# Public'Interest'Energy'Research' (PIER)' Program

FINAL'PROJECT'REPORT



# EMISSIONS'REDUCTIONS'FROM'

RENEWABLE'ENERGY'AND'ENERGY'

EFFICIENCY'IN'CALIFORNIA'AIR'

QUALITY'MANAGEMENT'DISTRICTS

NOVEMBER 2011

CEC

- 500

- 2011

- XXX 

### Prepared!for: California!Energy!Commission

Prepared!by: Synapse!Energy!Economics,!Inc.

### Prepared by:

Primary Author(s):

Jeremy Fish er, PhD

Chris James

Nicole Hughes

David White, PhD

Rachel Wilson

Bruce Biewald

Synapse Energy Economics, Inc.

485 Massachusetts Ave., Suite 2

Cambridge, Massachusetts 02139

(617) 661

- 3248

http://synapse

- energy.com/

Contract Number: 500

- 02

- 004

P

rep ared for :

California Energy Commission

Marla Mueller

Contract Manager



### Linda Spiegel

Project Manager



### Ken Koyama

Office Manager

Energy Generation Research



### Laurie ten Hope

Deputy Director

ENERGY RESEARCH AND DEVELOPMENT DIVISION



### Melissa Jones

Executive Director

DISCLAIMER

This report was prepared as the result of work sponsored by the California Energy Commission. It

does not necessarily represent the views of the Energy Commission, its employees or the State of

California. The Energy Commiss ion, the State of California, its employees, contractors and

subcontractors make no warrant, express or implied, and assume no legal liability for the information

in this report; nor does any party represent that the uses of this information will not infri nge upon privately owned rights. This report has not been approved or disapproved by the California Energy

Commission nor has the California Energy Commission passed upon the accuracy or adequacy of

the information in this report.

### i 

# PREFACE

The California Energy Commission Public Interest Energy Research (PIER) Program supports public interest energy research and development that will help improve the quality of life in
California by bringing environmentally safe, affordable, and reliable energy services and products to the marketplace.

The PIER Program conducts public interest research, development, and demonstration (RD&D)
projects to benefit California.

The PIER Program strives to conduct the most promising public interest energy research by partnering with RD&D entities, including individuals, businesses, utilities, and public or private research institutions.

PIER funding efforts are focused on the following RD&D program areas:

• Buildings End

- Use Energy Eff iciency

• Energy Innovations Small Grants

• Energy

- Related Environmental Research

• Energy Systems Integration

• Environmentally Preferred Advanced Generation

• Industrial/Agricultural/Water End

- Use Energy Efficiency

• Renewable Energy Technologies

• Transportation

Emis sions Reductions from Renewable Energy and Energy Efficiency in California Air Quality
Management District s is the final report for the Air Emissions Reductions Through Energy and

Peak Load Reductions and Renewable Generation project (contract number 500

- 0 2

- 004 , work

authorization number MRA 026 ) conducted by Sy napse Ene rgy Economics, Inc. The information from this pro ject contributes to PIER’s Energy

- Related Environmental Research

Program.

For more information about the PIER Program, please visit the Energ y Commission ’ s website at www.energy.ca.gov/research/ or contact the Energy Commission at 916

- 6 54

- 4878.

### ii

### iii 

# ABSTRACT

In California and other states, air quality management districts are considering us ing energy efficiency and renewable energy to target both criteria pollutants ( to meet air quality standards ) and greenhouse gas emissions. This report presents a pilot study, designed to quantitatively

model the emissions avoided through new energy effici ency / renewable energy programs not already required by state or federal statute. Th is work use d California as a test case

, producing an analysis that could theoretically satisfy U.S. E nvironmental Protection Agency requirements for using energy efficiency / renewable energy in State I mplementation P lan compliance.

Synapse Energy Economics used a Western Electricity Coordinating Council

- scale production

cost simulation model (PROSYM) t o examine the impact of incremental energy efficiency / renewable energy on ge neration and emissions of oxides of nitrogen (NO

X 

### ) , sulfur
dioxide (SO

2 

### ) , and carbon dioxide . Sixteen energy efficiency / renewable energy scenarios were tested against a reference “base case

, ” representing the expected structure of Western Electricity

Coord inating Council in 2016 under a full implementation of the California 33 percent renewable electricity standard and other energy statutes. The scenarios were comprised of four
energy efficiency / renewable energy programs (solar, wind, and both baseload and peaking energy efficiency ) enacted in four different California service territories ( San Diego Gas and

Electric

, Southern California Edison, Los Angeles Department of Water and Power, and Pacific

Gas and Electric ).

It was observed that in many cases, a lar ge fraction of generation was displaced out

- of

- state,

and total NO

X 

### and SO

2 

### displaced in California tended to be small. Generation displaced out

- of

state included coal resources, and therefore resulted in far larger emissions benefits in Western

Electricit y Coordinating Council regions other than California. The large range of criteria pollutant displacement from energy efficiency / renewable energy programs across California suggests that examining output variance and uncertainty is important, and that both model construct and input assumptions are key.

Keywords: California Energy Commission, energy efficiency, renewable energy, simulation model, displaced emissions, State I mplementation P lan , criteria emissions, greenhouse gas

emissions, Synapse Energy Econ omics

Please use the following citation for this report:

Fisher

, Jeremy, Chris James

, Nicole Hughes ( Synapse Energy Economics )

. 2011

. Emissions Reductions from Renewable Energy and Energy Efficiency in California Air Quality

Management Districts

. Califor nia Energy Commission. Publication number: CEC

- 500

- 201 1

- XXX.

### iv

### v 

# TABLE OF CONTENTS

PREFACE

................................ ................................ ................................ ................................ ..................... i

ABSTRACT

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

TABLE OF CONTENTS

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

Executive Summary

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

Purpose

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

Approach

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

Results

................................ ................................ ................................ ................................ ...................... 2

Summary of Conclusions

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

CHAPTER 1: Introduction

................................ ................................ ................................ ....................... 7

Background

................................ ................................ ................................ ................................ ............. 7

Quantifying Emissions Reduction Potential from EE/RE

................................ ............................... 8

Regulatory Requirements for Criteria Emissions Reductions

................................ ......................... 9

U.S. EPA Guidance on Emissions Reductions from EE/RE

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

Purpose of Project Relative to U.S. EPA Guidance

................................ ................................ ..... 11

Modeling Emissions Reductions from EE/RE

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

CHAPTER 2: Model Framework

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

Modeling Basis

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

The Western Interconnect

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

Economic Dispatch

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

Dispatch Model

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

Topology

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

Regions of Interest

................................ ................................ ................................ ................................ 17

Incremental New EE/RE Resource Zones

................................ ................................ .................... 17

WECC and Air District Region Definitions

................................ ................................ .................. 17

Emission Rates Corrections

................................ ................................ ................................ ................ 18

Base Case and EE/RE Scenarios

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

Base Case

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

Energy Efficiency and Renewable Energy (EE/RE) Scenarios

................................ .................. 22

Wind

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

Solar

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

Baseload Energy Efficiency

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

### vi Peak

- Targeting Energy Efficiency

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

CHAPTER 3: Analysis Framework

................................ ................................ ................................ ...... 29

Overview

................................ ................................ ................................ ................................ ............... 29

Displaced Energy

................................ ................................ ................................ ................................ . 29

Displaced Emissions

................................ ................................ ................................ ............................ 31

Uncertainty and Error Analysis

................................ ................................ ................................ ......... 33

Structural Uncertainty and Systematic Error

................................ ................................ ............... 33

Random Error

– Forced Outage Noise

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

CHAPTER 4: Results

................................ ................................ ................................ ............................. 37

Displaced Energy Fraction, by Region and Fuel Type

................................ ................................ ... 37

D isplaced Energy Fraction, by Air District Region

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

Displaced NO

X 

### Emissions, by Region and Fuel Type

................................ ................................ ..... 41

Displaced NO

X 

### Emissions, by Air District Region

................................ ................................ .......... 43

Displaced S O

2 

### Emissions, by Region and Fuel Type

................................ ................................ ...... 45

Displaced SO

2 

### Emissions, by Air District Region

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

Displaced CO

2 

### Emissions, by Region and Fuel Type

................................ ................................ ...... 47

CHAPTER 5: Discussion and Conclusions

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

Use of Results for Displaced Emissions Analysis

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

Patterns of Displacement and Future Research Opportunities

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

Out

- of

- State Coal

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

Negative Displacement Patterns

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

Concentration of Displacement in LADWP Service Territory

................................ .................. 55

Model Assumptions and Caveats

................................ ................................ ................................ ...... 56

Regulatory Implementat ion

................................ ................................ ................................ ................ 57

Conclusions

................................ ................................ ................................ ................................ ........... 58

Dispersed Criteria Emissions Benefits

................................ ................................ .......................... 58

Greenhouse Gas Benefits

................................ ................................ ................................ ................. 59

Verify Emissions from Existing Resources

................................ ................................ ................... 59

Peak Reduction Uncertainty

................................ ................................ ................................ ........... 60

Uncertainty in Emissions Benefits

................................ ................................ ................................ . 60

Closing

................................ ................................ ................................ ................................ ............... 60

References

................................ ................................ ................................ ................................ ................. 62

### vii Appendix A: Emi ssions Corrections for California EGU

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

Appendix B: Creation of Hybrid Base Case

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

Appendix C: Detailed Results

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

### viii 

# LIST OF FIGURES

Figure 1: Displaced Energy Fraction (MWh Displaced Generation per MWh of Energy Efficiency/Renewable Energy) by Western Electricity Coordinating Council Region and Fuel Type in 2016, Relative to the Base Case

................................ ................................ ................................ .. 3

Figure 2: Displaced NO

X 

### Emissions Rate (in lbs NO

X 

### /GWh) by California Air District Region in

2016, Relative to the Base Case

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

Fi gure 3: Displaced CO

2 

### Emissions (tons of CO

2 

### / MWh of Energy Efficiency/Renewable Energy) by Western Electricity Coordinating Council Region and Fuel Type in 2016, Relative to

the Base Case

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

Figure 4: Western Electric Coordinating Council (WECC) and Analysis Regions

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

Figure 5: Generation and Demand Balance in the Western States of the WECC 6



### ......................... 14

Figure 6: Ventyx PROSYM Topology of WECC

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

Figure 7: Generation by Fuel Type in 2009 (Recorded), and 2016 in Three Modeled Cases.

........ 22

Figure 8: Example Load Profile and Reduction from Wind in LADWP: July 11

– 12, 2016

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

Figure 9: Average Hourly Wind Capacity by Hour for 1000 MW Wind Farms in Four Service

Territories

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

Figure 10: Average Monthly Wind Capacity by Hour for 1000 MW Wind Farms in Four Service

Territories

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

Figure 11: Example Load Profile and Reduction from Solar in LADWP: July 11

– 12, 2016

.......... 25

Figure 12: Average Hourly Solar PV Capacity for 1000 MW of Solar Installations in Four Service

Territories

................................ ................................ ................................ ................................ .................. 25

Figure 13: Average Monthly Solar PV Capacity for 1000 MW of Solar Installation in Four

Service Territories

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

Fi gure 14: Example Load Profile and Reduction from a Baseload EE Program in LADWP: July

11

– 12, 2016

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

Figure 15: Example Load Profile and Reduction from a Peak

- Targeting EE Program in LADWP: July 11

– 12, 2016

................................ ................................ ................................ ................................ ......... 28

Figure 16: Average Hourly Peak EE Reduction Model for Four Service Territories

..................... 28

Figure 17: Average Monthly Peak EE Reduction Model for Four Service Territories

................... 28

Figure 18: Example of Displaced Energy Fraction by WECC Region and Fuel Type: MWh of Generation Displaced per MWh of New Wind in SCE

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

Figure 19: Example of Displaced Energy Fraction within California, by Air District Region: MWh of Generation Displaced in California per MWh of New Wind in SCE

............................... 31

Figure 20: Example of Displaced Emissions Rate by WECC Region and Fuel Type: SCE Wind

. 32

Figure 21: Example of California Component of Displaced NO

X 

### Emissio ns Rate, by Air District Region: SCE Wind

................................ ................................ ................................ ................................ .... 32

### ix Figure 22: Example of California Component of Displaced NO

X 

### Emissions Rate, by Fuel Type

within California: SCE Wind

................................ ................................ ................................ .................. 33

Figure 23: CO

2 

### Emissions, Displaced Emissions, and Iteration Run Emissions in California, All Fuel Types. Note, the y

- axis does not begin at zero.

................................ ................................ ................. 35

Figur e 24: Displaced Energy Fraction (MWh Displaced Energy per MWh of EE/RE) by WECC
Region and Fuel Type

................................ ................................ ................................ .............................. 37

Figure 25: Displaced Energy Fraction (MWh Displaced Energy Per MWh of EE/RE) in

Californ ia, Parsed by Air District Region

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

Figure 26: Displaced Energy Fraction (MWh Displaced Energy per MWh EE/RE) in California,
Parsed by Fuel Type

................................ ................................ ................................ ................................ 41

Figure 27: Total Modeled NO

X 

### Emissions by Month in Each WECC Region in 2016

.................... 42

Figure 28: Displaced NO

X 

### Emissions Rate (in lbs NO

X 

### per GWh of EE/RE Generation), by

WECC Re gion and Fuel in 2016

................................ ................................ ................................ ............. 42

Figure 29: Displaced NO

X 

### Emissions Rate (in lbs NO

X 

### /GWh) in California Air District Region 43

Figure 30 : Displaced NO

X 

### (in lbs NO

X 

### / GWh) by Fuel Type in California

................................ .... 45

Figure 31: Displaced SO

2 

### Emissions Rate (in lbs SO

2 

### /GWh) in by WECC Region and Fuel Type

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

Figure 32: Displaced SO

2 

### Emissions Rate (in lbs SO

2 

### /GWh) by Air District Region

..................... 47

Figure 33: Displaced CO

2 

### Emissions (tCO

2 

### / MWh of EE/RE) by Region and Fuel Type

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

Figure 34: Screenshot of Example Project in Selected Region

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

Figure 35: Example Screenshot from Data Annex of Displaced Em issions in California Air District Regions from a 250 MW Wind Project in SCE Service Territory

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

Figure 36: Difference in Monthly Generation across All of WECC between Base Case and

SDG&E Win d Scenario, by Fuel Type. The impacts shown result from the addition of 1000 MW of onshore wind in the SDG&E service territory

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

Figure 37: Difference in Hourly Generation across All of WECC between Base Case and SDG&E Wind Scenario, by Fuel Type for a Week in March (2016). The impacts shown result from the addition of 1000 MW of onshore wind in the SDG&E service territory.

................................ ......... 52

Fig ure 38: Difference in Hourly Generation across All of WECC Between Base Case and SDG&E Wind Scenario, By Fuel Type for a Week in July (2016). The impacts shown Result from the addition of 1000 MW of onshore wind in the SDG&E service territory.

................................ ......... 52

Figure 39: Difference in Monthly Generation across All of WECC between Base Case and SDG&E Peaking EE Scenario, by Fuel Type. Much of the observed change in dispatch is due to
random generating unit ou tages, which have a large effect relative to the modest energy

- saving impact of a peaking EE program.

................................ ................................ ................................ .......... 54

Figure 40: Difference in Hourly Generation across All of WECC between the Base Case and SDG&E Peaking EE Scenario, by Fuel Type for a Week in March (2016). The impacts shown result from the addition of an aggressive EE peak demand program targeting the 90th percentile highest load hours in SDG&E service territory.

................................ ................................ .................. 54

### x Figure 41: Difference in Hourly Generation across All of WECC Between Base Case and SDG&E Peaking EE Scenario, by Fuel Type for a Week in March (2016). The impacts shown result from the addition of an aggressive EE p eak demand program targeting the 90th percentile highest load hours in SDG&E service territory.

................................ ................................ ................................ 55

Figure B

- 1: Generation (GWh) in California from Recorded 2009 EIA Data, and 2016 Modeled High Wind, High Solar, and Hybrid Scenarios

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

Figure C

- 1: Displaced Energy Fraction by WECC Region and Fuel Type

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

Figure C

- 2: Di splaced lbs of NO

X 

### per GWh of Energy Displaced, by WECC Region and Fuel

Type

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

Figure C

- 3: Displaced lbs of SO

2 

### per GWh of Energy Displaced, by WECC Region and Fuel Type

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

Figure C

- 4: Displaced Tons of CO

2 

### per MWh of Energy Displaced, by WECC Region and Fuel

Type

................................ ................................ ................................ ................................ .............................. 7

Figure C

- 5: Displaced Energy Fraction by Air Distri ct (MWh per MWh)

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

Figure C

- 6: Displaced lbs of NO

X 

### per GWh of Energy Displaced, by Air District

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

Figure C

- 7: Displaced lbs o f SO

2 

### per GWh of Energy Displaced, by Air District

........................... 14

Figure C

- 8: Displaced Tons of CO

2 

### per MWh of Energy Displaced, by Air District

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

Figure C

- 9: Displaced Energy Fraction, by Fuel in California

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

Figure C

- 10: Displaced lbs of NO

X 

### per GWh of Energy Displaced, by Fuel in CA

........................ 17

Figure C

- 11: Displaced lbs of SO

2 

### per MWh of Energy Displaced, by Fuel in CA

......................... 18

Figure C

- 12: Displaced Tons of CO

2 

### per MWh of Energy Displaced, by Fuel in CA

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

Unless otherwise noted, all figures are provided by the author s

.

### xi 

# LIST OF TABLES

Table 1: Air District Aggregations into Air District Zones Used for this Analysis

........................ 18

Table 2: Aggregate Emissions Rates of NO

X 

### and SO

2 

### by Unit Categorization, in California only, Before and After Adjustment (2012)

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

Table 3: Displaced Energy Fraction by WECC Region for Eight EE/RE Scenarios. The top value is the displaced energy fraction, and the lower value is the uncertainty range. If the signal

exceeds the noise, the value is bold. (1 of 2)

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

Table 4: Displaced Energy Fraction by WECC Region for Eight EE/RE Scenarios. The top value is displaced energy fraction; the lower value is the uncertainty range. If the signal exceeds the

noise, the value is bold. (2 of 2)

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

Table 5: Displaced NO

X 

### Rate (in lbs NO

X 

### per GWh of EE/RE) for Eight Scenarios by California

Air District Region

................................ ................................ ................................ ................................ ... 44

Table 6: Displaced NO

X 

### Rate (in lbs NO

X 

### per GWh of EE/RE) for Eight Scenarios by California

Air District Region

................................ ................................ ................................ ................................ ... 44

Table A

- 1: Fractions of Generation, CO

2 

### , NO
X 

### , and SO

2 

### by Unit Cate gorization, in California, in Model Test Year (2012) Before Adjustment. Only units in the category marked “Gas (in CEMS)” report emissions directly to the U.S. EPA via CEMS.

................................ ................................ ........... 2

Table A

- 2: Aggr egate Emissions Rates of NO

X 

### and SO

2 

### by Unit Categorization, in California Only, Before and After Adjustment (2012)

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

Table A

- 3: SO

2 

### Emissions Rate Corrections for California Electric Generat ing Facilities Based on
CEMS (2009) Emissions Rates

................................ ................................ ................................ .................. 7

Table A

- 4: SO

2 

### Emissions Rate Corrections for California Electric Generating Facilities Based on
eGRID (2007) Emissions Rates

................................ ................................ ................................ ................. 8

Table A

- 5: NO
X 

### Emissions Rate Corrections for California Electric Generating Facilities Based on
CEMS (2009) Emissions Rates

................................ ................................ ................................ .................. 9

Table A

- 6: NO
X 

### Emissions Rate Corrections for California Electric Generating Facilities Based on
eGRID (2007) Emissions Rates (1 of 3)

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

Table A

- 7: NO
X 

### Emissions Rate Corrections for California Elect ric Generating Facilities Based on
eGRID (2007) Emissions Rates (2 of 3)

................................ ................................ ................................ .. 11

Table A

- 8: NO
X 

### Emissions Rate Corrections for California Electric Generating Facilities Based on
eGRID (2007) Emissi ons Rates (3 of 3)

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

Table A

- 9: SO

2 

### Emissions Rate Adjustments for Future California Electric Generating Facilities; Based on Lowest 10th Percentile Emissions for Fuel Type

................................ ............................... 14

Table A

- 10: NO
X 

### Emissions Rate Adjustments for Future CA Facilities; Based on Lowest 10th Percentile Emissions for Fuel Type (1 of 2)

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

Table A

- 11: NO
X 

### Emissions Rate Adjustments for Future Facilities; Based on Lowest 10th Percentile Emissions for Fuel Type (2 of 2)

................................ ................................ .......................... 17

Table B

- 1: Summary of Name Plate Capacity Adjustments for Hybri d Base Case

.......................... 2

### xii Table B

- 2: Summary of Generation (GWh) from High Wind, High Solar, and Hybrid Scenarios 3

Table C

- 1: Displaced E nergy Fraction by WECC Region (MWh/MWh)

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

Table C

- 2: Displaced NO

X 

### Emissions Rate by WECC Region (lbs NO

X 

### /GWh)

................................ 2

Table C

- 3: Di splaced SO

2 

### Emissions Rate by WECC Region (lbs SO

2 

### /GWh)

................................ ... 3

Table C

- 4: Displaced CO

2 

### Emissions Rate by WECC Region (t CO

2 

### /MWh)

................................ .... 3

Table C

- 5: Displaced Energy Fraction by Air District (MWh / MWh)

................................ .............. 8

Table C

- 6: Displaced NO

X 

### Emissions Rate by Air District (lbs NO

X 

### /GWh)

................................ ..... 9

Table C

- 7: Displaced SO

2 

### Emissions Rate by Air District (lbs NO

X 

### /GWh)

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

Table C

- 8: Displaced CO

2 

### Emissions Rate by Air District (t CO

2 

### /MWh)

................................ ........ 11

### 1 

# Executive Summary

Purpose

Energy and the environment are inextricably linked. Fossil

- fuel combustion is a primary source of pollutants that contribute to unhealthy concentrations of fine particulates, ground

- level
ozone, and airborne toxics. Both national ly and in California, air districts have successfully reduced criteria pollutants such as oxides of nitrogen (NO

X 

### ), sulfur dioxide (SO

2 

### )

, and fine particulates (PM

2.5 

### ) through end

- of

- pipe controls. In the face of increasingly stringent air quality

standard s however, states and air districts have progressively turned toward energy demand

reduction measures and non

- emissive sources of energy to meet standards cost

- effective ly

.

In California and other states, air quality management districts are considering us ing energy efficiency and renewable energy to target both criteria pollutant and greenhouse gas emissions.

The U.S. Environmental Protection Agency continues to improve guidance on the use of energy efficiency and renewable energy to meet State Implementat ion Plan targets, and states and

utilities are working to quantify emissions reductions potential of energy efficiency / renewable

energy programs.

Synapse Energy Economics conducted research for the California Public Interest Energy Research (PIER) program in two phases. The first phase surveyed existing analytical methods available to assess the energy savings from energy efficiency / renewable energy and identified air district efforts to incorporate energy efficiency / renewable energy into air quality State Implementation Plan s. The second phase, presented in this report , is a pilot study to quantitatively model the emissions avoided through new energy efficiency / renewable energy programs not already required by state or federal statute. The goal of the work was to use

California as a test case

, producing an analysis that could theoretically satisfy U.S. EPA
requirements for us ing energy efficiency / renewable energy in State Implementation Plan compliance.



# Approach

Each unit of energy saved by a new efficienc y program or supplied by a new renewable energy resource displaces energy from an existing generator (or eliminates the need for a new generator to be built). In most cases, the generating resources that are most readily displaced are

fossil

- fired; however , the type and location of these resources are not clearly delineated, and can be geographically widespread. In the case of California, the geography of potential generators

that might be displaced by new energy efficiency / renewable energy can span the ful l Western Interconnect (the Western Electricity Coordinating Council)

— an eleven

- state (and two

Canadian

- province) region. It is therefore feasible that energy efficiency / renewable energy programs implemented in California could displace generators that do not impinge on California air quality, and even generators that are well outside of state lines. The goal of this research is to identify which resources are displaced by incremental California energy efficiency / renewable energy programs in a future test y ear (2016), and which regions or air

districts can claim a benefit for these energy efficiency / renewable energy programs.

Synapse used a Western Electricity Coordinating Council

- scale production

- cost simulation

model t o examine the impact of incremental e nergy efficiency / renewable energy on generation

and emissions of NO

X 

### , SO

2 

### , and CO

2 

### . 1 

### Sixteen energy efficiency / renewable energy scenarios

1 

### In implementing the model, Synapse found significant errors in baseline assumed model emissions rates
for criteria pollutants of NO x and SO

2 

### in California, which would have resulted in emissions up to an

### 2 were tested against a reference “base case

, ” representing the expected structure of the California

and Western electri city system in 2016 under a full implementation of California’s 33 percent renewable electricity standard and other energy statutes. The scenarios were comprised of four
energy efficiency / renewable energy programs enacted in four different California servi ce territories. The programs included wind, solar, and both baseload and peaking efficiency programs in San Diego Gas and Electric (SDG&E), Southern California Edison (SCE), the Los Angeles Department of Water and Power (LADWP), and Pacific Gas and Electri c (PG&E)

service areas.

Previous work in displaced emissions from energy efficiency / renewable energy have almost exclusively focused on using either generic “emissions factors” for either all plants in a region or those which are thought to be on the margi n, or on hourly emissions factors, derived from historic data. While these methods have merit both in concept and approach simplicity, they are
not simulation models and cannot represent subtle, but important

, factors such as transmission

constraints, impo rts, and the economic considerations of generators. This research presents one of the most comprehensive simulation model studies designed strictly for the purposes of
evaluating displaced emissions from energy efficiency / renewable energy programs , and is therefore a new direction of research for examining displaced emissions at the operating

margin.

In the analysis, Synapse examined potential sources of error and explicitly accounted for
“forced outages

, ” wherein generators require emergency or unforeseen maintenance. Forced outages, represented as random events in the model, create potentially spurious results in marginal emissions analyses: the degree to which patterns of displaced energy and emissions are driven by forced outages were estimated and repor ted.

Results from the model were aggregated into four Western Electricity Coordinating Council regions (California, the Southwest, the Rocky Mountain West, and the Northwest) and into air district “regions” within California. 2 

### Synapse examined displaced en ergy and emissions, scaled by the size of the energy efficiency / renewable energy program implemented (i.e. , megawatt

hour [ MWh ] of generation displaced per MWh of energy efficiency / renewable energy implemented, or pounds of emissions displaced per MWh of e nergy efficiency / renewable

energy implemented). The purpose of this scaling is to allow users of the data to examine the
potential benefits from variously sized energy efficiency / renewable energy programs.



# Results

It was observed that in many cases, a larg e fraction of generation was displaced outside of California state lines. However, the degree to which program benefits were experienced in

California, or even in any air district, were observed to be both a function of the program type and the area of imp lementation. Figure 1 below shows generation displaced by the sixteen scenarios relative to the base case in 2016, parsed by region and fuel type.

order of magnitude greater in an early test year (2012) than recorded by air districts or the U.S. EPA. Synapse corrected e missions rates from 165 generators in California prior to modeling.

2 

### “Air district regions” include larger air districts as well as clusters of smaller air districts for the

pu rposes of examining results.

### 3 Figure 1 : Displaced E nergy F raction (MWh D isplaced Generation per MWh of Energy

Efficiency /R enewable Energy ) by Western Electricity Coordinating Council R egion

and F uel T ype in 2016, Relative to the Base Case



### In this chart, the color of the bars represent s the region of displacement (California is in green) and the pattern represents the generic fuel type displaced (solid is natural gas, hashed is coal,

and dotted is “other

, ” including biomass and petroleum). The amount of generation displaced is scaled by the amount of generation produced by th e energy efficiency / renewable energy program; in all cases, the net generation displaced is equal to the amount of energy produced by

the energy efficiency / renewable energy program (i.e.

