESO 154 3,572 15,437 12,019 1.0% 22.9% 3.8% 1.5%

BCTC 2,000 2,923 12,077 11,153 14.2% 20.8% 1.1% 0.4%

AVA 178 728 2,675 2,125 6.2% 25.5% 1.9%

- 0.2%

BPA 880 2,770 10,376 8,486 7.8% 24.6% 0.5%

- 1.3%

CHPD 26 199 717 544 3.5% 26.8% 1.6%

- 0.9%

DOPD 47 105 415 357 10.2% 22.7% 1.3%

- 0.1%

GCPD 127 254 846 719 13.0% 26.1% 1.9% 0.4%

IPC 338 696 4,788 4,429 6.6% 13.6% 3.2% 2.5%

NWMT 142 259 1,820 1,703 7.3% 13.2% 0.6% 0.0%

PACW 346 1,048 4,238 3,536 7.5% 22.9% 1.0%

- 0.6%

PGE 650 1,201 4,159 3,608 13.5% 25.0% 1.3% 0.0%

PSE 943 1,292 5,303 4,954 15.1% 20.7% 0.9% 0.3%

SCL 225 533 1,898 1,589 10.6% 25.1% 0.3%

- 1.3%

TPWR 114 265 1,035 884 9.9% 23.1% 0.4%

- 1.0%

WAUW 5 14 147 138 3.3% 9.4% 2.2% 1.6%

APS 1,178 2,111 9,473 8,540 11.1% 19.8% 2.7% 1.8%

CFE 0 0 3,353 3,353 0.0% 0.0% 4.0% 4.0%

EPE 203 275 2,191 2,119 8.5% 11.5% 2.8% 2.5%

NEVP 402 1,186 6,659 5,874 5.7% 16.8% 0.9%

- 0.2%

PACE 616 2,512 11,015 9,119 5.3% 21.6% 3.0% 1.3%

PNM 267 357 2,905 2,815 8.4% 11.2% 0.9% 0.6%

PSCO 1,278 2,069 7,821 7,030 14.0% 22.7% 0.3%

- 0.7%

SPP 224 416 2,102 1,909 9.6% 17.9% 0.8%

- 0.1%

SRP 999 1,911 7,373 6,461 11.9% 22.8% 1.1%

- 0.1%

TEP 477 758 3,096 2,814 13.3% 21.2% 0.0%

- 0.8%

WACM 206 998 4,605 3,813 4.3% 20.8% 2.2% 0.4%

WALC 74 339 1,585 1,320 4.5% 20.5% 1.0%

- 0.7%

WECC 24,874 43,592 191,678 172,960 11.5% 20.1% 1.3% 0.3%



### 70

### Alberta

Summary Tables

As explained further below, the Common Case Load Forecast for Alberta is identical to the load

forecast that AESO submitted to WECC’s Load and Resources Subcommittee.

Table 1. High DSM Load Forecasts for 2021: Annua l Electricity Consumption (GWh)

Balancing Authority A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

AESO 110,704 1,107 22,141 89,670

- 19%

Provincial Total 110,704 1,107 22,141 89,670

- 19%



### Table 2

. High DSM Load Forecasts for 2021: Annua l Peak Demand (MW)

Balancing Authority A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

AESO 15,437 154 3,572 12,019

- 22%

Provincial Total 15,437 154 3,572 12,019

- 22%



### Table 3. Comparison of Reference Case and High DSM Case (2021 Cumulative Savings)

Balancing Authority Common Case Savings (% of No

- EE Load Forecast) High DSM Case Savings (% of No

- EE Load Forecast)

GWh MW GWh MW

AESO 1% 1% 20% 23%

Provincial Total 1% 1% 20% 23% Notes: The percentages in this table were calculated by dividing the savings projection by the sum of the savings

projection and the post

- savings load forecast (e.g., High DSM savings divided by High DSM savings plus High DSM load forecast)



### High DSM Savings Projection

We develop High DSM savings projections for AESO on a sector

- by

- sector basis. To do so, it is necessary to first disaggregate AESO’s LRS load forecast for 2020 into individual sectors (see Table 4). This allocation is based on the sector

- level load forec asts from AESO’s published load forecast, Future Demand and Energy Outlook (2009

– 2029)

.

71

### Table 4. Projected 2021 Energy Sales to Alberta Internal Load

Sector % of Total 1 GWh 2

Residential 11% 12,177

Commercial 18% 19,927

Farm 2% 2,214

Oil Sands 19% 21,034

Industrial (excl. Oil Sands) 50% 55,352

Total 100% 110,704

1 Source: AESO Future Demand and Energy Outlook (2009

– 2029), Table 4

- 1

2 GWh values for each sector are calculated by multiplying the corresponding percentage and the total LRS forecast

for AESO in 2021 ( 110,704 GWh).



### To estimate the economic potential for the AESO balancing authority, the DSM working group

relied on the recent energy efficiency potential study conducted by the Canadian Manufacturers and Exporters (CME) for Alberta’s industrial and manufacturing sector (CME, 2010), as well as a number of other recent energy efficiency potential studies conducted for utilities/st ates in the surrounding region. The economic potential estimates produced by these studies is summarized in Table 5, disaggregated by sector and expressed in terms of the baseline sales for that sector. In general, the baseline sales projections in each study represent projected electricity consumption in the absence of future programs and policies to support energy efficiency, but typically do account for some amount of “naturally occurring” conservation in response to stock turnover and market prices.

Table 5. Electric Energy Efficiency Economic Potential by Sector (% of Baseline Electricity Sales)

Utility/Region Study Target Year Residential Commercial Industrial Irrigation

Alberta CME (2010) 2020 n/a n/a 19% n/a

Public Service

Colorado KEMA (2010) 2020 28% 24% 12% n/a

Western Montana NPCC (2009) 2020 31% 16% 13% 9%

NorthWestern Energy Nexant (2009) 2029 18% 12% 18% n/a

Rocky Mountain

Power Quantec (2007) 2027 17% 14% 8% 11%

BC Hydro Marbek (2007) 2020 23% 28% 32% n/a

Tri

- State (CO & WY) Nexant (2010) 2020 19% 18% 16% 10%

Median Value 21% 17% 15% 10%



### We apply the potential study results to AESO on a sector

- by

- sector basis. For the residential, commercial, and irrigation/farm sectors, we apply the median values among the set of studies in Table 4 (21%, 17%, and 10% of baseline electricity sales, respec tively). For the industrial sector, we separately estimate the economic potential for the oil sands sub

- sector and for the remaining portion of Alberta’s industrial sector. The CME potential study scope does not

include the oil sands sub

- sector; thus, we apply the electric potential results from that study (19% of baseline sales in 2020) to estimate the economic potential for the non

- oil sands sub

- sectors of Alberta’s industrial load. That estimate is largely consistent with the industrial sector economi c

72

### potential estimates from the other studies listed in Table 5. For the oil sands sub

- sector, we estimate economic potential by examining potential study results for the petroleum refining sub

sector, among those studies that present sub

- sector level resu lts (see Table 6). We assume that the economic potential for Alberta’s oil sands sub

- sector, as a percentage of baseline sales, is equal to the median value from these studies (24% of baseline electricity sales).

Table 6. Economic Potential Estimates for Petroleum Refining Sub

- Sector

Utility/Region Study Target Year % of Baseline Electricity Sales

Public Service Colorado KEMA (2010) 2020 28%

Rocky Mountain Power Quantec (2007) 2027 11%

NorthWestern Energy Nexant (2009) 2029 21%

BC Hydro Marbek (2007) 2020 28%

Median Value 24%



### We estimate the total 2021 economic potential in AESO by applying the potential percentage values, as estimated above, to the projected sectoral load in 2021, as shown in Table 7. This calculation yields a total estimated economic potential for AESO of 22,141 GWh in 2021.

Table 7. Estimated Electric Energy Efficiency Economic Potential for AESO in 2021 (GWh)

Sector Baseline Load (GWh) Economic

P otential (% of Baseline) Economic

Potential (GWh)

Residential 12,177 21% 2,557

Commercial 19,927 17% 3,388

Farm 2,214 10% 221

Oil Sands 21,034 24% 5,048

Industrial (excl. Oil Sands) 55,352 19% 10,517

Total 110,704 20% 22,141



### The final step in the High DSM scenario analysis is to estimate the peak demand savings associated with the energy efficiency potential estimate presented in Table 7. To do so, we first calculate the sectoral peak

- to

- energy savings ratios from those pote ntial studies that present peak and energy savings by sector (see Table 8). We then apply the median peak

- to

- energy savings ratio for each sector to the previously estimated energy savings potential (see Table 9). This calculation yields an estimated pea k demand savings of 3,572 MW in 2020.

Table 8. Sectoral Peak

- to

- Energy Savings Ratios Derived from Economic Potential Studies

Utility/Region Study Residential Commercial Irrigation Industrial Petroleum

Public Service Colorado KEMA (2010) 0.29 0.20 n/a 0.14 0.12

BC Hydro Marbek (2007) 0.26 0.14 n/a 0.12 n/a

Tri

- State (CO & WY) Nexant (2010) 0.17 0.21 0.23 0.20 n/a

Median Value 0.20 0.26 0.14 0.23 0.12



### 73

### Table 9. Peak Demand Savings Associated with AESO Energy Efficiency Economic Potential (2021)

Sector GWh MW/GWh Peak MW

Residential 2,557 0.26 665

Commercial 3,388 0.2 678

Farm 221 0.23 51

Oil Sands 5,048 0.14 707 Industrial (excl. Oil Sands) 10,517 0.14 1,472

Total 22,141 0.17 3,572



### 74

### Arizona

Summary Tables

Table 1. High DSM Load Forecasts for 2021: Annua l Electricity Consumption (GWh)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

APS 41,444 6,919 11,022 37,341

- 10%

SRP 35,283 4,235 10,011 29,506

- 16%

TEP 14,857 2,649 4,221 13,286

- 11%

WALC 6,239 222 1,577 4,884

- 22%

State Total 97,823 14,025 26,831 85,017

- 13%



### Table 2

. High DSM Load Forecasts for 2021: Annua l Peak Demand (MW)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

APS 9,473 1,178 2,111 8,540

- 10%

SRP 7,373 999 1,911 6,461

- 12%

TEP 3,096 477 758 2,814

- 9%

WALC 1,310 61 303 1,068

- 18%

State Total 21,252 2,715 5,083 18,884

- 11%



### Table 3. Comparison of Common Case and High DSM Case (2021 Cumulative Savings)

Balancing Authority (In

- State Portion) Common Case Savings (% of No

- EE Load Forecast) High DSM Case Savings (% of No

- EE Load Forecast)

GWh MW GWh MW

APS 14% 11% 23% 20%

SRP 11% 12% 25% 23%

TEP 15% 13% 24% 21%

WALC 3% 4% 24% 22%

State Total 13% 11% 24% 21% Note: The percentages in this table were calculated by dividing the savings projection by the sum of the savings projection and the post

- savings load forecast (e.g., High DSM savings divided by High DSM savings plus High DSM load forecast)



### 75

### High DSM Case Savings Projection

To estimate the economic potential for the balancing authorities in Arizona, the DSM working group relied on the 2010 energy efficiency potential s tudy conducted for Salt River Project (Cadmus, 2010), and extrapolated the results of that study to the rest of the state. 22 

### Table 4 summarizes the technical and economic potential estimates from the SRP study, net of naturally occurring energy efficiency savings, for the year 2020. For the purpose of the TEPPC High DSM Case, we assume that the economic potential for SRP in 2021 is equal to the study’s estimated potential for the year 2020.

Table 4. SRP Energy Efficiency Potential Estimates (2020)

Sector 2020 Baseline

Sales (GWh) 1 Net Technical Potential Net Economic Potential

GWh % of Baseline GWh % of Baseline

Residential 14,882 5,438 37% 5,015 34%

Commercial 10,268 3,562 35% 3,370 33%

Industrial 6,420 747 12% 677 11%

Total 31,571 9,747 31% 9,063 29%

Source: Cadmus (2010), Table 13

1 Note that the 2020 baseline sales projection in the energy efficiency potential study, which is based on end

- use

level forecasting model, is significantly lower than the 2020 load forecast that SRP submitted to WECC in early 2010 (40,382 GWh, or 41,160 GWh with embedded energy efficiency savings “added back in”). In principle, had the baseline forecast used in the potential study been calibrated to the WECC forecast, the resulting energy efficiency potential estimate would likely be higher in absolute term s.



### The potential estimates presented in the SRP study represent savings at the customer meter. For the purpose of developing the High DSM load forecast, the savings must be scaled up to the bus

bar to account for avoided T&D losses (see Table 5).

Table 5. Estimated Economic Potential at the Generator Bus

- Bar

Sector Marginal T&D

Losses 1 Net Economic Potential

Customer

Meter Bus

- Bar

Residential 10% 5,015 5,572

Commercial 9% 3,370 3,703

Industrial 8% 677 736

Total n/a 9,063 10,011

1 Marginal T&D loss factors are the relevant metric for estimating

avoided T&D losses from DSM, and are higher than average T&D loss factors because resistive losses increase exponentially with load.



### The SRP potential study provided estimates of energy savings, but not peak demand savings. We therefore estimate the peak demand savings associated with SRP’s economic potential

22 An energy efficiency potential study was conducted for Arizona Public Service in 2007 (ICF, 2007). However, that study provides potential estimates only for the year 2006. Applying those results to estimate the economic potential for APS in 2020 would arguably entail a more tenuous set of assumptions than extrapolating the results of the SRP potentia l study. Therefore, the DSM working group opted to rely solely on the results of the SRP study.



### 76

### energy efficiency estimate by applying a stipulated peak

- to

- energy savings ratio for each sector, based on the median value across other potential studies conducted for utilities in the Southwest (see Table 6). As shown in Table 7, applying these sectoral peak

- to

- energy savings ratios to SRP yields an estimated economic potential peak demand savings of 1,911 MW.

Table 6. Peak

- to

- Energy Savings Rat ios from a Sample of Energy Efficiency Potential Studies

Utility/Region Study Residential Commercial Industrial

Public Service Colorado KEMA (2010) 0.29 0.20 0.14

Public Service New Mexico Itron (2006) 0.21 0.18 0.10

Tri

- State (NM) Nexant (2010) 0.17 0.19 0.21

Arizona Public Service ICF (2007) 0.18 0.21 n/a

Median Value 0.19 0.20 0.14



### Table 7. Estimated Peak Demand Savings Economic Potential

Sector Net Economic Potential

GWh MW/GWh MW

Residential 5,572 0.19 1078

Commercial 3,703 0.20 728

Industrial 736 0.14 105

Total 10,011 0.19 1,911



### Finally, we extrapolate the SRP potential study results to the other balancing authorities in Arizona, on a sector

- by

- sector basis, in proportion to the 2008 retail sales in each balancing authority (see Table 8). While we recognize that this is a simplis tic approach, and ignores potential differences in demographics, climate, and end

- use characteristics, we also believe that it is a reasonable approximation given the data and resources available.

77

### Table 8. Extrapolation of Net Economic Potential to Othe r Arizona Balancing Authorities
Balancing Authority

(In

- State Portion) Residential Commercial Industrial Total

Retail Sales 1

SRP 12,775 11,245 3,379 27,399

APS 13,673 13,265 3,166 30,104

TEP 4,689 2,927 5,563 13,179

WALC 1,652 2,321 424 4,397

Net Economic Potential in 2021 (GWh)

SRP 5,572 3,703 736 10,011

APS 5,964 4,368 690 11,022

TEP 2,045 964 1,212 4,221

WALC 721 764 92 1,577

Net Economic Potential in 2021 (MW)

SRP 1,078 728 105 1,911

APS 1,154 859 98 2,111

TEP 396 190 172 758

WALC 139 150 13 303

1 Data Source: EIA

- 861 retail sales data for 2008.



### 78

### British Columbia

Summary Tables

Table 1. High DSM Load Forecasts for 2021: Annua l Electricity Consumption (GWh)

Balancing Authority A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

BCTC 66,892 11,076 17,931 60,037

- 10%

Provincial Total 66,892 11,076 17,931 60,037

- 10%



### Table 2

. High DSM Load Forecasts for 2021: Annua l Peak Demand (MW)

Balancing Authority A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

BCTC 12,077 2,000 2,923 11,153

- 8%

Provincial Total 12,077 2,000 2,923 11,153

- 8%



### Table 3. Comparison of Reference Case and High DSM Case (2021 Cumulative Savings)

Balancing Authority (In

- State Portion) Common Case Savings (% of No

- EE Load Forecast) High DSM Case Savings (% of No

- EE Load Forecast)

GWh MW GWh MW

BCTC 14% 14% 23% 21%

Provincial Total 14% 14% 23% 21% Notes: The percentages in this table were calculated by dividing the savings projection by the sum of the savings

projection and the post

- savings load forecast (e.g., High DSM savings divided by High DSM savings plus High DSM load forecast)



### High DSM Scenario Savings Projection

For the BCTC balancing authority, we estimate the economic potential based on the results of BC Hydro’s 2007 Conservation Potential Review. That study estimates the economic potential for five “Analysis Areas”: (1) Energy Efficiency and Peak Load Technologies and O&M, (2)

Cust omer Supplied Renewable Energies, (3) Behavior, (4) Lifestyle, and (5) Fuel Switching. For consistency with the potential studies used in other states, we focus exclusively on the results from Analysis Area 1. Furthermore, that Analysis Area includes bot h energy efficiency measures as well as demand response, but for the purpose of the present analysis, we focus only on the impact of energy efficiency measures.

79

### Table 6 excerpts the relevant potential estimates from the 2007 Conservation Potential Review, for Fiscal Year 2021 (year ending March 31, 2021), which we use as a proxy for calendar year 2021 (the horizon year in the TEPPC study). Note that the peak demand values refer to the peak

hour demand reduction.

Table 6. BC Hydro Economic Potential for En ergy Efficiency Measures (FY2021)

Residential Commercial Industrial Total

GWh 4,820 5,019 8,064 17,903

MW 1,238 715 962 2,915

Source: Residential Sector report, Exhibits E3 and 7.11; Commercial Sector report, Exhibits E3 and 7.16; Industrial

Sector report, Exhibits E3 and 7.13. Notes: All values represent savings at the customer

- meter. The baseline in the CPR accounts for naturally

occurring conservation; thus, the economic potential estimates represent savings “net of naturally

- occurring

conservati on.”



