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 Fisher, 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

Prepared 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 Commission, 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 infringe 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 Efficiency

• 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

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

Management Districts is the final report for the Air Emissions Reductions Through Energy and 

Peak Load Reductions and Renewable Generation project (contract number 500-02-004, work 

authorization number MRA 026) conducted by Synapse Energy Economics, Inc. The 

information from this project contributes to PIER’s Energy-Related Environmental Research

Program.

For more information about the PIER Program, please visit the Energy Commission’s website at 

www.energy.ca.gov/research/ or contact the Energy Commission at 916-654-4878.
ii
iii

ABSTRACT

In California and other states, air quality management districts are considering using 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 efficiency/renewable energy programs not 

already required by state or federal statute. This work used California as a test case, producing 

an analysis that could theoretically satisfy U.S. Environmental Protection Agency requirements 

for using energy efficiency/renewable energy in State Implementation Plan compliance.

Synapse Energy Economics used a Western Electricity Coordinating Council-scale productioncost simulation model (PROSYM) to examine the impact of incremental energy 

efficiency/renewable energy on generation and emissions of oxides of nitrogen (NOX), sulfur 

dioxide (SO2), and carbon dioxide. Sixteen energy efficiency/renewable energy scenarios were 

tested against a reference “base case,” representing the expected structure of Western Electricity 

Coordinating 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 large fraction of generation was displaced out-of-state, 

and total NOX and SO2 displaced in California tended to be small. Generation displaced out-ofstate included coal resources, and therefore resulted in far larger emissions benefits in Western 

Electricity 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 Implementation Plan, criteria emissions, greenhouse gas 

emissions, Synapse Energy Economics

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. California Energy Commission. Publication number: CEC-500-

2011-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

Displaced Energy Fraction, by Air District Region......................................................................... 39

Displaced NOX Emissions, by Region and Fuel Type..................................................................... 41

Displaced NOX Emissions, by Air District Region .......................................................................... 43

Displaced SO2 Emissions, by Region and Fuel Type ...................................................................... 45

Displaced SO2 Emissions, by Air District Region............................................................................ 46

Displaced CO2 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 Implementation................................................................................................................ 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: Emissions 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 NOX Emissions Rate (in lbs NOX/GWh) by California Air District Region in 

2016, Relative to the Base Case................................................................................................................. 4

Figure 3: Displaced CO2 Emissions (tons of CO2 / 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

Figure 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 NOX Emissions Rate, by Air District 

Region: SCE Wind.................................................................................................................................... 32
ix

Figure 22: Example of California Component of Displaced NOX Emissions Rate, by Fuel Type 

within California: SCE Wind.................................................................................................................. 33

Figure 23: CO2 Emissions, Displaced Emissions, and Iteration Run Emissions in California, All 

Fuel Types. Note, the y-axis does not begin at zero. ................................................................................. 35

Figure 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 

California, 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 NOX Emissions by Month in Each WECC Region in 2016.................... 42

Figure 28: Displaced NOX Emissions Rate (in lbs NOX per GWh of EE/RE Generation), by 

WECC Region and Fuel in 2016 ............................................................................................................. 42

Figure 29: Displaced NOX Emissions Rate (in lbs NOX/GWh) in California Air District Region 43

Figure 30: Displaced NOX (in lbs NOX / GWh) by Fuel Type in California.................................... 45

Figure 31: Displaced SO2 Emissions Rate (in lbs SO2/GWh) in by WECC Region and Fuel Type

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

Figure 32: Displaced SO2 Emissions Rate (in lbs SO2/GWh) by Air District Region..................... 47

Figure 33: Displaced CO2 Emissions (tCO2 / 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 Emissions 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 Wind 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

Figure 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 outages, 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 peak 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: Displaced lbs of NOX per GWh of Energy Displaced, by WECC Region and Fuel 

Type.............................................................................................................................................................. 5

Figure C-3: Displaced lbs of SO2 per GWh of Energy Displaced, by WECC Region and Fuel Type