, displaced energy fraction = 100 percent). However, the regions and f uel types displaced by the energy efficiency / renewable energy programs are highly variable. For example, p rograms implemented in LADWP and PG&E tend to displace more resources in California, while programs implemented in SDG&E and SCE tend
to displace ener gy out

- of

- state generators

, and includ e significant fractions of coal. This out

- of

state displacement is a non

- intuitive finding, discussed in depth in the Discussion section of th e report

.

Due to California’s cleaner

- burning natural gas units, the emissio ns produced by California generators are very small relative to surrounding states: therefore, total NO

X 

### and SO

2 

### displaced in California tend to be small. Results of the displaced NO

X 

### emissions rate within California

(i.e.

, emissions represented by the gre en bars in Figure 1

, are shown in Figure 2 , below, parsed by air district region. Note that the scale in this figure is in pounds of NO

X 

### per gigawatt

- hour

( GWh ) of energy e fficiency / renewable energy

.

### 4 Figure 2 : Displaced NO X 

### E missions R ate (in lbs NO X 

### /GWh) by California A ir D istrict R egion

in 2016, R elative to the B ase C ase



### Energy efficiency / renewable energy programs implemented in SDG&E and SCE te nd to displace generators throughout California, and have little impact in any given California region. On the other hand, programs in LADWP tend to have a disproportionate impact on South Coast generators, and programs in PG&E territory tend to have a dis proportionate impact in northern air district regions. The negative displaced emissions (i.e.

, increasing emissions) patterns seen in peaking efficiency programs in all four service territories are hypothesized to be an artifact

, and

are addressed in more depth in the discussion section of this report

. Finally, although energy efficiency / renewable energy in California are found to have a
relatively modest impact on California criteria emissions, Synapse finds a more significant impact in displaced CO

2 

### , both in California and from out

- of

- state coal generators (see Figure 3

, below).

### 5 Figure 3 : Displaced CO 2 

### E missions (t ons of CO 2 

### / MWh of Energy Efficiency /R enewable Energy )

by Western Electric ity Coordinating Council R egion and F uel T ype in 2016,

Relative to the Base Case



# Summary of Conclusions

• Dispersed emissions benefits: The Western grid is highly interconnected, and therefore changes in load, generation, or resource availability in Calif ornia affect generators throughout the entire Western Electricity Coordinating Council system. As a result,

criteria emissions benefits from the energy efficiency / renewable energy programs implemented in California are highly dispersed. Further, programs i mplemented in different parts of California appear to have varying impacts across the Western Electricity Coordinating Council and within California. It is concluded that a comprehensive modeling approach is required to estimate the emissions reduction

pot ential of energy efficiency / renewable energy in a highly interconnected and highly diverse region such as Western Electricity Coordinating Council

.

• Large benefit out

- of

- state: This research finds that while California does not necessarily

realize significa nt criteria emissions benefit from energy efficiency / renewable energy

programs in S tate, other regions of the West see significant emissions reductions from
demand reductions in California, posing important questions about interstate energy and emissions p lanning. This out

- of

- state energy displacement, and particularly the displacement of coal in the Intermountain West, does not conform to conventional concepts about the nature and cost of energy resources in the Western Electricity Coordinating Council reg ion. However, the results consistently show reductions in out

of

- state coal, which have higher emissions than California generators, and hence deliver a significant benefit to other Western Electricity Coordinating Council regions.

• Greenhouse gas benefits: A notable benefit identified in this analysis is that energy efficiency / renewable energy programs have a large displacement out side of the state

, often displacing coal

- fired resources in the Rocky Mountain and Southwest regions of

### 6 Western Electricity Coor dinating Council . Because of this coal displacement, the greenhouse gas benefit of the energy efficiency / renewable energy programs is higher than would be seen were the displacement within California only. In many of the
programs, displacing a combination of California natural gas and out

- of

- state coal (such

as in the SDG&E wind scenario) results in a 50 percent increase in GHG emissions benefit (0. 6 tons of carbon dioxide [ tCO

2 

### ] /MWh) relative to displacing in

- state natural

gas only (such as in the LADWP ba se load energy efficiency scenario, 0.4 tCO

2 

### /MWh).

• Uncertainty in emissions reductions: Emissions reductions from energy efficiency / renewable energy occur from generating units at the margin, or units which are least economic at any given time. These same u nits are highly influenced by small changes in electrical dispatch due to forced outages at large units: therefore, modeling research at this scale and detail necessarily requires an extensive and detailed analysis of error and uncertainty. In this researc h, it was found that uncertainty can equal or exceed the magnitude of displaced energy and emissions, suggesting that all patterns in similar studies should be examined for relevance relative to error and uncertainty. When
uncertainty, or noise, exceeds di splaced energy or emissions , the results are termed

“non

- meaningful” in this research. For example, the negative displacement patterns (generation and emissions increase) seen in the results of the peaking energy efficiency programs are, by this definition , non

- meaningful.

• Emissions rate accuracy: This research found, incidentally, that a small number of
generators in California are responsible for a large fraction of the emissions, but that the default model emission rates for these generators were also no t based in verified data. Verifying and correcting emissions rates in the model, particularly in a low

- emissions region such as California, is critical to a viable and useful result.

### 7 

# CHAPTER 1:

Introduction

Background

Energy and the environment are inex tricably linked. Fossil

- fuel combustion to produce electricity is a primary source of pollutants that contribute to California’s exceedances of the
fine particulate and ground

- level ozone National Ambient Air Quality Standards (NAAQS).
Electricity generati ng plants are also responsible for significant and growing quantities of greenhouse gas emissions. Both nationally and in California, air districts have successfully

reduced oxides of nitrogen ( NO
X 

### )

, sulfur dioxide ( SO

2 

### )

, and fine particulate ( PM
2.5 

### ) emiss ions (known as “criteria pollutants”) from the power sector through state and local regulations and the federal acid rain program. California’s rigorous efforts to control these criteria pollutant emissions have resulted in reductions by over two

- thirds si nce the early 1990s, yet greenhouse gas emissions, which have not been covered by any of these regulations, have continued to rise. In fact, certain policy choices

, like end

- of

- pipe emissions controls for stationary sources

, have
increased parasitic energy requirements at power plants, leading to a further increase in greenhouse gas emissions. 3



### Along with the success of some air quality regulations

, and increasing public awareness of the

value of clean air

, have come increasingly stringent emissions standar ds, but also numerous failures to meet existing standards in California and elsewhere. This has led to interest in combining energy and emissions planning efforts in the hope that greater coordination could

lead to lower overall emissions. In California an d in other states, air districts are using energy efficiency (EE) and renewable energy (RE) to target both criteria pollutant and greenhouse gas

emissions. The U.S. Environmental Protection Agency ( U.S. EPA ) continues to improve guidance on the use of EE a nd RE to meet State Implementation Plan (SIP) targets, and states and utilities continue to examine the emissions reductions potential of EE and RE initiatives.

The U

. S

. EPA has issued a host of new federal regulations and standards covering criteria and toxic emissions from the power sector , s tarting in 2010 and expected to be promulgated through

at least 2012. T hese new regulations dramatically i ncrease the importance of synchronizing state
energy and environmental programs, 4 

### but even in the absence of t hese new regulations, there

are still ample reasons to consider comprehensive energy planning when structuring SIPs. For example, economic energy resource plans considered by state energy offices and Public Utility Commissions (PUCs) may prove to be incons istent with state or local air district environmental requirements, particularly if (as is usually the case) the public health and environmental impacts of new resources are not fully considered as part of the plant’s costs. Similarly,

3 

### Certain emissions control technologies, such as flue gas desulfurization (FGD) for SO

2 

### control or selective catalytic reduction (SCR) for NOx control, require additional energy to operate; this energy
consumption can be consi dered “parasitic

, ” as it reduces the net output of the generator. Since these
energy reductions are not accompanied by an equivalent reduction in CO

2 

### emissions, the net result is an

increase in the CO

2 

### emissions rate from these criteria emissions controlle d generators.

4 

### The scope of these new environmental regulations, including the implementation of the regional haze rule, the air toxics or hazardous air pollutants rule, new National Ambient Air Quality Standards (NAAQS), and the newly promulgated Cross

- S tate Air Pollution Rule (effective in eastern states) are changing which power plants require new control equipment and which may cease operations for economic purposes. These environmental rules are, in some cases, dramatically affe cting the shape of the energy sector. Similarly, proactive energy planning may help states comply cost

- effectively with these
new and emerging environmental regulations.

### 8 decisions made by en vironmental regulators may impose economic constraints that turn out to be inconsistent with state or regional energy plans.

In California, the energy planning process at the California Energy Commission ( Energy

Commission ) and California PUC (CPUC) d o tak e environmental policies, regulations, and even intent into consideration; however, these processes have not historically been well coordinated with environmental planning efforts at the California Air Resources Board (CARB), much less
specific local air d istricts. Similarly, the energy planning authority recently granted to CARB to implement the Scoping Plan for AB 32, the Global Warming Solutions Act of 2006, did not have

a well

- established mechanism to coordinate with the Energy Commission or C PUC. These dynamics can impose unnecessary tension between the Energy Commission

, C PUC, CARB, and

the air districts.

In the past, a simple and internally consistent resolution between the agencies was to frame emissions reductions through end

- of

- pipe controls. Howe ver, this approach ignores certain

important ( if more complex to coordinate ) options that may protect health and the environment
equally as well (or more so)

, and at lower costs

, such as energy efficiency and renewable energy

. This two

- phased research proj ect emphasizes the environmental benefits of EE/RE and how air

districts can consider these benefits in their SIPs.



# Quantifying Emissions Reduction Potential from EE/RE

Phase 1 of this research surveyed existing analytical methods available to assess the e nergy savings from energy efficiency and renewable energy, and identified efforts that had been completed by air districts to date to incorporate the air quality benefits from EE/RE into their SIPs. That phase concluded that few U.S. states had explicitly included EE/RE as an air quality measure, or considered the possibility of doing so in a future SIP. Early efforts to include the air quality benefits from EE/RE suffered from two factors:

• High transaction costs: U.S. EPA guidance from 1999 and 2004 provid ed a path for air districts to include the air quality benefits from EE/RE into SIPs. However, this guidance focused on specific EE/RE measures, and the transaction costs to measure and verify the energy saved or produced by each specific measure was high. For example, avoiding generation might yield an avoided emissions rate of 1 lb NO

X 

### / megawatt

- hour

( MWh ) , yet an end

- of

- pipe solution (such as selective catalytic reduction, or SCR) could yield an order of magnitude steeper results with a significantly simp ler analysis.

• Incomplete understanding about how EE/RE affect s generation : These effects can vary by time
of day, season

, and location. For example, EE/RE during peak hours may avoid different resources than EE/RE during off

- peak hours.

Phase 2 of this res earch is an attempt to bridge these concerns in California, and to model quantitatively the emissions avoided through EE/RE. The goal of this phase is to use California

as a test case and produce an analysis which could satisf y emerging U.S. EPA criteria f or the use
of

EE/RE in meeting SIP compliance obligations

. Air district resources are limited, and the efforts required to quantify emissions reduced through EE/RE are potentially a significant barrier toward using these tools to meet SIP requirements. The re are rigorous protocols required to evaluate, measure, and verify energy saved by EE or produced through EE programs, and there are similarly rigorous protocols required to quantify the emissions that could be reduced by EE/RE. From the standpoint of an air district, there are significant economies of scale to be harnessed by engaging in this process

at a broader level than an individual district.

Further, the electric system in California and elsewhere is highly interconnected, ensuring that

the benefit s from EE/RE measures will not necessarily be realized in full in the air district, or

### 9 even in the state, where the measure is implemented. Due to the scope of the effort and the broad geographical reach of this type of reduction measure, estimating displa ced emissions from EE/RE is more effectively considered at a broad scale (i.e.

, multiple air districts in multiple states, and for multiple measures). This Phase 2 research both establishes a framework and performs analysis allowing the evaluation of EE/RE measures implemented in California, for the purposes of reducing criteria and greenhouse gas emissions.

The timing and focus of this document is developed to inform California air districts and energy planning authorities in advance of emerging U.S. EPA r egulatory guidance for criteria

emissions reductions.



# Regulatory Requirements for Criteria Emissions Reductions

Sections 108 and 109 of the Clean Air Act Amendments of 1990 (CAAA) specify the timing and
procedures for U.S. EPA’s review of the National Ambi ent Air Quality Standards (NAAQS).
Every five years U.S. EPA is required to review public health and epidemiological data, and based on this evidence, to issue findings on whether the NAAQS should be revised or be kept at the existing level. Once U.S. EPA issues a new or revised NAAQS, the air quality planning process is triggered pursuant to Section 110 of the CAAA. Within three years of adopting a new
or revised NAAQS, Section 110 requires states 5 

### to develop plans that implement, maintain

, and

enforce lim its that will improve air quality to meet and maintain the revised NAAQS. These
plans are referred to as S tate I mplementation P lans

, or SIPs.

During 2010, U.S. EPA reviewed the fine particle (PM

2.5 

### ) and ozone standards, and concluded that existing levels w ere not adequately protective of public health and the environment. The

U.S. EPA was expected to issue a revised ozone standard during July 2010, more stringent than the existing standard of 75 parts per billion (ppb). The U.S. EPA initially indicated that it would delay issuance of a revised ozone standard until December 2010, and then in December,

U.S. EPA indicated that it needed additional time to review data, and that the agency was further delaying issuance of the revised standard until July 2011. At the request of the p resident
of the United States, the U.S. EPA decided not to revise the ozone standard at this time.

The PM

2.5 

### NAAQS was last revised in 2006, when the 24

- hour standard was lowered from 65 to

35 micrograms per cubic meter ( g/m 3 

### ) . The ann ual 15 g/m 3 

### standard was not changed at

that time. The U.S. EPA has indicated that recent data suggest that the annual standard be lowered, and that the agency expects to issue a revised PM

2.5 

### annual standard during 2011.

Air districts would be required t o submit revised SIPs that contain new or revised control

measures to meet U.S. EPA’s revised ozone NAAQS by July 2014, and measures to meet

U.S. EPA’s revised fine particle NAAQS by December 2014 (three years after adoption of the
revised NAAQS, per Secti on 110). 6 

### This timing requires that air districts complete several steps:

• Analyze what control measures will reduce ozone and fine particle emissions and the efficacy of such measures, including updating emissions inventories and projecting

future emissio ns;

• Where required, update, revise

, or adopt new regulations and provisions to enforce them, including holding public hearings and taking public comment on the new or

revised rules;

5 

### “ S tates” also refers to local air quality planning agencies, where such authority has been delegated

.

6 

### Whi le considerable uncertainty exists as to the actual date by which U.S. EPA will issue its revised fine particle standard, it is assumed that U.S. EPA will issue it at the end of 2011 or in early 2012.

### 10 • Write a description of the regulations and control measures that will resu lt in air quality meeting the NAAQS, and provide technical support for the district’s conclusions.

Because government administrative procedure requirements can mean that the rulemaking process stretches out over several months or a year, air districts shou ld begin during 2011 (even prior to the issuance of the revised NAAQS) to assess what control measures are available to help reduce fine particle and ozone precursor emissions. The U.S. EPA has consistently stated that it expects that both the fine particl e and ozone NAAQS will be made more stringent. Beginning early is especially important for air districts that may be considering control measures that rely on concurrent implementation with other districts. This would include those
energy savings and renew able energy policies that are part of California’s statewide efforts to improve air quality and reduce greenhouse gas emissions. Districts can work together to jointly assess the efficacy of the energy savings and renewable energy measures, and to determin e what procedures may be needed to evaluate, measure

, and verify the energy saved or produced and the resulting emissions benefits.



# U.S. EPA Guidance on Emissions Reductions from EE/RE

States can choose one of four routes to incorporate the emissions bene fits of energy

- saving and renewable energy measures into their air quality control programs. These are identified as

follows:

• Future baseline

• Control measure

• Voluntary/emerging measure

• Weight of evidence

Each of the four routes has different requirements a nd entail s different levels of effort to demonstrate the quantity of emissions benefit.

The future baseline route requires states to review their existing policies and regulations, and to account for the anticipated impacts in the initial emissions projec tion, i.e.

, the baseline. As policies such as renewable portfolio standards ( RPS ) and energy efficiency resource standards increase in the quantity of energy and capacity involved over time, their displaced emissions

affect the state’s projection of future pollutant levels. The U.S. EPA has included these state policies in its own emissions projections, using the Integrated Planning Model (IPM). At present, the U.S. EPA has actively coordinated IPM input assumptions with states affected by

the 2010 Clean Ai r Transport Rule, and if these states concur with U.S. EPA ’s assessment, no additional work is required: the emissions benefits from EE and RE are integrated in U.S. EPA ’s baseline assessment. While the Clean Air Transport Rule does not apply to California

, U.S. EPA ’s IPM is loaded with state energy efficiency and renewable energy policies such as those included in the EIA Annual Energy Outlook 2010. New policies and existing policies that are amended in the future to increase California energy savings or p roduction 7 

### would need to

be accounted for as part of an adjustment to the future baseline.

The control measure route also requires states to review their existing policies and regulations but, instead of including the emissions benefits as part of the futu re baseline, these benefits are characterized as incremental to the baseline. The requirements to include EE and RE as control

measures are the same as those for other measures that states may develop and implement; the

7 

### An adjustment to the baseline could entail, for examp le, a change in RPS requirements or timing or new efficiency standards, implemented after the U.S. EPA completes modeling.

### 11 criteria for these reductions are th at they be quantifiable, real, surplus, enforceable, and

permanent.

The requirement that measures be “quantifiable” and “real” means that the emissions savings must be evaluated and verified over time. “Surplus” means that the emissions reductions must

not be currently relied on to meet NAAQS. Qualifying allowances from energy

- efficiency that
are captured in state cap

- and

- trade programs must be forfeited in order to be certified as emissions reductions. An “enforceable” emissions reduction has an originatin g and tracking organization, responsible for assuring the energy savings from the energy

- efficiency activities actually occur, and that the methodology used to calculate the resulting emissions savings is
credible and replicable. For energy

- efficiency, the air quality agency must be able to either independently determine such emissions savings or rely upon the program administrators responsible for ensuring those emissions reductions occur. “Permanence” refers to the savings persisting throughout the life o f the energy

- efficiency program or measure.

For EE and RE as SIP control measures, another agency besides the air quality agency , such as the state’s utility commission

, may be the responsible enforcement authority. Having a state utility commission as th e responsible agency is acceptable to U.S. EPA as long as that state agency has the ability to issue penalties for non

- compliance and to order corrective actions to be expeditiously completed. Also, states are responsible for making up any gaps in emission s benefits from EE/RE, just as for any SIP control measure.

The voluntary/emerging measure route refers to EE/RE programs that may have future
effective dates, are just beginning implementation, or are not required by a state regulation or

statute. The U.S

. EPA will give provisional SIP credits to states that wish to include the emissions benefits of voluntary/emerging measures in their air quality program. The process to calculate the benefit of these programs is similar to that described above for the con trol measure route. However, since the emissions benefits are based on projections rather than upon
evaluation of past programs and actual data, U.S. EPA is likely to discount the quantity of provisional SIP credit. The specific discount will be based upon negotiations with the responsible state agency. Also, the state must commit to review the EE/RE measures at least once every three years, and to evaluate their efficacy. Any gaps or deficits in the actual reductions compared to the provisional credit gran ted by U.S. EPA must be remedied.

Finally, the weight of evidence route requires a modeling analysis that quantifies differences
between a baseline and a state’s air quality programs or regulations to attain National Ambient Air Quality Standards. The U.S. EPA recommends that states also complete additional analyses to further prove NAAQS attainment. Where the results of the additional analyses differ from the preliminary attainment demonstration, states may qualitatively describe why U.S. EPA

should base i ts approval on both the initial analysis and the additional work the state has completed. The additional analyses attempt to prove that a state will attain NAAQS, based on a preponderance of evidence, despite modeling results that may reflect otherwise (i. e., showing
that the state is close to attainment). E nergy efficiency and renewable energy measures can be included as part of this weight of evidence. Their inclusion can give the state a bit of an insurance policy that the anticipated emissions reduction s will occur, and over the expected
time period.

The state does need to calculate the expected level of emissions reduction that will occur from the EE/RE measures. However, the state must complete a review at least once every three years

to evaluate the emissions benefits from the included EE/RE policies, and to make up any gaps

or deficits that occur.



## Purpose of Project Relative to U.S. EPA Guidance

This project provides the air districts with the means to calculate the expected benefits from

incremental efficiency and renewable energy policies, i.e.

, the ability to utilize a control

### 12 measure standard. As such, the baseline chosen for this research already includes existing efficiency and renewable energy policies that are required by statute by or through a test year, 2016. This project specifically examines incremental EE/RE above and beyond those which are required by statute; in doing so, the project assumes that in the baseline, aggressive California EE and RE policies (such as the 33 percent RES assoc iated with the AB 32 law) are well into
implementation.

Synapse came to an agreement with a program advisory committee (PAC), comprised of

U.S. EPA , air district, and Energy Commission members, that the project would examine

emissions benefits of incremen tal EE and RE programs not currently required by law as an additional mechanism to capture emissions reductions. There are compelling questions associated with the expected or likely emissions benefits of the existing California statutes, but this project is focused on additional programs that can be implemented by air districts to meet state and federal air quality regulations.

Ultimately, which of the four routes outlined above is to be chosen is an air district

- level decision. Different districts may cho ose different routes for how EE and RE benefits are to be included, even if the districts abut one another. The route chosen will likely relate to the staff resource capacity at a district. A district could choose to examine the benefits of California’s

ex isting RPS and energy efficiency requirements as part of a future baseline, and utilize

U.S. EPA assistance to examine those benefits. The district can then select one of the three other routes to account for the benefits of future programs (such as additi onal EE in a local district); this research provides a pilot study to illustrate a rigorous control measure approach

.


# Modeling Emissions Reductions from EE/RE

Phase 2 of this work emphasized e lectric system impacts from EE/RE

. As detailed in the next

secti on, Synapse used the PROSYM electric system dispatch model used by the Energy

Commission (and also by many utilities and system planners) to determine how new EE/RE initiatives would affect electricity output by location/generating plant, season, and time of day. The modeling approach is consistent with the methods U.S. EPA applies nationally to evaluate potential time

- and location

- varying air quality benefits from EE/RE.

The main body of this document details the methodology and results of this modeling effort. The accompanying Data Annex , an Excel workbook, is intended for use by air districts or other parties to evaluate the air district, state, and regional emissions benefits of EE/RE. This document provides detail not only on the actual emissions redu ctions expected from specific EE/RE load shapes, but also how these particular EE/RE measures affect energy dispatch throughout the Western Interconnect (also known as the Western Electricity Coordinating Council region, or WECC). The purpose of providing this detail is to illustrate how energy, emissions, and dispatch are coordinated throughout the West, and provide a degree of insight into how various regional environmental policies might be realized through changes in
California’s energy demand.

This rep ort is organized into the following sections:

• Modeling Framework discusses the heterogeneous structure of the Western
Interconnect and California’s role in an electric grid spanning 11 states and parts of Canada and Mexico; the electricity dispatch model u sed for modeling EE/RE; and the EE/RE scenarios modeled in this analysis.

• Analytical Framework details how results from this analysis are parsed to examine changes in generation and in NO

X 

### , SO

2 

### , and CO

2 

### emissions; how a user should interpret

results in ter ms of impact of EE/RE on a state, regional, and air district level; and the role of random error and uncertainty in the results presented here.

### 13 • Results steps through individual analyses of displaced energy and emissions, focusing on principal findings and important caveats at the state, regional, and air district level;

detailed results for displaced energy and emissions at all scales of analysis are found in the accompanying Data Annex ( an Excel workbook)

.

• Discussion highlights unusual or unexpected findin gs, details important caveats and assumptions in the model and model architecture, and draws conclusions based on the
analysis.



# CHAPTER 2 :

Model Framework

Modeling Basis

To determine the impacts of EE/RE on emissions in a future year, Synapse used a utilit y scale model of the Western Interconnect to examine generation and emissions in a “base case” and in
several scenarios, representing incremental EE and RE programs above and beyond existing statutory requirements. Synapse chose a test year of 2016 to exam ine these benefits. The following sections describe the geography of the region, the concept and execution of economic dispatch used in this research, as well as the base case and scenarios used to evaluate emissions

benefits.



# The Western Interconnect

Disp laced energy (generation) and emissions in California cannot be examined outside the context of the much larger Western Interconnect, also demarcated as the Western Electricity Coordinating Council (WECC) region. The WECC region includes California, Oregon

, Washington, Idaho, most of Montana, Wyoming, western South Dakota, Colorado, Utah,

Nevada, most of New Mexico, the El Paso region of Texas, Arizona, Northern Baja California,

Mexico, and the Canadian p rovinces of Alberta and British Columbia (WECC 2011)

. The WECC region as defined in this report can be seen in Figure 4

.

Figure 4 : Western Electric Coordinating Council (WECC) and A nalysis R egions

### 14 Source: NERC 2011



### The WECC region is highl y interconnected electrically throughout the West. The electrical grid structure in the West is comprised of high

- capacity transmission that runs primarily in two directions: from the Rocky Mountain region (RM: U tah , M ontana , W yoming, and C olorado ) to

the Northwest (NW: W ashington

, O regon

, I daho , Northern N evada , British Columbia, and
Alberta), and from the Southwest (SW: Southern N evada

, A rizona , and N ew Mexico ) into California. There is also significant transfer capacity from the Northwest to California, and between northern and southern California. Much of the electricity which serves California, and therefore many of the emissions associated with California electrical consumption, are served
from generators that lie outside of state lines.

The least expe nsive generation in terms of direct generating cost (i.e., not including either capital costs or environmental costs) lies outside of California > It includes coal generation in Utah, Wyoming, and Montana and hydropower in the Pacific Northwest and Canada.

The relationship between generation and electricity use in the Rocky Mountain region, the Northwest, and California is complex and important to understanding California’s energy picture. In 2009, the Rocky Mountain region and the Southwest produced approxi mately

150 percent and 145 percent more energy, respectively, than these regions required to meet their own demand needs. In the same year, California imported 25 percent of its electricity from other states in the West, while the Northwest remained approx imately energy balanced, on net. 8 

### The overall balance by region in 2009 is shown in Figure 5

.

Figure 5 : Generation and Demand Balance in the Western S tates of the WECC 8



### Approximately 70 percent of the electricity in the Northwest states (in 2009) is produced from hydroelectric sources; this electricity source is seasonally dependent on spring runoff, which continues through the summer. In t he spring and early summer, when hydroelectric energy is abundant, the Northwest supplies energy to California. In autumn and winter, when the rivers

8 

### Author calculations from 2009 U.S. Department of Energy (DOE) Energy Information Administration (EIA) Forms 923 (Page 1) aggregate d net generation and 861 (File 2), aggregated total sales by state.

### 15 run low, the Northwest imports significantly more energy from the Rocky Mountain region, and supplies much less to California (WECC 2009)

.



# Economic Dispatch

The displacement of generation and emissions from EE/RE is fundamentally a function of economic dispatch. Economic dispatch governs how most generating units in an interconnected
electrical system are run: the units with the least expensive operating costs (fuel and operations) are typically dispatched first, and increasingly expensive units are then dispatched to meet load requirements, subject to transmission availability. The last unit, or cohort of unit s, dispatched to meet load in a particular area are called the marginal unit s : the last to come online, they would also be the first to be taken offline if load is reduced. A displaced energy and emissions analysis is therefore usually an analysis of only the marginal units in the system: as demand is increased or decreased due to EE/RE, it is the marginal units that respond.

In the WECC region, units with very low running costs at the base of the supply curve, i.e.

, those least likely to be marginal, are nuclear, hydroelectric, solar, wind, and geothermal generators. These resources, with high fixed costs but low operating costs, are typically dispatched to run whenever they are available. Large hydroelectric facilities with reservoirs are
partially dispat chable, but can be optimized to deliver more power closer to times of peak usage

under some circumstances. Coal units, particularly in the West, are relatively inexpensive to fuel and operate, and are also dispatched early in the loading order. Finally, na tural gas units of various sorts (steam, combined cycle, and turbines) have higher running costs, and make up a majority of the marginal (also called load

- following) units in WECC. Some oil, petroleum, and jet fuel units are reserved for very high peaks, a nd can be on the margin a few hours of the year.