### Several adjustments to these estimates were performed in order to develop a High DSM savings estimate for the BCTC balancing authority.

• First, BC Hydro represents about 95% of the load within the BCTC balancing authority.
We extrapolate the econom ic potential estimate for BC Hydro to the entire balancing
authority in proportion to retail sales.

• Second, the 2007 Conservation Potential Review was performed in 2007, and thus the
economic potential for FY2021 estimated in that study includes some savin gs that were
since acquired in 2008

- 2010. We estimate that savings achieved in 2008

- 2010 reduce the
economic potential in FY2021 by 2,169 GWh and 349 MW. 23



### • Third, because the potential estimates represent savings at the customer

- meter, we gross up these estimates (after the preceding adjustments) to the bus

- bar, assuming 7% T&D

losses.
These adjustments are shown in Table 7; the High DSM case savings projection for BCTC is equal to Adjusted Potential at the bus

- bar.

Table 7. Adjustments to the Economic Potential Estimate to Calculate the High DSM Saving Projection

BC Hydro

Economic

Potential (2008

- 2020) BCTC

Economic

Potential (2008

- 2020) Reduction to

Account for

2008

- 2010

Savings BCTC Adjusted

Potential (customer

meter) BCTC Adjusted

Potential (bus

- bar)

GWh 17,903 18,845

- 2,169 16,676 17,931

MW 2,915 3,068

- 349 2,719 2,923

23 Specifically, the estimate assumes annual incremental savings from ratepayer funded programs (BC Hydro and Fortis) of 519 GWh in 2008 and 825 GWh in both 200 9 and 2010 , and annual incremental savings from appliance standards equal to zero GWh in 2008 and 41 GWh in both 2009 and 2010

. The estimates for energy savings in 2008

and 2009 were provided by Katherine Muncaster of the BC Ministry of Energy, Mines & Pe troleum

. We assume savings in 2010 was equal to the 2009 achievement, and w e estimate peak demand savings in 2008

- 2010 based on

the same peak

- to

- energy savings ratio as the potential estimate ( 0.161 MW/GWh)

.



### 80

### California

W e first describe the approach taken to develop these estimates for the CAISO balancing authority, and then describe the approach for the other California balancing authorities.

Summary Tables

Table 1. High DSM Load Forecasts for 2020: Annua l Electricity Consumption (GWh)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

CISO 249,856 39,884 44,288 245,452

- 2%

IID 4,260 412 804 3,868

- 9%

LDWP 30,789 3,535 5,403 28,920

- 6%

SMUD 17,975 2,251 3,467 16,758

- 7%

TIDC 2,829 279 507 2,602

- 8%

State Total 305,709 46,361 54,470 297,601

- 3%



### Table 2

. High DSM Load Forecasts for 2020: Annua l Peak Demand (MW)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

CISO 49,324 11,469 14,388 46,405

- 6%

IID 1,176 92 180 1,088

- 7%

LDWP 8,044 750 1,146 7,648

- 5%

SMUD 4,357 401 617 4,140

- 5%

TIDC 667 65 119 614

- 8%

State Total 63,568 12,777 16,450 59,895

- 6%



### 81

### Table 3. Comparison of Reference Case and High DSM Case (2020 Cumulative Savings)

Balancing Authority (In

- State Portion) Common Case Savings (% of No

- EE Load Forecast) High DSM Case Savings (% of No

- EE Load Forecast)

GWh MW GWh MW

CISO 14% 19% 15% 24%

IID 9% 7% 17% 14%

LDWP 10% 9% 16% 13%

SMUD 11% 8% 17% 13%

TIDC 9% 9% 16% 16%

State Total 13% 17% 15% 22% Note: The percentages in this table were calculated by dividing the savings projection by the sum of the savings projection and the post

- savings load forecast (e.g., High DSM savings divided by High DSM savings plus High DSM load forecast)



### High DSM Saving s Projection and Load Forecast: CAISO Balancing Authority

The High DSM savings projection for the CAISO balancing authority is based on the “High Goals” scenario from the California Energy Commission’s 2010 report, Incremental Impacts of Energy Efficiency Policy Initiatives Relative to the 2009 Integrated Energy Policy Report Adopted Demand Forecast , plus additional savings associated with decay replacement. These targets are intended to be a proxy for the economic potential, and are almost identical to t he economic potential estimate contained in Itron’s 2008 Goals Update report for the CPUC.

Table 4 identifies the total savings associated with the High DSM case for the three investor

owned utilities (IOUs), and also identifies the portion of savings t hat is incremental to the Energy Commission’s December 2009 demand forecast (and that is therefore also incremental to the forecast that CAISO submitted to WECC, which is based directly on the Energy Commission’s December 2009 demand forecast).

Table 4. IOU High DSM Case Savings (Cumulative Savings in 2021 at the Customer Meter)

Total Committed & Uncommitted Incremental Uncommitted

GWh MW GWh MW
High Goals Scenario 1 

### 33,720 11,187 16,085 7,548
Decay Replacement 2 

### 1,959 405 1,959 405

Total 35,679 11,591 18,044 7,953

1 Source: CEC 2010, Incremental Impacts of Energy Efficiency Policy Initiatives Relative to the 2009 Integrated

Energy Policy Report Adopted Demand Forecast , Attachment A, Tables 4

- 11, 4

- 12, 4

- 13, and 4

- 14. Values in the

source document have been linearly extrapolated to 2021.

2 Source: CEC 2010, Incremental Impacts of Energy Efficiency Policy Initiatives Relative to the 2009 Integrated

Energy Policy Report Adopted Demand Forecast , Table 12. Values in the source document have been linearl y extrapolated to 2021.



### The values in Table 4 apply only to the IOU

- portion of CAISO, and represent savings at the customer meter. For the TEPPC High DSM Case, these projections must be grossed up to include T&D losses and extrapolated to the remainder o f the CAISO balancing authority, as

82

### shown in Table 5. Based on these assumptions and calculations, the projected cumulative savings over 2011

- 2021 is equal to 44,288 GWh and 14,388 MW.

Table 5. High DSM Case Savings for CAISO (Cumulative Savings in 2021, Bus

- Bar)

Total Committed & Uncommitted Incremental Uncommitted

GWh MW GWh MW

IOUs 1 

### 39,193 12,733 19,821 8,736

POUs in CAISO 2 

### 5,095 1,655 2,576 1,136

Total 44,288 14,388 22,398 9,871

1 These numbers have been grossed up from the IOU totals in Table 4, based on an assumed T&D loss factor of

9%, which is roughly the average value used by the CPUC in its projection of energy efficiency savings for the
LTPP. See http://www.cpuc.ca.gov/NR/rdonlyres/C382EBDD

- 7E00

- 4D2F

- 863B

7380EDBF843C/0/TechnicalAttachme ntSpreadsheetv5.xls

2 A High DSM savings projection was extrapolated to the POUs in CAISO in proportion to their net energy for

load, as projected in the Energy Commission’s December 2009, California Energy Demand 2010

- 2020 Adopted

Forecast , Form 1.5a.



### High DSM Savings Projections and Load Forecasts: Other California Balancing Authorities

The High DSM savings projection for the other California balancing authorities is estimated by extrapolating the High DSM potential estimate for the IOUs’ annual energy sa vings, in proportion to each balancing authority’s net energy for load. However, rather than also extrapolating peak demand saving in this manner, the peak demand savings for each non

- CAISO balancing authority was calculated based on the peak

- to

- energy s avings ratio implied by the Common Case savings assumption for the same balancing authority. This approach was taken in order to maintain greater consistency with the underlying load shapes inherent in each balancing authority’s Common Case forecast, and because the peak

- to

- energy savings ratio implied by the CEC’s analysis for the IOUs is unusually high. The results of these calculations are shown in

Table 6.

Table 6. Extrapolation of IOU High DSM Savings to Non

- CAISO Balancing Authorities

Balancing

Au thority Utility/ Utilities Net Energy

for Load

(2020 GWh) 1 High DSM Savings (2011

- 2021 Cumulative)

GWh 

### MW/GWh MW

CAISO IOUs 235,286 39,193 0.32 12,733

POUs 30,584 5,095 0.32 1,655

IID IID 4,828 804 0.22 180

LADWP LADWP, Burbank, Glendale 32,437 5,403 0.21 1,146

SMUD SMUD, Redding, Roseville, Shasta, Modesto 20,816 3,467 0.18 617

TID TID, Merced 3,041 507 0.23 119

1 Source: 2009 IEPR Demand Forecast, Form 1.5a



### 83

### Colorado

Summary Tables

Table 1. High DSM Load Forecasts for 2021: Annua l Electricity Consumption (GWh)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

PSCO 46,552 6,826 8,542 44,836

- 4%

WACM 20,003 713 4,928 15,788

- 21%

State Total 66,555 7,538 13,470 60,623

- 9%



### Table 2

. High DSM Load Forecasts for 2021: Annua l Peak Demand (MW)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

PSCO 7,821 1,278 2,069 7,030

- 10%

WACM 3,088 138 1,009 2,217

- 28%

State Total 10,909 1,416 3,078 9,247

- 15%



### Table 3. Comparison of Reference Case and High DSM Case (2021 Cumulative Savings)

Balancing Authority (In

- State Portion) Common Case Savings (% of No

- EE Load Forecast) High DSM Case Savings (% of No

- EE Load Forecast)

GWh MW GWh MW

PSCO 13% 14% 16% 23%

WACM 3% 4% 24% 31%

State Total 10% 11% 18% 25% Note: The percentages in this table were calculated by dividing the savings projection by the sum of the savings projection and the post

- savings load forecast (e.g., High DSM savings divided by High DSM savings plus High DSM load forecast)



### PSCO Balancing Authority

The High DSM assumptions for the PSCO balancing authority are based on the March 2010 energy efficienc y market potential study for PSCo, the utility (KEMA, 2010). The results of that study are summarized below in Table 4, which presents cumulative savings over the 2010

- 2020 period. The table presents all types of potential estimate that were reported in the study: technical potential, economic potential, net economic potential (which excludes naturally

84

### occurring energy efficiency), and three estimates of achievable potential under varying incentive levels (expressed as a percentage of incremental measure cost).

Table 4: Results of PSCo Energy Efficiency Market Potential Study (Cumulative Savings for

2010

- 2020)*

Cumulative Savings Technical Economic Net

Economic Achievable

100%

Incentives 75%

Incentives 50%

Incentives
Energy Savings

GWh 8,938 7,563 6,420 4,892 2,806 1,802

% Base 2020 Retail Sales 27% 23% 20% 15% 9% 6%

Peak Demand Savings

MW 2,161 1,730 1,572 1,198 538 328

% Base 2020 Peak Demand 33% 26% 24% 18% 8% 5%

Source: KEMA (2010), Table 1

- 1 and Figures 1

- 1, 1

- 2, 5

- 4, and 5

- 5

* Excludes savings from emerging technologies and behavioral measures



### For the purpose of the High DSM scenario, we assume that the full economic potential is

achieved in the PSCO balancing authority region. However, we also assume that naturally occurring energy ef ficiency improvements are captured within the LRS load forecast (the load forecast provided by the balancing authority to WECC) and the Common Case load forecast. Thus, for our purposes, we focus on the Net Economic Potential. Furthermore, we assume that the net economic potential estimate for the 2010

- 2020 period is a reasonable proxy for the 2011

- 2021 forecast period (i.e., that we can shift the EE potential period forward by one year).

Developing a High DSM load forecast for the PSCO balancing authori ty involves two additional

steps: 1) Adding avoided T&D losses to the net economic potential estimate for the utility, PSCo

2) Extrapolating from the PSCo service territory to the entire PSCO balancing authority

Adding Avoided T&D Losses to the PSCo Potential E stimate

The potential estimates presented in the PSCo study represent savings at the customer meter. For the purpose of developing a High DSM load forecast, the savings must be scaled up to the bus

bar to account for avoided T&D losses. We scale up the net economic potential estimate by an assumed marginal T&D loss factor of 9.5%, which was derived from data presented in PSCo’s load forecast update to its 2007 IRP. 24 

### The results of this adjustment are shown in Table 5.

24 PSCo’s updated load forecast for its 2007 IRP presents total T&D losses (Table 2.7

- 6) and retail sales (Table 2.7

- 3), from which we calculated average loss factors for the utility’s retail load (residential and C&I customers)

. See: http://www.xcelenergy.com/SiteCollectionDocuments/docs/CRPLoadForecast.pdf

. We assume that marginal T&D loss factors are equal to 1.4 times the average T&D loss factor , based on preliminary an alysis conducted by Jim Lazar and shared with members of the DSM working group

. Marginal loss factors are the relevant metric for estimating avoided T&D losses from DSM and are higher than average loss factors, because resistive losses increase exponentia lly with load.



### 85

### Table 5. PSCo Net Economic Potenti al Savings Including Avoided T&D Losses 1

Res. Comm. Ind. Total

GWh 2,639 3,888 567 7,094

MW 821 829 86 1,737

1 The KEMA potential study presents the sectoral split for the gross economic potential, but not for the net

economic potential; we apply the sectoral distribution for gross economic potential to estimate the net economic potential for each sector, and scale these numbers to the bus

- bar level to include avoided T&D losses.



### Extrapolating the PSCo Potential Study Results to the Entire PSCO balanc ing authority

The PSCO balancing authority consists of PSCo, Black Hills (Colorado service territory), and a number of municipal utilities and rural electric cooperatives (the largest of which are Intermountain and Holy Cross). We extrapolate the PSCo potential study results to the entire PSCO balancing authority by simply scaling up the energy and peak demand savings potential for each sector (residential, commercial, and industrial) based on the ratio of the total balancing authority retail sales to PSCo’s retail sales for that sector. While we recognize that this is a simplistic approach, and ignores potential differences in demographics, climate, and end

- use characteristics, we also believe that it is a reasonable approximation given the data and r esources available. This extrapolation is presented below in Table 6, which shows the net economic potential and retail sales by sector, for both PSCo and the entire PSCO balancing authority. This extrapolation yields an estimated net economic potential for the PSCO balancing authority equal to 8,542 GWh and 2,069 MW by 2021.

Table 6. Extrapolation of Net Economic Potential to PSCO Balancing Authority

Residential Commercial Industrial Total

2008 Retail Sales 1

PSCo utility 8,905 13,044 6,273 28,271

PSCO balancing authority 11,667 14,927 7,035 33,678

Net Economic Potential in 2021 (GWh)

PSCo utility 2,639 3,888 567 7,094

Total PSCO balancing authority 2 3,457 4,449 636 8,542

Net Economic Potential in 2021 (MW)

PSCo utility 821 829 86 1,737

Total PSCO balancing authority 2 1,076 949 97 2,069

1 Data Source: EIA

- 861 retail sales data for 2008.

2 Calculated by multiplying the PSCo net economic potential for each sector by the ratio of PSCo utility retail sales

to PSCO balancing authority retail sales for that sector.



### WACM Balancing Authority (Colorado Portion only)

The High DSM scenario savings estimate for the WACM balancing authority is based on the results of two recent energy efficiency studies conducted for utilities in the region: the 2009 study for Colorado Springs Utilities (Summit Blue Consulting, 2009); and the 2010 study for

Tri

- State Generation and Transmission Cooperative (Nexant, 2010). The results of those studies, for the year 2020, are presented below in Tables 7 and 8. For the purpose of the TEPPC High DSM Case, we assume that the same values are applicable to the year 2021.

86

### Note that the potential study for Colorado Springs does not explicitly indicate whether or how the analysis accounts for naturally occurring savings. Based on the study’s description of the methodology used to develop baseline end

- use saturation levels, it appears that the potential estimates reported in the study represent gross savings. I n contrast, the Tri

- State study is explicit that naturally occurring savings are incorporated into its baseline projection of end

- use saturations, and therefore the potential estimates represent net savings.

Table 7. Colorado Springs Utilities Energy Ef ficiency Potential Study Results (2020)

Gross

Technical Gross

Economic Achievable (mid

- case) 1

GWh 2,291 1,758 348

MW 2 530 367 80

% 2008 Retail Sales 50% 38% 8%

1 The CSU potential study estimated the achievable potential under three different incentive scenarios. The mid

case corresponds to incentives that cover 50% of incremental measure cost. The study does not present the low and high case results in tabular form, and therefore the results for those scenarios are not included in this table

. 2 The Colorado Springs potential study does not report the potential peak demand savings (Technical or Economic)

from energy efficiency measures alone; it only reports the combined peak demand savings from both energy efficiency and demand response. We estimate peak demand reductions from energy efficiency by applying the ratio of peak

- to

- energy savings implied by the technical and economic potential estimates in the PSCo potential study.



### Table 8. Tri

- State Energy Efficiency Potential Study Results for Colorado (2010

- 2020)

Net

Technical Net

Economic Max.

Achievable 1

GWh 2,582 1,820 1,084

MW 568 345 205

% of 2008 retail sales 31% 22% 13% Source: Calculated from detailed savings tables in Nexant (2010), Appendix A

1 Max. Achievable scenario assumes incentives cover 100% of incremental measure cost. The study also estimated

achievable potential under three other incentive levels.



### As in the case of the PSCO balancing authority, for the purpose of the High DSM scenario we

assume that the full economic potential is achieved in the Colorado portion of the WACM balancing authority region. Developing a High DSM load forecast for the balancing authority involves four additional steps: 1) De

- rating the economic potential estimate for Colorado Springs to accoun t for naturally

occurring savings

2) Adding avoided T&D losses to the potential estimates from both studies

3) Extrapolating the potential study results to the entire Colorado portion of the WACM
balancing authority

De

- rating the Colorado Springs Economic Poten tial to Account for Naturally Occurring Savings

As indicated above, we assume that the economic potential estimates reported in the Colorado Springs potential study represent gross savings, and we therefore de

- rate those estimates

87

### according to an assumed net

- to

- gross (NTG) ratio. The study disaggregates the potential estimates into two sectors, Residential and Commercial/Industrial. We estimate the NTG for each sector based on the NTG ratios implied by the PSCo potential study (i.e., the ratio of net

ec onomic potential to gross economic potential for each sector). This calculation and the results

are shown in Table 9.

Table 9. Estimated Net Economic Potential for Colorado Springs

Gross Economic Potential NTG ratio * Net Economic Potential

Res C/I Total Res C/I Res C/I Total

GWh 302 1455 1758 85% 85% 257 1235 1492

MW 88 279 367 91% 91% 80 254 334

* The NTG ratio is equal to the ratio of the Net Economic Potential and Economic Potential estimates from PSCo’s

potential study. These ratios were calculated for the Res. and C/I sectors individually, though the calculated

values for the two sectors are identical.