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

Figure C-4: Displaced Tons of CO2 per MWh of Energy Displaced, by WECC Region and Fuel 

Type.............................................................................................................................................................. 7

Figure C-5: Displaced Energy Fraction by Air District (MWh per MWh)....................................... 12

Figure C-6: Displaced lbs of NOX per GWh of Energy Displaced, by Air District ......................... 13

Figure C-7: Displaced lbs of SO2 per GWh of Energy Displaced, by Air District........................... 14

Figure C-8: Displaced Tons of CO2 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 NOX per GWh of Energy Displaced, by Fuel in CA........................ 17

Figure C-11: Displaced lbs of SO2 per MWh of Energy Displaced, by Fuel in CA......................... 18

Figure C-12: Displaced Tons of CO2 per MWh of Energy Displaced, by Fuel in CA..................... 19

Unless otherwise noted, all figures are provided by the authors.
xi

LIST OF TABLES

Table 1: Air District Aggregations into Air District Zones Used for this Analysis ........................ 18

Table 2: Aggregate Emissions Rates of NOX and SO2 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 NOX Rate (in lbs NOX per GWh of EE/RE) for Eight Scenarios by California 

Air District Region ................................................................................................................................... 44

Table 6: Displaced NOX Rate (in lbs NOX per GWh of EE/RE) for Eight Scenarios by California 

Air District Region ................................................................................................................................... 44

Table A-1: Fractions of Generation, CO2, NOX, and SO2 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............................................................................ 2

Table A-2: Aggregate Emissions Rates of NOX and SO2 by Unit Categorization, in California 

Only, Before and After Adjustment (2012)............................................................................................. 6

Table A-3: SO2 Emissions Rate Corrections for California Electric Generating Facilities Based on 

CEMS (2009) Emissions Rates .................................................................................................................. 7

Table A-4: SO2 Emissions Rate Corrections for California Electric Generating Facilities Based on 

eGRID (2007) Emissions Rates ................................................................................................................. 8

Table A-5: NOX Emissions Rate Corrections for California Electric Generating Facilities Based on 

CEMS (2009) Emissions Rates .................................................................................................................. 9

Table A-6: NOX Emissions Rate Corrections for California Electric Generating Facilities Based on 

eGRID (2007) Emissions Rates (1 of 3) .................................................................................................. 10

Table A-7: NOX Emissions Rate Corrections for California Electric Generating Facilities Based on 

eGRID (2007) Emissions Rates (2 of 3) .................................................................................................. 11

Table A-8: NOX Emissions Rate Corrections for California Electric Generating Facilities Based on 

eGRID (2007) Emissions Rates (3 of 3) .................................................................................................. 12

Table A-9: SO2 Emissions Rate Adjustments for Future California Electric Generating Facilities; 

Based on Lowest 10th Percentile Emissions for Fuel Type ............................................................... 14

Table A-10: NOX Emissions Rate Adjustments for Future CA Facilities; Based on Lowest 10th 

Percentile Emissions for Fuel Type (1 of 2) .......................................................................................... 15

Table A-11: NOX 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 Hybrid Base Case.......................... 2
xii

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

Table C-1: Displaced Energy Fraction by WECC Region (MWh/MWh) .......................................... 1

Table C-2: Displaced NOX Emissions Rate by WECC Region (lbs NOX/GWh)................................ 2

Table C-3: Displaced SO2 Emissions Rate by WECC Region (lbs SO2/GWh)................................... 3

Table C-4: Displaced CO2 Emissions Rate by WECC Region (t CO2/MWh) .................................... 3

Table C-5: Displaced Energy Fraction by Air District (MWh / MWh).............................................. 8

Table C-6: Displaced NOX Emissions Rate by Air District (lbs NOX/GWh) ..................................... 9

Table C-7: Displaced SO2 Emissions Rate by Air District (lbs NOX/GWh)..................................... 10

Table C-8: Displaced CO2 Emissions Rate by Air District (t CO2/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 nationally and in California, air districts have successfully 

reduced criteria pollutants such as oxides of nitrogen (NOX), sulfur dioxide (SO2), and fine 

particulates (PM2.5) through end-of-pipe controls. In the face of increasingly stringent air quality 

standards however, states and air districts have progressively turned toward energy demandreduction measures and non-emissive sources of energy to meet standards cost-effectively.