As a rule, EE/RE displaces generation and emissions from units at the margin; therefore, it is expected that most of the reduced generation and subsequent reduced emissions will be
derived from natural gas. However, a certain amount of displacement will occur for coal or other dispatchable resources, depending on the operational details of the EE/RE resource.

As described above, the WECC region is highly interconnected and geographically heterogeneous, and co ntains important transmission and operational constraints that strongly affect the displacement of generation resulting from EE/RE. For this reason, a dispatch model is required to take into account transmission constraints, along with and more complex beh avior such as capacity reserves and voltage support. For the purposes of this research, an industry

standard model, PROSYM, is utilized.



# Dispatch Model

Synapse chose to utilize the PROSYM model to maintain consistency with approaches and
assumptions utiliz ed by the Energy Commission for the purposes of modeling in the 2007 and 2009 Integrated Energy Policy Reports (2009 IEPR) (CEC 2009) . The PROSYM model is a commonly utilized production cost model, designed to optimize dispatch to achieve a lowest

cost pri ce of electricity on an hourly basis. The model takes into account fuel prices, individual unit constraints such as heat rate, ramping time, and maintenance requirements, as well as
transmission linkages and constraints. Given a series of constraints and r equirements, the

model solves for the least

- cost dispatch at any given time.

The PROSYM model has been used extensively in the electric industry, including by some independent system operators ( ISOs ) , to evaluate likely system behavior ; evaluate transmiss ion and generation alternatives ; and predict future emissions, costs

, and prices. This type of model is useful both for estimating the costs of generating or obtaining electricity, and examining how different units are dispatched based on changes in the sy stem. In this case, Synapse use d the

### 16 model to evaluate how different units with different emissions characteristics are dispatched given changes in renewable energy and efficiency penetration.



## Topology

The model provides a fairly flexible framework to impl ement different existing or theoretical electrical systems, including generation, transmission, and load (demand) zones. Collectively, the mapping of these structures, either existing or theoretical, is referred to as a “topology

. ” Synapse utilized default parameterizations for the WECC system as supplied by the vendor, Ventyx, and as modified by the Energy Commission for the 2009 IEPR. Characteristics of existing units were generally consistent with vendor assumptions, with the exception of
expected retire ments or repowered plants as assumed by the Energy Commission , and significant changes to the emissions rates of generators in California, as described below and
extensively in Appendix A

. The model topology is identical to that used by the Energy Commiss ion for the 2009 IEPR. The model utilizes transmission zones, which are geographical areas that contain both generation

sources a nd load. At this scale of aggregation, the zones represent regions in which

transmission is not constrained (i.e.

, it flows fre ely between generation and load). The zones are connected by transmission pipelines that represent aggregations of physical interregional transmission capacity. The model topology for the WECC region is shown in Figure 6 below.

Figure 6 : Ventyx PROSYM Topology of WECC



### Transmission allows for low

- cost resources from neighboring zones to be chosen over local generation sources, including congestion and wheeling charges. However, if the line is fully loaded, then more expensive, local generation sources are used. Within California, the lines that maintain a high utilization rate originate from the Northwest and from Idaho to N orthern

California, from Utah to S outhern California, and between S CE and SDG&E.

In PROSYM, California’s electric grid is represented as seven interconnected zones; these are Los Angeles Department of Water and Power (LADWP), San Diego Gas and Electric (SDG&E),

### 17 Southern California Edison (SCE), and Northern California (p rimarily Pacific Gas and Electric,
PG&E), 9 

### Imperial Irrigation District (IID), California Oregon Border (COB), and IV

- NG.



# Regions of Interest

Incremental New EE/RE Resource Zones

Synapse focused on new EE/RE resources in the service territories of four pri mary utilities for this analysis: LADWP, SDG&E, SCE, and PG&E. Each of these utilities is represented in the model as an independent zone. The zone titles serve as shorthand: often the zones include multiple smaller utilities and municipalities as well. Th e major zones of interest for this research

are:

• The San Diego Gas & Electric (SDG&E) zone of the model is comprised solely of the SDG&E service territory. In PROSYM, SDG&E is connected to SCE and IID with

medium

- scale transmission lines.

• The Southern Ca lifornia Edison (SCE) zone in the model is comprised of not only the SCE service territory, but also the service territories of Anaheim Public Utilities
Department, Department of Water Resources

– South, Metropolitan Water District of

Southern California, Pasadena Water and Power Department, Riverside Utilities Department, and Vernon Municipal Light Department. SCE is highly connected to the surrounding zones in the model, and has direct lines to Northern California, LADWP,

Southern Nevada, Palo Verde, IID, and SDG&E.

• The Los Angeles Department of Water and Power (LADWP) area in the model is primarily comprised of the LADWP service territory, as well as Burbank Public Service Department and Glendale Public Service Department. In the model, the LADWP zone is connected to the Northwest and Utah via a dedicated direct current line as well as large

scale transmission into Southern Nevada and SCE.

• The Pacific Gas & Electric (PG&E) zone contains the service territory of PG&E, as well

as the service territories of Department of Water Resources

– North, Modesto Irri g ation District, Northern California Power Agency, Redding Electric Department, Sacramento
Municipal Utilities District, Santa Clara Electric Department, Turlock Irri g ation District,

and Western Area Powe r Administration

– Mid Pacific. This zone is connected to SCE and COB (and therefore the Northwest) with large

- scale transmission lines, and to the

Pacific Northwest and Northern Nevada with smaller

- scale transmission lines.



## WECC and Air District Region D efinitions

To examine generation and emissions impacts, Synapse delineated four WECC

- wide regional zones and twelve air district zones, comprised of one to many air districts, depending on size

and location.

The four WECC

- wide regional zones include Calif ornia (CA), Northwest (NW), Rocky

Mountain (RM), and Southwest (SW); see Figure 4 for a map. The Northwest is made up of Oregon, Washington, British Columbia, Alberta, Idaho, and northern Nevada; the Rocky

Mountain region contains Montana, Utah, Wyoming and Colorado; the Southwest includes Arizona, New Mexico, and the El Paso region of Texas.

California has thirty

- five air districts. Many of these districts are small, and have few stationary

sources of emissions. F or this study, these small districts have been aggregated into zones to increase the accuracy and reduce the margin of error. The aggregations are shown in Table 1

.

9 

### In the model, PG&E is primarily encompassed by a transmission hub, NP15.

### 18 Table 1 : Air District Aggregations into A ir District Zones Used for this Analysis

Air District Zone Air District Air District Zone Air District

Lassen County APCD Amador County APCD

Modoc County APCD Calaveras County APCD

North Coast Unified AQMD El Dorado County APCD Shasta County AQMD Mariposa County APCD North CA

Siskiyou County APCD Sierra South

Tuolumne County APCD

Colusa County APCD Sacramento Sacramento Metro AQMD

Glenn County APCD San Joaquin San Joaquin Valley APCD

Lake County AQMD Monter ey Bay Unified APCD

Mendocino County AQMD San Luis Obispo Co. APCD

Northern Sonoma Co. APCD Santa Barbara County APCD Tehama County APCD Central Coast

Ventura County APCD Bay North

Yolo/Solano AQMD Antelope Valley AQMD

Bay Area Bay Area AQMD Great Basin Unified APCD

Butte County AQMD Kern County APCD

Feather River AQMD Desert Region

Mojave Desert AQMD

Northern Sierra AQMD South Coast South Coast AQMD Sierra North

Placer County APCD San Diego San Diego County APCD

Imperial Imperial County APCD



### I ndividual generators in California were mapped into air districts via ZIP code affiliation, or closest city or town (CARB 2010 a ) . Many, but not all

, individual generators in the PROSYM model are tagged with ZIP codes, however, accuracy was found to be vari able. For all California generators, a federal code ( ORISPL ) identifying the plant at which the generator

resides was located using the name and service region of the generator. These codes were then associated, through a federal database, to ZIP codes, mu nicipalities, and specific latitude

longitude locations. Using ZIP codes first, municipalities second, and map

- checking specific coordinates when required, Synapse mapped each California generator to a specific Air District. Using the lookup table above, t he generators were mapped back into air district regions. Demand response generators (such as interruptible load) were not mapped into air districts.



# Emission Rates Corrections

This analysis was scoped to examine the results of locally increasing renewable energy and energy efficiency on emissions at the air district scale. To achieve improved accuracy, Synapse examined baseline emissions as estimated by the model in a test year, 2012, and compared these emissions to other data sources, including the U.S. E PA ’s Continuous Emissions Monitoring System (CEMS) database and the U.S. EPA ’s Emission and Generation Resource Integrated
Database (eGRID) . I t was found that:

• a small fraction of generation in California contributed to a vast majority of emissions of

SO

2 

### and NO

X 

### , and

• the assumed emissions rates for these generators were generally incorrect,
overestimating emissions of SO

2 

### and NO

X 

### by anywhere from 20 percent to several orders of magnitude.

Synapse corrected the emissions rates for 120 existing fossil and b iomass units in California, and

45 fossil and biomass

- fired units expected to be built by the 2016 model year. The type, method, and specific changes made to generators are given in Appendix A . In general, the most problematic units in the model were high

- emissions units that are not required to submit

emissions information to the U.S. EPA CEMS system. The most notable problems occurred at industrial boilers which generate grid

- available electricity (cogenerators); despite the changes

### 19 made to very high emis sions rates, these units still produce a vast majority of stationary source

SO

2 

### emissions in California (see Table s A

- 3 and A

- 4 in Appendix A )

.

Emissions of NO

X 

### and SO

2 

### before and after correction are given in Table 2

, characterized by fuel type. Three

- qu arters of natural gas generat ors report to the U.S. EPA CEMS dataset (labeled below). No other fuel types within California are required to report to the CEMS system. It is notable that of fossil and biomass units, only the natural gas units which report t o the CEMS system are close to accurate in the default model framework.



# Base Case and EE/RE Scenarios

The California Energy Commission used PROSYM to model California energy policy and western dispatch through 2020 for the 2009 Integrated Energy Policy Re port (IEPR) (CEC 2009)

. By agreement with the Energy Commission, Synapse used these Energy Commission model
runs and assumptions as a baseline to retain internal consistency between Energy Commission assumptions and the current study. Synapse adopted the E nergy Commission’s assumed build

out to 2016, and created a base case from two Energy Commission cases compliant with California Assembly Bill 32 (AB32). The Energy Commission data includes its assumptions on of renewable energy and energy efficiency pursu ant to AB32, the once

- through cooling law, and
an updated demand forecast.

### 20 Table 2 : Aggregate E missions R ates of NO X 

### and SO 2 

### by U nit C ategorization , in California only,

B efore and A fter A djustment (2012)

NOx (to ns) SO 2 

### (tons)

Generation

(GWh) Pre

Adjustment Post

Adjustment Pre

Adjustment Post

Adjustment

Coal 2,699 7,448 1,468 12,593 3,376

Natural Gas (in CEMS) 55,160 2,481 2,136 129 127

Natural Gas (not in CEMS) 23,328 24,250 8,556 74 74

Petroleum 1,322 3,806 871 58,013 5,337

Fuel Oil 0 1 1 1 1

Biomass 2,426 1,498 1,084 9 9

Refuse 414 752 680

- -

Wood 3,293 5 ,907 3,990

- -

Future Generation 15,170 4,020 3,559 26 25

Non

- Emitting 113,365

- - - -

Total 217,177 50,162 22,343 70,844 8,949

U.S. EPA Estimate for

C alifornia 199,925 22,302 13,577



## Base Case

The purpose of a base or reference case is to provide a baseline from which to evaluate the

impacts of particular strategies or programs. This research evaluates the potential for incremental emissions reductions in California from EE/RE programs rel ative to assumed existing conditions in a future year. Because the research is specifically oriented to additional and

incremental EE/RE above and beyond that which is required by statute (i.e.

, renewable and efficiency standards), the research utilizes a base case in which fairly aggressive, yet statutorily required EE/RE has already been implemented.

The Synapse base case is an expansion and combination of Energy Commission

- estimated resource expansion cases in the year 2016, assuming a full implementatio n of several existing laws and regulations, discussed below. The year 2016 was chosen to represent a near

- term year which would potentially fall within the window of the next California SIP for emissions reductions. The year is close enough to the present such that the system is likely to look analogous to today’s electrical grid (with some marked changes) ; 10 

### however, 2016 is far enough out that additional EE/RE programs might be feasibly implemented.

10 

### The Western Interconnect is a changing electrical grid. New transmission, large new renewable energy

projects, potential retirements of existing generators, and a potential regional price on carbon dioxide are all likely to change the shape of WECC over the next decade. However, for the purposes of this study, not

### 21 The base case meets a best understanding of several Cali fornia regulations that were enacted prior to or during the study period.

• Renewable Electricity Standard under AB 32 (33 percent Renewable Electricity
Standard [ RES ] ) : The Energy Commission modeled a best understanding of the evolving
rules emerging from AB32 (the California Global Warming Solutions Act of 2006) and

the resource build

- out that would be required to meet the RES required by the law :

33 percent renewable energy by 2020. A vast majority of new renewable energy added by the Energy Commission in the resource build

- out to 2016 appears in California; at the time of the modeling, it was unclear if the RES would require new resources in

- state or allow renewable energy credit trading out

- of

- state. As of January 13, 2011, it was

determined that 25 perc ent of new renewable energy can be built outside of California
(CPUC 2011a) . It is unclear if this ruling would markedly affect the results of the model as implemented in the Energy Commission build

- out assumptions.

• Energy e fficiency requirements under AB 2021: Assembly Bill 2021 of 2006 requires a

10 percent reduction in forecasted energy consumption in California over a ten

- year period; in 2008, the CPUC created an eight

- year compliance plan for energy efficiency in

the state from 2012 to 2020 (CPUC 2011b )

.

• Cooling w ater intake structures: In May 2010, the California State Water Board adopted the Use of Coastal and Estuarine Waters for Power Plant Cooling policy (California State

Water Control Board 2010) . This policy requires that power plant cooling stru ctures

reflect the “best technology available” for minimizing adverse environmental impacts, consistent with California’s interpretation of the federal Clean Water Act Section 316(b). The California policy effectively requires that 17 natural gas

- fired pow er plants and both

nuclear stations in the state re

- permit once

- through

- cooling (OTC) structures, which are likely to be noncompliant with the regulation. Based on its early understanding of this policy, the Energy Commission has assumed in its modeling t hat all OTC generators will
be repowered as new combined

- cycle and combustion turbine natural gas plants.

.

The base case does not account for any changes made in the WECC region to comply with new Clean Air Act Regional Haze Regulations (“BART”) or the Air Toxics Rule (“Utility MACT”), as the implementation of these rules were still evolving or not yet proposed at the time of the

research.

Other changes made by the Energy Commission include modified transmission capacity to accommodate the changing balance of California resources, and zero emissions costs for SO

2 

### , NO
X 

### , and CO

2 

### (see Discussion for details and additional assumptions.) Using the Energy

Commission data ensured that the underlying base assumptions for this analysis were the same

and that the resu lts would be comparable to Energy Commission modeling efforts.

Two main Energy Commission cases were used as the basis for this analysis: (1) a scenario in which California meets the AB32 law primarily by building new wind resources , and (2) the law

is met primarily with solar resources instead of wind. These cases were called “High Wind” and “High Solar” cases, respectively. T o meet the 33 percent RES, the Energy Commission incrementally added further wind and solar resources. The future economics of wind versus solar are unclear, and these two extreme cases illustrate how the future would look with just

one or the other resource available to meet the RES.

all of these changes can be modeled without a far m ore extensive process and broad

- scale assumptions. Synapse used Energy Commission assumptions of the likely impacts of California energy policy in 2016 to guide this analysis.

### 22 With input from the Program Advisory Committee, Synapse determined that, in the absence of

additional information about the evolution of the RES market in California, a reasonable Base Case would be halfway between the High Wind and High Solar cases. A hybrid base case was created that blended the High Wind and High Solar cases by taking approximately 50 percent of

the wind resources and 50 percent of the solar resources from High Wind and High Solar, respectively. The differences between the High Wind and High Solar cases and the Hybrid Base

Case can be seen in Figure 7 below. In 2016, the S tate of California is on the trajectory toward

33 percent renewables; the Hybrid C ase shows approximately 27 percent of energy within

California being generated from renewable sources. For more information on how the Hybrid

Base Case was created

, please see Appendix B.

Figure 7 : Generation by F uel T ype in 2009 ( R ecorded ), and 2016 in T hree M odeled C ases

. 11



## Energy Efficiency and Renewable Energy (EE/RE) Scenarios

Once the Hybrid Base Case was established , Synapse added various scenarios; the scenarios incrementally add EE/RE to the Hybrid Case such that the displaced emissions from EE/RE on top of the baseline scenario can be determined.

These incremental EE/RE measures are designed, in this case, to rep resent load shape

“endmembers

, ” where each type had a unique, if not completely realistic, impact on the grid at different hours of the day. Each of the programs chosen has a different temporal pattern (peak

or off

- peak coincident, flat, or stochastic)

, an d thus would be expected to affect the grid
differently, displacing peak or non

- peak resources. For the purposes of this research, it was important to evaluate a diverse array of programs, rather than strictly realistic programs.

The incremental EE/RE meas ures considered were new wind resources , new solar resources,

new base load energy efficiency programs, and a new peak

- shaving energy efficiency program. These incremental measures can be conceptualized with the following: (1) a new wind resource

as one add itional wind farm, (2) a new solar resource as one additional solar farm, (3) a new
base load energy efficiency initiative as refrigeration or industrial lighting efficiency program s

, and (4) a new peak

- targeting efficiency savings as demand response or eff icient air

- conditioning

program s

. 12 

### These four measures were chosen for a variety of reasons. Each of the measures

11 

### 2009 data from EIA Form 923, annual generation by plant, aggregated by state a nd fuel type.

12 

### A program such as a “Cool Environments Program,” designed to reduce cooling requirements by reflecting solar heat and providing shade, would meet the peak

- targeting program criteria.

### 23 have fundamentally different load shapes, and therefore affect the WECC system in varying ways; further, these four measures are an efficient way to represent a wide variety of programs that could theoretically and practically be chosen to reduce emissions.

Each EE/RE measure had to be sized such that it would make a measurable impact on the system. Because the WECC system is extensive, the siz e of the new measures had to be
sufficiently large to perturb the system enough to distinguish signal from “ noise. ” However, the measures could not be unrealistically large, as programs that are too large in size fundamentally change the dispatch dynamics and are therefore not representative of incremental changes to the system. The measures also needed to be plausible programs that could be implemented in California and used for SIP emissions credit. Each measure should also be comparable to the

other EE/R E measures. A complete description of the sizing for each measure is detailed below.

Each of the four measures was modeled in the four California zones detailed above in the Topology Section: LADWP, SDG&E, SCE, and PG&E. These zones were chosen for modeli ng the measures because they are the largest utilities in California. PG&E, SCE, and SDG&E are the major utilities dispatched by the California Independent System Operator (CAISO). LADWP is not part of the CAISO system, but it is the next

- largest utility i n California. These zones are geographically distinct and therefore provide a comprehensive picture of how measures will affect the different geographical zones of California. Because these zones do represent the
largest utilities in California, it is more likely that large

- scale wind or solar resources would be built or large

- scale efficiency programs would be implemented in these zones than in smaller
municipalities.

To use the dispatch model to determine displaced emissions from EE/RE, Synapse required an estimated hourly load shape for each load

- reduction measure that would serve as a proxy for a specific modeled incremental energy reduction program

. These load shapes are detailed below.



## Wind

Synapse added 1000 MW of wind to four zones: LADWP, SDG&E, SC E, and PG&E. In SDG&E, SCE, and PG&E, Synapse was able to use hourly wind profiles from the Energy Commission ’s model assumptions. The Energy Commission hourly wind profile assumptions come from the 2009 IEPR. The hourly profiles developed by the Energy Co mmission were scaled to reach

1000 MW. For LADWP, no Energy Commission assumption existed, so Synapse assumed that wind turbines could be built at Tehachapi and connected to the Los Angeles Basin. The hourly wind data for Tehachapi was obtained from the WE CC

- N ational Renewable Energy Laboratory
(N REL ) Wind Integration Study. All additional wind units ranged in capacity factor from

20 percent in PG&E (copying a pre

- existing Energy Commission unit) to 40 percent in Southern

California.

Figure 8

, below, shows changes in effective load requirements in LADWP after the
implementation of a large

- scale solar PV project directly connected to the service territory. The blue line represents the hourly load shape of the node as represented in the model, while the red line indicates the reduced load equivalent from the simulated wind farm. While the wind turbine simulation does have significant hour

- to

- hour stochastic elements, it is seen in these example hours that the load is r educed primarily off

- peak and in the morning hours. These hours are not necessarily representative of reductions from wind throughout the year.

### 24 Figure 8 : Example L oad P rofile and R eduction from W ind in LADWP: July 11

– 12, 2016



### A r epresentative picture of how the new wind measures operate during the year are shown in the following figures. Figure 9 shows the average annual capacity of the wind resource at each

hour. Figure 10 shows the average monthly capacity of the wind resource in each location.

Figure 9 : Average H ourly W ind C apacity by H our for 1000 MW W ind F arms

in F our S ervice T erritories

Figure 10 : Average M onthly W ind C apacity by H our for 1000 MW W ind F arms

in F our S ervice T erritories



## Solar

To maintain an approximate level of energy output consistency with the wind scenarios, each of the new solar units were given a rated capacity of 100 0 MW and were added to the same utility

### 25 service zones as the wind turbines. All solar units are assumed to be utility

- scale photovoltaic units as modeled by the Energy Commission . Hourly profiles for solar units all existed in the

Energy Commission dataset as assumptions from the 2009 IEPR, and these profiles were used

and scaled to 1000 MW.

Figure 11

, below, shows changes in effective load requirements in LADWP after the
implementation of a large

- scale solar PV pro ject directly connected to the service territory.

Figure 11 : Example L oad P rofile and R eduction from S olar in LADWP: July 11

– 12, 2016



### The following figures represent how the new solar measures operate during the ye ar. Figure 12 shows the average a nnual capacity of the solar PV resource at each hour, while Figure 13 shows average monthly capacity.

Figure 12 : Average H ourly S olar PV C apacity for 1000 MW of S olar I nstallations

in F our S ervice T erritories

### 26 Figure 13 : Average M onthly S olar PV C apacity for 1000 MW of S olar I nstallation

in F our S ervice T erritories



## Basel oad Energy Efficiency

Synapse c reated s cenarios that modeled both base load energy efficiency and peak

- targeting energy efficiency. The energy efficiency scenarios are not meant to represent one particular, targeted program, but to examine the overall effec t of cutting base load demand or peak
demand. However, the base load efficiency characterizes the expected effect of refrigerator or industrial lighting efficiency programs, while the peak

- shaving efficiency corresponds to
demand response or air conditioning efficiency programs. Energy ef ficiency is modeled in PROSYM as a power unit, with all the characteristics of a thermal power plant.

T o depict a “pure” base lo ad efficiency program, the base load efficiency units are given a capacity factor of 100 percent

, meaning that they are always ru nning in the model. Because of the capacity factor, and in order to ensure comparability of energy generated in the win d and

solar scenarios, all base load efficiency units were assigned a maximum capacity value of

333 MW. A base load efficiency unit was pla ced in and modeled in each of the four zones:

LADWP, SDG&E, SCE, and PG&E.

Figure 14

, below, shows the impact that 333 MW of base load efficiency makes on the load in the LADWP service territory. The blue line show s the hourly load shape of LADWP as represented

in the model, while the red line indicates the reduc ed load from the simulated base load

effic iency program. Because the base load efficiency is set to be continuously running in the

model, load in LADWP is red uced by the full 333 MW in all hours

, with no hourly variation.

### 27 Figure 14 : Example L oad P rofile and R eduction from a B aseload EE P rogram in LADWP: July 11

– 12, 2016



## Peak

- Targeting Energy Efficiency

Peak

- targeting energy efficien cy was designed to decrease load only on days with very high demand. This was modeled by creating a thermal unit in the model with an hourly profile, much like a solar PV or wind unit. Synapse examined the hourly load from the Hybrid C ase for

each in order to create an hourly profile for peak

- targeting efficiency. The profile was generated by first finding which hours the load was in the 90 th percentile or above, and then by shaving

10 percent off of those hours.

• In the LADWP zone in PROSYM, the maximum of the load is 6,499 MW, and the top 10

percent of hours are those where load exceeds 4,418 MW. The maximum decrease of
load from a peak

- targeting efficiency program in LADWP is 650 MW when the load

reaches its absolute maximum.

• In SDG&E, the maximum load is 4,703 MW, and the top 10 percent of hours are those

where load exceeds 3,300 MW. When load reaches an absolute peak, the maximum decrease from the peak

- targeting efficiency program is 470 MW.

• In the SCE zone of PROSYM, the maximum of the load is 24,474 M W, and the top

10 percent of hours are those where load exceeds 15,912 MW. The maximum decrease of
load from a peak

- targeting efficiency program in SCE is 2,447 when the load reaches its

absolute maximum.

• In PG&E’s zone, the maximum load reaches 25,306 MW, and the top 10 percent of hours

are those where load exceeds 16,711. In the hour that PG&E hits its maximum load, the
peak

- targeting efficient program shaves off 2,530 MW.

Figure 15 shows the impact that a peak

- t argeting efficiency program that shaves 10 percent of load off the top 10 percent of hours has on the load in the LADWP service territory. The blue line shows the hourly load shape of LADWP as represented in the model, while the red line

indicates the redu ced load from the simulated peak

- targeting efficiency program. The two days represented in the figure are in July, when load approaches the peak, so the decrease in load is approaching the maximum of 650 MW. The top 10 percent of hours occur during daytime peak

hours between late morning and late evening. These hours are not necessarily representative of the reductions seen from peak

- targeting programs throughout the year due to the distribution of peak hours.

### 28 Figure 15 : Example L o ad P rofile and R eduction from a P eak

- T argeting EE P rogram in LADWP: July 11

– 12, 2016



### The following figures represent how the peak

- targeting EE measures operate during the year.

Figure 16 s hows the average annual capacity of the EE resource at each hour, while Figure 17

shows average monthly capacity. It should be noted that the peak EE programs were created to

target 10 percent of the service area load

, as to not be unrea sonably large. Therefore these programs appear to have very different capacities

, depending on their implementation location.

Figure 16 : Average H ourly P eak EE R eduction M odel for F our S ervice T erritories

Figure 17 : Average Monthly P eak EE R eduction M odel for F our S ervice T erritories

### 29 

# CHAPTER 3:

A nalysis Framework

Overview

The purpose of this analysis is to determine the emissions benefit from specific types of renewable energy (RE) and energy efficienc y (EE) implemented within California (collectively, EE/RE). Ultimately, because this research is designed to estimate how emissions could be
displaced in a forward

- going fashion, the scale, type, or location of programs which could be
implemented was unkno wn. Therefore, this analysis was designed to estimate the rate of displaced emissions, measured as the physical units of pollution displaced for each unit of
alternative energy implemented.

This “displaced emissions rate” is designed to be scalable, withi n reasonable bounds, such that various amounts of EE/RE will accomplish estimated pollution reductions as a simple function of the rate and the amount of EE/RE implemented, within a particular implementation region.

In this California

- based study, the disp laced emissions rate is broken into its component parts, by both geography and emissions source (fuel type). In the Western Interconnect (the WECC region), the electric sector is largely divided along geographical lines, with hydroelectric

resources in the Northwest, coal in the Intermountain West and Southwest, and natural gas in

California. Even in the absence of additional information, reason suggests that EE/RE in California will have highly diversified impacts throughout the West in both generation and emissions. It is therefore critical that the displaced emissions rate for California be parsed by, at the very least, the geography of displaced energy resources.

A California

- wide displaced emissions rate is not useful for the purposes of determining

emi ssions benefits within specific air districts. Transmission constraints, unique long

- distance interconnections, and the variance in energy resources across California render the impact of any given EE/RE initiative very different in each air district. Ther efore, within the state, the displaced emissions rate is parsed into the air districts in which the emissions reduction benefit

occurs.