### Adding Avoided T&D Losses to the Potential Estimates

The potential estimates presented in the Color ado Springs and Tri

- State studies represent savings at the customer meter. As in the case of the foregoing analysis for PSCO, these potential estimates must be scaled up to the bus

- bar to account for avoided T&D losses, based on an estimated marginal T&D loss factor (5.9% for Colorado Springs and 7.7% for Tri

- State). 25 

### The results of this adjustment are shown below in Table 10.

Table 10. Colorado Springs and Tri

- State Net Economic Potential Savings Including Avoided

T&D Losses

Utility Energy or Peak Demand Savings Res. Com. Ind. Irrig. Total

Colorado Springs GWh 301 1,450 1,752

MW 85 270 355

Tri

- State GWh 844 350 694 84 1,972

MW 144 74 139 17 373



### Extrapolating the Study Results to the Entire Colorado Portion of WACM

The Colorado portion of the WACM balancing authority consists of numerous municipal utilities (the largest being Colorado Springs and Fort Collins) and numerous rural electrical cooperatives.

Tri

- State G&T serves most of the state’s cooperatives within WA CM, as well as several small municipal utilities.

For the purpose of extrapolating the potential study results to the Colorado portion of WACM, we divide the WACM utilities into two groups: Group 1 consists of Colorado Springs and Fort Collins (the two largest utilities, representing about 30% of the load in the Colorado portion of WACM) and Group 2 consists of all other utilities. We extrapolate the Colorado Springs

25 Colorado Springs potential study cites an average T&D loss factor of 4.2%, and Tri

- State’s potential study cites an averge T&D loss factor of 5.5%. Marginal T&D loss factors are assumed to be equal to 1.4 time s the average

T&D loss factor

.



### 88

### potential study results to Group 1 and the Tri

- State study to Group 2. The logic behin d this

division is that we assume that the relevant characteristics of Fort Collins are more similar to Colorado Springs than to Tri

- State, while the relevant characteristics of the other utilities are

more similar to Tri

- State.

To extrapolate each potential study to the applicable group of utilities, we follow a similar procedure as with the PSCO balancing authority. Namely, we scale up the adjusted potential study results for each sector, in proportion to 2008 retail sales for that sector. The extrapolations for Groups 1 and 2, respectively, are presented below in Tables 11 and 12. Table 13 combines these results to present the total estimated net economic potential for the Colorado portion of WACM, equal to 4,928 GWh and 1,07 6 MW in 2021. All results represent savings at the bus bar and are net of naturally occurring savings.

Table 11. Estimated Net Economic Potential for WACM Group 1

Res. C&I Total

2008 Retail Sales 1

Colorado Springs 1,409 3,167 4,576

WACM

- Colorado Group 1 1,882 4,124 6,006

Net Economic Potential in 2021 (GWh)

Colorado Springs 273 1,313 1,585

WACM

- Colorado Group 1 364 1,709 2,074

Net Economic Potential in 2021 (MW)

Colorado Springs 85 270 355

WACM

- Colorado Group 1 113 351 465

1 Data Source: EIA

- 861 retail sales data for 2008.



### Table 12. Estimated Net Economic Potential for WACM Group 2

Res. Com. Ind./Irr. Total

2008 Retail Sales 1

Tri

- State (Colorado) 3,101 1,913 3,505 8,519

WACM

- Colorado Group 2 4,172 3,099 5,188 12,459

Net Economic Potential in 2021 (GWh)

Tri

- State (Colorado) 844 350 779 1,972

WACM

- Colorado Group 2 1,135 566 1,153 2,854

Net Economic Potential in 2021 (MW)

Tri

- State (Colorado) 144 74 156 373

WACM

- Colorado Group 2 194 119 231 544

1 Data Source: EIA

- 861 retail sales data for 2008.



### Table 13. Estimated Net Economic Potential for the Colorado Portion of WACM

Group 1 Group 2 Total

GWh 2,074 2,854 4,928

MW 465 544 1,009



### 89

### New Mexico

Summary Tables

Table 1. High DSM Load Forecasts for 2021: Annua l Electricity Consumption (GWh)

Balancing Authority A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

PNM 16,887 1,419 1,899 16,407

- 3%

EPE 10,679 914 1,239 10,354

- 3%

WALC 1,310 47 132 1,225

- 6%

State Total 28,876 2,379 3,271 27,985

- 3%



### Table 2

. High DSM Load Forecasts for 2021: Annua l Peak Demand (MW)

Balancing Authority A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

PNM 2,905 267 357 2,815

- 3%

EPE 2,191 203 275 2,119

- 3%

WALC 275 13 36 252

- 9%

State Total 5,372 482 668 5,186

- 3%



### Table 3. Comparison of Reference Case and High DSM Case (2020 Cumulative Savings)

Balancing Authority Common Case Savings (% of No

- EE Load Forecast) High DSM Case Savings (% of No

- EE Load Forecast)

GWh MW GWh MW

PNM 8% 8% 10% 11%

EPE 8% 8% 11% 11%

WALC 3% 4% 10% 13%

State Total 8% 8% 10% 11% Note: The percentages in this table were calculated by dividing the savings projection by the sum of the savings projection and the post

- savings load forecast (e.g., High DSM savings divided by High DSM savings plus High DSM load forecast)



### High DSM Scenario Savings Projection

T he High DSM savings projections for the New Mexico balancing authorities are base d on the 2011 energy efficiency potential study conducted for the state (GEP, 2011). Table 4 presents the

90

### statewide economic potential estimates for each sector, in the year 2021, which includes the portion of New Mexico outside of WECC. The table also i dentifies the economic potential for each sector as a percentage of the sectoral baseline load forecast. The baseline load forecast represents the hypothetical load with no new energy efficiency programs or updates to federal standards beyond what was in place as of the date that the potential study was conducted.

Table 4. New Mexico Statewide Economic Potential (2021)

Residential Commercial Industrial Total

Baseline Load Forecast (GWh) 7,032 7,607 8,265 22,904

Economic Potential (% of Baseline Load) 12.6% 10.7% 6.8% 9.9%

Economic Potential (GWh) 886 814 562 2,262 Source: GEP (2011), Tables H

- 2, H

- 3, and H

- 4.



### The High DSM Case savings projection for each New Mexico balancing authority was derived by applying percentages in Table 4 to the hypothetical “no

- future

- EE” load forecast for each balancing authority. The hypothetical “no

- future3

- EE” forecasts for each balancing authority are equal to the Common Case load forecast plus the corresponding Common Case energy efficiency savings project ion, and were disaggregated by sector based on historical EIA retail sales data for each utility in the balancing authority. These calculations and the resulting potential estimate for each of the three New Mexico balancing authorities are shown in Table s 5

– 7 (see notes to tables for further details).

Table 5. PNM Balancing Authority Economic Potential Estimate (2021)

Residential Commercial Industrial Total

Baseline Load Forecast (% of Total) 1 34% 41% 25% 100%

Baseline Load Forecast (GWh) 2 6,232 7,517 4,557 18,306

Economic Potential (% of Baseline Load) 3 12.6% 10.7% 6.8% 10.4%

Economic Potential (GWh) 4 785 804 310 1,899

1 These percentages are derived from 2008 EIA sector

- level retail sales data for each utility in the balancing authority.

2 The total baseline load forecast for the balancing authority was calculated as the sum of the 2021 Common Case load forecast plus the Common Case energy efficiency savings projection, and is represents the hypothetical load forecast with no future energy e fficiency programs or new federal standards. Note that the PNM balancing authority includes the portion of Navopache Electric’s service territory in Arizona; for simplicity, we include that load within these tabulations, rather than developing a separate High DSM forecast for only that small amount of balancing authority load in Arizona.

3 The sector

- level economic potential percentages for PNM are assumed to be the same as the corresponding statewide percentages shown in Table 4.

4 The sector

- level econom ic potential in GWh was calculated by multiplying the economic potential percentages for each sector by the corresponding baseline load forecast.



### Table 6. EPE Balancing Authority Economic Potential Estimate (2021)

Residential Commercial Industrial Total

Baseline Load Forecast (% of Total) 32% 53% 16% 100%

Baseline Load Forecast (GWh) 3,673 6,103 1,817 11,593

Economic Potential (% of Baseline Load) 12.6% 10.7% 6.8% 10.7%

Economic Potential (GWh) 463 653 124 1,239



### 911 These percentages are derived from 2008 EIA sector

- level retail sales data for each utility in the balancing authority.

2 The total baseline load forecast for the balancing authority was calculated as the sum of the 2021 Common Case load forecast plus the Common Case energy efficiency savin gs projection, and is represents the hypothetical load forecast with no future energy efficiency programs or new federal standards. Note that the EPE balancing authority includes the portion of El Paso Electric’s service territory in Texas; for simplicity , we include that load within these tabulations, rather than developing a separate High DSM forecast for only the small amount of WECC load in

Texas.

3 The sector

- level economic potential percentages for EPE are assumed to be the same as the corresponding statewide percentages shown in Table 4.

4 The sector

- level economic potential in GWh was calculated by multiplying the economic potential percentages for each sector by the corresponding baseline load forecast.



### Table 7. WALC

- New Mexico Balancing Authorit y Economic Potential Estimate (2021)

Residential Commercial Industrial Total

Baseline Load Forecast (% of Total) 20% 46% 35% 100%

Baseline Load Forecast (GWh) 265 620 471 1,357

Economic Potential (% of Baseline Load) 12.6% 10.7% 6.8% 9.7%

Economic Potential (GWh) 33 66 32 132

1 These percentages are derived from 2008 EIA sector

- level retail sales data for each utility in the balancing authority.

2 The total baseline load forecast for the balancing authority was calculated as the sum of the 2021 Com mon Case load forecast plus the Common Case energy efficiency savings projection, and is represents the hypothetical load forecast with no future energy efficiency programs or new federal standards. Note that the tabulation here includes only the portion of WALC within New Mexico; the High DSM savings assumptions for the Arizona portion of WALC are included in the High DSM write

- up for Arizona.

3 The sector

- level economic potential percentages for WALC are assumed to be the same as the corresponding

statew ide percentages shown in Table 4.

4 The sector

- level economic potential in GWh was calculated by multiplying the economic potential percentages for each sector by the corresponding baseline load forecast.



### Tables 5

- 7 include the High DSM Case annual energy savings for each balancing authority. The corresponding peak demand savings were calculated by applying the peak

- to

- energy savings ratio from the Common Case for each balancing authority, as shown in Tabl e 8.

Table 8. High DSM Case Peak Demand Savings Projection (2021)

Balancing Authority Common Case Savings High DSM Savings

GWh MW MW/GWh GWh MW

PNM 1,419 267 0.19 1,899 357

EPE 914 203 0.22 1,239 275

WALC 47 13 0.28 132 36

Total 2,379 482 0.20 3,271 668



### 92

### Nevada

Summary Tables

Table 1. High DSM Load Forecasts for 2021: Annua l Electricity Consumption (GWh)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

NEVP 26,665 1,659 6,509 21,815

- 18%

SPP 12,694 795 2,297 11,191

- 12%

State Total 39,359 2,454 8,806 33,007

- 16%



### Table 2

. High DSM Load Forecasts for 2021: Annua l Peak Demand (MW)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

NEVP 6,659 402 1,186 5,874

- 12%

SPP 2,102 224 416 1,909

- 9%

State Total 8,761 626 1,603 7,784

- 11%



### Table 3. Comparison of Reference Case and High DSM Case (2020 Cumulative Savings)

Balancing Authority (In

- State Portion) Common Case Savings (% of No

- EE Load Forecast) High DSM Case Savings (% of No

- EE Load Forecast)

GWh MW GWh MW

NEVP 6% 6% 23% 17%

SPP 6% 10% 17% 18%

State Total 6% 7% 21% 17% Note: The percentages in this table were calculated by dividing the savings projection by the sum of the savings projection and the post

- savings load forecast (e.g., High DSM savings divided by High DSM savings plus High DSM load forecast)



### High DSM Scenario Savings Projection

There have no t been any recent energy efficiency potential studies for Nevada utilities that include an estimate of the economic potential. The SPSC DSM Work Group therefore developed an estimate of economic potential for Nevada, by extrapolating the results of two

re cent economic potential studies for other utilities in the desert southwest: the 2010 potential study for Salt River Project (Cadmus, 2010) and the 2007 potential study for Rocky Mountain

93

### Power (Quantec, 2007). 26 

### Table 4 summarizes the economic potential estimates from these two

studies.

Table 4. Economic Potential Estimates (GWh)

Market Sector Salt River Project (2020) Rocky Mountain Power (Utah service territory, 2027)

Residential 5,015 1,997

Commercial 3,370 2,365

Industrial 677 885

Total 9,063 5,247

Sources: Cadmus (2010), Table 13; Quantec (2007), Tables 53, 55, and 57.



### We extrapolate the SRP and RMP potential study results to the two balancing authorities in Nevada, on a sector

- by

- sector basis, in proportion to the 2008 retail sales in each balancing authority (see Table 5). Specifically, for each sector, we calculate the economic potential as a percentage of 2008 retail sales for both SRP and RMP, then compute the average of the two percentages, and multiply the resulting average to the se ctoral retail sales for each Nevada balancing authority to estimate the economic potential for that sector. While we recognize that this is a simplistic approach, and ignores potential differences in demographics, climate, and end

use characteristics, we also believe that it is a reasonable approximation given the data and

resources available.

26 A different potential study for Rocky Mountain Power was issued more recently (Cadmus, 2011), but that study

does not include an estimate of economic potential, and therefore is not used for the purpose of this extrapolation. In addition, several othe r energy efficiency potential studies for southwestern utilities have been issued within the past five years, but , for various reasons, none are suitable for extrapolation to Nevada.



### 94

### Table 5. Extrapolation of Economic Potential to Nevada Balancing Authorities

Residential Commercial Industrial Total

2008 Retail Sales (Bundled + Delivery) 1

SRP 12,775 11,245 3,379 27,399

PacifiCorp (Utah) 6,561 7,934 8,126 22,621

NEVP 9,600 6,010 8,842 24,452

SPP 2,262 3,086 3,795 9,143

Net Economic Potential in 2021 (% of 2008 retail sales)

SRP 39% 30% 20% 33%

PacifiCorp (Utah) 30% 30% 11% 23%

NEVP 35% 30% 15% 27%

SPP 35% 30% 15% 25%

Net Economic Potential in 2021 (GWh)

SRP 5,015 3,370 677 9,063

PacifiCorp (Utah) 1,997 2,365 885 5,247

NEVP 3,345 1,796 1,367 6,509

SPP 788 922 587 2,297

1 Data Source: EIA

- 861 retail sales data for 2008.



### The SRP and RMP potential studies provide estimates of annual energy savings, but not peak demand savings. We therefore estimate the peak demand savings associated with achieving the economic potential by applying a stipulated peak

- to

- energy savings ratio for each sector, based on the median value across other potential studies conducted for utilities in the Southwest (see Table 6). The resulting estimates of peak demand savings for the two Nevada balancing

authorities are shown in Table 7.

Table 6 . Peak

- to

- Energy Savings Ratios from a Sample of Energy Efficiency Potential Studies

Utility/Region Study Residential Commercial Industrial

Public Service Colorado KEMA (2010) 0.29 0.20 0.14

Public Service New Mexico Itron (2006) 0.21 0.18 0.10

Tri

- State (NM) Nexant (2010) 0.17 0.19 0.21

Arizona Public Service ICF (2007) 0.18 0.21 n/a

Median Value 0.19 0.20 0.14



### Table 7. Estimated Peak Demand Savings Economic Potential

Residential Commercial Industrial Total

Assumed Peak

- to

- Energy Savings Ratio (MW/GWh) 0.19 0.20 0.14 n/a

NEVP 636 359 191 1,186

SPP 150 184 82 416



### 95

### Pacific Northwest States

This section explains how the High DSM Case efficiency savings projections were developed for each state, and then how the state

- level savings were allocated to the balancing authorities in the

PNW.

Summary Tables

- Results by State

Table 1. High DSM Load Forecasts for 2021: Annua l Electricity Consumption (GWh)

State A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

ID 30,713 3,040 6,431 27,323

- 11%

MT 44,197 4,786 9,641 39,342

- 11%

OR 56,589 7,362 13,514 50,437

- 11%

WA 70,625 8,860 16,885 62,600

- 11%

PNW Region Total 202,124 24,048 46,470 179,702

- 11%



### Table 2

. High DSM Load Forecasts for 2021: Annua l Peak Demand (MW)

State A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

ID 6,878 475 1,243 6,109

- 11%

MT 8,200 676 1,968 6,908

- 16%

OR 10,586 1,181 2,889 8,879

- 16%

WA 13,413 1,719 3,412 11,720

- 13%

PNW Region Total 39,077 4,051 9,512 33,615

- 14%



### 96

### Table 3. Comparison of Reference Case and High DSM Case (2021 Cumulative Savings)

State Common Case Savings (% of No

- EE Load Forecast) High DSM Case Savings (% of No

- EE Load Forecast)

GWh MW GWh MW

ID 9% 6% 19% 17%

MT 10% 8% 20% 22%

OR 12% 10% 21% 25%

WA 11% 11% 21% 23%

PNW Region Total 11% 9% 21% 22% Note: The percentages in this table were calculated by dividing the savings projection by the sum of the savings projection and the post

- savings load forecast (e.g., High DSM savings divided by the sum of High DSM savings and High DSM load forecast)



### High DSM Savings Projections by State

In order to implement economic potential criterion for the Pacific Northwest states, the DSM working group relied o n the conservation potential assessment conducted by the Northwest Power and Conservation Council (NPCC) for its 6 th 

### Power Plan. At the request of the DSM working group for last year’s High DSM Case, NPCC staff provided an estimate of the “total

economic potential” in 2020, for the NPCC planning area as a whole. This estimate is greater than the 2020 conservation target presented in the 6 th 

### Plan for two primary reasons:

• The conservation targets in the 6 th 

### Plan assume that only 85% of the technical poten tial is
“achievable,” whereas the total economic potential does not include this constraint.

• The conservation targets in the 6 th 

### Plan are based on assumptions about how fast
conservation savings can be acquired, given certain practical limitations of energ y efficiency programs. The total economic potential does not impose these constraints.