In California and other states, air quality management districts are considering using 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 Implementation 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 Plans. 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 using energy efficiency/renewable energy in State Implementation Plan 

compliance.

Approach

Each unit of energy saved by a new efficiency 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 full 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 year (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 to examine the impact of incremental energy efficiency/renewable energy on generation 

and emissions of NOX, SO2, and CO2.

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 NOx and SO2 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 electricity 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 service 

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 Electric (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 margin, 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, imports, 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 reported.

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 energy and emissions, scaled 

by the size of the energy efficiency/renewable energy program implemented (i.e., megawatthour [MWh] of generation displaced per MWh of energy efficiency/renewable energy 

implemented, or pounds of emissions displaced per MWh of energy 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 large 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 implementation. 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 emissions 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 

purposes of examining results.
3

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

In this chart, the color of the bars represents 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 the 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 fuel types displaced by the energy efficiency/renewable energy

programs are highly variable. For example, programs implemented in LADWP and PG&E tend 

to displace more resources in California, while programs implemented in SDG&E and SCE tend 

to displace energy out-of-state generators, and include significant fractions of coal. This out-ofstate displacement is a non-intuitive finding, discussed in depth in the Discussion section of the 

report.

Due to California’s cleaner-burning natural gas units, the emissions produced by California 

generators are very small relative to surrounding states: therefore, total NOX and SO2 displaced 

in California tend to be small. Results of the displaced NOX emissions rate within California 

(i.e., emissions represented by the green 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 NOX per gigawatt-hour 

(GWh) of energy efficiency/renewable energy.
4

Figure 2: Displaced NOX Emissions Rate (in lbs NOX/GWh) by California Air District Region

in 2016, Relative to the Base Case

Energy efficiency/renewable energy programs implemented in SDG&E and SCE tend 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 disproportionate 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 CO2, both in California and from out-of-state coal generators (see Figure 3, 

below). 
5

Figure 3: Displaced CO2 Emissions (tons of CO2 / MWh of Energy Efficiency/Renewable Energy) 

by Western Electricity Coordinating Council Region and Fuel Type 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 California 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 implemented 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 

potential 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 significant criteria emissions benefit from energy efficiency/renewable energy

programs in State, other regions of the West see significant emissions reductions from 

demand reductions in California, posing important questions about interstate energy 

and emissions planning. 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 region. However, the results consistently show reductions in outof-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 outside of the state, 

often displacing coal-fired resources in the Rocky Mountain and Southwest regions of 
6

Western Electricity Coordinating 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 [tCO2]/MWh) relative to displacing in-state natural 

gas only (such as in the LADWP baseload energy efficiency scenario, 0.4 tCO2/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 units 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 research, 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 displaced 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 not 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 inextricably 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 generating 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 (NOX), sulfur dioxide (SO2), and fine particulate (PM2.5) emissions 

(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 since 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 standards, 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 and 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 and 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, starting in 2010 and expected to be promulgated through 

at least 2012. These new regulations dramatically increase the importance of synchronizing state 

energy and environmental programs,4 but even in the absence of these 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 inconsistent 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 SO2 control or 

selective catalytic reduction (SCR) for NOx control, require additional energy to operate; this energy 

consumption can be considered “parasitic,” as it reduces the net output of the generator. Since these 

energy reductions are not accompanied by an equivalent reduction in CO2 emissions, the net result is an 

increase in the CO2 emissions rate from these criteria emissions controlled 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-State 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 affecting 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 environmental 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) do take 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 districts. 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 CPUC. These

dynamics can impose unnecessary tension between the Energy Commission, CPUC, 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. However, 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 project 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 energy 

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 provided 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 NOX/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 simpler analysis.