At a fine

- grained level of resolution, particularly for air districts with little generation, there is
significant uncer tainty from the model results as to what amount of displaced energy and emissions are due to EE/RE, and what can be attributed to random error or uncertainty within the model construct itself. It is important for this analysis to explicitly estimate model

uncertainty.

The following sections describe, in a stepwise fashion, how this research examines :

• displaced energy (generation) in general,

• displaced emissions on a regional and air district basis, and

• random error and uncertainty.



# Displaced Energy

Displ aced energy is defined as the energy which is not served by marginal generators ( either

reduced or curtailed ) when new renewable energy produces electricity or efficiency is available

. Given a balance between load (demand) and generation, a generating unit (or, more likely, a cohort of generators) will be displaced by new “must take” energy or load reductions from

### 30 EE/RE. The amount of energy displaced should be approximately equal to the amount of energy in the EE/RE, plus or minus transmission losses if th ese occur in different locations.

In this research, the “ displaced energy fraction ” ( gf disp 

### ) is calculated as:

where g baseline 

### represents the baseline energy from a set of generators i over time period t

, g scenario 

### is

the energy fr om the same generators in the scenario of interest, and g EE/RE 

### is the energy from the EE/RE in the same time period t . The subset of generators i can refer to either all generators in the model region, in which case the displaced emissions rate represents a total sum rate, or a subset of generators in a particular region or air district or of a fuel type (or both). Over the entire WECC region, this analysis examined displaced energy at both the full WECC regional

scale and within C alifornia at the air distr ict scale.

In this research, all displaced energy and emissions are given relative to the amount of added energy in the EE/RE:

• displaced energy is the amount of energy (MWh) curtailed per unit of added EE/RE energy (MWh), and

• displaced emissions are phy sical units of pollution (tons or lbs

. ) reduced per unit of added EE/RE energy (MWh or GWh ).

In the displaced energy analysis, the total sum displaced energy should be approximately

100 percent

, but the location and fuel types of the displaced resources a re also of significant interest; these fractions will add up to 100 percent

.

An example of a displaced energy analysis for WECC regions is given in Figure 18 , below.

Figure 18 : Example of D isplaced E nergy F raction by WECC R egion and F uel T ype : MWh of

Generation Displaced per MWh of New Wind in SCE



### The bar represents the fraction of energy displaced, which cumulatively should add up to
approximately 100 percent

. The colors of the bar repre sent the areas where EE/RE displacement occurs: green in CA (32 percent ), blue in the NW ( 14 percent ), yellow in the RM ( 17 percent ),
and the remainder in the SW (37 percent ); similarly, the patterns on the bar colors represent the
major classes of fuel ty pe displaced by the EE/RE, with solid colors representing natural gas (overall, 63 percent ), stripes representing coal (27 percent ), and dots “other” ( primarily comprised of oil, petroleum products, and biomass

, including refuse and wood) (10 percent ).

In the example shown, the scenario is identified as an EE/RE program of new wind in the SCE service territory. All of the energy added into the system (in this case, approximately 2600 GWh from the equivalent of a 1000 MW wind farm) is displaced throughout th e WECC region; it is displaced only partially in C alifornia , while the rest is displaced throughout WECC.

### 31 The implications for this wide distribution of displaced energy from EE/RE in C alifornia is that
only about one

- third of the displaced energy has an emissions benefit in C alifornia , while the remainder is spread throughout other regions of the West. The displaced fuel within California

is natural gas over 90 percent of the time.

The component of generation which is displaced within C alifornia can be f urther parsed by air

distri ct. Figure 19 , be low examines the net 32 percent of generation displaced within the state, parsed by air district region (see WECC and Air District Region D efinitions on p age 17 for air district regions). In this case, the largest single beneficiary of new wind in SCE territory are generators in the Bay A rea , at about 9 percent of total generation

, or about 30 percent of

generation wit hin California.

Figure 19 : Example of D isplaced E nergy F raction within California, by A ir D istrict R egion : MWh of

Generation Displaced in California per MWh of New Wind in SCE



### This analysis shows a small negative displaced energ y in the Central Coast region (i.e.

, an apparent increase in generation relative to the base case. These increases, though small, represent significant potential random errors within the analysis, which are explored at the end of this chapter.



# Displaced E missions

Displaced emissions are emissions which are avoided when EE/RE cause a reduction or
curtailment in generation from fossil

- fired generators. Of particular interest to this research are

NO
X 

### , SO

2 

### , and CO

2 

### from coal, natural gas, petroleum

, and other hydrocarbons, as well as

biomass and waste sources. The PROSYM dispatch model tracks these criteria and greenhouse gas emissions; emissions results were output at a monthly time step. For the purposes of this analysis, hourly output was not pursued for all WECC generators.

For this research, displaced emissions are estimated as a rate , in physical units of pollutant avoided per each unit of EE/RE energy (lbs/MWh). This rate allows the user to scale avoided

emissions to the amount of RE or EE in the EE/RE. A s with the energy analysis, the displaced emissions rate can be parsed by geography (region or air district) and fuel type.

The displaced emissions rate ( er disp 

### ) is calculated as:

where e baseline 

### represents the baseline emissions ( in lbs of NO

X 

### or SO

2 

### , or tons of CO

2 

### ) from a set of

generators i over time period t

, e scenario 

### are the emissions from the same generators in the scenario

of interest, and g EE/RE 

### is the generation (in G Wh) from the EE/RE in the same time period t . This

equa tion yields the physical units of pollution displaced for each unit of energy produced by

EE/RE, or the estimate d displaced emissions rate. The sum of all regional displaced emissions

rates is equal to the total sum rate, as shown in the example in Figure 20

, below:

### 32 Figure 20 : Example of D isplaced E missions R ate by WECC R egion and F uel T ype : SCE Wind



### Similar to the displaced energy fraction in Figure 19 , the bar i n Figure 20 shows the regional displaced emissions rate, parsed by the major WECC regions and fuel types. In this case, the net
aggregate displaced em issions rate is approximately 1 01 0 lbs NO

X 

### displaced for each G Wh 13 

### of new wind g eneration in the SCE service territory. However, a vast majority (85 percent ) of the
aggregate rate is a function displacing out

- of

- state coal power; the benefit realized within

California is about 6 0 lbs NO

X 

### per G Wh of new wind. This low rate is a combine d function of both a fairly small fraction of energy displaced within California (32 percent ) and the very low emissions rate of generators within the state.

Like generation, the displaced emissions rate can be parsed to air districts within California.

H owever, it should be noted that as the displaced emissions rate is parsed further, the accuracy of the solution decreases markedly due to random events intrinsic to both the model and

realit y. Figure 21

, below, sho ws the displaced emissions of NO

X 

### subdivided among air district

regions. 14



### Figure 21 : Example of California C omponent of D isplaced NO X 

### E missions R ate , by A ir D istrict

R egion : SCE Wind



### The figure shows that the displaced NO

X 

### emissi ons from a new wind project in the SCE service territory are distributed over numerous air districts. The highest displaced emissions rate is

se en in the Bay Area, at about 13 lbs NO

X 

### /G Wh.

13 

### Emissions in the power sector are often given in lbs/M Wh of output. However, due to the fairly small values in this analysis, the output for criteria emissions are given here in lbs/GWh, where one gigawatt

hour (GWh) is equal to 1000 megawatt

- hours (MWh). Emissions of CO

2 

### are given in tCO

2 

### /MWh.

14 

### It should be noted that these displaced emissions represent annual emissions from generators which

reside within the air district regions, and do not represent expected changes in ambient air quality or
population exposure to those emissions.

### 33 Finally, it is illustrative to examine the displaced emissions rate in California by fuel type, such

as shown i n Figure 22

, below.

Figure 22 : Example of California C omponent of D isplaced NO X 

### E missions R ate , by F uel T ype

within C alifornia : SCE Wind



### The f igure illustrates that while natural gas accounts for a vast majority of the displaced energy
in California (over 90 percent

, see Figure 18 , above) it is a smaller fraction of the displaced

emissions rate within Ca lifornia (~40 percent ), at least for this example EE/RE (wind in SCE).

This small reduction is because natural gas in C alifornia has, by a large fraction, the lowest NO

X 

### emissions rate. Wood, which is only about 3 percent of the displaced energy within Cal ifornia,

accounts for another 40 percent of the displaced emissions rate. Generally speaking, the reason for this is that displaceable wood generation in C alifornia has a far higher emissions rate than

natural gas (about 2.4 lbs NO

X 

### /MWh [240 lbs/GWh] for wood, versus < 0.4 lbs NO

X 

### /MWh

[ 40 lbs/GWh] for natural gas), and therefore makes up a larger fraction of the emissions margin.

The very large component of California displaced emissions that appear to come from wood

fired generation raise s the question of whether these emissions benefits are “real” or not

— that

is, if they are more readily explained by random error or by actual displaced energy . The following section describes our error analysis for this research.



# Uncertainty and Error Analysis

The displace d emissions calculated in this analysis must be contextualized within the framework of the model, and specifically

, the errors and uncertainties associated with both the model and real dispatch operations. The model used here is a commercial

- scale utility dispatch model (see earlier Modeling Framework section), but nonetheless has significant limitations,
particularly as applied to a displaced energy analysis. In particular, the model used here is highly simplified in order to solve a complex problem in rea sonable time. To some extent, the errors and uncertainty described here and applied to this analysis are a function of the model,
but to a larger degree, may represent a wide uncertainty experienced in a complex and dynamic electric system.

One can charact erize aspects of systematic or architectural errors, as well as uncertainties which

are purpose

- built into the model itself.



## Structural Uncertainty and Systematic Error

Systematic and architectural errors are defined as fundamental aspects of the electrici ty market which this model cannot capture, as well as simplified assumptions used to streamline model use, runtime, and results. These uncertainties are, in large parts, fertile ground for future

research efforts, and as such are described in the discussio n section later. Among these

uncertainties are model topology (including both temporal and spatial resolution)

, future fuel

and emissions costs, new generation and retirement, expected emissions controls in place in the

### 34 analysis year, and stochastic behavi ors for wind and the EE/RE load profiles chosen for this
analysis.



## Random Error

– Forced Outage Noise

Random errors are defined as inconsistent measurements over repeated observations. The model utilized for this research does have a random component which describes forced outages, or times when specific units are unavailable due to unpredictable maintenance requirements. These forced outages occur randomly with a given probability for many electricity generating
units ( EGU ) ; the random element is that in a ny given run or scenario, a unit may spend a fraction of its time offline. In the model, forced outages occur randomly with a given frequency.

When an EGU is forced offline, other EGU ramp up to meet load requirements; these adjustments in the loading cha nge both generation patterns and emissions. As an extreme example, if one of the large nuclear units has a forced outage, its equivalent energy will be
served by additional natural gas and coal units across WECC, apparently “adding” emissions to the system

. In any given model run (scenario), it is not possible to predict which changes in generation and emissions are due exclusively to the modeled EE/RE and which are due to random forced

outages

— all occur simultaneously. Without numerous (and highly time

- in tensive) runs of each scenario to estimate an average behavior, the expected value behavior of the displacement due

to EE/RE alone (versus outages) cannot be explicitly determined. Therefore, an analysis mechanism is required to account for forced outage u ncertainty , termed “noise” in the system.

To account for this uncertainty , the base case was run six times; from these six iterations, it was possible to estimate changes in generation due only to forced outages. In each region,

transmission area, and air district, Synapse estimated the standard deviation of the generation and emissions within each fuel group. When the noise due to forced outages (the standard deviation) exceeds the change in generation or emissions between the scenario and base case, an

i ndividual estimate of displaced energy or emissions is termed “non

- meaningful

. ” 15



### Figure 23 , below, illustrates an example of how the error is evaluated in this model. This case

examines the total CO

2 

### emissions fro m generators in California. In the baseline run, slightly

more than 60.4 million tons of CO

2 

### are emitted from C alifornia generators. In the SCE Wind scenario, the EE/RE displaces approximately 380,000 tons of CO

2 

### in 2016 (represented by the

dark red bar).

15 

### Both the base case and each scenario have a random draw component in choosing the timing of forced outages. If any given difference between a scenario and the base case is due to a forced outage only, then the difference is not valuable from a displaced emissions standpoint, and is therefore “not meaningful”.

### 35 Figure 23 : CO 2 

### E missions, D isplaced E missions , and I teration R un E missions in California, A ll

F uel T ypes

. Note, the y

- axis does not begin at zero.



### To estimate if the displaced emissions are significant, or likely due only to ran dom forced outages in this particular run, Synapse calculated the standard deviation of all CO

2 

### emissions in California in the iterations of the baseline runs (the six separated bars). The standard deviation,

denoted in Figure 23 as an error bar on the baseline, is approximately 80,000 tons of CO

2 

### . Synapse estimates that the actual displaced CO

2 

### emissions from this EE/RE cannot be predicted

within an error of 80,000 tons (or 22 percent of the displaced emissions). T he total displaced

energy for this particular EE/RE is 2923 GWh per year; therefore, the estimated displaced emissions rate is approximately 0.13 tCO

2 

### /MWh (380/2923 = 0.13 ), but the error in that rate is

0. 03 tCO

2 

### /MWh (80/2923 = 0.03 ).

In numerous scenario s and particular areas, the error (noise) exceeds the displaced emissions or generation (the signal). In these cases, there is lower confidence of the displaced emissions generation or emissions rate. This circumstance occurs under circumstances and region s where the displacement is relatively small and the forced outage rate is relatively high.

Formally, the uncertainty in generation and emissions are estimated as the standard deviation of the six baseline runs, as in the following equations:

and

where g std 

### and e std 

### are the standard deviation of generation (in G Wh) and emissions (in tons or lbs) over cohort of generators i in time period t , and g baseline 

### and e baseline 

### are the six baseline runs a (one through s ix) over the same cohort of generators in the same time period.

The error rate in generation ( gf std 

### ) and the emissions rate ( er std 

### ) are estimated similarly to the fraction of generation displaced by the EE/RE and the displaced emissions rate due to the

EE/ RE, as:

and

### 36 In the following results section, the displaced generation fraction and displaced emissions rates are followed by the error rate, in the same units. This value can be interpreted as a plus

- or

minu s value.

### 37 

# CHAPTER 4:

Results

The following results section is comprised primarily of tables and graphs with the estimated
displaced energy fraction and displaced emissions rates. Results are presented for generation,

NO
X 

### , SO

2 

### , and CO

2 

### , shown regionally a nd then by air district. All detailed results are given in

both Appendix C and the attached Data Annex

.



# Displaced Energy Fraction, by Region and Fuel Type

The displaced energy fraction for all sixteen EE/RE scenarios, parsed by WECC region and fuel

type is shown in Figure 24 , below.

Figure 24 : Displaced E nergy F raction (MWh D isplaced E nergy per MWh of EE/RE) by WECC

R egion and F uel T ype



### The first four bars represent the displaced energy f raction of EE/RE implemented in the San Diego Gas & Electric (SDG&E) service territory, followed by EE/RE impl e mented in Southern California Edison (SCE), the Los Angeles Department of Water and Power (LADWP), and

Pacific Gas and Electric (PG&E). Each of t he four EE/RE are ordered by onshore wind, utility

scale solar PV, a baseload EE load shape, and a peak

- reducing EE program.

The color scheme and fuel type demarcation are identical to those presented in example Figure

18 , above; green bars (of all textures) represent the fraction of the EE/RE that is displaced in California, blue in the Northwest, yellow in the Rocky Mountains (Intermountain West), and orange in the Southwest. Solid bars represent the fraction disp laced by natural gas, stripes represent coal displacement, and dots are “other” forms of displaceable generation.

The chart is a useful illustration of how particular types of programs affect generation throughout WECC. In SDG&E and SCE, most programs app ear to displace primarily outside of California, only affecting California generation 20 to 40 percent of the time, while in LADWP and PG&E, EE/RE tend to displace primarily within California (and, as will be seen later, primarily in their own service terr itories). Displaced energy outside of California is a mix of coal

and natural gas, with some “other” (primarily petroleum and oil outside of C alifornia ), while

### 38 displaced generation in California is largely natural gas, with very small fractions of “other” (primarily load control mechanisms

— see section below on displaced fuel types).

A striking attribute of the chart is the negative displaced energy value in three of the EE peak reduction programs, and even in some of the EE baseload programs; these negative values of displaced energy represent increases in coal generation in the Southwest relative to the baseline when these types of EE programs are implemented. The model indicates that SDG&E peak
reduction program, for example, increases coal generation in t he Southwest by approximately

16 percent of the magnitude of the EE/RE ; 16 

### however, the uncertainty on this value is

24 percent

, meaning that the noise far exceeds the signal in this example.

A more subtle pattern which emerges from this analysis is that of f

- peak wind programs displace more out

- of

- state (coal) generation than on

- peak solar EE/RE; this pattern might be
expected

, as off

- peak programs may be able to cut into baseload generation more effectively than peak

- targeting programs.

Aggregates of the d ata in the chart above are represented in Table 3 and 4

, below ( with eight

scenarios in each table)

.

Table 3 : Displaced E nergy Fraction b y WECC Region f or Eight EE / RE Scenario s

. T he t op v alu e is

the d isplaced e nergy f raction

, and the l ower v alue i s the u ncertainty r ange

. If the s ignal e xceeds

the n oise

, the v alue i s b old

. (1 of 2)

Displaced Energy Fraction (%)SDGE,WindSDGE,Solar (PV)SDGE, EEBaseloadSDGE, EEPeakSCE,WindSCE,Solar (PV)SCE, EEBaseloadSCE, EEPeak

20.6% 32.3% 30.6% 22.0% 32.3% 36.3% 35.7% 40.8% CA

9.5% 9.4% 5.7% 52.7% 5.7% 9.3% 5.7% 10.6%

17.2% 13.4% 11.1% 6.4% 14.0% 12.4% 11.4% 15.0% NW

7.2% 7.1% 4.3% 39.9% 4.3% 7.0% 4.3% 8.0%

16.9% 12.3% 13.4% 39.4% 16.7% 11.4% 11.7% 11.7% RM

3.9% 3.9% 2.4% 21.7% 2.4% 3.8% 2.4% 4.4%

45.2% 42.0% 44.9% 31.8% 36.8% 39.8% 41.1% 32.3% SW

9.6% 9.5% 5.8% 53.4% 5.8% 9.4% 5.8% 10.7%

16 

### F or a 100 GWh of energy reductions during peaking hours, coal generators in the Southwest would increase generation by 16 GWh.

### 39 Table 4 : Displaced Energy Fraction b y WECC Region f or Eight EE / RE Scena rios. T he t op v alue i s

d isplaced e nergy f raction ; the l ower v alue i s the u ncertainty r ange

. If the s ignal e xceeds the n oise

, the va lue i s b old

. (2 of 2)

Displaced Energy Fraction (%)LADWP,WindLADWP,Solar (PV)LADWP,EEBaseloadLADWP,EE PeakPG&E,WindPG&E,Solar (PV)PG&E, EEBaseloadPG&E, EEPeak

77.0% 80.0% 85.6% 43.0% 55.5% 78.3% 80.8% 91.1% CA

5.9% 9.4% 5.7% 38.5% 13.2% 9.7% 5.7% 10.2%

1.9% 4.8% 1.4% 23.7% 8.9% 7.0% 7.3%

- 4.4% NW

4.5% 7.1% 4.3% 29.1% 10.0% 7.4% 4.3% 7.7%

5.9% 4.3% 3. 1% 28.9% 14.8% 7.3% 6.4% 8.2% RM

2.4% 3.9% 2.4% 15.9% 5.4% 4.0% 2.4% 4.2%

15.1% 10.8% 9.9% 4.2% 20.6% 7.2% 5.5% 5.0% SW

6.0% 9.5% 5.8% 38.9% 13.3% 9.8% 5.8% 10.3%



### In this table (as all others following), the top value represents the displaced energy fr action (or in later charts, the displaced emissions rate) for the column EE/RE and row region. The lower value represents the uncertainty range in the same units as the displaced energy fraction (or emissions rate, respectively). When error values exceed t he signal, both values are gray. In all cases, except the SDG&E peak reduction EE/RE, the fraction of generation displaced within California exceeds the noise (barely in LAWDP), although this fraction can vary significantly.

The large variance in many of t hese values indicate s just how deeply interconnected the WECC region is; small changes in forced outages throughout the region can steeply vary where generation is displaced, in many cases even exceeding the amount of generation that is displaced by a larg e new renewable energy or efficiency program. In some regions, such as LADWP and PG&E territory (northern California), the fraction of generation displaced within California is far higher, and therefore the uncertainty component much lower.



# Displaced Energ y Fraction, by Air District Region

Within California, various air district regions see different levels of generation displaced by EE/RE, depending on the type and service territory in which the program is implemented (see

Figure 25

, below). The layout of EE/RE across the x

- axis is identical in this chart, grading through wind, solar, and the two EE programs in each of the four service territories. However, instead of examining generation across all WECC regions, th e displaced energy fraction is parsed within California air district regions (see definitions in Table 1 ). The net total of both
positive and negative displaced energy equals the total displaced energy fraction in California,

from Figure 24 , above.

### 40 Figure 25 : Displaced E nergy Fraction (M W h Displaced Energy Per M W h o f EE / RE ) i n California

, Parsed b y Air District Region



### It is notable that, similarly to the WECC

- wid e results, EE/RE in SDG&E and SCE tend to displace generation across a much wider area than EE/RE in LADWP and PG&E. In fact, EE/RE in the two southern ISO utilities displace in nearly equal proportion in northern and

southern California. In contrast, EE/R E in the LADWP service territory are highly specific to LADWP, displacing almost exclusively within the LADWP zone. Finally, EE/RE in the PG&E
territory target s generators in the northern reaches of the state, with only fairly small fractions

in the south.

In accordance with the WECC

- wide results, peaking EE/RE appear to reduce generation in some regions, yet increase generation in others (negative displaced energy fraction). In particular, the increase in generation in the Central Coast air district regio n (yellow) usually exceeds the noise, suggesting that this increase in generation is real.

Tables for the displaced energy fraction by air district region are given in Appendix C and the

Data Annex ( an Excel workbook)

. The net impact of these EE/RE scenar ios on fuel types in California is shown in Figure 26

, below. The figure indicates that a large fraction of the displacement in California is from natural gas generators. Note that there is almost a negligible cont ribution in displaced energy from

wood

- fired generators; a factor which will become important when reviewing displaced

emissions.

### 41 Figure 26 : Displaced Energy Fraction (M W h Displaced Energy p er M W h EE / RE ) i n California

, Parsed b y Fu el Type



# Displaced NO X 

# Emissions, by Region and Fuel Type

Oxides of nitrogen (NO

X 

### ) are emitted by all combustion fuels, but the native emissions rate from

coal exceeds that of natural gas by an order of magnitude or more. Most combustion sources

can be co ntrolled for NO

X 

### emissions, and these controls are largely represented in the model

with lower NO

X 

### rate inputs at controlled generators. As described in Appendix A , NO
X 

### emissions rates were corrected for mischaracterized California generators, but not for generators outside of the state.

In general, generators in California are highly controlled for NO

X 

### emissions, and the state
contributes a relatively small fraction of NO

X 

### in the Western Interconnect (< 5 percent of the

NO
X 

### in this analysis, see Figure 27 ). E missions out

- of

- state are much higher, mostly due to uncontrolled coal generators. Therefore, it is expected that significant NO

X 

### reductions will mostly be r ealized out

- of

- state if a program displaces non

- California generation.

### 42 Figure 27 : Total M odeled NO X 

### E missions by M onth in E ach WECC R egion in 2016



### Figure 28 , b elow shows the displaced NO

X 

### emissions rate for all of WECC, in pounds of NO

X


### per gigawatt

- hour of EE/RE generation. The rate is given in an unusual unit (on a per GWh basis) for readability only, due to the small value in California.

Figure 28 : Displaced N O X 

### E missions R ate (in lbs NO X 

### per GWh of EE/RE G eneration ), by WECC

R egion and F uel in 2016



### A vast majority of the NO

X 

### reduction benefit is realized out

- of state for most EE/RE. Nonetheless, the displacement is highly distributed across WECC. The net rat e is negative in the LADWP peak EE EE/RE because the benefit in California and the NW is outweighed by an

### 43 apparent increase in coal generation in the Southwest; a similar story is seen in both SDG&E
and PG&E peaking programs.



# Displaced NO X 

# Emissions, by Ai r District Region

The small component of NO

X 

### displaced emissions rate that is realized in California air district

region s is graphed in Figure 29

, below.

Figure 29 : Displaced NO X 

### E missions R ate (in lbs NO X 

### /GWh) in California A ir D istrict R egion



### For the most part, displaced emissions rates in the southern air districts are significant for EE/RE implemented in the three southern utilities (SDG&E, SCE, and LADWP), while
displaced emissions ra tes in the air districts in Northern California (i.e.

, Northern California to the Central Coast) are significant for EE/RE implemented in the PG&E service territory. The negative displaced emissions rates (i.e.

, increases in emissions) in the north from pe aking EE/RE in SDG&E and LADWP are generally on the same order of magnitude as noise, suggesting that while real patterns may exist, they are more easily explained by random variance than by load changes from EE/RE. This pattern is explored in more depth i n the
Negative Displacement Patterns section of Chapter 5.

Table 5 and Table 6

, below, show the NO

X 

### displaced emissions rate by air district. Each table

includes results for eight different scenarios. Again, the rate is given as the first value, and uncertainty values are given in the second value in each cell. Values where the noise exceeds the
value are grayed

, and negative values are given in red.