For the High DSM Case, we rely on the total economic potential rather than the conservation targets in the 6 th 

### Plan, in order to maintain consistency between the PNW and other states, where the High DSM savings estimates are based on acquiring the full economic potential without any

non

- economic “achievability” constraints. For this year’s High DSM Case, we use the same potential estimate as provided last year, which applied to the period 2010

- 2020. Thus, in effect, we assume that the potential estimate can be “shifted” one year forward (i.e., that the additional potential gained by going out one year further into the future is more or less offset by the actual

conser vation achievements in 2010). Throughout the remainder of this document, references to potential estimates for year 2020 should therefore be understood to apply to the year 2021 for the purpose of this year’s High DSM Case.

The analysis provided by NPCC staff indicates that, in 2020, the total economic potential for the NPCC planning area (Idaho, Oregon, Washington, and Western Montana) is equal to 5,000 aMW. In comparison, the 2020 conservation potential target in the 6 th 

### Plan is 3,225 aMW. We

estimate the total economic potential for each state in the Pacific Northwest by scaling up the corresponding 6 th 

### Plan conservation target, based on the ratio of the total economic potential and the conservation target for the region (i.e., 5,000 aMW divided by 3, 255 aMW = 1.55 scaling factor), as shown in Table 4. Note that the conservation numbers for Montana are statewide, and

97

### were calculated by extrapolating the NPCC conservation potential estimates for Western Montana to the entire state; thus the total 6 th 

### p lan conservation target for the region shown in Table 4 slightly exceeds the value reported in the 6 th 

### plan (which is limited to Western Montana).

Table 4. Total Economic Potential for PNW States

State 6 th Plan Conservation Target (2020) 1 Total Economic Potential (2020)

aMW GWh Peak MW aMW GWh Peak MW

ID 473 4,148 948 734 6,431 1,470

MT 116 1,013 224 179 1,571 347

OR 995 8,716 1,870 1,543 13,514 2,899

WA 1,837 16,096 3,246 2,849 24,954 5,033

Total 3,422 29,973 6,288 5,305 46,470 9,749

1 The state

- level conservation targets were provided to the DSM working group by NPCC staff in the course of

developing the High DSM Case for the 2010 TEPPC Study Program.



### The state

- level High DSM load forecasts were then allocated to individual balancing autho rities, in proportion to retail sales, as shown in Table 5. There are two minor items to note related to this extrapolation. First, Table 5 includes an estimate of the economic potential for the

California

- portion of PACW, which was derived simply by ext rapolating the estimated economic potential for the Oregon portion of PACW; thus, the total economic potential energy savings (GWh) shown in Table 5 is slightly greater than the corresponding total shown in Table 4. Second, in the course of modeling last year’s High DSM Case, it was determined that, in the case of the IPC balancing authority, allocating state

- level peak demand savings based simply on retail sales yielded an overly compressed load shape. Therefore, the peak demand savings for IPC was, inst ead, calculated from the energy savings, based on an assumed peak

- to

- energy savings ratio of 0.17 MW/GWh. This is the peak

- to

- energy savings ratio implied by the Common Case savings assumptions for IPC, and is significantly lower than the peak

- to

- energy s avings ratio implied by the 6 th 

### Plan conservation target for Idaho (0.23 MW/GWh). As a result of this adjustment to the NPCC economic potential estimate, the total peak demand savings shown in Table 5 is slightly lower than the corresponding value shown in Table 4.

Based on the economic potential estimates in Table 5, the High DSM load forecasts are shown for each balancing authority in Tables 6 and 7, for energy and peak load, respectively. Note that for balancing authorities spanning multiple states, the DSM Work Group will “roll

- up” the numbers across states into a single load forecast for the balancing authority (including portions of the PACE balancing authority that are outside of the PNW and therefore excluded from these tables).

98

### Table 5. Alloca tion of State

- Level Economic Potential Estimates to Balancing Authorities

Balancing Authority (PNW portion) State Balancing Authority

Percent of StateLoad 1 Full Economic Potential (2020)

aMW GWh MW

AVA ID 18% 133 1,164 266

WA 9% 261 2,289 462

BPA ID 8% 60 526 120

MT 21% 38 333 74

OR 30% 469 4,108 881

WA 34% 959 8,403 1,695

CHPD WA 4% 113 987 199

DOPD WA 2% 59 520 105

GCPD WA 5% 144 1,258 254

IPC 3 ID 60% 441 3,866 657

OR 2% 26 231 39

NWMT MT 75% 134 1,173 259

PACE ID 14% 100 875 200

PACW OR 26% 408 3,575 767

WA 5% 129 1,130 228

CA 2 n/a 28 245 53

PGE OR 41% 639 5,600 1,201

PSE WA 26% 731 6,407 1,292

SCL WA 11% 302 2,644 533

TPWR WA 5% 150 1,316 265

WAUW MT 4% 7 65 14

PNW Total 5,333 46,716 9,565

1 The distribution of each state’s load across balancing authorities is based on the load forecast data provided to

WECC by individual balancing authorities.

2 To estimate the economic potential in the California portion of PACW, we multiplied the potential results for the

Oregon po rtion of PACW by the ratio of the PACW load in California and Oregon (0.07). That ratio was derived from the load forecast data provided in PacifiCorp’s March 2010 IRP.

3 The peak demand savings (MW) for IPC was calculated from the energy savings, based o n an assumed peak

- to

energy savings ratio of 0.17 MW/GWh, rather than by allocating state

- level peak demand savings estimates in proportion to retail sales.



### 99

### Table 6. Balancing Authority High DSM Load Forecasts for 2021: Annua l Electricity Consumption (GW h)

Balancing Authority

(PNW portion) State A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) ( GWh) (%)

AVA ID 

### 5,388 570 1,164 4,794

- 11%

WA 

### 9,077 960 2,289 7,748

- 15%

BPA ID 

### 2,343 285 526 2,103

- 10%

WA 

### 3,016 367 333 3,049 1%

OR 

### 15,983 1,945 4,108 13,821

- 14%

MT 

### 32,108 3,907 8,403 27,612

- 14%

CHPD WA 

### 4,037 135 987 3,185

- 21%

DOPD WA 

### 1,931 117 520 1,528

- 21%

GCPD WA 

### 5,103 314 1,258 4,160

- 18%

IPC ID 

### 18,570 1,924 3,866 16,629

- 10%

OR 

### 969 100 231 839

- 13%

NWMT MT 

### 11,268 851 1,173 10,946

- 3%

PACE ID 

### 4,411 261 875 3,797

- 14%

PACW OR 

### 16,570 1,501 3,575 14,496

- 13%

WA 

### 5,016 454 1,130 4,340

- 13%

CA 

### 1,163 105 245 1,023

- 12%

PGE OR 

### 23,067 3,816 5,600 21,283

- 8%

PSE WA 

### 26,340 4,667 6,407 24,600

- 7%

SCL WA 

### 10,662 1,258 2,644 9,275

- 13%

TPWR WA 

### 5,443 588 1,316 4,715

- 13%

WAUW MT 

### 821 28 65 784

- 4%

Total 203,287 24,154 46,716 180,725

- 11%



### 100

### Table 7 . Balancing Authority High DSM Load Forecasts for 2021: Annua l Peak Demand (MW)

Balancing Authority

(PNW portion) State A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

AVA ID 997 66 266 797

- 20%

WA 1,679 112 462 1,329

- 21%

BPA ID 455 39 120 373

- 18%

WA 585 50 74 561

- 4%

OR 3,103 263 881 2,485

- 20%

MT 6,233 529 1,695 5,067

- 19%

CHPD WA 717 26 199 544

- 24%

DOPD WA 415 47 105 357

- 14%

GCPD WA 846 127 254 719

- 15%

IPC ID 4,550 321 657 4,214

- 7%

OR 238 17 39 215

- 9%

NWMT MT 1,820 142 259 1,703

- 6%

PACE ID 876 49 200 725

- 17%

PACW OR 3,087 252 767 2,572

- 17%

WA 935 76 228 783

- 16%

CA 217 18 53 182

- 16%

PGE OR 4,159 650 1,201 3,608

- 13%

PSE WA 5,303 943 1,292 4,954

- 7%

SCL WA 1,898 225 533 1,589

- 16%

TPWR WA 1,035 114 265 884

- 15%

WAUW MT 147 5 14 138

- 6%

Total 39,293 4,069 9,565 33,797

- 14%



### 101

### Utah

Summary Tables

Table 1. High DSM Load Forecasts for 2020: Annua l Electricity Consumption (GWh)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

PACE 37,482 2,217 6,412 33,287

- 11%

State Total 37,482 2,217 6,412 33,287

- 11%



### Table 2

. High DSM Load Forecasts for 2020: Annua l Peak Demand (MW)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = D/A

- 1

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

PACE 7,441 416 2,030 5,827

- 22%

State Total 7,441 416 2,030 5,827

- 22%



### Table 3. Comparison of Reference Case and High DSM Case (2020 Cumulative Savings)

Balancing Authority (In

- State Portion) Reference Case Savings (% of Load Forecast w/o EE) High DSM Case Savings (% of Load Forecast w/o EE)

GWh MW GWh MW

PACE 6% 5% 16% 26%

State Total 6% 5% 16% 26% Note: The percentages in this table were calculated by dividing the savings projection by the sum of the savings projection and the post

- savings load forecast (e.g., High DSM savings divided by High DSM savings plus High DSM load forecast)



### 102

### High DSM Savings Projection

The High DSM savings projection for the PACE balancing authority is based, in part, on PacifiCorp’s 2011 energy efficiency potential st udy (Cadmus, 2011). Table 4 presents the technical potential estimate from that study, for the Utah portion of PacifiCorp’s service territory, in the year 2030.

Table 4. Technical Potential Estimate for 2030 (PacifiCorp Utah Service Territory)

Market S ector aMW GWh MW 1

Residential 429 3,758 1,190

Commercial 304 2,663 843

Industrial 158 1,384 438

Irrigation 3 25 8

Street Lighting 4 32 10

Total 897 7,861 2,489 Source: Cadmus (2011), Tables 63, 65, 67, 69, and 70

1 The study provides peak demand savings potential for the Utah service territory as a whole, but not for each individual market segment within the service territory. The peak demand savings shown in this table were estimated by applying the peak

- to

- energy savings ratio implied by the peak and energy savings estimates for the entire service territory (2489 MW / 7861 GWh = 0.32 MW/GWh) to each market sector.



### In order to use the potential estimates in Table 4 for developing a High DSM savings projection

for the entire Utah portion of the PACE balancing authority, three additional steps are required: 1) Estimate the technical potential for the year 2021

2) Estimate economic potential based on the technical potential estimate

3) Extrapolate the results to the entire Uta h portion of the PACE balancing authority

Estimating Technical Potential for the Year 2021

The SPSC DSM Work Group is initially developing High DSM Case savings projections and load forecasts for the year 2021. The technical potential estimate in the Cadmus study, however, is based on the year 2030. Those results must therefore be adjusted to account for the fact that technical potential increases over time. To inform this adjustment, Table 5 segments the technical potential for each market sector into “discretionary” and “lost opportunity” measures.

Table 5. PacifiCorp Technical Potentia l Estimate: Discretionary vs. Lost Opportunity Measures

Sector Discretionary Lost Opportunity

Residential 38% 62%

Commercial 72% 28%

Industrial 100% 0%

Irrigation 100% 0%

Street Lighting 100% 0%

Total 64% 36%

1 Source: Calculated from Cadmus (2011), Tables 54.



### 103

### To estimate the available technical potential for 2021, we assume that all discretionary measures are available immediately, but that the lost opportunity potential for each market sector increases linearly with time. Based on this assumption, Table 6 pr esents the estimated technical potential for the PacifiCorp’s Utah service territory in 2021. Given the relative contribution of discretionary and lost opportunity measures, the estimated technical potential in 2021 (721 aMW) is equal to approximately 80% of the technical potential in 2030 (879 aMW).

Table 6. Technical Potential Estimate for 2021 (PacifiCorp Utah Service Territory)

Market Sector aMW GWh MW

Residential 295 2,584 818

Commercial 261 2,289 725

Industrial 158 1,384 438

Irrigation 3 25 8

Street Lighting 4 32 10

Total 721 6,314 1,999



### Estimating Economic Potential Based on the Technical Potential Estimate

The 2011 PacifiCorp potential study provides estimates of technical potential and achievable

technical potential; it does not, however, provide an estimate of economic potential. Therefore, we also rely on PacifiCorp’s 2007 energy efficiency potential study (Quantec, 2007), which provides estimates of both technical and economic potential. In that study, the economic potential for the Utah portion of PacifiCorp’s service territory is equal to 80% of the corresponding technical potential. We apply the same percentage to the updated technical potential estimate in Table 6, in order to estimate the economi c potential for the Utah portion of PacifiCorp’s service territory, as shown in Table 7.

Table 7. Updated Economic Potential Estimates for PacifiCorp Utah Service Territory in 2021

Market Sector aMW GWh MW

Residential 235 2,061 653

Commercial 208 1,826 578

Industrial 126 1,104 350

Irrigation 2 20 6

Street Lighting 3 25 8

Total 575 5,036 1,595



### Extrapolating the Results to the Entire Utah Portion of the PACE Balancing Authority

The Utah portion of the PACE balancing authority includes PacifiCorp, as well as numerous municipal utilities and cooperatives. We extrapolate the PacifiCorp potential study results for each sector (residential, commercial, and industrial) to the entire U tah portion of PACE by simply scaling up the energy and peak demand savings potential based on the ratio of the total balancing authority retail sales to PacifiCorp’s retail sales, for that sector. While we recognize that this is a simplistic approach, an d ignores potential differences in demographics, climate, and

104

### end

- use characteristics, we also believe that it is a reasonable approximation given the data and resources available. This extrapolation, presented below in Table 8, yields an estimated 2021

e conomic potential for the entire Utah portion of PACE equal to 6,412 GWh and 2,030 MW.

Table 8. Extrapolation of Economic Potential to Utah Portion of PACE

Res. Com. Ind. Total

Retail Sales 1

PacifiCorp (Utah) 6,561 7,934 8,126 22,621

PACE (Utah) 8,786 10,286 9,086 28,159

Net Economic Potential in 2021 (GWh)

PacifiCorp (Utah) 2,061 1,826 1,149 5,036

PACE (Utah) 2,760 2,367 1,284 6,412

Net Economic Potential in 2021 (MW)

PacifiCorp (Utah) 653 578 364 1,595

PACE (Utah) 874 750 407 2,030

1 Data Source: EIA

- 861 retail sales data for 2008.



### 105

### Wyoming

Summary Tables

Table 1. High DSM Load Forecasts for 2021: Annua l Electricity Consumption (GWh)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(GWh) (GWh) (GWh) (GWh) (%)

PACE 13,594 804 1,939 12,459

- 8%

WACM 9,827 350 1,539 8,638

- 12%

State Total 23,421 1,154 3,478 21,098

- 10%



### Table 2

. High DSM Load Forecasts for 2021: Annua l Peak Demand (MW)

Balancing Authority (In

- State Portion) A B C D = A

- (C

- B) E = (D/A

- 1)

Common Case

Load Forecast Common Case Efficiency

Savings High DSM Efficiency

Savings High DSM

Load Forecast Percent Change

from Common

Case Load

Forecast

(MW) (MW) (MW) (MW) (%)

PACE 2,699 151 282 2,568

- 5%

WACM 1,517 68 301 1,284

- 15%

State Total 4,216 219 583 3,851

- 9%



### Table 3. Comparison of Reference Case and High DSM Case (2020 Cumulative Savings)

Balancing Authority (In

- State Portion) Common Case Savings (% of No

- EE Load Forecast) High DSM Case Savings (% of No

- EE Load Forecast)

GWh MW GWh MW

PACE 6% 5% 13% 10%

WACM 3% 4% 15% 19%

State Total 5% 5% 14% 13% Note: The percentages in this table were calculated by dividing the savings projection by the sum of the savings projection and the post

- savings load forecast (e.g., High DSM savings divided by High DSM savings plus High DSM load forecast)



### PACE Balancing Authority

The High DSM savings projection for the PACE balancing authority is based, in part, on PacifiCorp’s 2011 energy efficiency potential study (Cadmus, 2011). Table 4 presents the technical potential estimate from that study, for the Wyoming portion of PacifiCorp’s service territory, in the year 2030.

106

### Table 4. Technical Potential Estimate for 2030 (PacifiCorp Wyoming Service Territory)

Market Sector aMW GWh MW 1

Residential 47 412 60

Commercial 53 464 68

Industrial 158 1,384 201

Irrigation 0 3 0

Street Lighting 1 5 1

Total 259 2,269 330 Source: Cadmus (2011), Tables 63, 65, 67, 69, and 70

1 The study provides peak demand savings potential for the Wyoming service territory as a whole, but not for each individual market segment within the service territory. The peak demand savings shown in this table were estimated by applying the peak

- to

- ener gy savings ratio implied by the peak and energy savings estimates for the entire service territory (330 MW / 2269 GWh = 0.15 MW/GWh) to each market sector.



### In order to use the potential estimates in Table 4 for developing a High DSM savings projection

fo r the entire Wyoming portion of the PACE balancing authority, three additional steps are required: 4) Estimate the technical potential for the year 2021

5) Estimate economic potential based on the technical potential estimate

6) Extrapolate the results to the entire Wyoming portion of the PACE balancing authority

Estimating Technical Potential for the Year 2021

The SPSC DSM Work Group is initially developing High DSM Case savings projections and load forecasts for the year 2021. The technical potential estimate in the Cadmus study, however, is based on the year 2030. Those results must therefore be adjusted to account for the fact that technical potential increases over time. To inform this adjustment, Table 5 segments the technical potential for each market sector into “discretionary” and “lost opportunity” measures.

Table 5. PacifiCorp Technical Potentia l Estimate: Discretionary vs. Lost Opportunity Measures

Sector Discretionary Lost Opportunity

Residential 38% 62%

Commercial 72% 28%

Industrial 100% 0%

Irrigation 100% 0%

Street Lighting 100% 0%

Total 64% 36%

1 Source: Calculated from Cadmus (2011), Table 54.



### To estimate the available technical potential for 2021, we assume that all discretionary measures are available immediately, but that the lost opportunity potential for each market sector increases linearly with time. Base d on this assumption, Table 6 presents the estimated technical potential for the PacifiCorp’s Wyoming service territory in 2021. Given the relative contribution of discretionary and lost opportunity measures, the estimated technical potential in 2021 (237 aMW) is equal to approximately 91% of the technical potential in 2030 (259 aMW).