• Incomplete understanding about how EE/RE affects 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 research 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 satisfy emerging U.S. EPA criteria for 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. There 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 benefits 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 displaced 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 regulatory 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 Ambient 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 states5 to develop plans that implement, maintain, and 

enforce limits that will improve air quality to meet and maintain the revised NAAQS. These 

plans are referred to as State Implementation Plans, or SIPs.

During 2010, U.S. EPA reviewed the fine particle (PM2.5) and ozone standards, and concluded 

that existing levels were 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 president 

of the United States, the U.S. EPA decided not to revise the ozone standard at this time.

The PM2.5 NAAQS was last revised in 2006, when the 24-hour standard was lowered from 65 to 

35 micrograms per cubic meter (g/m3). The annual 15 g/m3 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 PM2.5 annual standard during 2011.

Air districts would be required to 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 Section 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 emissions;

• 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

 “States” also refers to local air quality planning agencies, where such authority has been delegated.

6 While 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 result 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 should 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 particle 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 renewable 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 determine 

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 benefits 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 and entails 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 projection, 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 Air 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 production7 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 future 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 example, 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 that 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 originating 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 of 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 the 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 emissions 

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 control 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 granted 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 its 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). Energy 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 reductions 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 associated 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 incremental 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 choose 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 

existing 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 additional 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 electric system impacts from EE/RE. As detailed in the next 

section, 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 reductions 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 report 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 used 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 NOX, SO2, and CO2 emissions; how a user should interpret 

results in terms 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 findings, 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 utility 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 examine 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

Displaced 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 provinces 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 Analysis Regions
14

Source: NERC 2011

The WECC region is highly 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: Utah, Montana, Wyoming, and Colorado) to 

the Northwest (NW: Washington, Oregon, Idaho, Northern Nevada, British Columbia, and 

Alberta), and from the Southwest (SW: Southern Nevada, Arizona, and New 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 expensive 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 approximately 

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 approximately 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 States 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 the 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) aggregated 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 units, dispatched to 

meet load in a particular area are called the marginal units: 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 dispatchable, 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, natural 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, and 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 contains 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 behavior 

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 utilized 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 price 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 requirements, 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 transmission 

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 system. In this case, Synapse used 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 implement 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 retirements 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 Commission for the 2009 IEPR. The 

model utilizes transmission zones, which are geographical areas that contain both generation 

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

transmission is not constrained (i.e., it flows freely 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 Northern

California, from Utah to Southern California, and between SCE 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 (primarily 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 primary 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. The 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 California 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 largescale 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 Irrigation 

District, Northern California Power Agency, Redding Electric Department, Sacramento 

Municipal Utilities District, Santa Clara Electric Department, Turlock Irrigation District, 

and Western Area Power 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 Definitions

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 California (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. For 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 Air 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 Monterey 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

Individual generators in California were mapped into air districts via ZIP code affiliation, or 

closest city or town (CARB 2010a). Many, but not all, individual generators in the PROSYM 

model are tagged with ZIP codes, however, accuracy was found to be variable. 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, municipalities, and specific latitudelongitude 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, the 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. EPA’s Continuous Emissions Monitoring 

System (CEMS) database and the U.S. EPA’s Emission and Generation Resource Integrated 

Database (eGRID). It was found that:

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

SO2 and NOX, and

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

overestimating emissions of SO2 and NOX by anywhere from 20 percent to several orders 

of magnitude. 

Synapse corrected the emissions rates for 120 existing fossil and biomass 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 emissions rates, these units still produce a vast majority of stationary source 

SO2 emissions in California (see Tables A-3 and A-4 in Appendix A). 