### 44 Table 5 : Displaced NO X 

### R ate (in lbs NO X 

### per GWh of EE/RE) for E ight S cenarios

by California Air District Region

Displaced lbs of NOx

per GWh of Energy

Displaced, by Air

DistrictSDGE, WindSDGE, Solar(PV)SDGE, EEBaseloadSDGE, EEPeakSCE, WindSCE, Solar(PV)SCE, EEBaseloadSCE, EE Peak

1.0 0.0 1.5

- 15.7 1.4 1.2 0.3

- 0.8 North CA 2.7 2.7 1.6 15.0 1.6 2.6 1.6 3.0

0.5 1.1 1.0

- 1.9 1.0 0.7 1.8 0.1 Bay North 0.7 0.7 0.4 4.1 0.4 0.7 0.4 0.8

11.6 7.2 11.4

- 19.5 13.1 13.6 8.6

- 6.7 Bay Area 9.1 9.0 5.5 50.2 5.5 8.9 5.5 10.1

4.4

- 0.8 4.1

- 10.7 2.9 3.1 2.9

- 2.6 Sierra North 2.7 2.7 1.6 15.0 1.6 2.7 1.6 3.0

0.7 0.4 0.5 0.0 1.0 0.8 0.7

- 0.1 Sierra South 0.3 0.3 0.2 1.8 0.2 0.3 0.2 0.4

- 0.6

- 1.5 0.2

- 11.3 0.2

- 0.7 0.4

- 1.5 Sacramento 0 .9 0.9 0.6 5.1 0.6 0.9 0.6 1.0

7.5 2.8 7.2

- 46.4 5.4 11.8 5.2 0.5 San Joaquin 7.4 7.3 4.4 40.8 4.4 7.2 4.4 8.2

0.7 3.1 2.7 8.2 3.6 2.4 3.6 9.8 Central Coast 0.6 0.6 0.4 3.5 0.4 0.6 0.4 0.7

5.3 4.3 3.3 16.3 4.9 4.9 4.1 8.7 Desert Region 3.7 3.6 2.2 20.4 2.2 3.6 2.2 4.1

7.0 7.9 9.6 18.2 8.6 8.2 9.6 39.0 South Coast 6.0 5.9 3.6 33.0 3.6 5.8 3.6 6.6

6.6 9.3 9.8 45.8 4.3 5.4 4.2 26.4 San Diego 4.4 4.3 2.6 24.3 2.7 4.3 2.6 4.9

8.4 5.6 6.4 18.8 8.6 7.4 6.6 9.8 Imperial 1.2 1.2 0.7 6.9 0.8 1.2 0.7 1.4



### Table 6 : Displaced NO X 

### R ate (in lbs NO X 

### per GWh of EE/RE) for E ight S cenarios

by California Air District Region

Displaced lbs of NOx

per GWh of Energy

Displaced, by Air

DistrictLADWP,WindLADWP,Solar(PV)LADWP,EEBaseloadLADWP,EE PeakPG&E,WindPG&E,Solar(PV)PG&E,EEBaseloadPG&E,EE Peak

- 0.6 0.1

- 0.4

- 6.4 7.7 5.6 12.8 22.2 North CA 1.7 2.7 1.6 10.9 3.7 2.8 1.6 2.9

0.1

- 0.2

- 0.1 0.9 3.3 3.6 4.0 2.5 Bay North 0.5 0.7 0.4 3.0 1.0 0.8 0.4 0.8

2.1 1.7

- 2.5

- 26.1 27.3 39.5 47.2 69.9 Bay Area 5.6 9.0 5.5 36.6 12.5 9.3 5.5 9.7

0.8 1.2

- 0.7

- 9.2 11.3 12.9 20.5 29.4 Sierra North 1.7 2.7 1.6 11.0 3.8 2.8 1.6 2.9

0.1

- 0.1

- 0.1

- 0.9 0.8 0.8 0.5

- 0.1 Sierra South 0.2 0.3 0.2 1.3 0.4 0.3 0.2 0.3

- 0.2

- 0.4

- 0.2

- 4.6 0.9 2.4 4.4 8.5 Sacramento 0.6 0.9 0.6 3.7 1.3 0.9 0.6 1.0

1.5 3.2

- 0.1

- 10.1 22.9 30.6 43.6 75.9 San Joaquin 4.6 7.3 4.4 29.7 10.2 7.5 4.4 7.8

1.5 1.7 0.3 10.5 6.6 12.5 11.8 8.8 Central Coast 0.4 0.6 0.4 2.5 0.9 0.6 0.4 0.7

2.7 3.8 2.9 15.1 4.5 4.9 2.8 1.1 Desert Region 2.3 3.6 2.2 14.8 5.1 3.8 2.2 3.9

57.0 76.3 74.6 107.1 3.0 0.6 2.9

- 8.2 South Coast 3.7 5.9 3.6 24.1 8.3 6.1 3.6 6.4

4.3 4.0 4.0 41.2 3.1 2.3 1.0

- 2.4 San Diego 2.7 4.3 2.6 17.7 6.1 4.5 2.6 4.7

3.0 2.5 2.1 19.7 8.0 5.7 5.6 2.5 Imperial 0.8 1.2 0.7 5.0 1.7 1.3 0.7 1.3

### 45 Much of the displaced NO

X 

### is derived from offset natural gas

- fired units in California (see

Figure 30 , b elow), although displaced petroleum and biomass are also s ignificant fractions in some EE/RE programs. Overall, the absolute magnitude of displaced N O
X 

### emissions is small in California. However, it is useful to note that the biomass and petroleum fractions of displaced

NO
X 

### are a significantly larger fraction of d isplaced emissions than of generation because of their high absolute emissions rate.

Figure 30 : Displaced NO X 

### (in lbs NO X 

### / GWh) by F uel T ype in California



# Displaced SO 2 

# Emissions, by Region and Fuel Type

Sulfur dioxide (SO

2 

### ) is emitted during the combustion of sulfur

- contaminating fuels, particularly coal and petroleum products. These fuel types are limited in California, and therefore, there is a fairly small displaced SO

2 

### benefit in the state. However, EE/RE
implemented in Cali fornia appear to affect coal generators throughout the West, and result in reduced emissions across the WECC region. Figure 31

, below, shows displaced SO

2 

### emissions throughout the WECC region, again in pounds of SO

2 

### per GWh, due to the fairly small values,
particularly in California.

### 46 Figure 31 : Displaced SO 2 

### E missions R ate (in lbs SO 2 

### /GWh) in by WECC R egion and F uel T ype



### The displaced SO

2 

### pattern differs from NO

X 

### in that nearly all of the displaced emissions (and added emissions in EE peaking programs) are seen at coal generators, except in California, which is comprised of the “other” category. As will be seen below, these values are primarily associated with displaced coal ( or pet roleum c oke) and petroleum consumption. The negative
net displaced emissions of SO

2 

### in the peaking EE/RE in SDG&E and LADWP are due to increased coal generation in the Southwest; with little SO

2 

### benefit elsewhere, the result is a net

negative emissions rate. Again , the negative displaced emissions rate here is termed “non meaningful ” because the uncertainty from multiple model runs exceeds the signal given by the

difference between the base case and the scenarios.



# Displaced SO 2 

# Emissions, by Air District Region

Dis placed SO

2 

### emissions rate by air district region are given in Figure 32

, below. Much of the displacement is seen in the South Coast and Bay Area air district regions ; regions where there
are more fuel oil and petco ke displacement opportunities. Overall, the total displaced emissions

rate for SO

2 

### is insignificant (relative to random noise) in nearly all air districts and scenarios except in the Bay Area and South Coast, where significant displacement ranges from 18

– 4 5 lbs

SO

2 

### /GWh; in no circumstances are any negative displacements (i.e.

, increase in emissions) significant at a rate of more than 1 lb SO

2 

### /GWh. Tables showing the actual values and
significance can be found in Appendix C

.

### 47 Figure 32 : Displaced SO 2 

### E missions R ate (in lbs SO 2 

### /GWh) by A ir D istrict R egion



# Displaced CO 2 

# Emissions, by Region and Fuel Type

All combustion sources release carbon dioxide (CO

2 

### ), a greenhouse gas pollutant. As a climate change pollutant, CO

2 

### does not impose d amages on populations near the generation source as
do criteria pollutants of NO

X 

### and SO

2 

### . Displaced CO

2 

### emissions are evaluated across all generators in the WECC region. From the perspective of attempting to reduce overall CO

2 

### emissions from the U

. S

. gene rating fleet, the fact that EE/RE in California displace coal
generators out of state is a net benefit.

Since most displaceable resources are fossil

- fired, net displaced CO

2 

### emissions are highly

related to net displaced energy , with the important caveat th at natural gas generation has a CO

2 

### rate approximately half that of coal generation. 17 

### It is expected that the overall displaced

emissions rate for CO

2 

### will range between natural gas and coal emissions rates. The regional
displaced CO

2 

### rate s for all EE/RE a re given in Figure 33

, below.

17 

### In this model, the WECC

- wide average gas CO

2 

### rate is approximately 0.5 tCO

2 

### /MWh, while the c oal

rate is about 1.1 tCO

2 

### /MWh.

### 48 Figure 33 : Displaced CO 2 

### E missions (tCO 2 

### / MWh of EE/RE) by R egion and F uel T ype



### In regions where the displaced energy is primarily coal, the displaced CO

2 

### e missions rate is higher (although not markedly so) than regions where the displaced fuel is primarily natural

gas.

Detailed results for the displaced CO

2 

### rate by region and air district region are given in
Appendix C

.

### 49 

# CHAPTER 5:

Discussion and Conclusio ns

Use of Results for Displaced Emissions Analysis

The analysis presented here has produced values for a displaced emissions rate, designed to be
scalable to moderately sized new installations of EE/RE. To estimate the emissions that could
be displaced by a new EE/RE program, these values can be applied to the total net saved energy (in MWh) associated with each program. This analysis can be applied to estimate the displaced emissions throughout the WECC region, or within a WECC sub

- region or air district

r egion.

Using the accompanying Data Annex workbook, one can calculate the displaced emissions, in tons, from any given EE/RE program in any given region. For example, for a program designed

to build an additional 250 MW of wind at 35 percent capacity facto r wind near the SCE service territory, the expected displaced NO

X 

### emissions would be calculated as follows. The wind farm could be expected to generate approximately 895 GWh of energy per year. Multiplying this energy times the displaced emissions rate for California, the total displacement would be
expected at approximately 25.3 tons of NO

X 

### per year, with an error bound of approximately

4. 3 tons per year. This project would have the highest impact in the Bay Area, South Coast, and
Imperial air district s , d isplacing approximately 6, 4, and 4 tons of NO

X 

### in each, respectively. The impact of this project on CO

2 

### is realized primarily outside of California; overall, the project
would be estimated to displace 494,000 tons of CO

2 

### , but only about 25 percent of this benefit

would be realized in

- state.

The above example can be calculated simply using the attached Data Annex workbook . In the

“Calculator” tab of the worksheet, the user selects “wind” under the EE/RE measure,

“Southern California Edison” under the utilit y region, and enters a capacity of 250 MW. The capacity factor of the project, as implemented in this project, is given, as is the annual energy of the project ( see screenshot in Figure 34

, b elow )

.

Figure 34 : Screenshot of E xample P roject in S elected R egion



### The average daily load shape and average monthly energy output for this RE project are shown in the accompanying graphs in the Data Annex

.

Displaced energy and emissions by WECC region and air district region are calculated from model output, and shown in the tables below the input section; results are given in total displaced energy and total displaced emissions (in tons) by WECC region and air district region. In each table cell, the first value is the displaced energy or emissions, and the second value represents the uncertainty range, in the same units. Results that are smaller than the

random error are termed “non

- meaningful

. ” These non

- meaningful results are greyed out. An

### 50 example results

- table screenshot for displaced emissions in air district region is given in Figure

35 , below.

Figure 35 : Example S creenshot from Data Annex of D isplaced E missions in California A ir D istrict

R egions from a 250 MW W ind P roject in SCE S ervice T erritory



### The results presented here are based on EE/RE projects with specific prototypical load shapes, and should be applicable for many or most EE/RE projects contemplated in the region.

Howev er, projects with significantly different load shapes than these four for each region would likely result in different, though comparable, emissions benefits.



# Patterns of Displacement and Future Research Opportunities

In the West, natural gas

- fired resour ces are primarily on the margin, meaning that in most hours of the year they are more likely to be displaced by EE/RE than other types of resources, such as coal or oil. Therefore, it would be expected first that most displaced generation

associated with E E/RE in California and out of state will be natural gas

- fired (and will therefore have a fairly low emissions displacement for SO

2 

### and NO

X 

### ), and that these displaced generators will be distributed across the WECC region.

Generally, this pattern holds; howe ver, there are at least three noteworthy exceptions seen in

this research:

• A non

- intuitive displacement of out

- of

- state coal was observed in many of the scenarios

.

• A n apparent increase in coal

- fired generation for some of the peaking EE programs

.

• A high concentration of natural gas

- fired resources are displaced within the LADWP service territory for EE/RE programs enacted within LADWP.

These patterns either illustrate useful lessons about dispatch dynamics within the West or are
potentially symptomatic of either inaccurate model constructs or model inputs. The patterns are described in detail below. While some hypotheses can be posed on the reason these patterns

emerge, an in

- depth exploration of these findings is required to align general expectations or historical system behavior with the patterns seen in this research.

### 51 

## Out

- of

- State Coal

One surprising aspect of this analysis is that the model indicates that coal

- fired generation in the Southwest and Rocky Mountain States can be displaced by some renewabl e energy and energy efficiency programs in California. In particular, additional wind energy in SDG&E, SCE, and PG&E service territories appear to displace between 26 percent and 33 percent coal, 18 

### almost exclusively out of state. 19



### A graph of the wind gene ration and absolute displaced energy in SDG&E appears to confirm that coal is displaced in response to additional wind generation ( Figure 36 ). T his graph shows the differences in generation, by fuel type, in the SD G&E wind scenario relative to the base case in 2016. Shaded regions above the horizontal line at zero indicate increased energy output by
fuel type

— in this case, generally wind, but sometimes natural gas. Shaded regions below the
line indicate reduced outp ut by fuel type.

Figure 36 : Difference in Monthly Generation a cross All o f WECC b etween Base Case a nd SDG & E

Wind Scenario, b y Fuel Type. The i mpacts s hown r esult f rom t he a ddition o f 1000 MW o f o nshore

w ind i n t he SDG & E s ervice t er ritory



### This chart shows that the greatest coal displacement occurs during the spring and early winter,

so

- called “shoulder” seasons where regional demand is relatively low and hydroelectric availability is greatest. According to the model results, coal g eneration in the Intermountain is primarily displaced during the shoulder seasons.

A more detailed examination of hourly patterns of displacement in the shoulder and peak seasons (March and July, in Figure 37 and Figure 38 , respectively) for the SDG&E wind scenario shown above suggests that in periods of low demand, coal is displaced on a regular basis, while during the highest consumption months, natural gas is displaced a lmost exclusively. The spike

18 

### SCE and SDG&E, respectively. Coal fractions displaced by utility EE/RE are SCE: 27 ± 4 percent;

SDG&E 33 ± 7 percent; PG&E 26 ± 10 percent.

19 

### The only California in

- state coal generation which appears in this model is petr oleum coke burned at refineries; these loads are generally modeled as non

- dispatchable because the energy is primarily used

on

- site for industrial purposes. Therefore there is little available coal in California to be displaced.

### 52 in nuclear generation for a day in March is an artifact of a forced outage which occurs in the

base case but not in the scenario.

Figure 37 : Difference in Hourly Generation a cross All o f WECC b etween B ase Case a nd SDG & E

Wind Scenario, b y Fuel Type f or a Week i n March (2016). The i mpacts s hown r esult f rom t he

a ddition o f 1000 MW o f o nshore w ind i n t he SDG & E s ervice t erritory

.

Figure 38 : Difference in Hourly Generation a cross Al l o f WECC Between Base Case a nd SDG & E Wind

Scenario, By Fuel Type f or a Week i n July (2016). The i mpacts s hown Result f rom t he a ddition o f 1000 MW o f

o nshore w ind i n t he SDG & E s ervice t erritory

.



### This seasonal pattern of displacement of out

- of

- state coal generation, while perhaps

counterintuitive, is a consistent feature of the model results.

### 53 There are several lines of further investigation that are suggested by the displacement of coal from incremental EE/RE. These hypotheses suggest either erroneous ass umptions in the model construct, or legitimate questions of operation in the WECC region. One potential hypothesis is that during shoulder seasons, reliability or transmission constraints prevent additional natural gas displacement in the WECC region. Anot her potential hypothesis is that coal units are forced offline during trough hours and are not able to recommit cost

- effectively for on

- peak hours. Additionally, it is feasible that the model construct is ill equipped to effectively dispatch

thermal resour ces around deep penetrations of wind. It is notable, for example, that hydroelectric dispatch is never altered in the model runs; as a very inexpensive and flexible resource, one might hypothesize that some amount of hydroelectric energy might be re

dispat ched to balance stochastic wind, rather than compel less flexible generation (such as coal) to dispatch around wind. Finally, the model may portray wind patterns in a non

- realistic manner, forcing large amounts of wind onto the system simultaneously simply because multiple wind farms are assumed to have an identical load shape. These large, instantaneous insertions of wind might be sufficient to force coal offline in select circumstances.



## Negative Displacement Patterns

A second unexpected pattern in this an alysis is an apparent increase in coal

- fired generation in the Southwest associated with EE programs that seek to reduce peak energy use. In both the SDG&E and LADWP peaking EE scenarios, the model predicts that coal generation in the

Southwest will increa se by 10 to 16 percent of the total EE/RE savings

— i.e.

, rather than being
displaced, coal generation actually appears to increase.

Peaking EE programs are designed to reduce usage during the highest demand hours, mostly in

the summer and winter months. The y generally serve to reduce capacity needs, but have a relatively small impact on overall energy use. Parsing the results for the SDG&E analysis by
month ( Figure 39 ) , the model confirms that the primary impact of t he peaking program occurs
during mid

- summer. However, because the modeled peaking EE program does not have a significant energy impact, the role of noise and random outages is exacerbated relative to the other EE/RE scenarios, and dispatch changes in the m odel appear somewhat erratic as a result. For example, the coal “additions” in January and November appear to be a response to short forced outages at nuclear units (in this case an outage of several days at Palo Verde, Arizona) rather than explicit respon ses to energy efficiency programs. This random effect, unrelated to the EE modeled in this scenario, appears large relative to the small energy impact of the

program.

### 54 Figure 39 : Difference in Monthly Generation across A ll of WECC b etween Base Case and SDG&E

P eaking EE S cenario , by F uel T ype . Much of the observed change in dispatch is due to random

generating unit outages, which have a large effect relative to the modest energy

- saving impact of

a peaking EE program.



### Again, examinin g the hourly patterns of displacement in both April and July (in Figure 40 and
Figure 41 , respectively) it is clear that the EE peaking program tends to target natural gas

- fired resources for most hours. The large increase in coal and decrease in natural g as during the middle of the March period is likely due to a forced outage difference and not due to the peaking program. This representation suggests that the total energy displaced by an EE peaking program provides insufficient signal for a relatively hig h level of noise.

Figure 40 : Difference in H ourly G eneration across A ll of WECC between the B ase C ase and

SDG&E P eaking EE S cenario , by F uel T ype for a W eek in March (2016). The impacts shown result

from the addition of an aggress ive EE peak demand program targeting the 90th percentile highest

load hours in SDG&E service territory.

### 55 Figure 41 : Difference in H ourly G eneration across A ll of WECC B etween B ase C ase and SDG&E

P eaking EE S cenario , by F uel T ype for a W eek in March (2016). The impacts shown result from the

addition of an aggressive EE peak demand program targeting the 90 th percentile highest load

hours in SDG&E service territory.



### The poor signal

- to

- noise given in the EE peaking scenarios suggest s further lines of research to overcome this particular obstacle. Many modeling constructs allow users to fix forced outages to a particular schedule, such that individual scenarios can be compared on a one

- to

- one basis. However, an analysis examining only the operating margin (as in this research) should be aware of the impact of random events on the shape of the outcome. It should be considered

whether a small program that affects the margin in a large and complicated system such as

WECC should be conside red in an explicit dispatch model, as presented here, or whether it is sufficiently characterized by simpler analyses. In addition, in other regions aside from California, the displacement of potentially high emissions peakers may have a larger impact

than that found in this analysis.



## Concentration of Displacement in LADWP Service Territory

New EE/RE resources in LADWP service territory appear to displace, almost exclusively,

natural gas resources in LADWP. This is potentially a surprising result: if transm ission is unconstrained between LADWP and adjacent service territories, it would be expected that displacement in LADWP might look similar, if not identical, to nearby service territories (such as SCE). Even more surprising is that, according to spot check s, the displacement occurs with

new and efficient combined

- cycle (CC) units ( i.e., the Valley and repowered Haynes and Magnolia CC units). It would normally be assumed that these units are lower cost, and therefore less likely to be displaced at the operat ing margin, suggesting that the displacement is potentially due to either a commit or transmission constraint.

One potential explanation as to why , in the model, the displacement occurs with these new

and/or efficient units has to do with how the model mig ht choose to dispatch local versus non

local resources

. LADWP obtains a large fraction of power directly from the Intermountain Power Project (IPP), a coal plant in Utah. Further, IPP is directly linked, both in the model and in reality, with LADWP through a direct current connection, and the utility maintains a take

- or

pay contract with the plant. However, IPP also serves other utilities in California and throughout the West. It is feasible that in the model construct, IPP is dispatched not on behalf of

LA DWP, but in consideration of other requirements, and in the model LAWDP essentially is compelled to accept the coal. If the model, in reaching equilibrium, does not re

- dispatch resources outside of LADWP to otherwise transfer this power out of the LADWP se rvice territory, existing resources in LADWP might need to derate to accept IPP power. Given a large insertion of yet additional wind power, the only option (again, according to this model construct) might be for LADWP resources to derate and accept must

- t ake wind power. It is

### 56 unclear if LADWP dispatch would operate in the manner described above, but clearly this is a
critical question for understanding displaced energy and emissions in the L os A ngeles basin.



# Model Assumptions and Caveats

Assumptions and mo del constructs used in this research may result in systematic errors; other fundamental aspects of the electricity market simply cannot be captured by this model; and constructing an analysis based on a theoretical build

- out in a future year invites additi onal uncertainty. There are significant caveats and assumptions used to forecast future policies,

decisions

, and economic trends, as well as to streamline model use, runtime, and results. These
assumptions include, but are not limited to the following :

• Mod el topology: The PROSYM model uses a simplified topology, or mapping, to achieve faster runtime and simpler outputs. This nodal topology assumes that generation within specific power control areas (“nodes”) are unconstrained by

transmission within that nod e, and only transmission

- constrained between nodes. The transmission between nodes is also simplified and aggregated, rather than tracing specific power lines. The effect on generators in the model is that real transmission
constraints may not be captured due to a user ’s choice of topology ; subtle differences between prices at different generators may not be captured ; and some ancillary requirements may be lost. These types of errors are more important at the margin, and therefore may have a more profound e ffect on this study’s results.

• Fuel costs: Generation dispatch relies heavily on the estimated variable costs of operation, which include fuel costs, operation and maintenance costs , and any emissions costs. Changes in relative generation merit due to fuel prices or emissions costs could markedly change our expectations of the marginal units affe cted by EE/RE. This

research uses fixed Energy Commission assumptions of fuel prices in 2016 as of 2009, which may not reflect our current estimate of fuel prices ( or uncertainty in fuel prices)
by 2016. 20 

### Indeed, run output from the scenarios indicate s that natural gas and coal are often on the margin (i.e.

, displaced by EE/RE), suggesting that changes in fuel prices could change the expected avoided generation and

, subsequently, avoided emissions.

• Emissions costs: The model, in keeping with assumptions of the Energy Commission in the model construct, does not utilize any emissions costs. In California, as elsewhere,
federal and local environmental regulations enforce various cap

- and

- trade and other pricing mechanisms for criteria emissions. For example, under the U.S. EPA ’s Acid Rain Program, all U.S. electric generating units (EGU) over 25 MW are required to hold

allowances for SO

2 

### emissions, effectively establishing a market price for the pollutant. This price has fluctuated between under $100/tSO

2 

### to several hundred dollars per ton, spiking at over $1,500/tSO

2 

### in 2005 (nominal) (U.S. EPA 2010b) . At higher prices, these

SO

2 

### emissions costs can account for up to 5 to 10 percent of a coal unit’s running cost. 21 

### In California, CARB plans for the Assembly Bill 32 (AB32) create a cap

- and

- trade program for CO

2 

### (CARB 2010)

, which will similarly establish a market price for CO

2 

### emissions from power produced in or entering Cali fornia. These prices are specifically designed to change dispatch dynamics, discouraging high

- emissions units and

20 

### For example, since 2009, significant new natural gas discoveries have lowered gas prices in 2010

– 2011 (as well as in most near

- term forecasts); while increasing international demand for U.S. coal has driven up coal costs. While a dispatch tradeoff between gas and coal may not be t riggered only by these fuel prices, changes in fuel costs could significantly change the marginal resource.

21 

### Author’s estimate: Assuming SO

2 

### emissions rate of 4 lbs SO

2 

### /MWh, an example price of $500/tSO

2 

### results in a cost adder of $1/MWh, or 5 percent of a $20/MWh running cost.

### 57 promoting low

- CO

2 

### generation. However, a price reflecting this program is also absent

from the model construct.

• Generation retirements: The mo del, which was originally designed to review California policy, may not reflect expected changes in out

- of

- state generation over the next decade.
In particular, recently proposed and expected U.S. EPA regulations further restricting

criteria emissions and water use are expected to result in numerous changes to elements of the existing fleet, particularly coal EGU. At the time of this writing, at least four utilities in WECC had announced various coal retirements or repowering over the next

two decades (but with highly uncertain timing)

, including Portland General Electric
( O regon ) (Reuters 2010) , TransAlta Corporation ( W ashington ) (Reuters 2011) , Arizona Public Service Co. ( A rizona ) (Navajo Nation Council 2011) , and Xcel ( C olorado ) (Xcel Energy. 2010.) . Some of these changes in the fleet composition may precede the
compliance deadline of 2015 for U.S. EPA ’s rules, meaning that in our analysis year (2016), the fleet composition may look different than that expected by the Energy

Commission as of 2009. Without extensive and ongoing forward

- going planning, these types of changes are not feasibly captured within this type of analysis, but must be
recognized as a significant source of uncertainty.

• Stochastic w ind d ispatch : In the model structure, the stochastic na ture of wind is

handled in at least two distinct mechanisms.

o In some areas, wind farms are given an explicit hourly load shape, often derived from anemometer data and physical wind

- flow models. These patterns,

however, are fixed: in each run, the wind far m generates exactly the same amount of energy at each hour. When this mechanism is used in a comparative model, such as the analysis presented here, it ensures that each run represents a similar wind behavior. However, the model, which has “perfect foresig ht” may unrealistically compensate for wind patterns when in reality, a dispatcher may have little notice of rising or falling wind availability. All wind and solar farms represented for displacement purposes are modeled with an explicit load shape.

o As a d efault setting, in some regions, wind farms are simply assigned a random outage factor for each month, and a short “recovery” period after a forced outage. This mechanism yields a random wind pattern with no distinct hourly
pattern, and no consistency betw een model runs. The inconsistent approach may yield greater variance (error, or noise) between runs than is warranted for this analysis, but may represent the stochastic nature of wind more accurately

overall.

The simplifying assumptions used here have an unknown impact on the model or the results. For the purposes of this research, it is assumed that the impacts are less severe than the random nature of the forced outages discussed in the Random Error section of Chapter 3. These questions

and uncertainties are important, and should be considered carefully in future research.



# Regulatory Implementation

To transition this model from a pilot study to a n analysis of real, enforceable EE and RE programs, there are a variety of steps which should be taken, includi ng:

Physical Build

- O ut A ssumptions: The model used here relied on a best estimate of build

- out as given by the California Energy Commission, in an early representation of the 33 percent renewable energy standard, compliance with a CO

2 

### emissions reduction p rogram, new efficiency standards, and conversion of old natural gas units with once

- through cooling to more rigorous controls or new plants. However, the model does not represent regulatory changes

### 58 that occur in other states, new transmission being built i n WECC, or other state policies. An accurate model would correctly represent both California policies, as well as existing and expected environmental regulations (including the Air Toxics rule, improvements and retirements due to the Regional Haze Rule, an d renewable energy standards in other states) and new plants expected across the WECC region. This broad base of assumptions may require a
regionally accepted model framework, but could also be used to accurately model emissions

reductions in other states.

Fuel and Emissions Costs : The model is currently built with default fuel costs for coal, oil, and

natural gas, but has no emissions costs. With changing fuel availability and prices (lower

natural gas price projections since the time this model was create d) and emissions regulations, the model would require updated projections and potentially a range of fuel and emissions costs for the purposes of evaluating multiple uncertainties.

Monte Carlo Analysis: This research provides a mechanism for evaluating unc ertainty due only to forced outage rates. However, for forward

- looking models, uncertainty in prices for

fuels and emissions, as well as uncert ainty in hyd rological conditions

, can affect the emissions

benefit of EE/RE at the margin. A preferred approach m ight be to run a true Monte Carlo analysis within each scenario, evaluating random combinations of forced outages, prices, and hydrology in order to generate an expected value outcome and a broadly applicable uncertainty to each element of this analysis, r ather than simply stating that some results are “non

meaningful

. ”

Viable EE/RE Programs: This research utilized four distinct load shapes for EE/RE programs of

wind, solar, baseload

, and peaking efficiency programs, all implemented at a large

- scale (1000

M W). In a regulatory implementation using a Monte

- Carlo analysis to quantify error and uncertainty, using regionally or locally viable EE/RE program load shapes might produce more
meaningful output.

Evaluate Generator Constraints: The results of this study include non

- intuitive findings, such as the fairly large displacement of out

- of

- state coal. These results imply potentially incorrectly parameterized coal and hydroelectric behavior under certain conditions. Even small changes in

how these and other units respond could significantly affect behavior at the margin. Therefore, a regulatory implementation of this study should thoroughly evaluate parameterizations that

result in behaviors non

- typical in the WECC region.

Peak Emissions Reporting: Air quality stan dards usually regulate short

- term exposures, rather than annual average exposures. Therefore, while the annual displaced emissions rate is a useful metric in this analysis, it is not necessarily the metric that would be required by an air district to

quant ify the efficacy of a particular emissions

- r eduction program. The model used here works natively as an hourly model, but this study only extracted final output at a monthly time step. In a regulatory framework, the peak emissions displacement, or an estima te of the range of peak displacement, would hold a high value for regulatory compliance.