107

### Table 6. Technical Potential Estimate for 2021 (PacifiCorp Wyoming Service Territory)

Market Sector aMW GWh MW

Residential 32 283 41

Commercial 46 399 58

Industrial 158 1,384 201

Irrigation 0 3 0

Street Lighting 1 5 1

Total 237 2,074 302



### Estimating Economic Potential Based on the Technical Potential Estimate

The SPSC DSM Work Group determined that the savings projections for the High DSM Case are to be based on achieving the full economic potential throughout the WECC. The 2011 PacifiCorp potential study provides estimates of technical potential and achievabl e technical potential; it does not, however, provide an estimate of economic potential. Therefore, we also rely on PacifiCorp’s 2007 energy efficiency potential study (Quantec, 2007), which provides estimates of both technical and economic potential. In that study, the economic potential for the Wyoming portion of PacifiCorp’s service territory is equal to 89% of the corresponding technical potential. We apply the same percentage to the updated technical potential estimate in Table 6,

in order to estim ate an updated economic potential for the Wyoming portion of PacifiCorp’s service territory, as shown in Table 7.

Table 7. Updated Economic Potential Estimates for PacifiCorp Wyoming Service Territory in

2021

Market Sector aMW GWh MW

Residential 29 253 37

Commercial 41 356 52

Industrial 141 1,235 180

Irrigation 0 2 0

Street Lighting 1 5 1

Total 211 1,851 269



### Extrapolating the Results to the Entire Wyoming Portion of the PACE Balancing Authority

The Wyoming portion of the PACE balancing authority consists primarily of PacifiCorp’s load, but also includes several small utilities and cooperatives. We extrapolate the PacifiCorp potential study results for each sector (residential, commercial, and in dustrial) to the entire Wyoming portion of PACE by simply scaling up the energy and peak demand savings potential based on the ratio of the total balancing authority retail sales to PacifiCorp’s retail sales, for that sector. While we recognize that this is a simplistic approach, and ignores potential differences in demographics, climate, and end

- use characteristics, we also believe that it is a reasonable approximation given the data and resources available. This extrapolation, presented below in

108

### Table 8 , yields an estimated economic potential for the entire Wyoming portion of PACE equal to 1,939 GWh and 282 MW.

Table 8. Extrapolation of Economic Potential to Wyoming Portion of PACE

Res. Com. Ind. Total

Retail Sales 1

PacifiCorp (Wyoming) 1,083 1,519 6,734 9,335

PACE (Wyoming) 1,254 1,691 6,775 9,720

Net Economic Potential in 2021 (GWh)

PacifiCorp (Wyoming) 253 356 1,242 1,851

PACE (Wyoming) 293 397 1,250 1,939

Net Economic Potential in 2021 (MW)

PacifiCorp (Wyoming) 37 52 181 269

PACE (Wyoming) 43 58 182 282

1 Data Source: EIA

- 861 retail sales data for 2008.



### WACM Balancing Authority

The High DSM scenario savings estimate for the WACM balancing authority is based on the 2010 energy efficiency potential study for Tri

- State Generation and Transmission Cooperative (Nexant, 2010). The potential estimates from that study, for the Wyoming portion of Tri

- State’s service territory in the year 2020, are presented below in Table 9.

Table 9. Tri

- State Energy Efficiency Potential ( Wyoming Service Territory, 2020)

Technical Economic Max.

Achievable 1

GWh 402 308 179

MW 83 62 36

% of baseline retail sales 20% 15% 9% Source: Calculated from detailed savings tables in Nexant (2010), Appendix A Notes: The potential study indicates that the baseline projection accounts for naturally occurring savings, thus the

potential estimates are net of naturally occurring savings.

1 Max. Achievable scenario assumes incentives cover 100% of incremental measure cost. The study also estimated

ach ievable potential under three other incentive levels.



### For the purpose of the High DSM scenario we assume that the full economic potential is achieved in the Wyoming portion of the WACM balancing authority region. Developing a High

DSM load forecast for t he balancing authority involves three additional steps: 4) Adding avoided T&D losses to the economic potential estimate
5) Extrapolating the potential study results to the entire Wyoming portion of the WACM
balancing authority

Adding Avoided T&D Losses to the P otential Estimates

The potential estimates presented in the Tri

- State study represent savings at the customer meter. The economic potential estimate must therefore be scaled up to the bus

- bar to account for

109

### avoided T&D losses, based on an estimated margi nal T&D loss factor (7.7%). 27 

### The results of this adjustment are shown below in Table 10.

Table 10. Tri

- State Economic Potential (Wyoming, 2020) Including Avoided T&D Losses

Res. Com. Ind. Irrig. Total

GWh 83 14 224 12 334

MW 14 3 45 5 67



### Extrapolating the Study Results to the Entire Wyoming Portion of WACM

The Wyoming portion of the WACM balancing authority consists of numerous utilities (the largest being Powder River, Cheyenne, High Plains, and Lower Valley). Tri

- State G&T serves

about one quarter of the total load within the Wyoming portion of WACM.

To extrapolate the Tri

- State potential study, we follow a similar procedure as with the PACE balancing authority. Namely, we scale up the potential study results for each sector, in

propo rtion to 2008 retail sales for that sector. This extrapolation is presented below in Table 11, which indicates that the total economic potential in the Wyoming portion of WACM is estimated to be 1,539 GWh and 301 MW in 2020. These values represent saving s at the bus bar and are net of naturally occurring savings.

Table 11. Estimated Economic Potential for Wyoming Portion of WACM

Res. Com. Ind. & Irr. Total

2008 Retail Sales

Tri

- State (WY) 387 56 1,210 1,653

WACM (WY) 1,465 2,721 2,785 6,970

Net Economic Potential in 2020 (GWh)

Tri

- State (WY) 83 14 237 334

WACM (WY) 314 681 545 1,539

Net Economic Potential in 2020 (MW)

Tri

- State (WY) 14 3 50 67

WACM (WY) 53 132 116 301

1 Data Source: EIA

- 861 retail sales data for 2008.

27 Tri

- State’s potential study cites an aver a ge T&D loss factor of 5.5%. We assume that marginal T&D loss factors are equal to 1.4 times the average T&D loss factor , based on preliminary analysis conducted by Jim Lazar and shared with members of the DSM working group

. Marginal loss factors are the relevant metric for estimating avoided T&D losses from DSM and are higher than average loss factors, because resistive losses increase exponentially with load.



### 110

## Appendix C. SPSC 20

- Year High DSM Case : Additional Methodological Details, Assumptions, and Analysis

This appendix contains further details on the assumptions, methods, and data sources to develop

the SPSC 20

- year High DSM Case load forecasts, along with details on the resulting load forecasts themselves. This information is intended to supplement the overview provided within Chapter 4 of the main body of the report

C.1 Weather and economic data and analyses for the SAE Base Case Forecast

Historical weather data of the National Weather Service’s Automated Surface Observation System (ASOS), from 100 stations selected to cover the WECC region and covering the period

1991

- 2011, were obtained from the commercial vendor DTN. The raw data were da ily average temperatures by station. These data were processed in a series of steps for use in the SAE econometric model estimation. First, daily and monthly degree days by station were calculated. Second, these statistics were calculated for “zones” defin ed by mapping the ASOS station geography to WECC BAs. Next, in a preliminary analysis, spline approximations were computed relating daily energy and peak demand to average temperatures in terms of heating and cooling degree days. These results were used to calculate the XCool 

### and XHeat 

### indices in the SAE regression model.

For economic and demographic variables, both historical data (from 1998) and 10

- and 20

- year forecasts were obtained from Moody’s, Inc., including ho useholds, population, household income, employment, and gross state products manufacturing gross products (for states in WECC). These purchased data were in monthly form at the state and province level, and were processed for use in the SAE econometric an alysis. The following indices were created: For the residential models, numbers

- of

- households, household sizes, and household income; for the commercial models, population, non

- manufacturing employment, and gross manufacturing produce (value of output).

C.2

. End

- U se C ategories and E fficiency U nits

As discussed within the main body of the report , the end

- use structure of the SAE model mirrors

the end

- use categories in EIA’s National Energy Modeling System (NEMS) . The stock efficiency for each end

- use is therefore defined in the same units as in NEMS, as summarized in

Table C

- 1

.

111

### Table C

- 1 . End

- Use Categories and Stock Efficiency Units in the SAE Load Forecasting Model

Sector End

- Use Category Abbreviation Stock Efficiency UnitResidential Electric furnaces EFurn Energy factor

Heat

- pump heating HPHeat HSPF

Ground

- source heat pump heating GHPHeat COP
Secondary heating SecHt kWh/year
Central air conditioning CAC SEER
Heat pump cooling HPCool SEER

Ground

- source heat pump cooling GHPCool EER
Room air conditioning RAC EER
Electric water heating EWHeat Energy factor
Electric cooking ECook kWh/year
Refrigerators Ref1 kWh/year
Secondary refrigerators Ref2 kWh/year

Dishwashers Dish kWh/year

Freezers Frz kWh/year

Clothes washers CWash kWh/cycle
Electric dryers EDry Energy factor

TVs (a) TV kWh/yr

Furnace fans FurnFan kWh/year
Lighting Light kWh/household/year

Miscellaneous (b) Misc kWh/household/yearCommercial Heating Heat btu

- out/btu

- in (c)

Cooling Cool btu

- out/btu

- in (c)

Ventilation Vent 1000 cfm

- hours output/ 1000 btu input
Water heating EWHeat btu

- out/btu

- in (c)

Cooking Cooking btu

- out/btu

- in (c)

Refrigeration Refrig btu

- out/btu

- in (c)

Outdoor Lighting OLight lumens per watt
Indoor Lighting ILight lumens per watt
Office equipment Office n/a (d)

Miscellaneous Misc n/a (d)

(a) Composite of televisions and set

- top boxes (b) Composite of ceiling fans, coffee makers, DVD players, unspecified electric devices, electric printers, home audio systems, personal computers, rechargeable electronics, spas, security systems, and video games (c) For commercial heating, cooling, water heating, cooking, and refrig eration, the unit of efficiency is described as

follows in the NEMS Commercial Module documentation: “[Energy] service demand is defined as Btus out (amount of delivered energy). Equipment efficiency or equipment Coefficient of Performance (COP) of the technologies that meet required service demands, together with the distribution of that equipment and the levels of service demanded, determines th e fuel consumption, or Btu input. Efficiency is defined as the ratio of Btus out to Btus in for a closed system, which is a system that does not draw from external sources for Btu transference. The COP is a more appropriate measure of...performance where the system is more open, as in the case of a heat pump...The terms efficiency and COP are used interchangeably [in the documentation] when referring to the ratio of delivered to consumed energy” (USEIA 2011).

(d) For t he Commercial “Office” and “Miscellaneous” cate gories, t he SAE framework does not define an efficiency metric or index for these categories; instead, levels of efficiency, and their changes, are incorporated from exogenous assumptions that affect electricity demand for these end

- uses in the NEMS model.



### 112

### C.3

. Data S ources for End

- Use Assumptions in the SAE Base Case

When constructing the SAE Base Case load forecasts, EIA’s Annual Energy Outlook 2012

Reference Case was the default data source for residential and commercial average stock

efficiencies and saturation , covering both the historical model estimation period (1998

- 2010) and the forecast period (2011

- 2032)

. The EIA data , however, are specified at the census region level

, which for the west ern U.S. includes the Pacific Region (CA, OR, WA) and the Mountain Region (all other U.S. states with in WECC). This coarse level of geographical granularity is less than ideal, as both stock efficiency levels and saturation can vary substantially within a given census region (especially saturations for heating and cooling end

- uses).

Where possible, more localized data sources were used as either a substitute or complement to

the EIA

- NEMS data , as described below

. In some cases, alternate time series data sources were available that could be substituted for the EIA data in whole . I n other cases, however, the alternate data were available only for a single “base year”, which was then used as a benchmark to calibrate the corresponding EIA trajectory

.

Stock Efficiency Data Sources ( Table C

- 2 ) : For residential end uses

, alternate time series data

for a sub

- set of end uses were available from the No rthwest Power and Conservation Council (NPCC) and the National Research Council Canada (NRC) and were used for Northwestern and Canadian BAs, respectively. For a number of other regions, base

- year benchmark data were available to calibrate the EIA traject ories

. This included data from the California Energy Commission (CEC)

, 28 

### and base

- year data from recent energy efficiency potential studies by Public Service of New Mexico (PNM) and PacifiCorp (PAC)

. For commercial stock efficiency

, EIA data was used for all BAs, but base

- year benchmark data from the CEC, PAC , and NRC

were used for the corresponding regions to calibrate the EIA trajectories

.

Saturation Data Sources ( Table C

- 3 ) : NPCC and NRC data were again used in place of EIA

data for the Northwestern and Canadian BAs, respectively. Alternate time series saturation data

from the CEC ’s Residential Appliance Saturation Survey was used for California BAs, and time series data on residential heating shares for Arizona was available from the U.S. Census and was

used for Arizona BAs. In addition, base

- year benchmark were available for a number of other regions to calibrate the EIA sa turation trajectories. This included data from the CEC’s Commercial End Use Saturation Survey

, central air conditioning share data by state from a Federal Energy Regulatory Commission (FERC) study , and base

- year saturation data from PNM and PAC efficiency potential studies.

28 Note that the present section pertains to the SAE Base Case; however, the SAE Reference Case relied on stock efficiency projections that reflect achievement of the state’s energy efficiency goals, based on a potential study

conducted for the California Public Utilities Commission.



### 113

### Table C

- 2

. Data Sources for Stock Efficiency Trajectories and Base

- Year Benchmarks

Sector End Use California Southwest (excl. NM and PACE) New Mexico PACE Pacific Northwest Canadian

ProvincesResidential EFurn EIA (CEC) EIA EIA (PNM) EIA (PAC) NPCC EIA

HPHeat EIA (CEC) EIA EIA (PNM) EIA (PAC) NPCC EIA

GHPHeat EIA (CEC) EIA EIA (PNM) EIA (PAC) EIA EIA

SecHt EIA EIA EIA (PNM) EIA (PAC) EIA EIA

CAC EIA (CEC) EIA EIA (PNM) EIA (PAC) EIA NRC

HPCool EIA (CEC) EIA EIA (PNM) EIA (PAC) EIA EIA

GHPCool EIA (CEC) EIA EIA (PNM) EIA (PAC) EIA EIA

RAC EIA (CEC) EIA EIA (PNM) EIA (PAC) EIA NRC

EWHeat EIA (CEC) EIA EIA (PNM) EIA (PAC) NPCC EIA

ECook EIA (CEC) EIA EIA (PNM) EIA (PAC) NPCC EIA

Ref1 EIA (CEC) EIA EIA (PNM) EIA (PAC) NPCC EIA

Ref2 EIA (CEC) EIA EIA (PNM) EIA (PAC) NPCC EIA

Frz EIA (CEC) EIA EIA (PNM) EIA (PAC) NPCC EIA

Dish EIA EIA EIA (PNM) EIA (PAC) NPCC EIA

CWash EIA EIA EIA (PNM) EIA (PAC) NPCC EIA EDry EIA (CEC) EIA EIA (PNM) EIA (PAC) NPCC EIA

TV EIA (CEC) EIA EIA (PNM) EIA (PAC) EIA EIA

FurnFan EIA (CEC) EIA EIA (PNM) EIA (PAC) EIA EIA

Light EIA (CEC) EIA EIA (PNM) EIA (PAC) NPCC NRC

Misc EIA EIA EIA (PNM) EIA (PAC) EIA EIACommercial Heat EIA (CEC) EIA EIA EIA (PAC) EIA EIA ( NRC )

Cool EIA (CEC) EIA EIA EIA (PAC) EIA EIA (NRC)

Vent EIA (CEC) EIA EIA EIA (PAC) EIA EIA (NRC)

EWHeat EIA (CEC) EIA EIA EIA (PAC) EIA EIA (NRC) Cooking EIA (CEC) EIA EIA EIA (PAC) EIA EIA (NRC)

Refrig EIA (CEC) EIA EIA EIA (PAC) EIA EIA (NRC) OLight EIA (CEC) EIA EIA EIA (PAC) EIA EIA (NRC) ILight EIA (CEC) EIA EIA EIA (PAC) EIA EIA (NRC)

Office EIA (CEC) EIA EIA EIA (PAC) EIA EIA (NRC)

Misc EIA (CEC) EIA EIA EIA (PAC) EIA EIA (NRC)

* Note: Data sources within parentheses refer to benchmark values available for a single base year, which were used to calibrat e the EIA census

- region

trajectory.



### 114

### Table C

- 3

. Data Sources for Residential Saturation Trajectories and Base

- Year Benchmarks

Sector End Use California Arizona Colorado Nevada New Mexico PACE Pacific

Northwest Canadian

ProvincesResidential EFurn CEC Census EIA (Census) EIA EIA (PNM) EIA (PAC) NPCC NRC

HPHeat CEC Census EIA (Census) EIA EIA (PNM) EIA (PAC) NPCC NRC

GHPHeat CEC Census EIA (Census) EIA EIA EIA (PAC) EIA NRC

SecHt EIA EIA EIA EIA EIA (PNM) EIA (PAC) EIA EIA

CAC CEC EIA (FERC) EIA (FERC) EIA (FERC) EIA (PNM) EIA (PAC) NPCC NRC

HPCool CEC EIA (FERC) EIA (FERC) EIA (FERC) EIA EIA (PAC) NPCC NRC

GHPCool CEC EIA (FERC) EIA (FERC) EIA (FERC) EIA (PNM) EIA (PAC) EIA NRC

RAC CEC EIA EIA EIA EIA (PNM) EIA (PAC) NPCC NRC

EWHeat CEC EIA EIA EIA EIA (PNM) EIA (PAC) NPCC NRC

ECook CEC EIA EIA EIA EIA (PNM) EIA (PAC) NPCC NRC

Ref1 CEC EIA EIA EIA EIA (PNM) EIA (PAC) EIA EIA

Ref2 CEC EIA EIA EIA EIA (PNM) EIA (PAC) NPCC NRC

Frz CEC EIA EIA EIA EIA (PNM) EIA (PAC) NPCC NRC

Dish CEC EIA EIA EIA EIA (PNM) EIA (PAC) NPCC NRC

CWash CEC EIA EIA EIA EIA (PNM) EIA (PAC) NPCC NRC EDry CEC EIA EIA EIA EIA (PNM) EIA (PAC) NPCC NRC

TV CEC EIA EIA EIA EIA (PNM) EIA (PAC) EIA EIA

FurnFan CEC EIA EIA EIA EIA (PNM) EIA (PAC) EIA EIA

Light CEC EIA EIA EIA EIA EIA (PAC) EIA EIA

Misc CEC EIA EIA EIA EIA EIA (PAC) EIA EIACommercial Heat EIA (CEC) EIA EIA EIA EIA EIA (PAC) NPCC NRC

Cool EIA (CEC) EIA EIA EIA EIA EIA (PAC) EIA NRC

Vent EIA (CEC) EIA EIA EIA EIA EIA (PAC) EIA NRC

EWHeat EIA (CEC) EIA EIA EIA EIA EIA (PAC) NPCC NRC Cooking EIA (CEC) EIA EIA EIA EIA EIA (PAC) EIA NRC

Refrig EIA (CEC) EIA EIA EIA EIA EIA (PAC) NPCC NRC OLight EIA (CEC) EIA EIA EIA EIA EIA (PAC) EIA NRC ILight EIA (CEC) EIA EIA EIA EIA EIA (PAC) NPCC NRC

Office EIA (CEC) EIA EIA EIA EIA EIA (PAC) EIA NRC

Misc EIA (CEC) EIA EIA EIA EIA EIA (PAC) EIA NRC

* Note: Data sources within parentheses refer to benchmark values available for a single base year, which were used to calibrat e the EIA census

- region

trajectory.