# Conclusions

This research was designed to model the criteria pollution and GHG emissions benefits in California from renewable energy (RE) and energy efficiency (EE) i mplemented within the State. The research resulted in several key findings:



## Dispersed C riteria E missions B enefits

As shown in this research and elsewhere, the Western grid is highly interconnected, and

therefore c hanges in load, generation, or resource av ailability in one part of the West impact generators throughout the entire WECC system. As a result, criteria emissions benefits from EE/RE programs implemented in California are highly dispersed throughout the WECC region.

### 59 Thus, while there are important emissions benefits associated with these programs, those

benefits are not all available for California Air Districts to comply with SIP requirements. However, this research finds that there are significant benefits accrued to other Western states

from Cali fornia demand reductions.

Those benefits which do accrue to California are generally distributed among multiple districts, often far beyond the original locus of the EE/RE programs themselves. According to the model results, EE/RE programs in LADWP and PG& E tend to displace local resources (in LADWP and Northern California, respectively) more often than EE/RE programs in SCE and SDG&E.

The analysis shown here was not originally scoped to include non

- California entities in results, with the exception of gre enhouse gas benefits. In comparing out

- of

- state criteria emissions benefits to California benefits, this analysis may unfairly overemphasize out

- of

- state emissions benefits. In particular, charts such as Figure 28

, whi ch shows displaced pounds of NO

X 

### per GWh of generation, show very little relative benefit of EE/RE in California and fairly large

benefits out of state. This disproportionate benefit is almost entirely a function of California’s
relatively low emissions ge nerating fleet and the large number of uncontrolled generators throughout the Intermountain West.

It is clear that there may be significant opportunities for cooperation across air districts and even throughout the WECC region to rely upon low

- cost EE/RE programs to displace emissions, even if those emissions are not confined to a single air district, or even a single state. Across states and regions, the combined emissions benefit of EE/RE can be quite high, suggesting that there may be significant benefi t to cooperative air and energy planning across air district, state, and regional boundaries. With a focus on single air districts, the programs examined in this analysis appear to be less effective.



## Greenhouse G as B enefits

A notable benefit identified in this analysis is that EE/RE programs have a large displacement

out

- of

- state, often displacing coal

- fired resources in the Rocky Mountain and Southwest regions of WECC. Because of this coal displacement, the greenhouse gas benefit of the EE/RE programs

is h igher than it would be if the displacement were within California only. In many of the
programs, displacing a combination of California natural gas and out

- of

- state coal (such as in
the SDG&E wind scenario) results in a 50 percent increase in GHG emissions benefit
(0. 6 tCO

2 

### /MWh) relative to displacing in

- state natural gas only (such as in the LADWP

baseload EE scenario, 0.4 tCO

2 

### /MWh).



## Verify E missions from E xisting R esources The utility of electric system dispatch models to analyze air quality benefits from EE/RE was significantly advanced by Phase 2 of this project, but not in the way originally intended. Synapse’s assessment of the input assumptions in the model found that the emissions data for
electric generating units was inconsistent, and was not based on actual stack emissions

, or was inconsistent with data reported to U.S. EPA by the same generating units. Since the focus of this phase was to assess how and to what degree EE/RE affects generating units, ensuring that

emissions data are correct wa s cru cial to the precision of the modeling results.

The importance of correcting emissions cannot be overstated, particularly in an already low

emissions state like California. Prior to the emissions correction undertaken in this analysis, the

estimated NO

X 

### em issions from California EGU were 230 percent higher than values reported by

the U.S. EPA , and SO

2 

### values in the model exceeded reported emissions by over 500 percent

. Without a correction, the emissions for the state would be incorrectly modeled, and the

e missions benefit could be incorrect by a large margin.

The most dramatic changes in the emissions correction exercise were observed in small, very high emissions generating resources, such as biomass generators and refineries ( see

### 60 Appendix A ). While these units contribute very little energy overall to California, their emissions are significant enough that small changes in generation at these units have a large
and disproportionate impact on overall California stationary source emissions. It is concluded

th at verifying, reporting, and correcting the emissions from these and other units in California
is of paramount importance for correct e missions modeling in the State.



## Peak R eduction U ncertainty

In nearly all of the examples of peak energy reduction EE prog rams, the displaced energy and emissions benefits are small and subsumed by random error in the model. In some cases, the net
regional emissions benefit appears to be negative, due to a displacement of low

- emissions

natural gas in C alifornia and an increas e in high

- emissions coal in the Intermountain West. These coal increases are almost all below the level of noise in this analysis. However, the consistent appearance of this pattern across multiple scenarios suggests that peak reduction

programs may be cou nterproductive as an emissions

- reduction mechanism for California . In particular, if decreases in peak energy demands (or load shifting from peak to off

- peak hours)
allow greater commitment of high

- emissions baseload units such as coal, then the net change
may not be beneficial from an emissions standpoint. 22 

### It should be emphasized that this result is highly uncertain and potentially an outcome specific to the modeling construct and assumptions of this research.



## Uncertainty in E missions B enefits

Where the benefit of EE/RE is broadly distributed, the generations and emissions “signal” associated with program energy savings is diluted in both random and non

- random changes throughout WECC (“noise” and systematic error or changes). Therefore, while Synapse has chosen EE/RE programs that are fairly ambitious in scale (1000 MW of wind and solar, 333 MW

of baseload reduction, or 10 percent of peak load reduction in each service territory) it is often difficult to distinguish the absolute changes in generation and e missions due to the EE/RE program from the random changes in the model. This is a persistent source of uncertainty in this sort of modeling exercise

, and indeed in empirical observation: random variance in the
electrical grid is often as large as, or large r than, displaced energy

.



## Closing

It is clear that there are reduced pollutant emissions benefits, both local and regional, to be gained from certain EE/RE programs in California. Many of these benefits are often found out

of

- state due to the integrated na ture of the western electricity grid. In some cases the benefits are quite modest, as for EE programs that target peak load reduction rather than a high volume of energy savings. In the peak

- load reducing cases, the model results even suggest that certain programs have the seemingly paradoxical effect of increasing emissions, if they lead to a dispatch scenario involving greater reliance on coal. It is unclear whether this is a real effect or

an artifact of the model structure.

The results of this research suggest that there is ample opportunity for inter

- district and even regional cooperation to define EE/RE emissions benefits. If air districts collaborate to reduce energy use, the emissions benefits throughout the region can be substantial. It is less clea r that
individual air qualit y regions can realize much local emissions benefit by acting alone. In fact, the model results indicate that regional modeling and coordination is critical for SIP compliance, and that regional energy and multi

- pollutant plannin g will be a crucial strategy for

22 

### Load shifting or reductions in peak load may allow baseload units to operate at a higher capacity factor or more often because either they do not need to ramp down during off

- peak hours, or they can maintain a higher level of co mmitment when peaking units are not required.

### 61 progress as traditional mechanisms for achieving emissions reductions, such as end

- of

- pipe

controls, become less available.

### 62 

# References

CARB (California Air Resources Board). 2010a. District Lookup by ZIP Code, City, or C ounty.

Available online at www.arb.ca.gov/app/dislookup/dislookup.php

.

CARB. 2001b. Resolution 10

- 42. California Cap

- and

- Trade Program. December 16.

CPUC ( California Public Utilities Commiss ion ) . 2011a. Decision resolving petitions for

modification of Decision 10

- 03

- 021 authorizing use of renewable energy credits for compliance with the California Renewables Portfolio standard and lifting stay and moratorium Imposed by

decision 10

- 05

- 018. Ava ilable at http://docs.cpuc.ca.gov/word_pdf/AGENDA_DECISION/129354.pdf

.

C PUC . 2011b. Decision adopting interim energy efficiency savings goals for 2012 through 2020,
and defining e nergy efficiency savings goals for 2009 through 2011.

California State Water Control Board. 2010. Statewide Water Quality Control Policy on the Use
of Coastal and Estuarine Waters for Power Plant Cooling. www.swrcb.ca.gov/water_issues/programs/npdes/docs/cwa316/policy100110.pdf

.

CEC (California Energy Commission). December 2009. 2009 Integrated Energy Policy Report.

CEC

- 100

- 2009

- 003

- CMF. Available at http://www.energy.ca.gov/2009publications/CEC

- 100

- 2009

- 003/CEC

- 100

- 2009

- 003

- CMF.PDF

.

Navajo Nation Council. 2011. Navajo Nation Council Approves APS Four Corners Leas e Extension. “... the Environmental Protection Agency issued a proposed rule that would force the plant to cut nitrogen oxide emissions by 80 percent through the closing of three of its oldest
five units by 2014...” February 15.

NERC ( North American Electric R eliability Corporation ). A ccessed January 2011. Available online at http://www.nerc.com/page.php?cid=1|9|119

.



### Reuters. November 22, 2010. “ PGE to stop burning coal at Oregon power plant in 2020. ”

Reu ters. March 5, 2011. “ TransAlta to phase out coal boilers in Wash ington State. ”

U.S. Department of Energy (DOE). 2009. Energy Information Administration (EIA) Forms 923.

U.S. EPA. 2010. 2005

– 2009. Acid Rain Program Progress Report(s). Available at http://www.epa.gov/airmarkets/progress/progress

- reports.html

.

WECC ( Western Electricity Coordinating Council ). 2009. Historical Analysis Work Group. 2008 Annual Report of the Western Ele ctricity Coordinating Council’s Transmission Expansion Planning Policy Committee: Part 3, Western Interconnection Transmission Path Utilization
Study. April.

WECC. A ccessed January 2011. Available online at www.wecc.biz/About/Pages/default.aspx

.

Xcel Energy. 2010. Clean Energy

– Clean Jobs. “Xcel Energy Emissions

- Reduction Plan

. ”

Available at http://www.xcelenergy. com/SiteCollectionDocuments/docs/10

- 12

- 303.pdf

.

### A

- 1 

# A ppendix A :

Emissions Corrections for California EGU

Background

For the purposes of evaluating criteria and greenhouse gas emissions displaced through energy efficiency (EE)/renewable energy (RE) project s in California Air Districts, an accurate emissions baseline is required and more specifically, an accurate baseline set of emissions for each generator is required. It is imperative that the emissions rates represented in the generation dispatch model re flect a best understanding of emissions rates at electric generating units (EGU) for both baseline and displaced emissions purposes.

In preliminary studies, Synapse found that total criteria emissions from combustion EGU in
California according to output f rom the dispatch model do not reflect recent estimates from

U.S. EPA datasets. Further investigation revealed that emissions from a fairly small fraction of combustion generators both contributed to total criteria emissions and were responsible for a large fraction of the discrepancies between the dispatch model (the Ventyx PROSYM model, or

here, simply “Ventyx”) and U.S. EPA reported emissions. To rectify these discrepancies, Synapse adjusted emissions rates at selected California EGU to reflect the most r ecently

available, vetted U.S. EPA emissions data.

This appendix describes adjustments made to the emissions rates of California EGU as represented in the Ventyx model to reflect our best understanding of emissions rates in advance

of the EE/RE incremental modeling performed for this study. The appendix is organized to discuss emissions before and after adjustment (Findings), the method used to estimate and

compare emissions against U.S. EPA data (Approach), and detailed changes made to emissions

for the la rgest emitting sources.



# Findings

Emissions by U nit T ype Synapse examined emissions from California EGU as modeled for analysis year 2012, the earliest year available in the dispatch software as calibrated for modeling in the 2009 California
Integrated Ene rgy Policy Report (2009 IEPR). Examining the Ventyx inputs, Synapse found that there were no significant adjustments made to criteria or greenhouse gas emissions from

individual EGU relative to recent historic data.

It is expected that the only difference s which would be found between individual EGU emissions in model year 2012 and reported historic U.S. EPA data are due to differences in historic and future dispatch. Generally, in the absence of adjustments, emissions rates on a pounds or tons per MWh or million Btu ( MMBtu ) basis would be expected to remain consistent between recent historic years and the 2012 model year. Adjustments which might be expected could include changes to default Ventyx emissions rates or emissions characteristics, or expected ch anges in emissions rates due to more stringent environmental regulations or controls. Synapse found no adjustments, suggesting that there should be reasonable comparisons between emissions rates from the U.S. EPA and the Ventyx model.

According to initial model outputs, Synapse found that a fairly small amount of generation within California state lines, accounting for only 13 percent of generation, emitted 68 percent of oxides of nitrogen ( NO
X 

### ) and 82 percent of sulfur dioxide ( SO

2 

### ) in the state. These uni ts fell into three categories: natural gas

- fired units which did not report emissions to the U.S. EPA via the

### A

- 2 continuous emissions monitoring system (CEMS), 23 

### petroleum

- fired units, and wood units. Natural g as

- fired units which did report emissions to the C EMS system account for a large fraction of remaining generation (25 percent) yet only emitted 5 percent of total NO

X 

### from stationary sources, according to the model output for 2012.

Table A

- 1 , below , shows a breakdown of the fraction of generation and emis sions from units of a particular type, subdivided by existing units and “Future Generation

. ” i.e.

, units which did

not exist in either the CEMS dataset from 2009 or at the time the last U.S. EPA validated emissions records were published (at the time of th is study, 2005 data in the 2007 Emissions and Generation Resource Integrated Database [eGRID] record). Non

- emitting resources, such as hydroelectric, geothermal, wind, and solar generators, account for the majority of generation in the test year.

Table A

- 1 : Fractions of Generation , CO 2 

### , NO X 

### , and SO 2 

### by Unit Categorization , in California, in

Model Test Year (2012) Before Adjustment . Only units in the category marked “Gas (in CEMS)”

report emissions directly to the U.S. EPA via CEMS.

Count Fraction of

Gene ration

(%) Fraction of

NOx

(%) Fraction of

SO 2

(%)

Coal 8 1 15 18

Natural Gas (in CEMS) 196 25 5 0

Natural Gas (not in CEMS) 156 11 48 0

Petroleum 8 1 8 82

Fuel Oil 9 0 0 0

Biomass 41 1 3 0

Refuse 3 0 1 0

Wood 28 2 12 0

Future Generation 36 7 8 0

Non

- Emitting 344 52 0 0

Total 829 100 100 100



## Erroneous E missions R ates

The significant discrepancy between emissions emanating from natural gas units which are in

the CEMS database and natural gas units which are not in the CEMS database, as well as a large discrepancy between U.S. EPA estimated total stationary source emissions in California and emissions as estimated in the Ventyx model

, compelled Synapse to check emissions rates from units in the model. Synapse found that emissions from non

- CEMS rep orting natural gas, petroleum, and biomass units represented in the model in California were not in agreement

with U.S. EPA estimates of source emissions. For example, using the default emissions rates in the Ventyx model, total NO

X 

### in California amounted to 50.1 thousand tons per year from stationary sources, yet the U.S. EPA estimates only 22.3 thousand tons of annual NO

X 

### in 2005 in

23 

### U.S. EPA. 2009. Clean Air Markets Division (CAMD) emissions data. Available online at http://camddataandmaps.epa.gov/gdm/

.

### A

- 3 California. 24 

### Further, while the U.S. EPA estimates approximately 13.6 thousand tons of SO

2 

### emissions in California in 2005, the model estimated nearly 71 thousand tons in the 2012 test

year.

Through investigation, Synapse found that the emissions rates from units which report to the
CEMS system were generally accurate and derived directly from CEMS data. However, many

other u nits, including coal, natural gas, petroleum, and wood

- fired units

, had anywhere from
moderately incorrect to egregiously incorrect emissions rates estimates, according to U.S. EPA data. Most units in the Ventyx model (as received) had emissions rates far in excess of those recorded in California. In a small number of cases, the Ventyx model underestimated SO

2 

### emissions rates. In general, new electrical generating units expected to be built in the future had a higher emissions rate than many existing units.



# Emissions Rates Adjustments

Synapse assumed that:

• U.S. EPA data represented in the CEMS and eGRID data were generally more accurate than the model assumptions, and

• new generators built in California would likely have emissions rates comparable to the

bes t existing generators in California today.

For the purposes of emissions correction, Synapse used a “base case” model run: the 20 percent renewable energy standard ( RES ) scenario for a near

- term reference year, 2012. To compare
emissions from the model, S ynapse used and cross referenced three sources of emissions data:

• the Ventyx Market Analytics 25 

### output from the “base case

, ”

• data reported to U.S. EPA ’s CEMS for 2009 , and

• the U.S. EPA ’s eGRID for 2005.

The eGRID database is an inventory of generation an d emissions characteristics of electric generating systems. It includes all plants that provide power to the electric grid and report data

to the U

. S

. government. Where possible, it uses CEMS data; elsewhere it integrates data from

U.S. EPA , the Energy Inf ormation Administration (EIA)

, and the Federal Energy Regulatory

Commission (FERC). 26



### Where feasible, each emitting unit in California was mapped from the Ventyx model output to a known generator in either the CEMS data or in the eGRID dataset. Units which could not be positively identified (“No Information”) were not adjusted; in total 68 units of 829, amounting

to 2.4 percent of generation, could not be positively identified.

To enact the above assumptions, Synapse first adjusted SO

2 

### and NO

X 

### emissions ra tes on a unit

by

- unit basis where U.S. EPA data were available, either from existing CEMs data or in the validated eGRID dataset, and where the unit could be positively identified and associated with

24 

### U.S. EPA. 2007. eGRID. Availab le online at http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2007_Version1

- 1_xls_only.zip

.

25 

### The actual results are from the Market Analytics inte rface for the PROSYM model, part of a Ventyx

software suite.

26 

### More information on eGRID can be found here: http://www.epa.gov/cleanenergy/energy

resources/egrid/faq.html

.

### A

- 4 U.S. EPA data. Further, the SO

2 

### and NO

X 

### emissions rates of units built in the future were adjusted to match the lowest 10 th percentile of emissions in the California today.



## Existing U nits

Emissions rates for existing units in the model were adjusted if the unit could be positively

identified (i.e.

, correlated w ith an existing U.S. EPA

- recorded unit), the output emissions rates
(in lbs/MWh) differed from an U.S. EPA recorded rate by ± 20 percent, and the unit generated at least 100 GW in the test year 2012. In this analysis, 120 units met the above criteria and we re adjusted.

• SO

2 

### emissions rates were adjusted using CEMS data for eight (8) natural gas

- fired units, reducing estimated emissions by 1.7 tons of SO

2 

### in California in 2012 ( Table A

- 3

, below).

• SO

2 

### e missions rates were adjusted dramatically using eGRID data for 11 petroleum and

coal

- fired units (a lmost all non

- dispatchable industrial users), reducing estimated
emissions by 61,892 tons of SO

2 

### in 2012. ( Table A

- 4, below)

• NO
X 

### emissions rates were adjusted using CEMS data for 26 natural gas units (almost all

non

- dispatchable industrial users), reducing estimated emissions by 345 tons of NO
X 

### in 2012. ( Table A

- 5 below)

• NO
X 

### emissions rates were adjusted using eGRID data for 75 natural gas, wood, and petroleum

- fired units, reducing estimated emissions by 27,012 tons of NO

X 

### in California
in 2012 ( Table A

- 6 through Table A

- 8 below).

Units for which both eGRID and CEMS data existed were corrected preferentially using the

CEMS data. Units for which SO

2 

### emissions were underestimated in the model could generally
not be adju sted upwards (17 units).



## Future U nits

Emissions rates for future units in the model were adjusted if the unit emitted SO

2 

### or NO
X 

### in the 2012 test year and was activated in the model (i.e

., built) after 2009. In total, 45 units met this

criteria and were ad justed.

• SO

2 

### emissions rates for future units were adjusted using the lowest 10 th percentile of
emissions for nine (9) biomass and natural gas

- fired units, reducing estimated emissions by 1. 1 tons of SO

2 

### in 2012 ( Table A

- 9

, below)

.

• NO
X 

### emissions rates for f uture units were adjusted using the lowest 10 th percentile of

emissions for 36 biomass, wood and natural gas

- fired units, reducing estimated emissions by 461.1 tons of NO

X 

### in 2012 ( Table A

- 10 and Table A

- 11 , below)

.



## Testing A djustments

To test if total emi ssions of NO

X 

### and SO

2 

### in the model were more closely aligned with measured values after the correction, Synapse calculated aggregate emissions from each unit type.
Aggregate emissions by fuel type before and after adjustment are given in Table A

- 2, below

, and compared against U.S. EPA estimates from the 2005 eGRID dataset.

NO
X 

### and SO

2 

### rates were altered markedly for coal, petroleum, and wood

- fired units, as well as

natural gas units not in the CEMS database. Overall, NO

X 

### estimated by the model in California dropped from 50.1 to 22.3 thousand tons. Primarily due to alterations of only 11 units (almost

all refineries), SO

2 

### emissions dropped in the model from 70.8 to 8.9 thousand tons. This reduction brings the overall SO

2 

### emissions in the model below U.S. EPA estimated SO

2 

### emissions in 2012. Seventeen (17) units, accounting for 2.3 percent of generation, reported SO

2 

### emissions rates above those estimated by the U.S. EPA . Due to the way SO

2 

### is estimated in the model

### A

- 5 construct, it was deemed restrictively difficu lt to alter SO

2 

### emissions in an upwards direction for

most units. 27

27 

### Em issions of SO

2 

### are modeled in the Ventyx system as a function of the fuel type burned at the unit, the

estimated sulfur content of the fuel, the heat rate of the unit, and the fraction of SO

2 

### which is captured by any scrubber technologies. Due to a lack of data on the units of interest, Synapse was unable to find sufficient information to justify either an alteration in unit fuel use, sulfur content, or heat rate

— any of which could change the SO

2 

### emissions rate. Therefore, Synapse only altered SO

2 

### capture r ates; these cannot be brought below 0 percent to simulate a higher emissions rate than that given by the fuel and

heat rates.

### A

- 6 Table A

- 2 : Aggregate Emissions Rates of NO X 

### and SO 2 

### by Unit Categorization , in California Only

, Before and After Adjustment (2012)

NOx (tons) SO 2 (tons)

Generation (GWh) Pre

Adjust ment Post

Adjustment Pre

Adjustment Post

Adjustment

Coal 2,699 7,448 1,468 12,593 3,376

Natural Gas (in CEMS) 55,160 2,481 2,13 6 129 127

Natural Gas (not in CEMS) 23,328 24,250 8,556 74 74

Petroleum 1,322 3,806 871 58,013 5,337

Fuel Oil 0 1 1 1 1

Biomass 2,426 1,498 1,08 4 9 9

Refuse 414 752 680

- -

Wood 3,293 5,907 3,990

- -

Future Generation 15,170 4,020 3,559 26 25

Non

- Emitting 113,365

- - - -

Total 217,177 50,162 22,343 70,844 8,949

U.S. EPA Estimate for CA 199,925 22,302 13,577



## No C hanges in E missions D ue to N ew R egula tions

Synapse did not make changes in existing or future unit generation to adjust for any future emissions regulations, including either the Clean Air Act Regional Haze (BART) rules or the proposed Clean Air Act Air Toxics rule. Synapse did not evaluate h ow these rules might change emissions, heat rate, or dispatch dynamics of generators in or outside of California.



## Detailed A djustments

The following sections of this appendix have tables indicating explicit adjustments made to 120
existing units and 45 fut ure units. As noted above, all adjusted existing units fell outside of a

±20 percent emissions rate threshold relative to U.S. EPA estimates (either CEMS or eGRID) and produced over 100 GWh in the 2012 test year. All fossil or biomass fired units expected to emit in the future were adjusted to match the best performing 10 th percentile of units today in

California.

Each table below gives the unit name as supplied by Ventyx, the unit name as given by either

the U.S. EPA in eGRID or in the CEMS dataset, the p rimary fuel type as given in the model, as well as the closest city (by ZIP code), and the air district encompassing the unit ZIP code.

### A

- 7 Table A

- 3 : SO 2 

### Emissions Rate Corrections for California E lectric Generating Facilities Based on CEMS (2009) Emissions Rates

Vent y x Model Name CEMS / eGRID Name Fuel

Type City, State Air District

Region Model

Generation

in 2012

(GWh) Pre

Adjustment

SO 2 Emissions

(tons) Ventyx

Assumed

SO 2 Removal

Rate (%) Ventyx

Assumed

SO 2 Emissions

Rate

(lbs/MWh) CEMS

Estimated

SO 2 Emiss ion

Rate

(lbs/MWh) Synapse

Adjusted

SO 2 Removal

Rate (%)

CARSON ICE CG

CC Carson Cogeneration Co. Gas Elk Grove, CA South

Coast 392 1.2 0% 0.006 0.005 21

COALINGA COG 100 Coalinga Cogeneration

Co. Gas Coalinga, CA S an Joaquin 289 1.2 0% 0.008 0.006 22

HUNTINGTON BEA

2 AES Huntington Beach Gas Huntington

Beach, CA South

Coast 486 2.0 0% 0.008 0.007 18

LA PALOMA 1 La Paloma Generating

Plant Gas Mc Kittrick, CA San

Joaquin 110 0.3 0% 0.005 0.004 23

MOUNTAINVIEW 4A Mountainview Power Co. Gas San Bernardino,

CA South

Coast 117 0.3 0% 0.005 0.004 18

ROSEVILLE ENE 1A Roseville Energy Park Gas Roseville, CA Sierra North 415 1.2 0% 0.006 0.004 24

ROSEVILLE ENE 1B Roseville Energy Park Gas Roseville, CA Sierra North 411 1.2 0% 0.006 0.004 25

YUBA CITY 1 Yuba City Energy Center Gas Yuba City, CA Sierra North 141 0.4 0% 0.006 0.005 18

### A

- 8 Table A

- 4 : SO 2 

### Emissions Rate Corrections for California Electric Generating Facilities Based on eGRID (2007) Emissions R at es

Vent y x Model Name CEMS / eGRID Name Fuel Type City, State Air

District

Region Model

Generation

in 2012

(GWh) Pre

Adjustment

SO 2 Emissions

(tons) Ventyx

Assumed

SO 2 Removal

Rate (%) Ventyx

Assumed

SO 2 Emissions

Rate

(lbs/MWh) eGRID

Estimated

SO 2 Emissi on

Rate

(lbs/MWh) Synapse

Adjusted

SO 2 Removal

Rate (%)

BP WILMINGTON 1 BP Wilmington Calciner Petroleum Tujunga, CA South

Coast 209 9,182.0 0 87.849 34.824 60

EAST THIRD STR 1 East Third Street Power

Plant Petroleum Pittsbu rg, CA Bay Area 171 7,504.2 0 87.754 1.358 98

HANFORD COGEN 1 Hanford Petroleum Hanford, CA San

Joaquin 183 8,013.7 0 87.435 0.446 99

LOVERIDGE ROAD 1 Loveridge Road Power

Plant Petroleum Pittsb urg, CA Bay Area 136 5,995.0 0 87.990 1.093 99

MT POSO COGEN 1 Mt Poso Cogeneration Coal Bakersfield,

CA San

Joaquin 419 2,931.6 0 13.996 2.979 79

NICHOLS ROAD 5 Nichols Road Power

Plant Petrole um Pittsburg, CA Bay Area 171 7,510.8 0 87.828 1.096 99

PORT OF STOCKT 1 Port of Stockton District

Energy Coal Stockton, CA San

Joaquin 380 2,671.0 0 14.073 0.898 94

SFAR CARBON 1 Phillips 66 Ca rbon Plant Petroleum Rodeo, CA Bay Area 126 5,542.4 0 87.725 18.564 79

STOCKTON COGEN 1 Stockton Cogen Coal Stockton, CA San

Joaquin 473 4,667.2 0 19.749 1.093 94

WILBUR EAST 4 Wilbur East Power Plant Petroleum Antioch, CA Bay Area 171 7,514.3 0 87.863 1.106 99

WILBUR WEST 3 Wilbur West Power Plant Petroleum Antioch, CA Bay Area 154 6,750.1 0 87.789 1.364 98

### A

- 9 Table A

- 5 : NO X 

### Emissions Rate Co rrections for California Electric Generating Facilities Ba sed on CEMS (2009) E missions Rates

Vent y x Model Name CEMS / eGRID Name Fuel

Type City, State Air District

Region Model

Generation

in 2012

(GWh) Pre

Adjustment

NOx

Emissions

(tons) Ventyx

Assu med

NOx

Emissions

Rate

(lbs/MWh) CEMS

Estimated

NOx

Emission

Rate

(lbs/MWh) Synapse

Adjusted

NOx Input

Rate

(lbs/MMBtu)

CARSON COGEN C 1 Carson Ice

- Gen Project Gas Carson, CA Sacramento 356 12.8 0.072 0.151 0. 013

CARSON ICE CG CC Carson Cogeneration Gas Elk Grove, CA South Coast 392 25.4 0.130 0.073 0.006

GILROY POWER P 1 Gilroy Peaking Energy Center Gas Gilroy, CA Bay Area 103 30.5 0.593 0.419 0.035

GRAYSON CC BC Grayson Gas Glendale, CA South Coast 217 52.2 0.480 0.321 0.027

HAYNES 5 Haynes Gas Long Beach, CA South Coast 271 8.1 0.060 0.044 0.004

HIGH DESERT P 1A High Desert Power Plant Gas Adelanto, CA Desert

Region 125 4.5 0.072 0.105 0.009

HIGH DESERT P 1B High Desert Power Plant Gas Adelanto, CA Desert

Region 144 5.2 0.072 0.100 0.008

HIGH DESERT P 1C High Desert Power Plant Gas Adelanto, CA Desert

Region 123 4.5 0.073 0.100 0.008

HUNTINGTON BEA 1 AES Huntington Beach LLC Gas Huntington Beach,

CA South Coast 489 14.1 0.058 0.226 0.019

KING CITY COGE 1 King City Power Plant Gas King City, CA Central Coast 890 196.6 0.442 0.157 0.0 13

LOS MEDANOS E 1A Los Medanos Energy Center Gas Pittsburg, CA Bay Area 1,962 62.0 0.063 0.048 0.004

MAGNOLIA REPOW 1 Magnolia Power Project Gas Burbank, CA South Coast 1,760 14.4 0.016 0.042 0.004

Malburg 1a Malburg Gas Los Angeles, CA South Coast 239 6.1 0.051 0.255 0.021

Malburg 1b Malburg Gas Los Angeles, CA South Coast 237 6.1 0.051 0.254 0.021

Otay Mesa 1a Otay Mesa Energy Center Gas Jamul, CA San Diego 1,327 50.7 0.076 0.049 0.004

Otay Mesa 1b Otay Mesa Energy Center Gas Jamul, CA San Diego 1,332 50.7 0.076 0.039 0.003

PALOMAR ESCON

1A Palomar Energy Center Gas Escondido, CA San Diego 1,393 35.0 0.050 0.041 0.003

PASTORIA CC 1B Pastoria Energy Facility LLC Gas Bakersfield, CA San Joaquin 1,193 30.7 0.051 0.040 0.003

PASTORIA CC 1C Pastoria Energy Facility LLC Gas Bakersfield, CA San Joaquin 1,273 32.5 0.051 0.040 0.003

ROSEVILLE ENE 1B Rosevil le Energy Park Gas Roseville, CA Sierra North 411 7.8 0.038 0.053 0.004

SARGENT CANYON 1 Sargent Canyon Cogeneration Gas King City, CA Central Coast 297 13.9 0.094 0.137 0.012

SCATTERGOOD 2 Scattergood Gas Playa Del Rey, CA South Coast 431 7.1 0.033 0.023 0.002

SCATTERGOOD 3 Scattergood Gas Playa Del Rey, CA South Coast 641 6.3 0.020 0.028 0.002

SOUTH BAY 1 South Bay Power Plant Gas Chula Vista, CA San Diego 344 22.9 0.133 0.110 0.009

SPA CAMPBELL C 1 SPA Cogen 3 Gas Sacramento, CA Sacramento 1,164 39.0 0.067 0.087 0.007

YUBA CITY 1 Greenleaf 2 Power Plant Gas Yuba City, CA Sierra North 141 368.6 5.237 1.685 0.142

### A

- 10 Table A

- 6 : NO X 

### Emissions Rate Corrections for California Electric Generating Facilities Base d on eGRID (2007) Emissions Rate s (1 of 3)

Vent y x Model Name CEMS / eGRID Name Fuel Type City, State Air District

Region Model

Generation

in 2012

(GWh) Pre

Adjustment

NOx

Emissions

(tons) Ventyx

Assumed

NOx

Emissions

Rate

(lbs/MW h) eGRID

Estimated

NOx

Emission

Rate

(lbs/MWh) Synapse

Adjusted

NOx Input

Rate

(lbs/MMBtu)

ACE COGEN 1 ACE Cogeneration Facility Coal Trona, CA Desert

Region 752 1,945.5 5.173 0.823 0.069

BADGER CREEK 1 Badger Cr eek Cogen Gas Bakersfield, CA San Joaquin 192 429.4 4.466 0.782 0.066

BEAR MOUNTAIN 1 Bear Mountain Cogen Gas Bakersfield, CA San Joaquin 366 817.5 4.465 0.782 0.066

BERRY COGEN Berry Cogen Gas Taft, CA San Joaquin 295 806.8 5.468 0.782 0.066

BERRY PLACERIT 1 Berry Placerita Cogen Gas Newhall, CA South Coast 137 358.1 5.215 0.782 0.066

BERRY PLACERIT 2 Berry Placerita Cogen Gas Newhall, CA South Coast 137 357.3 5.204 0.782 0.066

BP WILMINGTON 1 BP Wilmington Calciner Petroleum Tujunga, CA South Coast 209 602 .4 5.763 3.685 0.310

BURNEY FOR PRO 1 Burney Forest Products Wood Burney, CA North CA 217 388.6 3.576 7.000 0.589

CHALK CLIFF CG 1 Chalk Cliff Cogen Gas Maricopa, CA San Joaquin 366 817.3 4.464 0.782 0.066

CORONA COGEN 1 Corona Cogen Gas Corona, CA South Coast 389 1,049.2 5.394 0.782 0.066

DELANO ENERGY 1 Delano Energy Wood Delano, CA San Joaquin 138 245.2 3.549 1.660 0.140

DELANO ENERGY 2 Delano Energy Wood Delano, CA San Joaquin 113 193.9 3.440 1.660 0.140

DOUBLE C LTD DC1 Double C Gas Bakersfield, CA San Joaq uin 183 444.3 4.853 0.782 0.066

DOUBLE C LTD DC2 Double C Gas Bakersfield, CA San Joaquin 183 444.5 4.856 0.782 0.066

E F OXNARD 1 Indeck West Enfield Energy Gas Po rt Hueneme,

CA Central

Coast 132 348.5 5.268 3.672 0.309

EAST THIRD STR 1 East Third Street Power Plant Petroleum Pittsburg, CA Bay Area 171 492.3 5.757 0.558 0.047

FW MARTINEZ 1A Martinez Refining Gas Concord, CA Bay Area 348 79.1 0.455 0.800 0.067

FW MARTINEZ 1B Martinez Refining Gas Concord, CA Bay Area 348 79.1 0.454 0.800 0.067

HANFORD COGEN 1 Hanford Petroleum Hanford, CA San Joaquin 183 525.8 5.737 0.311 0.026

HIGH SIERRA HS1 High Sierra Gas Bakersfield, CA San Joaquin 183 484.3 5.290 0 .782 0.066

### A

- 11 HIGH SIERRA HS2 High Sierra Gas Bakersfield, CA San Joaquin 183 484.2 5.289 0.782 0.066

HL POWER PLANT 1 HL Power Wood Litchfield, CA North CA 141 252.8 3.579 1.524 0.128

KERN FRONT L KF1 Kern Front Gas Bakersfield, CA San Joaquin 183 484.6 5.294 0.782 0.066

KERN FRONT L KF2 Kern Front Gas Bakersfield, CA San Joaquin 183 484. 5 5.292 0.782 0.066



### Table A

- 7 : NO X 

### Emissions Rate Correctio ns for California Electric Generating Facilities Bas ed on eGRID (2007) Emissions Rates (2 of 3)

Vent y x Model Name CEMS / eGRID Name Fuel Type City, State Air District

Region Model

Generation

in 2012

(GWh) Pre

Adjustment

NOx

Emissions

(tons) Ventyx

Assumed

NOx

Emissions

Rate

(lbs/MWh) eGRID

Estimated

NOx

Emission

Rate

(lbs/MWh) Synapse

Adjusted

NOx Input

Rate

(lbs/MMBtu)

KERN RIVER C TAG Kern River Cogeneration Gas Bakersfield , CA San Joaquin 541 243.0 0.898 0.599 0.050

KERN RIVER C TBG Kern River Cogeneration Gas Bakersfield, CA San Joaquin 542 243.0 0.897 0.599 0.050

KERN RIVER C TCG K ern River Cogeneration Gas Bakersfield, CA San Joaquin 542 243.0 0.897 0.599 0.050

KERN RIVER C TDG Kern River Cogeneration Gas Bakersfield, CA San Joaquin 542 243.1 0.898 0.5 99 0.050

LASSEN 1 Wheelabrator Lassen Gas Redding, CA North CA 105 298.0 5.700 0.782 0.066

LIVE OAK LIMIT 1 Johnsonburg Mill Gas Bakersfield, CA San Joaquin 366 942.8 5.149 1.216 0.102

LOVERIDGE ROAD 1 Loveridge Road Power Plant Petroleum Pittsburg, CA Bay Area 136 393.3 5.773 0.560 0.047

MCKITTRICK LTD 1 McKittrick Cogen Gas Bakersfield, CA San Joaquin 366 947.7 5.176 0.782 0.066

MECCA 1 Mecca Plant Wood Mecca, CA South Coast 290 503.2 3.470 1.423 0.120

MENDOTA BIOMAS 1 AES Mendota Wood Kerman, CA San Joaquin 171 306.3 3.587 2.584 0.217

MID SET COGEN 1 Mid

- Set Cogeneration Gas Bakersfield, CA San Joaquin 294 782.5 5.320 0.782 0.066

MIDWAY SUNSET A Midway Sunset Cogen Gas Mc Kittric k, CA San Joaquin 628 539.4 1.717 0.495 0.042

MIDWAY SUNSET B Midway Sunset Cogen Gas Mc Kittrick, CA San Joaquin 634 544.9 1.718 0.495 0.042

MIDWAY SUNSET C Midway Sunset Cogen Gas Mc Kittrick, CA San Joaquin 628 539.5 1.718 0.495 0.042

### A

- 12 MT POSO COGEN 1 Mt Poso Cogeneration Coal Bakersfield, CA San Joaquin 419 1,176.0 5.615 0.908 0 .076

NICHOLS ROAD 5 Nichols Road Power Plant Petroleum Pittsburg, CA Bay Area 171 492.7 5.761 0.563 0.047

OILDALE ENERGY 1 Oildale Cogen Gas Bakersfield, CA San Joaquin 303 853.9 5.639 0.782 0.066

OXNARD 2 Oxnard Gas Oxnard, CA Central Coast 165 367.4 4.463 0.782 0.066

PORT OF STOCKT 1 Port of Stockton District Energy Fac Coal Stockton, CA San Joaquin 380 1,071.5 5.645 1.704 0.143

PUENTE HILLS 1 Puente Hills Energy Recovery Biomass Whittier, CA South Coast 388 358.1 1.845 0.372 0.031

REDDING CC 1 Redding Power Gas Redding, CA North C A 222 2.1 0.019 0.025 0.002

Richmond CG 1 Richmond Cogen Gas #N/A Bay Area 286 233.8 1.638 0.808 0.068

Richmond CG 2 Richmond Cogen Gas #N/A Bay Area 286 233.7 1.637 0.808 0.068

RIO BRAVO FRES 1 Rio Bravo Fresno Wood Fresno, CA San Joaquin 146 265.4 3.626 1.062 0.089

RIO BRAVO JASM 1 Rio Bravo Jasmin Coal Bakersfield, CA S an Joaquin 225 640.0 5.678 0.825 0.069



### Table A

- 8 : NO X 

### Emissions Rate Corrections for California Electric Generating Facilities Based on eGRID (2007) Emissions Rate s (3 of 3)

Vent y x Model Name CEMS / eGRID Name Fuel Type City, State Air District

Region Model

Generation

in 2012

(GWh) Pre

Adjustment

NOx

Emissions

(tons) Ventyx

Assumed

NOx

Emissions

Rate

(lbs/MWh) eGRID

Estimated

NOx

Emission

Rate

(lbs/MWh) Synapse

Adjusted

NOx Input

Rate

(lbs/MMBtu)

RIO BRAVO POS O 1 Rio Bravo Poso Coal Bakersfield, CA San Joaquin 338 961.5 5.696 0.900 0.076

RIO BRAVO ROCK 1 Rio Bravo Rocklin Wood Lincoln, CA Sierra North 144 259.0 3.591 1.270 0.107

RIPON COGEN 1 Ripon Mill Gas Ripon, CA San Joaquin 375 1,014.7 5.411 0.782 0.066

SAN GABRIEL CO 1 San Gabriel Facility Gas San Gabriel, CA South Coast 310 859.5 5.553 0. 782 0.066

SERRF MSW 1 Southeast Resource

Recovery Refuse Long Beach, CA South Coast 218 393.6 3.618 2.956 0.249

SFAR CARBON 1 Phillips 66 Carbon Plant Petroleum Rodeo, CA Bay Area 126 363.6 5.755 3.661 0.308

SHASTA WHLBRT 1 Wheelabrator Shasta Wood Redding, CA North CA 152 278.5 3.665 2.118 0.178

SHASTA WHLBRT 2 Wheelabrator Shasta Wood Redding, CA North CA 152 278.4 3.664 2.118 0.178

SHASTA WHLBRT 3 Wheelabrator Shasta Wood Redding, CA North CA 152 278.4 3.664 2.118 0.178

SPI BURNEY 1 Sierra Pacific Burney Facility Wo od Burney, CA North CA 109 202.1 3.700 0.774 0.065

SPI QUINCY 1 Sierra Pacific Quincy Facility Wood Quincy, CA Sierra North 180 332.5 3.693 0.888 0.075

### A

- 13 STOCKTON COG EN 1 Stockton Cogen Coal Stockton, CA San Joaquin 473 1,328.2 5.620 0.313 0.026

SYCAMORE COG GTA Sycamore Cogeneration Gas Bakersfield, CA San Joaquin 590 514.6 1.744 0.573 0.048

SYCAMORE COG GTB Sycamore Cogeneration Gas Bakersfield, CA San Joaquin 590 514.5 1.744 0.573 0.048

SYCAMORE COG GTC Sycamore Cogeneration Gas Bakersfield, CA San Joaquin 590 514.7 1.745 0.573 0.048

SYCAMORE COG GTD Sycamore Cogeneration Gas Bakersfield, CA San Joaquin 590 514.4 1.744 0.573 0.048

TRACY BIOMASS 1 Tracy Biomass Wood Tracy, CA San Joaquin 136 242.8 3.563 0.094 0.008

US BORAX 1 US Borax Gas Bakersfield, CA Desert Region 290 819.6 5.647 1.035 0.087

WADHAM ENERGY 1 Wadham Energy LP Biomass Williams, CA Bay Nort h 121 217.7 3.597 1.488 0.125

WATSON COGEN 1A Watson Cogeneration Gas Carson, CA South Coast 739 162.2 0.439 0.065 0.006

WATSON COGEN 1B Watson Cogeneration Gas Car son, CA South Coast 739 162.0 0.438 0.065 0.006

WATSON COGEN 1C Watson Cogeneration Gas Carson, CA South Coast 739 162.1 0.439 0.065 0.006

WATSON COGEN 1D Watson Co generation Gas Carson, CA South Coast 739 162.0 0.438 0.065 0.006

WILBUR EAST 4 Wilbur East Power Plant Petroleum Antioch, CA Bay Area 171 493.0 5.765 0.569 0.048

W ILBUR WEST 3 Wilbur West Power Plant Petroleum Antioch, CA Bay Area 154 442.8 5.759 0.562 0.047

WOODLAND BIOMA 1 Woodland Biomass Power Ltd Wood Woodland, CA Bay North 150 269.3 3.583 1.264 0.106

### A

- 14 Table A

- 9 : SO 2 

### Emissions Rate Adjustments for F uture California Electric Generating Facilities; Based on L owest

10 th Percentile Emissions for Fuel Typ e

Vent y x Model Name Ventyx Model Description Fuel

Type Vent y x Assumed Physical

Location Air

District

Region Model

Generation

in 2012

(GWh) Pre

Adjustment

SO 2 Emissions

(tons) Ventyx

Assumed

SO 2 Emissions

Rate

(lbs/MWh) 10th

Percentile

SO 2 Emission

Rate for

fuel Type

(lbs/MWh) Synapse

Adjusted

SO 2 Removal

Rate (%)

SOUTHERN S AN ST Southern San Diego Biomass

Energy Biomass SDG&E Bay

North 191 0.5 0.0052 0.0048 8.8

COLUSA GENER 1A Colusa Generating Station: CC1 Gas PG&E Bay

North 1,056 2.4 0.0045 0.0040 11.5

COLUSA GENER 1B Colusa Generating Station: CC1 Gas PG&E Bay

North 1,076 2.4 0.0045 0.0040 9.9

GENCC_IID Generic New CC for L/R Balance Gas IID Imperial 534 1.1 0.0041 0.0040 2.5

GENCC_SMUD2012_1 Generic New CC for 2012+ Gas SMUD Bay

North 1,454 3.0 0.0041 0.0040 2.6

GENCC_SMUD2012_2 Generic New CC for 2012+ Gas SMUD Bay

North 1,427 3. 0 0.0042 0.0040 4.4

INLAND EMPIRE 2 Inland Empire Energy Center: CS2 Gas SCE South

Coast 2,699 5.5 0.0041 0.0040 1.4

OTC REPLACE SD1 Generic New CC for L/R Balance Gas SDG&E San

Diego 538 1.2 0.0045 0.0040 9.9

OTC REPLACE SD2 Generic New CC for L/R Balance Gas SDG&E San

Diego 544 1.2 0.0044 0.0040 8.9

### A

- 15 Table A

- 10 : NO X 

### Emissions Rate Adjustments for Future CA Facilities; Ba sed on Lowest 10 th Percentile Emissions for Fuel Type (1 of 2)

Vent y x Model Name Ventyx Model Description Fuel

Type Vent y x Assumed

Physical Location Air

District

Region Model

Generation

in 2012

(GWh) Pre

Adjustment

NOx

Emissions

(tons) Ve ntyx

Assumed

NOx

Emissions

Rate

(lbs/MWh) 10th

Percentile

NOx

Emission

Rate for

fuel Type

(lbs/MWh) Synapse

Adjusted

NOx Input

Rate

(lbs/MMBtu)

SOUTHERN SAN ST Southern San Diego Biomass Biomass SDG&E Bay

North 191 285.7 2.996 0.449 0.038

COLUSA GENER 1A Colusa Generating Station: CC1 Gas PG&E Bay

North 1,056 38.0 0.072 0.047 0.004

COLUSA GENER 1B Colusa Generating Station: CC1 Gas PG&E Bay

North 1,076 38.6 0.072 0.047 0.004

CPV SENTINEL GT1 CPV Sentinel Energy Project: GT1 Gas SCE South

Coast 33 4.6 0.276 0.047 0.004

CPV SENTINEL GT2 CPV Sentinel Energy Project: GT2 Gas SCE South

Coast 30 4.1 0.275 0.047 0.004

CPV SENTINEL GT3 CPV Sentinel Energy Project: GT3 Gas SCE South

Coast 26 3.5 0.271 0.047 0.004

CPV SENTINEL GT 4 CPV Sentinel Energy Project: GT4 Gas SCE South

Coast 28 3.8 0.267 0.047 0.004

CPV SENTINEL GT5 CPV Sentinel Energy Project: GT5 Gas SCE South

Coast 33 4.3 0.262 0.047 0.004

ESCONDIDO POWE 2 Escondido: GEN2 Gas SDG&E San

Diego 4 0.6 0.267 0.047 0.004

GENAD_NBAJA_1001 Gas San

Diego 143 19.3 0.270 0.047 0. 004

GENCC_IID Generic New CC for L/R Balance Gas IID Imperial 534 18.5 0.069 0.047 0.004

GENCC_SMUD2012_1 Generic New CC for 2012+ Gas SMUD Bay

North 1,454 50.5 0.069 0.047 0.004

GENCC_SMUD2012_2 Generic New CC for 2012+ Gas SMU Bay

North 1,427 49.2 0.069 0.047 0.004

GENGT_NBAJA_1001 Gas San

Diego 20 6.1 0.619 0.047 0. 004

GENGT_NBAJA_1201 Gas San

Diego 27 8.4 0.633 0.047 0.004

HUMBOLDT BAY C1 Humboldt Bay: IC 1 Gas PG&E North

CA 41 5.3 0.256 0.047 0.004

HUMBOLDT B AY C10 Humboldt Bay: IC 10 Gas PG&E North

CA 42 5.3 0.252 0.047 0.004

HUMBOLDT BAY C2 Humboldt Bay: IC 2 Gas PG&E North

CA 42 5.3 0.254 0.047 0.004

HUMB OLDT BAY C3 Humboldt Bay: IC 3 Gas PG&E North 0.047 0.004

### A

- 16 CA 41 5.1 0.248

HUMBOLDT BAY C4 Humboldt Bay: IC 4 Gas PG&E North

CA 42 5.3 0.252 0.047 0.004

HUMBOLDT BAY C5 Humboldt Bay: IC 5 Gas PG&E North

CA 41 5.2 0.251 0.047 0.004

HUMBOLDT BAY C6 Humboldt Bay: IC 6 Gas PG&E North

CA 42 5.2 0.251 0.047 0.00 4

HUMBOLDT BAY C7 Humboldt Bay: IC 7 Gas PG&E North

CA 42 5.3 0.255 0.047 0.004

HUMBOLDT BAY C8 Humboldt Bay: IC 8 Gas PG&E North

CA 42 5.4 0.259 0.047 0.004

### A

- 17 Table A

- 11 : NO X 

### Emissions Rate Adjustm ents for Future Facilities; Base d on L owest 10 th Percentile Emissions for Fuel Typ e (2 of 2)

Vent y x Model

Name Ventyx Model Description Fuel

Type Vent y x Assumed

Physical Location Air

District

Region Model

Gen eration

in 2012

(GWh) Pre

Adjustment

NOx

Emissions

(tons) Ventyx

Assumed

NOx

Emissions

Rate

(lbs/MWh) 10th

Percentile

NOx

Emission

Rate for

fuel Type

(lbs/MWh) Synapse

Adjusted

NOx Input

Rate

(lbs/MMBtu)

HUMBOLDT

BAY C9 Humboldt Bay: IC 9 Gas PG&E North

C A 42 5.3 0.253 0.047 0.004

INLAND

EMPIRE 1 Inland Empire Energy Center: CS1 Gas SCE South

Coast 2,712 91.4 0.067 0.047 0.004

INLAND

EMPIRE 2 Inland Empire En ergy Center: CS2 Gas SCE South

Coast 2,699 90.6 0.067 0.047 0.004

OTC REPLACE

SD1 Generic New CC for L/R Balance Gas SDG&E San

Diego 538 18.6 0.069 0.047 0.004

OTC REPLACE

SD2 Generic New CC for L/R Balance Gas SDG&E San

Diego 544 18.8 0.069 0.047 0.004

SFO AIRPORT

GT1 San Francisco Electric Reliability Project Gas PG&E Bay

Area 4 1.0 0.516 0.047 0.004

CHOWCHILLA

BIO 1 Chowchilla Biomass: AB Wood DWR San

Joaquin 85 123.0 2.904 2.960 0.249

EL NIDO

BIOMAS 1 El Nido Biomass: AB Wood PG&E San

Joaquin 85 122.5 2.891 2.960 0.249

RPSBIO_IID Wood IID Imperial 921 1,362.6 2.960 2.960 0.249

RPSBIO_NP15 Wood PG&E Bay

Area 515 762.4 2.959 2.960 0 .249

RPSBIO_SCE Wood SCE South

Coast 282 417.4 2.961 2.960 0.249

RPSBIO_SDGE Wood SDG&E San

Diego 287 424.2 2.959 2.960 0.249

### B

- 1 

# Appendix B:

Creation of Hybrid Base Case

Background

Synapse Energy Economics required a base case of future build

- out and operations in order to estimate the difference in generation and emissions under each of the incremental energy efficiency (EE)/renewable energy (RE) scenari os. The base case was created by hybridizing two AB 32 and 33 percent renewable energy standard (RES )

- compliant scenarios for the year 2016 as created by California Energy Commission ( Energy Commission ) staff for the 2009 Integrated Energy Policy Report (I EPR). The 2009 IEPR utilizes two “bookend” cases to examine high penetrations of RE build

- out, one that examines a wind

- dominated build

- o ut (known

as the “ H igh W ind” scenario) and one that examines a solar

- thermal heavy build

- out (“ H igh S olar”). Both cases included at least

moderate amounts of in

- state and out

- of

- state wind, as well as photovoltaic and solar thermal resources.

Both the High Wind and High Solar pathways represent potentially extreme futures, with a likely AB 32

- compliant future emerging

som ewhere between depending on economic, environmental (siting), licensure, and grid integration considerations. Rather than
attempting to find an optimal build

- out, Synapse opted to create a Hybrid Base Case representing a more balanced mix of the

resources. The hybrid is a 50

- 50 mix of the high wind and high solar cases. While this scenario does not, and cannot, show how California will meet the RES in future years, it does represent a plausible future.



# Creation of Hybrid

Synapse created the Hybrid Case by surveying all inputs to both the High Wind and High Solar scenarios. Synapse recorded which resources were shown to have capacity changes specific to either the High Wind or High Solar scenario. Only units that were determined to exist in the analysis year 2016 were examined. Three categories of generating resource were delineated: units with the same capacities in both scenarios, units that existed in both scenarios with different capacities, and units that only existed in one of

the scenarios. Units with the same capacities in both scenarios were left unchanged in the Hybrid Base Case. For units that existed in both scenarios, but differed in nameplate capacity, the average of the two scenario capacities were used for the generating resource.

For the units that only existed in one of the two scenarios, a generating unit was created in the Hybrid Base Case with one

- half

(50 percent) of the nameplate capacity.



## Adjustments to Wind and Solar Resources

Wind and solar resources, as modeled in this system, have a “simulated” stochastic load shape, wherein the output of the unit is given explicitly for each hour of the year. For these units, taking the average between the High Wind and High Solar cases, or dividing the resource in half, required extracting 8760 hou rs of output and manipulating the data. A new index of expected capacity output was then input into the Hybrid Base Case scenario.

### B

- 2 A summary of changes between High Wind, High Solar, and Hybrid scenarios can be seen in Table B

- 1

, below. The name plate capacities shown in the table for wind and solar resources represent the maximum capacity of the index

— this is the one hour with the highest capacity factor.



## Geothermal Resources

New geothermal units were treated dif ferently than wind or solar units. Geothermal units have a static capacity factor. There was only one geothermal unit that needed adjustment in the model; this unit existed in the High Wind scenario, but not in the High Solar scenario. Following the patter n, the name plate capacity of this unit was halved for its inclusion in the Hybrid Base Case. A summary of changes between High Wind, High Solar, and Hybrid can be seen in Table B

- 1 below.