### 115

### C. 4. EE Adjustments for SAE Reference Case

The SAE load forecasting framework was used to develop an initial set of “SAE Base Case” forecasts using the end

- use efficiency and saturation assumptions described in the previous

section. Because those end

- use efficiency projections are not necessarily consistent with current set of energy efficiency policies and program plans, the initial SAE Base Case forecasts for many balancing authorities were adjusted in order to bring them in line with the expected impacts of current energy efficiency policies and program plans ; the resulting set of adjusted forecasts are termed the “SAE Reference Case.” The general process used to develop and apply these EE adjustments is described within Section 4.3.1 . Within this appendix, we provide further details on the specific assumptions employed for each BA and the resulting EE adjustment for the SAE Reference Case (if any).

Table C

- 4 summarizes the underlying EE policy and program assumptions for the SAE Reference Case. Based on these assumptions, EE adjustments were applied to the SAE Base

Case forecasts for eleven BAs: APS, AVA, BPA, EP E, IPC, PACE, PGE, PSC, PSE, SRP, TEP

. Table C

- 5 summarizes the resulting EE adjustments for those eleven BAs. For all other BAs,

the SAE Base Case forecasts were deemed to adequately reflect the expected impact of current EE policies and program plans over the 20

- year study period , and therefore no adjustment to the SAE Base Case forecasts was applied

.

As described previously within Section 4.3.1 , the determination of whether or not apply an EE adjustment, and the size of any EE adjustment applied, was based on a comparison of the projected savings from current EE policies and program plans to an extrapolation of historical EE program savings trends. Those comparisons are depicted graphically in Figure C

- 1 through Figure C

- 17 below , for each BA for which the requisite data were available

. In each of those figures, the left

- hand graphic depicts historical incremental annual en ergy savings, based on EIA

Form

- 861 data and any other sources available, along with projected incremental annual energy savings resulting from current EE policies and program plans , based on the assumptions outlined

in Table C

- 4 . The right

- hand graphic in each figure compares the extrapolated historical savings (based on a simple linear extrapolation) to the projected savings from current policies and program plans, in terms of cumulative savings from programs implemented in the year 2000

onward.

Table C

- 4 . Policy and Program Assumptions Used for EE Adjustments in the SAE Reference Case

B alancing Authority Policy and Program Assumptions

AESO No long

- term EE policies or plans

APS Planned cumulative savings through 2021 are based data submitted by APS to WECC, and reflect full compliance with Arizona’s Energy Efficiency Rule. It is assume that these values are net of measure decay, given that the EE Rule is specified in terms of cumulative savings. From 2022 onward, absent specific policy requirements, it is assumed that APS will simply maintain cumulative savings equal to 20% of its annual load, per the requirements of the Energy E fficiency Rule, which is roughly equivalent to the assumption that the annual growth in cumulative savings reverts back to

its historical trend.

AVA Planned cumulative savings through 2032 are based on the projection of utility program savings in

Avista's 2011 IRP, net of measure decay.

BCTC Insufficient historical EE savings data exist to perform the adjustment



### 116BPA Planned cumulative savings through 2032 are based on the portion of NPCC's 6th plan conservation targets allocated to the BPA BA load.

CFE No long

- term EE policies or plans

CISO Planned EE impacts are addressed directly through SAE end

- use efficiency forecasts

CHPD Small BA; no EE adjustment analysis warranted

DOPD Small BA; no EE adjustment analysis warranted

EPE Planned cumulative savin gs through 2021 are based data submitted by EPE to WECC, and reflect full compliance with New Mexico's and Texas' energy efficiency resource standards. From 2022 onward, it is assumed that growth in cumulative savings reverts back to its historical trend, absent specific policy or planning targets

GCPD Small BA; no EE adjustment analysis warranted

IID Small BA; no EE adjustment analysis warranted

IPC Planned cumulative savings through 2032 are based on the projection of utility program savings in

Idaho Power's 2011 IRP, net of measure decay.

LDWP Planned cumulative savings represent only a negligible acceleration over a continuation of historical

trends

NEVP Planned savings through 2021 correspond roughly to continuation of historical trend

NWMT Small BA; no EE adjustment analysis warranted

PACE Planned cumulative savings through 2021 are based data submitted by PacifiCorp to WECC, and reflect achievement of the EE savings targets in PacifiCorp's 2011 IRP. From 2022 onward, growth

in cumulative savings is assumed to revert back to its historical trend, given the lack of any specific policy or planning targets over that time period.

PACW Based on PacifiCorp's 2011 IRP planning assumptions, there is no acceleration in EE savings over

the forecast period relative to the historical trend.

PGE Planned cumulative savings through 2021 are based data submitted by PGE to WECC, and reflect achievement of EE savings targets planned by the Energy Trust of Oregon. From 2022 onward, growth in cumulative sav ings is assumed to revert back to its historical trend, given the lack of any specific policy or planning targets over that time period.

PNM Based on PNM's 2011 IRP, planned savings accelerate rapidly over the next decade, but program

activities in the fo llowing decade are expected to largely cease, with much of the cumulative savings

achieved over the 2011

- 2021 period decaying away by 2032. As a result, the planned cumulative savings in 2032 differ negligibly from what would occur under an extrapolation of historical cumulative savings trends.

PSC Planned cumulative savings through 2021 are based on data submitted by PSCO to WECC, and should be interpreted as net of decay. From 2022

- 2032, cumulative savings are assumed to grow at historical rate, given lack of policy or programmatic targets.

PSE Planned cumulative savings through 2032 are based on the projection of cumulative utility program savings from PSE's 2011 IRP, which is assumed to be net of measure decay.

SCL Based on SCL's 2010 IRP, planned savings accelerate somewhat over the next decade, but planned program activity in the following decade tapers off dramatically, with much of the cumulative savings achieved over the 2011

- 2021 period decaying away by 2032. As a result, the planned

cumulati ve savings in 2032 differ negligibly from what would occur under an extrapolation of historical cumulative savings trends.

SMUD The acceleration of planned savings relative to the historical trend is negligible

SPP Planned savings through 2021 correspond roughly to continuation of historical trend

SRP Planned cumulative savings through 2021 are based on achievement of SRP's "Sustainable Porfolio Plan" EE savings target, and account for the rapid measure decay associated with the M

- Power program (1

- year m easure life). From 2022

- 2032, growth in cumulative savings is assumed to revert back to the historical rate of growth, given the lack of program or policy targets over that period.

TEP Planned cumulative savings through 2021 are based data submitted by T EP to WECC, and reflect full compliance with Arizona’s Energy Efficiency Rule. It is assume that these values are net of measure decay, given that the EE Rule is specified in terms of cumulative savings. From 2022



### 117onward, absent specific policy requireme nts, it is assumed that TEP will simply maintain cumulative savings equal to 20% of its annual load, per the requirements of the Energy Efficiency Rule, which is roughly equivalent to the assumption that the annual growth in cumulative savings reverts back to
its historical trend.

TIDC Small BA; no EE adjustment analysis warranted

TPWR Small BA; no EE adjustment analysis warranted

WACM No long

- term EE policies or plans for most of the utility load within the BA (served by Tri

- State)

WALC No long

- term EE policies or plans

WAUW No long

- term EE policies or plans



### Table C

- 5

. Summary of EE Adjustmen ts for the SAE Reference Case

Balancing Authority Annual Energy (2032) Annual Peak (2032)

SAE Base

Case

Forecast (GWh) Adjustment

for SAE

Reference

Case (GWh) Adjustment

for SAE

Reference

Case (%) SAE Base

Case

Forecast

(MW) Adjustment

for SAE

Reference

Case (MW) Adjustment

for SAE

Reference

Case (%)

APS 56,031

- 4,391

- 7.8% 12,612

- 525

- 4.2%

AVA 17,064

- 828

- 4.9% 3,044

- 110

- 3.6%

BPA 72,509

- 2,126

- 2.9% 11,532

- 190

- 1.6%

EPE 11,949

- 490

- 4.1% 2,675

- 68

- 2.6%

IPC 18,154

- 788

- 4.3% 4,003

- 103

- 2.6%

PACE 61,415

- 1,502

- 2.4% 10,853

- 195

- 1.8%

PGE 27,424

- 1,723

- 6.3% 4,685

- 240

- 5.1%

PSC 57,234

- 3,040

- 5.3% 10,560

- 286

- 2.7%

PSE 30,430

- 1,023

- 3.4% 5,352

- 136

- 2.5%

SRP 48,380

- 2,216

- 4.6% 10,531

- 286

- 2.7%

TEP 17,201

- 2,145

- 12.5% 3,545

- 305

- 8.6%



### Figure C

- 1 . Projected EE Program Savings vs. Extrapolation of Historical Trends (APS) 0 100 200 300 400 500 600 700 800

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (SWEEP and APS data)

Planned EE Savings (APS submittal to WECC)

-1,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)



### 118

### Figure C

- 2 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( AVA )

Figure C

- 3 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( EPE )

Figure C

- 4 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( IPC )

Figure C

- 5 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( LADWP ) 0 20 40 60 80 100 120 140 160 180 200

2000 2005 2010 2015 2020 2025 2030 2035Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Avista IRP)

Planned EE Savings (Avista IRP)

0 500 1,000 1,500 2,000 2,500 3,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 10 20 30 40 50 60

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh)

Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-100 0 100 200 300 400 500 600 700 800

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 20 40 60 80 100 120 140 160 180 200

2000 2005 2010 2015 2020 2025 2030 2035Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (IPC IRP)

Planned EE Savings (IPC IRP)

-500 0 500 1,000 1,500 2,000 2,500 3,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)



### 119

### Figure C

- 6 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( NEVP )

Figure C

- 7 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( PACE )

Figure C

- 8 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( PACW )

Figure C

- 9 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( PGE ) 0 50 100 150 200 250 300 350 400

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-500 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 50 100 150 200 250 300 350 400 450

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh)

Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-1,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 50 100 150 200 250 300 350

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-1,000 0 1,000 2,000 3,000 4,000 5,000 6,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 50 100 150 200 250 300 350

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-1,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)



### 120

### Figure C

- 10 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( PNM )

Figure C

- 11 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( PSCO )

Figure C

- 12 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( PSE )

Figure C

- 13 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( SCL ) -80 -60 -40 -20 0 20 40 60 80 100

2000 2005 2010 2015 2020 2025 2030 2035Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings (net of decay)

-200 0 200 400 600 800 1,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 50 100 150 200 250 300 350 400 450 500

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings (net of decay)

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 50 100 150 200 250 300 350 400

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-1,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 200 400 600 800 1,000 1,200 1,400

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

0 500 1,000 1,500 2,000 2,500

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)



### 121

### Figure C

- 14 . Projected EE Program Savi ngs vs. Extrapolation of Historical Trends ( SMUD )

Figure C

- 15 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( SPP )

Figure C

- 16 . Projected EE Program Savings vs. Extrapolation of Historical Trends (SRP)

Figure C

- 17 . Projected EE Program Savings vs. Extrapolation of Historical Trends ( TEP ) 0 50 100 150 200 250

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-500 0 500 1,000 1,500 2,000 2,500 3,000 3,500

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 20 40 60 80 100 120

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-200 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 100 200 300 400 500 600 700 800

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-1,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)

0 50 100 150 200 250 300

2000 2005 2010 2015 2020 2025Incremental Annual Savings (GWh) Historical EE Savings (EIA-861)

Historical EE Savings (Alternate Source)

Planned EE Savings

-500 0 500 1,000 1,500 2,000 2,500 3,000

2000 2005 2010 2015 2020 2025 2030 2035Cumulative Savings (GWh) Historical Savings

Continuation of Historical Average

Planned Savings

Linear (Historical Savings)



### 122

### C. 5. SAE Reference Case Load Forecast s

The SAE Reference Case load forecasts are summarized graphically in Section 4.3.2 . Here within the technical appendix, we provide additional numeric details. In particular, Table C

- 6 presents the SAE Reference Case load forecasts for the year 2032 , in terms of energy and non

coincident peak demand, as well as the compound annual growth rates over the 2010

- 2032 period, as plotted within Figure 21

. Table C

- 7 displays 2032 energy and peak demand by sector and region , as shown earli er in Figure 23

. Table C

- 8 shows 2032 resi dential energy and peak demand by end use and region , and Table C

- 9 shows the corresponding informa tion for the commercial sector; the data from t hes e two tables are aggregated within Figure 24 in the main body of the report

.

Table C

- 6 . SAE Reference Case Load Forecast by WECC Load Zone (2032)

Load Zone Annual Energy Non

- Coincident Annual Peak Demand

GWh CAGR (2010

- 2032) MW CAGR (2010

- 2032)

AESO 129,624 2.7% 18,010 2.6%

APS 52,668 2.4% 11,988 2.4%

AVA 16,137 1.3% 2,865 1.3%

BCTC 73,858 1.1% 13,132 0.9%

BPA 64,781 1.0% 12,494 1.1%

CFE 18,361 2.5% 3,327 2.0%

CHPD 5,076 2.2% 820 1.4%

DOPD 2,184 2.2% 449 1.0%

EPE 11,361 1.6% 2,294 1.6%
FAR_EAST 3,741 2.7% 704 2.3%

GCPD 6,673 2.3% 1,065 2.0%

IID 5,991 2.4% 1,366 1.4%

LDWP 34,868 0.9% 7,466 0.4%
MAGIC_VLY 4,642

- 0.3% 1,025 0.0%

NEVP 37,334 1.9% 9,337 2.0%

NWMT 12,617 0.8% 2,056 0.9%
PACE_ID 4,830 1.3% 628

- 0.8%
PACE_UT 39,165 0.9% 8,237 1.6%
PACE_WY 15,983 1.9% 2,080 2.0%

PACW 27,811 1.4% 5,429 1.7%
PGE_BAY 49,869 0.4% 10,398 0.9%
PGE_VLY 80,873 1.6% 14,426 0.5%

PGN 25,858 1.1% 5,046 1.5%

PNM 21,344 1.6% 3,650 1.5%

PSC 53,267 1.1% 9,564 1.0%

PSE 29,561 0.9% 5,187 0.3%

SCE 133,054 1.1% 26,262 0.5%

SCL 11,915 0.8% 1,994 0.4%

SDGE 27,579 1.2% 5,567 0.7%

SMUD 20,737 0.8% 4,834 0.4%

SPP 14,631 1.2% 2,594 1.4%

SRP 46,477 1.9% 10,326 2.1%

TEP 16,093 0.5% 3,465 0.5%

TIDC 3,236 1.3% 767 1.2%

TPWR 5,299 0.4% 996 0.0%
TREAS_VLY 11,448 0.9% 2,423 1.4%

WACM 29,680 1.1% 4,836 1.3%



### 123WALC 8,190 0.9% 1,627 0.6%

WAUW 692 0.4% 120 0.1%

WECC Total 1,157,509 1.4% 218,854 1.2%



### Table C

- 7

. SAE Reference Case Load Forecast by Customer Sector and Region ( 2032 )

Customer

Sector Annual Energy (GWh) Contribution to Regional Peak (MW)*

WECC NW CA SW WECC NW CA SW

Residential 420,594 156,075 138,580 125,939 84,455 30,551 31,374 31,188

Commercial 479,580 156,594 168,895 154,092 87,688 23,692 31,714 29,372

Industrial 257,335 124,077 48,734 84,523 36,666 17,362 7,126 12,581

Total 1,157,509 436,746 356,208 364,554 208,809 71,604 70,214 73,140 * For each customer sector, the monthly peak demand is shown for the peak month in the region ( i.e., in December

for BAs in the NW and in July for all other regions and for WECC as a whole)



### Table C

- 8

. SAE Reference Case Load

– 2032 residential energy and peak demand by end

- use and region

End Use Annual Energy (GWh) Contribution to Regional Peak (MW)*

WECC* NW CA SW WECC NW CA SW

Electric furnace 21,768 16,874 1,201 3,692 0 6,451 0 0 Heat pump heating 6,806 4,692 523 1,591 0 1,762 0 0

Ground

- source heat pump heating 215 87 25 103 0 39 0 0 Secondary heating 3,118 2,097 533 488 0 778 0 0

Central air 35,049 3,905 11,811 19,333 31,699 0 12,978 13,397 Heat pump cooling 7,733 1,027 398 6,308 5,731 0 405 4,108

Ground

- source heat pump cooling 398 36 25 337 294 0 25 226

Room air 2,646 480 638 1,528 2,345 0 668 1,009 Electric water heating 37,280 22,125 2,429 12,726 4,153 4,123 280 1,491 Electric cooking 14,170 4,034 5,325 4,811 1,807 1,530 695 609 Refrigerator 35,626 8,399 19,489 7,739 5,519 1,113 3,014 1,248 Secondary refrigerator 7,055 2,075 3,367 1,613 1,084 276 519 259

Freezer 10,122 2,921 4,270 2,931 1,854 403 768 556

Dishwasher 10,052 3,774 3,149 3,128 1,411 435 448 456

Clothes washer 2,392 896 735 761 408 146 124 135 Electric dryer 19,020 5,598 7,357 6,065 4,252 928 1,626 1,422

TV 30,481 12,994 7,702 9,784 4,487 2,194 1,147 1,499

Furnace fan 9,083 4,848 772 3,463 0 1,731 0 0 Lighting 50,590 25,592 12,126 12,873 2,941 3,502 709 794

Miscellaneous 116,990 33,622 56,704 26,665 16,292 5,117 7,968 3,831 * For each customer sector, the monthly peak demand is shown for the peak month in the region ( i.e., in December

for BAs in the NW and in July for all other regions and for WECC as a whole)



### Table C

- 9

. SAE Reference Case

– 2032 commercial energy and peak demand by end

- use and region

End Use Annual Energy (GWh) Contribution to Regional Peak (MW)*

WECC NW CA SW WECC NW CA SW Heating 4,404 3,584 331 488 0 1,284 0 0 Cooling 27,799 2,811 10,037 14,951 12,357 0 5,492 4,550

Ventilation 61,270 21,794 19,044 20,432 10,174 3,038 3,155 3,552 Electric water heating 6,240 2,372 1,382 2,487 1,178 488 260 485 Electric cooking 6,002 692 4,866 444 1,326 143 1,078 102



### 124Refrigeration 42,073 11,695 20,654 9,724 6,614 1,706 3,257 1,600 Outside lighting 18,532 4,348 9,762 4,422 201 531 105 50 Interior lighting 115,539 36,889 40,166 38,484 21,636 5,740 7,515 7,531 Office equipment 30,143 8,899 15,245 5,999 4,695 1,241 2,402 980

Miscellaneous 167,578 63,508 47,409 56,660 29,323 9,503 8,450 10,384 * For each customer sector, the monthly peak demand is shown for the peak month in the region ( i.e., in December

for BAs in the NW and in July for all other regions and for WECC as a whole)



### C. 6

. D ata S ources and Assumptions for A verage S tock Efficiencies in the High DSM Case

As discussed in Section 4.4, t he 20

- Year High DSM study case focuses on a single year ( 2032, the end of the planning horizon ), stipulates average stock efficiencies for each end

- use in that year, and then adjusts the SAE Base C ase load forecast for each end

- use based on the efficiency improvements relative to the average stock efficiencies used for the SAE Base Case fore casts

. The average stock efficiencies stipulated for the High DSM case are intended to represent the most efficient equipment presently commercially available.

To develop stock efficiency assumptions for the High DSM Case, we relied primarily upon

stud ies conducted for the EIA by Navigant Consulting on present and future end

- use technology

characteristics (Navigant, 2007; Navigant, 2008; Navigant & SAIC, 2011a; Navigant & SAIC, 2011b) . For most residential and commercial end

- uses, th e se studies identif y the present

- day ( e.g., year 2010) commercially

- available high efficiency opt ion, and these are the default values used to define average stock efficiency levels for the High DSM case. 29



### Some of the end uses included within the SAE model, however, have not been included in recent Navigant analyses. In a number of other cases, the Navigant 2010 high efficiency option is lower than the projected 2032 stock efficiency in the base case. And in several cases, the SAE definitions of categories

– i.e., the tech nologies within them

– differ from their counterparts in NEMS. In these instances, where possible we used Navigant’s projected high efficiency option for a future year (rather than for 2010). In other cases, however, it was necessary to develop the High DSM case stock efficiency assumption from other sources, including LBNL’s recent “MaxTech” report and technical support documents from the U. S. DOE appliance standards

program (Desroches & Garbesi

, 2011)

.

We describe below the specific data sources used for each end

- use category, along with any additional assumptions or steps required in order to apply those data sources to the SAE model.

Residential End

- Uses

Electric furnaces and secondary heating: Electric furnaces are assumed in EIA

- NEMS to oper ate at 100% efficiency (i.e., presently and in all forecast years); this end use was not studied by Navigant, and no other source was identified for a higher efficiency scenario. It was therefore

29 Current high

- efficiency levels on the market are steadily increasing for a number of end uses, and at any given time, estimates as to what this level is for a given end use may vary. Thus, we are not interpreting the Navigant estimates as being definitive. Rather, they are reasonably co mprehensive and in addition were constructed in such a way as to conform with the structure of the NEMS model, upon which the SAE framework is based. Both these aspects motivated the use of the Navigant studies as our primary source.



### 125

### posited that by 2032 these furnaces would be converted to el ectric heat pumps, and the Navigant 2010 Typical HSPF of 8 was used as the 2032 high efficiency option. (For BAs in Washington and Oregon, stock efficiencies in the Itron SAE model are based on data provided by the

Northwest Power and Conservation Council (NPCC), for which the units are specified in terms of “thermal efficiency”; we interpreted these efficiencies as equivalent to COP and applied the above procedure with values converted appropriately.) For secondary heating, it was assumed that the percent age energy savings relative to the Base Case was the same as for electric

furnaces.

Electric heat

- pump heating: For BAs in the NPCC, the SAE 2032 Base Case efficiency exceeded the Navigant 2010 high efficiency level of 10.7 HSPF. We determined that ther e is a currently

- available Energy Star unit of 14 HSPF (4.10 COP), and used this as the 2032 target in

all BA. T his level, and a reported Energy Star currently available unit eq uivalent to 4.10 COP

was used.

Ground

- source heat

- pump heating, central air c onditioning, heat

- pump cooling, ground

- source heat

- pump cooling, and room air conditioning: The Navigant 2010 high efficiency values were

used for all BAs.

Electric water

- heating: The Navigant 2010 high efficiency was used for BAs in the NPCC. For

all oth er BAs, this 2010 high was exceeded by the SAE 2032 Base Case efficiency. For these, we applied the NPCC 2032 percentage energy savings for this end use from either Oregon/Washington (to California, Alberta, and British Columbia) or Idaho/Montana (all

othe rs). The result is an average stock efficiency greater than 1.00, the energy factor upper bound for standard units; we can interpret this as reflecting a switch by some fraction of households to heat pump models, which have energy factors of 2.00 or above.

Electric cooking: This end

- use was not analyzed by Navigant. For NPCC BAs, in which the stock efficiency units are specified as an energy factor, we used the proposed but not

- adopted “max tech” energy factor of 0.77 reported in USDOE (2009b). For all oth ers exclusive of New Mexico, stock efficiencies are specified as a UEC (kWh/year), and we used the technical assumptions in USDOE (2009a) to impute a SAE 2032 Base case efficiency of 0.72, and computed the implied reduction in UEC associated with an increa se in stock efficiency to a 0.77 energy factor. For New Mexico, where the reference case UEC was calibrated to state

- specific data, we applied the same percentage energy savings (or UEC reduction) as in Arizona and its neighboring states.

Refrigerators, second refrigerators, and freezers: For refrigerators

– which are refrigerator

freezers in EIA

- NEMS (either top

- mount or side

- mount)

– the Navigant 2010 high efficiency level is 285 kWh/year for a representative unit; this is a forty

- percent UEC reduction from the 2010 typical unit. Stand

- alone freezers were not analyzed by Navigant, but EIA

- NEMS assumes a 2010 high efficiency UEC that is also forty

- percent below the 2010 typical unit, corresponding to an Energy Star model (Comstock 2012). Secondary refri gerators are not an end

- use in EIA

NEMS; in the reference case forecast, this end

- use was assumed to have a UEC of ninety percent that of primary refrigerators in all forecast years. The assumed stock efficiencies for refrigerators, secondary refrigerator s, and freezers in the High DSM Case varied by region. For

126

### NPCC BAs, e fficiency units are a form of energy factor (cubic feet / kWh/ day). For all three end uses, we computed the efficiency increase corresponding to a forty

- percent decrease in unit

energ y consumption applied to the stock average represented by SAE 2011 Base Case efficiency, and used this as the 2032 High DSM benchmark. For all other BAs except those in New Mexico, we used the Navigant 2010 high efficiency UEC for the 2032 High DSM benchma rk (285 kWh/yr) for refrigerators . For secondary refrigerators, the UEC for the 2032 High DSM case is equal to ninety percent of the UEC for primary refrigerators (i.e., 256.5 kWh/year), applying the same relationship as in the SAE R efer ence Case. For free zers, the 2032 high efficiency level corresponds to a UEC forty percent below that of SAE Base Case 2011 value, the same assumption about current high efficiency freezers as in AEO

- NEMS. Finally for New Mexico

BAs , for all three of these end uses, we appl ied the High DSM percentage energy savings calculated for Arizona and neighboring states.

Dishwashers: For all BAs except those in New Mexico, the Navigant 2010 high efficiency was used as the High DSM 2032 benchmark. For New Mexico, the percentage energy savings calculated for Arizona and neighboring states was used.

Clothes washers and electric dryers: Clothes washers: For NPCC BAs, the Navigant 2010 high efficiency was used. For others excepting New Mexico, a high

- efficiency

- level machine energy kWh/cycle reported in the Technical Support documentation for the US DOE appliance standard was used. For New Mexico, because a difference of efficiency units complicated the use of the Navigant estimate, the percentage energy savings calculated for Arizona and neighboring states was used.

Dryers: For all BAs except New Mexico, the Navigant 2020 high efficiency estimate was used. (For these BAs, the Navigant 2010 high was exceeded by the SAE Base Case ef ficiency.) For New Mexico, the percentage energy savings calculated for Arizona and neighboring states was

used.

TVs: This end use was not analyzed by Navigant. Instead, detailed information on stock

efficiencies and saturations in the AEO 2012 Reference Case were obtained from EIA, and combined with estimates of energy used by current “best on market” technologies in the LBNL MaxTech report to calculate a high efficiency benchmark (UEC) for 2032; these calculations were performed for Census regions 8 and 9 separately, and applied to BAs in each accordingly. For New Mexico, the percentage energy savings calculated for Arizona and neighboring states

was used.

Furnace fans: For all BAs, we used an estimate of 6% energy savings potential from improved

effic iency reported by Franco et al. (2008) as a proxy for a current best

- available (Franco et. Al, 2008)

.

Lighting: This end use was analyzed by Navigant. However, the Navigant estimates specify efficiency levels in the standard units of lumens per Watt, wher eas the SAE model represents lighting efficiency as a UEC in units of kWh/year/household, so that estimates of high

- efficiency options must be adapted. As in the case of TVs, we obtained detailed information on stock

efficiencies and saturations in the AEO 2012 Reference Case from EIA. To construct the 2032

127

### High DSM benchmark, we assumed that saturations (i.e., total bulb counts) and utilizations were the same as in this Reference Case but that all bulbs were replaced by a CFL, and computed the resulting i mplied UEC. Here again, these calculations were performed for Census regions 8 and 9 separately, and applied to BAs in each accordingly.

Miscellaneous end uses: This category comprises a set of end uses not analyzed by Navigant.

Several of these were stu died in the LBNL MaxTech report. For these, we combined the latter’s

current best

- on

- market energy use estimates with EIA information on AEO 2012 stock efficiencies and saturations, and constructed new miscellaneous UECs (kWh/year/household)

– again for Ce nsus regions 8 and 9 separately

– incorporating the reductions in the modified end

uses.

Commercial End

- Uses

I n contrast to the NEMS residential module, in the commercial module each end

- use is represented by a set of technologies, and the commercial mo dule computes, and reports as outputs, aggregate or composite average efficiencies across the set for each end

- use. At the same time, however, the model does generate internal files containing efficiencies for all technologies that contribute, in the model solution, to meeting the service demand for that end

- use

– this applies to all seven end

- use categories in the model. Thus, for example, in the AEO reference case, electric boilers, electric resistance heaters, rooftop air

- source heat

- pumps, and ground

so urce heat

- pumps, all play a role in meeting heating demand, and the corresponding efficiencies of all four are reported for each year of the projection, as well as the overall, average heating efficiency.

The SAE model uses these “whole end

- use” efficien cy metrics; as with the residential sector, the analysis adjusts those computed by NEMS using intra

- WECC information, and these are part of the SAE reference forecast. By accessing the model’s internal data, however, we are able to disaggregate the effici ency information for each end

- use, and to use this greater detail to define our High DSM targets. In particular, the Navigant studies cited above also dealt with

commercial

- sector end

- use technologies, and also included in most information on 2010 high

e fficiency units. We were therefore able to use the Navigant information to define our 2032 High DSM targets.

For all the commercial end

- uses except “Office” and “Miscellaneous,” the Navigant 2010 high efficiency estimates were used to define the 2032 Hi gh DSM benchmarks. The “Office” and “Miscellaneous” categories are composites of different end

- uses. The SAE framework does not define an efficiency metric or index for these categories; instead, levels of efficiency, and their changes, are incorporated fr om exogenous assumptions that affect electricity demand for these

end

- uses in the NEMS model. The “Office” comprises electronic equipment including computers, monitors, and servers, among others. The preliminary energy savings estimate reported in the ta bles was derived by applying potential estimates for computers, desktop monitors, and servers reported in Navigant (2009). The “Miscellaneous” category comprises end uses including transformers, X

- ray equipment, elevators, and municipal water services. Th e preliminary energy savings estimate reported in the tables was derived by applying current “best on market” energy use levels reported in the LBNL MaxTech report for dry and liquid

128

### transformers, elevators, and escalators. It should be noted that the larg est fraction of energy use in this category

– on the order of one

- half the total

– is for water services, particularly pumping.

Industrial S ector Efficiency Assumptions

In contrast to the Residential and Commercial Modules, the NEMS Industrial Module is built on an econometric rather than technology framework, and does not contain end

- use detail. This carries over to the SAE model. Thus, a much simpler approach was taken to developing industrial 2032 High DSM targets. We drew upon the results of several recent industrial potential studies that focused on utility service territories within WECC. Table C

- 10 summarizes the key information, in particular

, estimates of cumulative energy savings in the studies’ final forecast years for technical, economic, and achievable potential cases. It was judged that the low

- to

moderate achievable p otentials generally reflected the amount of DSM

- induced savings already embedded in the SAE Base Case, so that the difference between tho se potentials and the corresponding technical potentials within each study area was an appropriate approximate magnitud e for a 2032 High DSM savings target. As indicated in the table, this delta ranges from roughly 3% to more than 15% across the studies. Absent further information, a uniform 10% savings was applied for all BAs. In other words , for each BA, the High DSM Ca se was derived by assuming a 10% reduction in industrial energy consumption from the SAE Base Case forecast

in 2032

.

Table C

- 10

. Industrial Sector Energy Efficiency Potential Study Results

Client Analyst Year of study Forecast

horizon Annual Energy Savings in Horizon Year as Percentage of Baseline Usage

Technical Economic Achievable

Idaho Power Nexant 2009 2028 11.57% 10.66% Low: 1.73%

Moderate: 2.63% Aggressive: 2.83%

Maximum: 3.03%

New

Mexico Global Energy

Partners 2011 2025 18.70% 15.10% Low : 5.00%

High: 11.5%

Pacific

Power Cadmus 2011 2030 19.30% N/A 15.79%

Rocky

Mountain Cadmus 2011 2030 11.51% N/A 8.77%

PSCo

- Colorado Kema 2010 2020 12.40% 11.70% 50% incentive : 3.55%

75% incentive : 5.50%

100% incentive : 7.22%

Tri

- State Nexant 2010 2025 23.00% 21.00% Low : 7.6%

Moderate: 9.5% Aggressive : 11.9%

Maximum : 14.9%



### 129

### C.7 Calculating Efficiency Gains in the High DSM Case

As described within Section 4.4.1 , the second critical step in deriving the High DSM forecasts, after defining the stock efficiency assumptions for the High DSM Ca se, is to c alculate

– for each

individual residential and commercial end

- use

– the percentage energy savings associated with moving from the average stock efficiency in the SAE Base Case for the year 2032 to the assumed average stock efficiency in 2032 for the High DSM Case. Here we further describe the mechanics of that step. The results of these calculations, expressed in terms of the percentage reduction in annual energy consumption relative to the SAE Base Case, are tabulated in Table C

- 11 and Table C

- 12 , for residential and commercial end

- uses respectively

. Within those tables, the results are grouped into combinations of states and/or load zones with identical end

- use assumptions.

Residential Sector End

- Uses

In both NEMS and the SAE model, residential

- sector efficiency is described for some end

- uses in terms of technical units

– such as COP or SEER

– and for others in terms of UECs, either kWh per year or kWh per household per year. The 2032 high

- efficiency b enchmarks were used to calculate percentage improvements in these efficiency indices for year 2032. For each WECC BA/LSE we can write the year 2032 reference case consumption as

2032 2032 Ref i Ref

i 

### Load Enduse = 

# ∑

, ( 1 )

where both sides of the equation are in GWh. For end

- uses with efficiency in technical units, the High DSM consumption was calculated as

2032 2032 

### 1

, 1 % i HighDSM i Ref 

### Enduse Enduse Eff = ⋅ + ∆ 

### ( 2 )

where 

### % Eff ∆ is the percentage improvement in efficiency for the given end

- use. For end

- uses with efficiency in UEC terms, the High DSM consumption was calculated as



# ( )

2032 2032 

### 1 %

, i HighDSM i Ref 

### Enduse UEC Enduse =−∆ ⋅ 

### ( 3 )

where % UEC ∆ 

### is the percentage reduction in the UEC for th e given end

- use. Combining both types, the total residential High DSM consumption for the given BA is then

2032 2032 

### . ighDSM i ighDSM

i 

### Load Enduse
Η Η 

### = 

# ∑ ( 4 )

Commercial Sector End

- Uses

Although both are based on engineering

- economic principles, the structure of the NEMS commercial module (sub

- model) differs in certain details from that of the residential, which is in turn reflected in the representation of end

- use efficiency. (Further de tails on the contents of this

130

### sub

- section are provided in the Appendix.) While the commercial module also uses technology input data including efficiencies in terms of technical units or UECs, these are converted within the model (for most end

- uses) into t he metric of btu

- out/btu

- in for the energy service demands computed by the model for a given scenario, and the module’s end

- use efficiency outputs are reported in terms of this metric. Furthermore, while as described above efficiency levels for most

end

- us es in the residential sector are represented by technical units or UECs of “representative” technologies, by contrast, in the commercial module the average efficiency for each end

- use is a composite across different technology types providing the energy se rvice within that end

- use.