Table B

- 1 : Summary o f Name Plate Capacity Adjust ments for Hybrid Ba se Case

Resource Name Resource Type Location Max Capacity in High

Wind Scenario (MW) Max Capacity in High

Solar Scenario (MW) Max Capacity in

Hybrid Scenario (MW)

RPSPV_NP15_REO Solar PV PG&E 214 962 589

RPSSol_SCE Solar Thermal SCE N/A 2,319 1,160

RPSP V_SP15_REO Solar PV SCE 29 356 193

RPSWT_SDGE_HW Wind SDG&E 893 N/A 446

RPSWT_SP15_2HW Wind SCE 3,170 N/A 1,585

RPSWT_NBAJ_HW Wind NBAJA 651 N/A 326

RPSGeo_IID Geothermal IID 1,000 N/A 500

RPSSol_NP15 Solar Thermal PG&E N/A 1,450 804

RPSSol_IID Solar Thermal IID N/A 860 464



# Results

After making the above changes, Synapse ran the model with the Hybrid Renewable Scenario and compared the results to results from both the High Wind and High Solar runs. They Hybrid run successfully hybridized the generat ion coming from renewable resources, while still being on track for meeting the RES. The High Wind scenario shows 25.9 percent of generation coming from renewable resources, High Solar shows 25.2 percent of generation from renewable sources, and Hybrid Bas e Case shows generation from renewables slightly higher, at 26.8 percent. The generation from each type of resource is shown below in Table B

- 2, and illustrated in Figure B

- 1. Figure B

- 1 also includes a comparison to generation in 2009 sourced from EIA’s F orm 923.

### B

- 3 Table B

- 2 : Summary of Generation (GWh) from High Wind, High Solar, and Hybrid ScenariosUraniumGeothermalCoalBiomassPetroleumHydroNatural GasWindSolarThermalSolar PVEnergyEfficiency% fromRenewable

2016

High Wind 37,113 17,873 2,6 94 7,981 1,298 40,098 97,661 33,033 1,350 2,369 13,552 25.9

2016

High Solar 37,113 16,576 2,705 7,826 1,312 40,080 96,285 19,777 11,456 4,254 13,552 25.2

2016

Hybrid Case 37,113 17,429 2,699 8,006 1,306 40,080 95,374 26,404 9,596 3,312 13,552 26.8

### B

- 4 Fi gure B

- 1 : Generation (G Wh) in California from Recorded 2009 EIA Data

, and 2016 Modeled High Wind,

High Solar, and Hybrid Scenarios 28

28 

### Historic and modeled data may not represent the same conditions (such as hydrologic conditions or maintenance outages), and

incl ude changes in generation fleet in California.

### C

- 1 

# Appendix C :

Detailed Results

This appendix includes tables and graphical charts showing the displacement generation fra ction and the displacement rate for NOx,

SO

2 

### , and CO

2 

### . The first series of tables and charts show displaced energy and emissions by WECC region (Tables and Figures C

- 1 through C

- 4), while the second series shows the same information by C alifornia air distr ict (C

- 5 through C

- 8). Figures C

- 9 through

C

- 12 show California displaced energy and emissions by fuel type. In all of the tables below

, the first value in the cell is the displaced energy fraction/emissions rate; the second value is an uncertainty range due to random forced outages (in the same units). Negative values are red. Cells in which the random forced outage error exceeds the displaced energy fraction/emissions rate are shaded gray.

Table C

- 1: Displaced Energy Fraction by WECC Region (MWh/MWh)DisplacedEnergyFraction(%)SDGE,WindSDGE,Solar (PV)SDGE, EEBaseloadSDGE, EEPeakSCE, WindSCE, Solar(PV)SCE, EEBaseloadSCE, EEPeakLADWP,WindLADWP,Solar (PV)LADWP, EEBaseloadLADWP, EEPeakPG&E,WindPG&E,Solar (PV)PG&E, EEBaseloadPG&E, EEPeak

20.6% 32.3% 30.6% 22.0% 32.3% 36.3% 35.7% 40.8% 77.0% 80.0% 85.6% 43.0% 55.5% 78.3% 80.8% 91.1% CA

9.5% 9.4% 5.7% 52.7% 5.7% 9.3% 5.7% 10.6% 5.9% 9.4% 5.7% 38.5% 13.2% 9.7% 5.7% 10.2%

17.2% 13.4% 11.1% 6.4% 14.0% 12.4% 11.4% 15.0% 1.9% 4 .8% 1.4% 23.7% 8.9% 7.0% 7.3%

- 4.4% NW

7.2% 7.1% 4.3% 39.9% 4.3% 7.0% 4.3% 8.0% 4.5% 7.1% 4.3% 29.1% 10.0% 7.4% 4.3% 7.7%

16.9% 12.3% 13.4% 39.4% 16.7% 11.4% 11.7% 11.7% 5.9% 4.3% 3.1% 28.9% 14.8% 7.3% 6.4% 8.2% RM

3.9% 3.9% 2.4% 21.7% 2.4% 3.8% 2.4% 4.4 % 2.4% 3.9% 2.4% 15.9% 5.4% 4.0% 2.4% 4.2%

45.2% 42.0% 44.9% 31.8% 36.8% 39.8% 41.1% 32.3% 15.1% 10.8% 9.9% 4.2% 20.6% 7.2% 5.5% 5.0% SW

9.6% 9.5% 5.8% 53.4% 5.8% 9.4% 5.8% 10.7% 6.0% 9.5% 5.8% 38.9% 13.3% 9.8% 5.8% 10.3%

### C

- 2 Table C

- 2: Displaced NO X 

### Emi ssions Rate by WECC Region (lbs NO X 

### /GWh)DisplacedEmissionsRate (lbsNOx/GWh)SDGE,WindSDGE,Solar (PV)SDGE, EEBaseloadSDGE, EEPeakSCE, WindSCE, Solar(PV)SCE, EEBaseloadSCE, EEPeakLADWP,WindLADWP,Solar (PV)LADWP, EEBaseloadLADWP, EEPeakPG&E,WindPG&E,Solar (PV)PG&E, EEBaseloadPG&E, EEPeak

55 44 59 19 57 61 51 88 73 98 79 140 103 124 164 213 CA

16 16 10 89 10 16 10 18 10 16 10 65 22 16 10 17

184 120 124

- 21 171 96 79 33 17 28

- 6 32 115 93 108

- 93 NW

172 170 104 952 104 168 104 191 107 170 104 694 238 176 104 183

308 122 182 322 282 130 156 120 64 12 3 162 324 116 140 112 RM

83 82 50 460 50 81 50 92 52 82 50 335 115 85 50 88

622 258 438

- 217 504 304 385 192 69 63 6

- 394 487 172 196

- 70 SW

153 152 92 850 93 150 92 170 9 5 152 92 620 212 157 92 164

1,170 545 803 103 1,013 592 672 433 223 200 82

- 59 1,029 505 608 162 Net Rate

231 229 139 1,278 139 225 139 256 143 228 139 932 319 236 139 246

### C

- 3 Table C

- 3: Displaced SO 2 

### Emissions Rate by WECC Region (lbs SO 2 

### /GWh)DisplacedEmissionsRate (lbsSO2/GWh)SDGE,WindSDGE,Solar (PV)SDGE, EEBaseloadSDGE, EEPeakSCE, WindSCE, Solar(PV)SCE, EEBaseloadSCE, EEPeakLADWP,WindLADWP,Solar (PV)LADWP, EEBaseloadLADWP, EEPeakPG&E,WindPG&E,Solar (PV)PG&E, EEBaseloadPG&E, EEPeak

70 19 31

- 70 55 29 31

- 3 11 14

- 3

- 20 47 28 61 42 CA

17 16 10 92 10 16 10 18 10 16 10 67 23 17 10 18

46 34 25

- 20 43 15 24 2 2 3

- 6

- 6 36 17 31

- 5 NW

20 20 12 113 12 20 12 23 13 20 12 82 28 21 12 22

162 54 68 50 157 56 68 19 36

- 9

- 3 24 185 64 74 25 RM

44 44 27 246 27 43 27 49 28 44 27 179 61 45 27 47

275 84 155

- 314 213 94 132 33 21 7

- 30

- 335 185 52 95

- 48 SW

70 70 43 391 43 69 43 78 44 70 43 285 98 72 43 75

553 192 279

- 353 469 195 255 51 71 15

- 42

- 338 454 161 261 14 Net Rate

57 57 35 318 35 56 35 64 36 57 35 231 79 59 35 61



### Table C

- 4: Displaced CO 2 

### Emissions Rate by WECC Region (t CO 2 

### /MWh)DisplacedEmissionsRate(tCO2/MWh)SDGE,WindSDGE,Solar (PV)SDGE, EEBaseloadSDGE, EEPeakSCE, WindSCE, Solar(PV)SCE, EEBaseloadSCE, EEPeakLADWP,WindLADWP,Solar (PV)LADWP, EEBaseloadLADWP, EEPeakPG&E,WindPG&E,Solar (PV)PG&E, EEBaseloadPG&E, EEPeak

0.07 0.12 0.13 0.08 0.13 0.14 0.15 0.19 0.32 0.32 0.36 0.23 0.23 0.34 0.39 0.44 CA

0.05 0.05 0.03 0.26 0.03 0.05 0.03 0.05 0.03 0.05 0.03 0.19 0.06 0.05 0.03 0.05

0.11 0.08 0.07 0.03 0.09 0.07 0.06 0.07 0.01 0.03 0.01 0.11 0.06 0.05 0.06

- 0.05 NW

0.08 0.08 0.05 0.43 0.05 0.07 0.05 0.09 0.05 0.08 0.05 0.31 0.11 0.08 0.05 0.08

0.13 0.07 0.09 0.23 0.12 0.07 0.07 0.07 0.04 0.02 0.01 0.16 0.12 0.05 0.05 0.06 RM

0.03 0.02 0.02 0.14 0.02 0.02 0.02 0.03 0.02 0.02 0.02 0.10 0.03 0.03 0.02 0.03

0.27 0.17 0.23

- 0.07 0.21 0.17 0.20 0.13 0.03 0.04 0.02

- 0.17 0.15 0.04 0.04

- 0.05 SW

0.07 0.07 0.04 0.40 0.04 0.07 0.04 0.08 0.04 0.07 0.04 0.29 0.10 0.07 0.04 0.08

0.57 0.44 0.52 0.27 0.55 0.45 0.49 0.47 0.39 0.40 0.39 0.32 0.56 0.47 0.54 0.41 Net Rate

0.07 0.07 0.04 0.38 0.04 0.07 0.04 0.08 0.04 0.07 0.04 0.28 0.10 0.07 0.04 0.07

### C

- 4 Figure C

- 1 : Displaced Energy Fraction by WECC Region and Fuel Type

### C

- 5 Figure C

- 2 : Displaced lbs of NO X 

### per GWh of Energy Displaced, by WECC Region and Fuel Type

### C

- 6 Figure C

- 3 : Displaced lbs of SO 2 

### per GWh of Energy Displaced, by WECC Region and Fuel Type

### C

- 7 Figure C

- 4 : Displaced Tons of CO 2 

### per M Wh of Energy Displaced, by WECC Region and Fuel Type

### C

- 8 Table C

- 5 : Displaced Energy Fraction by Air District (MWh / MWh)

Displaced Energy Fraction by

Air DistrictSDGE,WindSDGE,Solar (PV)SDGE, EEBaseloadSDGE, EEPeakSCE, WindSCE, Solar(PV)SCE, EEBaseloadSCE, EEPeakLADWP,WindLADWP,Solar (PV)LADWP, EEBaseloadLADWP, EEPeakPG&E,WindPG&E,Solar (PV)PG&E, EEBaseloadPG&E, EEPeak

0.4% 0.3% 0.4%

- 1.0% 0.4% 0.5% 0.4% 0.1% 0.1% 0.2%

- 0.1%

- 0.3% 1.6% 2.3% 2.0% 2.7% North CA

0.3% 0.3% 0.2% 1.8% 0.2% 0.3% 0.2% 0.4% 0.2% 0.3% 0.2% 1.3% 0.5% 0.3% 0.2% 0.3%

2.2% 3.4% 3.8%

- 6.6% 3.6% 3.1% 6.2%

- 0.3% 0.3%

- 0.6%

- 0.7%

- 1.5% 10.7% 11.9% 13.0% 7.6% Bay North

2.7% 2.7% 1.7% 15.2% 1.7% 2.7% 1.7% 3.0% 1.7% 2.7% 1.7% 11.1% 3.8% 2.8% 1.7% 2.9%

7.2% 8.8% 6.8% 7.0% 8.8% 9.4% 7.3%

- 1.5% 2.5% 2.7%

- 0.6%

- 9.5% 17.9% 26.1% 23.6% 29.3% Bay Ar ea

3.0% 3.0% 1.8% 16.8% 1.8% 3.0% 1.8% 3.4% 1.9% 3.0% 1.8% 12.3% 4.2% 3.1% 1.8% 3.2%

2.4% 0.1% 2.4% 6.8% 2.9% 1.1% 2.2% 0.1% 1.0% 1.1%

- 0.3% 1.2% 4.9% 3. 8% 7.1% 5.1% Sierra North

1.3% 1.3% 0.8% 7.3% 0.8% 1.3% 0.8% 1.5% 0.8% 1.3% 0.8% 5.3% 1.8% 1.3% 0.8% 1.4%

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Sierra South

0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

1.3% 1.9% 1.1%

- 0.7% 1.6% 2.1% 1.4% 0.1% 0.2% 0.7% 0.1%

- 0.8% 2.8% 4.8% 3.3% 6.6% Sacramento

0.7% 0.7% 0.4% 4.1% 0.4% 0.7% 0.4% 0.8% 0.5% 0.7% 0.4% 3.0% 1.0% 0.8% 0.4% 0.8%

1.4% 3.5% 2.9%

- 1.4% 2.9% 5.3% 3.4% 3.4% 1.1% 2.0% 0 .5% 1.2% 7.7% 10.8% 10.4% 17.7% San Joaquin

1.6% 1.6% 0.9% 8.7% 0.9% 1.5% 0.9% 1.7% 1.0% 1.6% 0.9% 6.3% 2.2% 1.6% 0.9% 1.7%

- 1.5% - 1.8% 0.6% - 23.9% 2.7% - 0.1% 2.2%

- 4.2%

- 1.6%

- 2.9%

- 4.3%

- 14.5% 5.8% 11.0% 14.2% 15.6% Central Coast

1.5% 1.5% 0.9% 8.2% 0.9% 1.4% 0 .9% 1.6% 0.9% 1.5% 0.9% 6.0% 2.1% 1.5% 0.9% 1.6%

0.6% 1.0% 0.8% 6.9% 0.6% 1.1% 1.0% 3.9% 0.5% 0.9% 0.7% 4.0% 0.0% 0.8% 0.4% 0.8% Desert Region

1.5% 1.5% 0.9% 8.3% 0.9% 1.5% 0.9% 1.7% 0.9% 1.5% 0.9% 6.1% 2.1% 1.5% 0.9% 1.6%

2.7% 7.7% 6.1% 15 .8% 4.8% 8.0% 6.3% 28.8% 71.5% 70.6% 89.3% 50.4%

- 0.5% 1.3% 0.0% 0.4% South Coast

4.3% 4.2% 2.6% 23.6% 2.6% 4.2% 2.6% 4.7% 2.6% 4.2% 2.6% 17.2% 5.9% 4.3% 2.6% 4.5%

2.9% 5.4% 5.0% 12.5% 2.3% 3.5% 3.0% 5.7% 0.6% 1.7% 1.0% 5.9%

- 0.9%

- 0.1%

- 1.3%

- 0.9% San Diego

0.9% 0.9% 0.5% 4.9% 0.5% 0.9% 0.5% 1.0% 0.5% 0.9% 0.5% 3.6% 1.2% 0.9% 0.5% 0.9%

1.1% 0.8% 0.9% 1.6% 1.1% 0.6% 0.8% 1.8% 0.1% 0.2% 0.0% 1.3% 0.4%

- 0.5% 0.0%

- 0.6% Imperial

0.3% 0.3% 0.2% 1.8% 0.2% 0.3% 0.2% 0.4% 0.2% 0.3% 0.2% 1.3% 0.5% 0.3% 0.2% 0.3%

### C

- 9 Tab le C

- 6 : Displaced NO X 

### Emissions Rate by Air District (lbs NO X 

### /GWh)

Displaced lbs of NOx

per GWh of Energy

Displaced, by Air

DistrictSDGE,WindSDGE,Solar (PV)SDGE, EEBaseloadSDGE, EEPeakSCE, WindSCE, Solar(PV)SCE, EEBaseloadSCE, EEPeakLADWP,WindLADWP,Solar (PV)LADWP, EEBaseloadLADWP, EEPeakPG&E,WindPG&E,Solar (PV)PG&E, EEBaseloadPG&E, EEPeak

1.0 0.0 1.5

- 15.7 1.4 1.2 0.3

- 0.8

- 0.6 0.1

- 0.4

- 6.4 7.7 5.6 12.8 22.2 North CA

2.7 2.7 1.6 15.0 1.6 2.6 1.6 3.0 1.7 2.7 1.6 10.9 3.7 2 .8 1.6 2.9

0.5 1.1 1.0

- 1.9 1.0 0.7 1.8 0.1 0.1

- 0.2

- 0.1 0.9 3.3 3.6 4.0 2.5 Bay North

0.7 0.7 0.4 4.1 0.4 0.7 0.4 0.8 0.5 0.7 0.4 3.0 1.0 0.8 0.4 0.8

11.6 7.2 11.4

- 19.5 13.1 13.6 8.6

- 6.7 2.1 1.7

- 2.5

- 26.1 27.3 39.5 47.2 69.9 Bay Area

9.1 9.0 5.5 50.2 5.5 8.9 5.5 10.1 5.6 9.0 5.5 36.6 12.5 9.3 5.5 9.7

4.4

- 0.8 4.1

- 10.7 2.9 3.1 2.9

- 2.6 0.8 1.2

- 0.7

- 9.2 11.3 12.9 20.5 29.4 Sierra North

2.7 2.7 1.6 15.0 1.6 2.7 1.6 3.0 1.7 2.7 1.6 11.0 3.8 2.8 1.6 2.9

0.7 0.4 0.5 0.0 1.0 0.8 0.7

- 0.1 0.1

- 0.1

- 0.1

- 0.9 0.8 0.8 0.5

- 0.1 Sierra South

0.3 0.3 0.2 1.8 0.2 0.3 0.2 0.4 0.2 0.3 0.2 1.3 0.4 0.3 0.2 0.3

- 0.6

- 1.5 0.2

- 11.3 0.2

- 0.7 0.4

- 1.5

- 0.2

- 0.4

- 0.2

- 4.6 0.9 2.4 4.4 8.5 Sacramento

0.9 0.9 0.6 5.1 0.6 0.9 0.6 1.0 0.6 0.9 0.6 3.7 1.3 0.9 0.6 1. 0

7.5 2.8 7.2

- 46.4 5.4 11.8 5.2 0.5 1.5 3.2

- 0.1

- 10.1 22.9 30.6 43.6 75.9 San Joaquin

7.4 7.3 4.4 40.8 4.4 7.2 4.4 8.2 4.6 7.3 4.4 29.7 10.2 7.5 4.4 7.8

0.7 3.1 2.7 8.2 3.6 2.4 3.6 9.8 1.5 1.7 0.3 10.5 6.6 12.5 11.8 8.8 Central Coast

0.6 0.6 0.4 3. 5 0.4 0.6 0.4 0.7 0.4 0.6 0.4 2.5 0.9 0.6 0.4 0.7

5.3 4.3 3.3 16.3 4.9 4.9 4.1 8.7 2.7 3.8 2.9 15.1 4.5 4.9 2.8 1.1 Desert Region

3.7 3.6 2.2 20.4 2.2 3.6 2.2 4.1 2.3 3.6 2.2 14.8 5.1 3.8 2.2 3.9

7.0 7.9 9.6 18.2 8.6 8.2 9.6 39.0 57.0 76.3 74.6 107.1 3.0 0.6 2.9

- 8.2 South Coast

6.0 5.9 3.6 33.0 3.6 5.8 3.6 6.6 3.7 5.9 3.6 24.1 8.3 6.1 3.6 6.4

6.6 9.3 9.8 45.8 4.3 5.4 4.2 26.4 4.3 4.0 4.0 41.2 3.1 2.3 1.0

- 2.4 San Diego

4.4 4.3 2.6 24.3 2.7 4.3 2.6 4.9 2.7 4.3 2.6 17.7 6.1 4.5 2.6 4.7

8.4 5.6 6.4 18.8 8.6 7.4 6.6 9.8 3.0 2.5 2.1 19.7 8.0 5.7 5.6 2.5 Imperial

1.2 1.2 0.7 6.9 0.8 1.2 0.7 1.4 0.8 1.2 0.7 5.0 1.7 1.3 0.7 1.3

### C

- 10 Table C

- 7 : Displaced SO 2 

### Emissions Rate by Air District (lbs NO X 

### /GWh)

Displaced lbs of SO 2 per GWh of Energy

Displaced, by A ir

DistrictSDGE,WindSDGE,Solar (PV)SDGE, EEBaseloadSDGE, EEPeakSCE, WindSCE, Solar(PV)SCE, EEBaseloadSCE, EEPeakLADWP,WindLADWP,Solar (PV)LADWP, EEBaseloadLADWP, EEPeakPG&E,WindPG&E,Solar (PV)PG&E, EEBaseloadPG&E, EEPeak

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Nor th CA

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.1 0.2 0.5 0.6 0.3 0.1 0.6

- 0.1 0.1 0.1 0.0 0.9 1.1 0.6 1.3 0.7 Bay North

0.4 0.4 0.3 2.5 0.3 0.4 0.3 0.5 0.3 0.4 0. 3 1.8 0.6 0.5 0.3 0.5

9.4 0.4 4.8

- 21.3 6.5 2.9 3.3

- 1.3 3.0 2.6 0.7

- 7.3 9.9 8.3 17.6 32.3 Bay Area

5.2 5.2 3.1 28.8 3.1 5.1 3.1 5.8 3.2 5.1 3.1 21.0 7.2 5.3 3.1 5.6

0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1

- 0.1 0.0 0.0 0.0 0.1 0.2 Sierra North

0.1 0 .1 0.1 0.6 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.5 0.2 0.1 0.1 0.1

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Sierra South

0.1 0.1 0.1 0.5 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.4 0.1 0.1 0.1 0.1

- 0.1 0.0

- 0.1 0.0 0.0

- 0.1 0.0 0.0 0.0

- 0 .1 0.0 0.0 0.0 0.1 0.0 0.2 Sacramento

0.1 0.1 0.1 0.8 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.6 0.2 0.1 0.1 0.1

5.8 2.0 2.3

- 3.1 3.1 1.9 1.4 1.0 1.9 1.5 1.6 0.9 4.2 3.7 6.4 12.0 San Joaquin

3.1 3.0 1.8 16.9 1.8 3.0 1.8 3.4 1.9 3.0 1.8 12.3 4.2 3.1 1.8 3.3

0 .2 0.2 0.3

- 0.6 0.4 0.7 0.3

- 0.3 0.1

- 0.1

- 0.1 0.0 0.9 1.2 1.0 1.1 Central Coast

0.2 0.2 0.1 1.0 0.1 0.2 0.1 0.2 0.1 0.2 0.1 0.7 0.3 0.2 0.1 0.2

8.6 4.2 3.9 5.0 7.7 4.8 4.4 0.5 1.4 0.8 0.1 2.3 7.2 4.2 4.2 1.3 Desert Region

1.9 1.9 1.1 10.4 1.1 1.8 1.1 2.1 1.2 1.9 1 .1 7.6 2.6 1.9 1.1 2.0

45.5 10.8 18.9

- 55.2 37.5 18.5 20.1

- 5.5 4.6 8.1

- 5.7

- 21.0 23.7 10.3 30.5

- 6.6 South Coast

21.4 21.3 12.9 118.8 13.0 21.0 12.9 23.8 13.3 21.2 12.9 86.6 29.7 21.9 12.9 22.9

0.3 0.4 0.4 3.1 0.1 0.2 0.3 1.1 0.0 0.4 0.1 1. 8 0.3 0.1 0.1 0.4 San Diego

0.3 0.3 0.2 1.9 0.2 0.3 0.2 0.4 0.2 0.3 0.2 1.4 0.5 0.4 0.2 0.4

0.0 0.1 0.1 0.6 0.0 0.1 0.1 0.4 0.0 0.1 0.1 0.5 0.0 0.0 0.0 0.0 Imperial

0.1 0.1 0.1 0.7 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.5 0.2 0.1 0.1 0.1

### C

- 11 Table C

- 8 : Displaced CO 2 

### Emis sions Rate by Air District (t CO 2 

### /MWh)

Displaced tons of

CO 2 per MWh of

Energy Displaced, by

Air DistrictSDGE,WindSDGE,Solar (PV)SDGE, EEBaseloadSDGE, EEPeakSCE, WindSCE, Solar(PV)SCE, EEBaseloadSCE, EEPeakLADWP,WindLADWP,Solar (PV)LADWP, EEBaseloadLADWP, EEPeakPG&E,WindPG&E,Solar (PV)PG&E, EEBaseloadPG&E, EEPeak

0.00 0.00 0.00

- 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.02 North CA

0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.0 0 0.00

0.01 0.01 0.01

- 0.03 0.01 0.01 0.03 0.00 0.00 0.00 0.00 0.00 0.04 0.05 0.06 0.03 Bay North

0.01 0.01 0.01 0.07 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.05 0.02 0.01 0.01 0.01

0.03 0.03 0.03 0.02 0.03 0.04 0.03

- 0.02 0.01 0.01

- 0.01

- 0.07 0. 08 0.12 0.12 0.15 Bay Area

0.02 0.02 0.01 0.09 0.01 0.02 0.01 0.02 0.01 0.02 0.01 0.06 0.02 0.02 0.01 0.02

0.01 0.00 0.01 0.03 0.01 0.01 0.01 0.00 0.00 0.01 0.00 0.00 0.02 0.02 0.03 0.02 Sierra North

0.01 0.01 0.00 0.03 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.02 0.01 0.01 0.00 0.01

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sierra South

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.01 0.00

- 0.01 0.01 0.01 0.01 0.00 0.00 0. 00 0.00

- 0.01 0.01 0.02 0.01 0.03 Sacramento

0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00

0.01 0.02 0.02

- 0.02 0.01 0.03 0.02 0.01 0.01 0.01 0.00 0.00 0.05 0.06 0.06 0.11 San Joaquin

0.01 0.01 0.00 0.04 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.03 0.01 0.01 0.00 0.01

- 0.01

- 0.01 0.00

- 0.14 0.01 0.00 0.01

- 0.02

- 0.01

- 0.02

- 0.02

- 0.08 0.02 0.05 0.08 0.08 Central Coast

0.01 0.01 0.01 0.06 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.04 0.01 0.01 0.01 0.01

0.00 0.01 0.00 0.04 0.00 0.01 0.01 0.02 0.00 0.00 0.00 0.02 0.00 0.01 0.00 0.00 Desert Region

0.01 0.01 0.00 0.04 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.03 0.01 0.01 0.00 0.01

0.01 0.03 0.03 0.09 0.02 0.03 0.03 0.16 0.30 0.30 0.38 0.31 0.00 0.01 0.00 0.00 South Coast

0.03 0.02 0.02 0.14 0.02 0.02 0.02 0.03 0.02 0.02 0.02 0.10 0.03 0.03 0.02 0.03

0.01 0.02 0.02 0.08 0.01 0.01 0.01 0.03 0.00 0.01 0.01 0.05 0.00 0.00 0.00 0.00 San Diego

0.01 0.01 0.00 0.03 0.00 0.01 0.00 0.01 0.00 0.01 0.00 0.02 0.01 0.01 0.00 0.01

0.00 0.00 0. 00 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 Imperial

0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00

### C

- 12 Figure C

- 5 : Displaced Energy Fraction by Air District (MWh per MWh)

### C

- 13 Figure C

- 6 : Displaced lbs of NO X 

### per GWh of Energy Displaced, by Air District

### C

- 14 Figure C

- 7 : Displaced lbs of SO 2 

### per GWh of Energy Displaced, by Air District

### C

- 15 Figure C

- 8 : Displaced Tons of CO 2 

### per MWh of Energy Displaced, by Air District

### C

- 16 Figure C

- 9 : Displaced Energy Fraction, by Fu el in California

### C

- 17 Figure C

- 10 : Displaced lbs of NO X 

### per GWh of Energy Displaced, by Fuel in CA

### C

- 18 Figure C

- 11 : Displaced lbs of SO 2 

### per MWh of Energy Displaced, by Fuel in CA

### C

- 19 Figure C

- 12 : Displaced Tons of CO 2 

### per MWh of Energy Displaced, by Fuel in C A