These differences are reflected in the calculations of end

- use

- level commercial High DSM savings. In the commercial module the electricity consumption within each end

- use

– indexed by

i

- is the sum of consumption provided by th e corresponding set of end

- use technologies, indexed by tech (in the following formulae we suppress the time index for simplicity):

, 

### . i i tech

tech 

### Enduse Enduse = 

# ∑ ( 5 )

For each technology type, we used the “2010 high efficiency” level reported by Navigant (or a
proxy),

, , i tech high 

## Eff , and defined a consumption share

- weighted high

- efficiency level for 2032 as

, , ,

, 

### . i tech high i tech
i high

tech i 

### Eff Enduse Eff Enduse ⋅

= 

# ∑ ( 6 )

The calculation of commercial 2032 High DSM energy savings was then similar to that for residential sector represented in equation (5) , above, but used this high

- efficiency composite.

Tables 3 and 4 display the High DSM 2032 energy savings percentages for the residential and commercial sectors, respectively, by end

- use and by “load groups”

– sub

- sets of BAs for which the savings factor s are equal. For the residential sector, these percentages are those referred to

above as % UEC ∆ 

### for end

- uses with efficiency measured by UEC . For residential end

- uses with

efficiency measured by technical units, the savings percentages are given by 

### % 1 % Eff Eff ∆
+ ∆ ;

commercial sector percentages are also given by the latter expression, where 

### % Eff ∆ is the

perce ntage improvement from the baseline efficiency to

, 

## " " i high 

## Eff (per equation 7).

For both sectors, the pattern of equal savings percentages within what we are calling “load groups” arises for several reasons. First, in the SAE Base Case, 2 032 efficiency levels are based upon those of U. S. Census Regions 8 and 9 in the EIA AEO 2012 Reference Case. For most BAs, the changes to these levels resulting from the SAE econometric analysis using localized

data are small

– or there are no changes

– and are the same within certain subsets geographically adjacent BAs, as reflected in the tables.

A second reason, pertaining to the residential sector, is that efficiency data for BAs in Washing ton, Oregon, Idaho, and Montana that was used in the SAE ana lysis was for several end

uses based upon different units than those in NEMS, and assumptions necessary for

131

### accommodating these units in the High DSM analysis resulted in some systematic differences (between BAs in these states, and those elsewhere).

A t hird reason arises in the end

- uses for which information to set the High DSM 2032 efficiency levels was not available from the Navigant studies noted previously and had to be assembled from other sources, and applied through additional calculations to dete rmine the 2032 high

efficiency benchmarks. In these cases, the High DSM percentage energy savings are either the

same for all BAs

– as is the case for residential furnace fans and the commercial “Office”

category

– or the same among all BAs within each of the Census Regions, as is the case for residential lighting and both the residential and the commercial miscellaneous categories.

Table C

- 11 . High DSM Case Energy Savings in 2032 Relative to SAE Base Case (Residential)

End

- U se Category States or Load Zone AZ, CO, NV,

PACE UT

- WY ID, MT,

PACE ID NM CA WA,

OR BPA PACW AB,

BC

Electric furnace 57.4% 57.4% 57.4% 57.4% 59.7% 59.4% 59.4% 57.4% Heat pump heating 42.5% 7.9% 42.5% 42.4% 6.8% 6.9% 8.2% 42.4%

Ground

- source heat pump heating 21.9% 21.9% 21.9% 21.9% 21.9% 21.9% 21.9% 21.9%

Secondary heating 57.4% 57.4% 57.4% 57.4% 59.7% 59.5% 59.5% 57.4%

Central air 37.9% 38.9% 37.9% 38.1% 38.9% 38.9% 38.8% 45.6% Heat pump cooling 35.7% 35.7% 35.9% 35.9% 35.7% 35.7% 35.7% 35.7%

Ground

- source heat pump cooling 36.9% 36.9% 36.9% 36.9% 36.9% 36.9% 36.9% 36.9%

Room air 11.4% 11.5% 11.4% 11.5% 11.5% 11.5% 11.5% 8.8%

Electric water heating 4.7% 4.7% 4.7% 4.6% 4.6% 4.7% 4.7% 4.6%

Electric cooking 6.0% 5.5% 6.0% 6.9% 4.9% 4.9% 4.9% 6.9% Refrigerator 47.0% 27.0% 47.0% 46.8% 26.7% 26.7% 26.7% 46.8% Secondary refrigerator 47.0% 27.0% 47.0% 46.8% 26.7% 26.7% 26.7% 46.8%

Freezer 27.6% 36.4% 27.6% 28.1% 36.2% 36.3% 36.2% 28.1%

Dishwasher 38.1% 36.0% 38.1% 38.1% 35.3% 35.4% 35.6% 38.1%

Clothes washer 24.8% 48.4% 24.8% 24.8% 47.8% 47.8% 47.8% 24.8% Electric dryer 5.6% 13.7% 5.6% 4.0% 13.8% 13.8% 12.8% 4.0%

TV 53.4% 53.6% 53.4% 55.7% 55.7% 55.5% 55.7% 55.7%

Furnace fan 6.0% 6.0% 6.0% 6.0% 6.0% 6.0% 6.0% 6.0% Lighting 51.4% 51.4% 51.4% 52.3% 52.3% 52.2% 52.3% 51.4%

Miscellaneous 9.8% 9.8% 9.8% 11.7% 11.7% 11.5% 11.7% 11.7%



### Table C

- 12 . High DSM Case Energy Savings in 2032 Relative to SAE Base Case (Commercial)

End

- Use Category States or Load Zone AZ, ID, MT, NM, NV,

PACE ID

- UT

- WY CO, UT, WY CA OR, WA, AB, BC BPA PACW

Heating 34.9% 31.9% 35.0% 31.5% 31.9% 31.9% Cooling 27.5% 34.5% 19.9% 26.4% 26.6% 25.8%

Ventilation 63.7% 64.2% 57.6% 63.4% 63.4% 62.8%



### 132Electric water heating 19.2% 25.9% 34.1% 34.7% 33.4% 34.6%

Electric cooking 1.8% 1.8% 3.0% 1.8% 1.8% 1.9% Refrigeration 45.5% 45.5% 59.8% 47.2% 47.0% 48.5% Outside lighting 21.5% 21.9% 28.1% 23.4% 23.2% 23.9% Interior lighting 21.5% 21.9% 18.4% 23.4% 23.2% 22.9%

Office 24.9% 24.9% 24.9% 24.9% 24.9% 24.9%

Miscellaneous 13.0% 13.0% 11.2% 11.2% 11.3% 11.2%



### C.8 High DSM Case Incremental Energy Savings

Section 4.5 compares the SAE High DSM Case load forecasts to the SAE Reference Case forecasts, i llustrating the incremental savings associated with the stock efficiency assumptions in the High DSM Case r elative to what is expected to occur under the current set of energy efficiency policies and program plans that form the basis for the SAE Reference Case . Here we provide additional details on this comparison.

To begin, Table C

- 1 3 and Table C

- 14 present the reduction in annual energy consumption in
2032, relative to the SAE R eference Case

, for each individual residential and commercial end

use across WECC as a whole. Table C

- 15 aggregates those data into the 10 end

- use categories

introd uced earlier in Section 4.3.2 , showing the distribution in total WECC

- wide annual energy savings across those end

- use categories. As shown, the percentage energy savings ranges from

10% for the Commercial Miscellaneous set of end

- uses to a 56% reduction in annual energy consumption fo r Commercial Refrigeration. These values in these three tables reflect the net

effect of various end

- use efficiency levels and saturation levels specified for each load zone.

Table C

- 16 presents the distribution in residential and commercial sector savings across the 10

end

- use categ ories, for each of the three major geographic regions and for WECC as a whole. These are the same data that are plotted within Figure 27 of the main body of the report, and the

values are calculated from the information contained in Table C

- 13 and Table C

- 14 . The table illustrates, for example, that Commercial HVAC end

- uses represent the largest source of energy saving s in the High DSM Case for WECC as a whole (20% of the total savings across all

residential and commercial end

- uses).

Finally, Table C

- 17 presents the reduction in annual energy and peak demand in 2032, relative the SAE Reference Case, for each load zone. These are the same data that are plotted within Figure 25 of the main bod y of the report and reflect both the saturation of the various end

- uses and stock efficiency levels specific to each individual load zone.

Table C

- 13

. WECC

- Wide Annual Energy Savings Relative to SAE Reference Case (Residential)

End Use Reduction in 2032 Annual Energy Consumption (GWh) Reduction in 2032 Annual Energy Consumption ( % )

Electric furnace 12,564 57.0% Heat pump heating 1,851 28.0%

Ground

- source heat pump heating 43 21.5% Secondary heating 1,817 57.6%

Central air 10,144 28.9% Heat pump cooling 2,389 31.2%

Ground

- source heat pump cooling 127 33.1%



### 133Room air 5 0.2% Electric water heating 160 0.4% Electric cooking 747 5.3% Refrigerator 16,363 45.9% Secondary refrigerator 3,162 44.9%

Freezer 3,136 30.6%

Dishwasher 3,610 35.8%

Clothes washer 670 27.8% Electric dryer 2,708 14.2%

TV 16,104 52.8%

Furnace fan 282 3.2% Lighting 23,913 46.8%

Miscellaneous 11,398 9.8%



### Table C

- 14

. WECC

- Wide Annual Energy Savings Relative to SAE Reference Case (Commercial)

End Use Reduction in 2032 Annual Energy Consumption (GWh) Reduction in 2032 Annual Energy Consumption ( % ) Heating 1,429 32.44% Cooling 5,994 21.56%

Ventilation 35,555 57.59% Electric water heating 1,620 26.07% Electric cooking 212 3.53% Refrigeration 23,755 56.37% Outside lighting 4,919 26.36% Interior lighting 20,230 17.41%

Office 7,075 23.52%

Miscellaneous 16,525 9.93%



### Table C

- 15

. High DSM Case Incremental Savings Relative to SAE Reference Case, by Residential

and Commercial End

- Use Group

End

- Use Group Reduction in Annual Energy Use

Relative to the SAE Reference Case Residential Appliances 31% Residential Cooling 28% Residential Lighting 47%

Residential Miscellaneous & TV 19% Residential Space & Water Heating 21%

Commercial HVAC 45% Commercial Lighting 19%

Commercial Miscellaneous 10%

Commercial Office 23% Commercial Refrigeration 56%



### Table C

- 16 . Distribution of High DSM Residential & Commercial Annual Energy Savings by End

Use Category and Region

End

- Use Group WECC California Northwest Southwest
Residential Appliances 13% 22% 8% 9% Residential Cooling 6% 2% 2% 14% Residential Lighting 10% 6% 15% 9%

Residential Miscellaneous & TV 12% 14% 12% 9% Residential Space & Water Heating 7% 1% 15% 5%

Commercial HVAC 20% 15% 19% 26%



### 134Commercial Lighting 11% 11% 11% 12%

Commercial Miscellaneous 7% 7% 7% 8%

Commercial Office 3% 5% 2% 2% Commercial Refrigeration 10% 18% 6% 6%

Total Residential & Commercial Savings 100% 100% 100% 100%



### Table C

- 17

. High DSM Case Incremental Savings Relative to SAE Reference Case, by Load Zone

Load Zone Percent age Reduction in Annual Energy Percent age Reduction in Non

Coincident Peak Demand

AESO 22% 22%

APS 19% 21%

AVA 19% 21%

BCTC 23% 24%

BPA 20% 25%

CFE 0% 0%

CHPD 23% 26%

DOPD 26% 28%

EPE 22% 22%
FAR_EAST 19% 22%

GCPD 16% 15%

IID 24% 20%

LDWP 25% 23%
MAGIC_VLY 19% 22%

NEVP 24% 29%

NWMT 22% 23%
PACE_ID 22% 23%
PACE_UT 22% 24%
PACE_WY 18% 20%

PACW 26% 26%
PGE_BAY 24% 22%
PGE_VLY 24% 22%

PGN 19% 22%

PNM 25% 26%

PSC 18% 16%

PSE 22% 25%

SCE 25% 22%

SCL 24% 24%

SDGE 23% 21%

SMUD 19% 17%

SPP 21% 23%

SRP 21% 23%

TEP 11% 18%

TIDC 20% 17%

TPWR 19% 20%
TREAS_VLY 19% 22%

WACM 17% 19%

WALC 26% 26%

WAUW 29% 24%

WECC 22% 22%



### 135

### C.9 SPSC High DSM Case Load Forecasts

The SPSC High DSM Case load forecasts are summarized graphically in Section 4.6 in terms of the compound annual growth rates for each load zone . Here we provide additional numeric

detail s. Specifically, Table C

- 18 and Table C

- 19 present the SPSC High DSM Case annual energy and non

- coincident peak demand forecasts for each load zone, respectiv ely. The tables present the forecasted load in 2032, along with the corresponding percentage reduction from the WECC Reference Case. In addition, the tables compare the CAGRs for the WECC Reference Case and SPSC High DSM Case (the latter of which is pres ented graphically in Figure 28 and Figure 29 within the main body of the rep ort).

Table C

- 18

. SPSC High DSM Case Annual Energy (2032)

Load Zone SPSC High DSM

Case Non

Coincident Peak Demand (M W ) Reduction from

WECC Reference Case (%) CAGRs (2010

- 2032)

WECC Reference

Case SPSC High DSM

Case

AESO 130,702

- 22.3% 4.0% 2.8%

APS 45,348

- 19.0% 2.7% 1.8%

AVA 13,787

- 19.2% 1.5% 0.5%

BCTC 56,395

- 23.5% 1.1%

- 0.2%

BPA 44,410

- 20.4% 0.3%

- 0.7%

CFE 19,834 0.0% 2.9% 2.9%

CHPD 3,851

- 23.1% 2.1% 0.9%

DOPD 2,017

- 25.8% 3.3% 1.9%

EPE 11,041

- 21.8% 2.6% 1.4%
FAR_EAST 3,427

- 18.8% 3.3% 2.3%

GCPD 5,316

- 16.1% 2.0% 1.2%

IID 3,888

- 23.9% 1.6% 0.4%

LDWP 25,811

- 25.1% 0.9%

- 0.4%
MAGIC_VLY 4,549

- 18.8% 0.5%

- 0.4%

NEVP 22,214

- 24.3% 0.8%

- 0.5%

NWMT 9,493

- 22.0% 0.7%

- 0.5%
PACE_ID 4,102

- 22.3% 1.7% 0.5%
PACE_UT 34,026

- 22.4% 1.4% 0.2%
PACE_WY 14,444

- 18.2% 2.4% 1.5%

PACW 18,546

- 25.8% 0.9%

- 0.5%
PGE_BAY 36,231

- 23.6% 0.2%

- 1.1%
PGE_VLY 54,324

- 23.6% 1.0%

- 0.3%

PGN 21,658

- 18.8% 1.3% 0.3%

PNM 14,508

- 24.8% 1.2%

- 0.1%

PSC 42,925

- 17.9% 1.0% 0.1%

PSE 22,063

- 21.8% 0.6%

- 0.5%

SCE 89,956

- 24.9% 0.6%

- 0.7%

SCL 8,775

- 23.8% 0.7%

- 0.5%

SDGE 20,161

- 23.0% 1.0%

- 0.2%

SMUD 14,789

- 19.0% 0.3%

- 0.7%

SPP 11,255

- 20.5% 1.0% 0.0%

SRP 32,774

- 20.9% 1.4% 0.3%

TEP 13,815

- 11.2% 0.4%

- 0.2%

TIDC 2,616

- 19.8% 1.3% 0.3%

TPWR 4,928

- 18.6% 1.0% 0.1%
TREAS_VLY 10,754

- 18.8% 1.6% 0.7%



### 136WACM 31,621

- 17.0% 2.2% 1.4%

WALC 6,220

- 26.1% 1.0%

- 0.4%

WAUW 783

- 28.8% 2.5% 1.0%

WECC Total 913,356

- 21.5% 1.4% 0.3%



### Table C

- 19

. SPSC High DSM Case Non

- Coincident Peak Demand ( 2032 )

Load Zone SPSC High DSM

Case Non

Coincident Peak Demand (M W ) Reduction from

WECC Reference Case (%) CAGRs (2010

- 2032)

WECC Reference

Case SPSC High DSM

Case

AESO 17,608 22.1% 3.7% 2.5%

APS 10,057 21.1% 2.7% 1.6%

AVA 2,522 21.1% 1.8% 0.7%

BCTC 9,762 24.2% 0.8%

- 0.4%

BPA 7,478 24.6% 0.0%

- 1.2%

CFE 4,541 0.0% 3.4% 3.4%

CHPD 532 26.5% 0.8%

- 0.6%

DOPD 309 28.3% 0.8%

- 0.7%

EPE 2,017 22.1% 2.2% 1.0%
FAR_EAST 809 21.7% 4.1% 3.0%

GCPD 871 14.9% 1.8% 1.1%

IID 1,023 20.4% 1.1% 0.1%

LDWP 6,322 23.4% 0.9%

- 0.3%
MAGIC_VLY 1,275 21.8% 2.1% 1.0%

NEVP 4,823 28.9% 0.5%

- 1.0%

NWMT 1,471 23.0% 0.5%

- 0.7%
PACE_ID 721 23.1% 1.0%

- 0.2%
PACE_UT 8,720 23.1% 3.1% 1.9%
PACE_WY 1,828 23.1% 2.6% 1.4%

PACW 3,402 26.2% 0.9%

- 0.5%
PGE_BAY 6,802 21.8% 0.0%

- 1.1%
PGE_VLY 9,296 21.8%

- 0.3%

- 1.4%

PGN 3,645 22.0% 1.2% 0.0%

PNM 2,287 25.6% 0.7%

- 0.6%

PSC 6,881 16.3% 0.3%

- 0.5%

PSE 4,255 25.1% 0.8%

- 0.6%

SCE 16,512 22.1%

- 0.4%

- 1.6%

SCL 1,449 23.9% 0.1%

- 1.1%

SDGE 3,702 21.3% 0.0%

- 1.1%

SMUD 2,767 17.0%

- 1.3%

- 2.1%

SPP 1,739 22.7% 0.7%

- 0.5%

SRP 6,466 23.2% 1.2% 0.0%

TEP 2,446 17.7%

- 0.2%

- 1.1%

TIDC 577 17.1% 0.7%

- 0.1%

TPWR 819 20.4% 0.2%

- 0.9%
TREAS_VLY 2,934 21.7% 3.5% 2.3%

WACM 4,740 18.8% 2.2% 1.2%

WALC 1,216 25.6% 0.6%

- 0.7%

WAUW 132 24.2% 1.9% 0.6%

WECC Total 164,759 21.9% 1.0%

- 0.1%



### 137