Emissions of NOX and SO2 before and after correction are given in Table 2, characterized by fuel 

type. Three-quarters of natural gas generators 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 to 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 Report (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 Energy Commission’s assumed buildout 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 pursuant to AB32, the once-through cooling law, and 

an updated demand forecast. 
20

Table 2: Aggregate Emissions Rates of NOX and SO2 by Unit Categorization, in California only, 

Before and After Adjustment (2012)

NOx (tons) SO2 (tons)

Generation 

(GWh)

PreAdjustmentPostAdjustmentPreAdjustmentPostAdjustment

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 

California 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 relative 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 implementation 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 California 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 percent 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 efficiency 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 water 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 structures 

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 power 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 that 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 SO2, 

NOX, and CO2 (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 results 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. To 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 more 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 State of California is on the trajectory toward 

33 percent renewables; the Hybrid Case 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 Fuel Type in 2009 (Recorded), and 2016 in Three Modeled Cases.

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 represent 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), and 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 measures considered were new wind resources, new solar resources, 

new baseload 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 additional wind farm, (2) a new solar resource as one additional solar farm, (3) a new 

baseload energy efficiency initiative as refrigeration or industrial lighting efficiency programs, 

and (4) a new peak-targeting efficiency savings as demand response or efficient air-conditioning 

programs.

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 and 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 size 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/RE 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 modeling 

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 in 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, SCE, 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 Commission 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 WECC-National Renewable Energy Laboratory 

(NREL) 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 reduced 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 Load Profile and Reduction from Wind in LADWP: July 11–12, 2016

A representative 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 Hourly Wind Capacity by Hour for 1000 MW Wind Farms 

in Four Service Territories

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

in Four Service Territories

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 1000 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 project directly connected to the service territory. 

Figure 11: Example Load Profile and Reduction from Solar in LADWP: July 11–12, 2016

The following figures represent how the new solar measures operate during the year. Figure 12

shows the average annual capacity of the solar PV resource at each hour, while Figure 13 shows 

average monthly capacity.

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

in Four Service Territories
26

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

in Four Service Territories

Baseload Energy Efficiency

Synapse created scenarios that modeled both baseload 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 effect of cutting baseload demand or peak 

demand. However, the baseload 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 efficiency is modeled in 

PROSYM as a power unit, with all the characteristics of a thermal power plant. 

To depict a “pure” baseload efficiency program, the baseload efficiency units are given a 

capacity factor of 100 percent, meaning that they are always running in the model. Because of 

the capacity factor, and in order to ensure comparability of energy generated in the wind and 

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

333 MW. A baseload efficiency unit was placed 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 baseload efficiency makes 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 reduced load from the simulated baseload 

efficiency program. Because the baseload efficiency is set to be continuously running in the 

model, load in LADWP is reduced by the full 333 MW in all hours, with no hourly variation. 
27

Figure 14: Example Load Profile and Reduction from a Baseload EE Program 

in LADWP: July 11–12, 2016

Peak-Targeting Energy Efficiency

Peak-targeting energy efficiency 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 Case 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 90th 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 MW, 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-targeting 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 reduced 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 Load Profile and Reduction from a Peak-Targeting EE Program 

in LADWP: July 11–12, 2016

The following figures represent how the peak-targeting EE measures operate during the year. 

Figure 16 shows 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 unreasonably large. Therefore these 

programs appear to have very different capacities, depending on their implementation location.

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

Figure 17: Average Monthly Peak EE Reduction Model for Four Service Territories
29

CHAPTER 3:

Analysis Framework

Overview

The purpose of this analysis is to determine the emissions benefit from specific types of 

renewable energy (RE) and energy efficiency (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 unknown. 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, within 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 displaced 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 

emissions 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. Therefore, 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 uncertainty 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

Displaced 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 these occur in different locations. 

In this research, the “displaced energy fraction” (gfdisp) is calculated as:

where gbaseline represents the baseline energy from a set of generators i over time period t, gscenario is 

the energy from the same generators in the scenario of interest, and gEE/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 regionalscale and within California at the air district 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 physical 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 are 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 Displaced Energy Fraction by WECC Region and Fuel Type: 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 represent 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 type 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 the WECC region; it is 

displaced only partially in California, while the rest is displaced throughout WECC. 
31

The implications for this wide distribution of displaced energy from EE/RE in California is that 

only about one-third of the displaced energy has an emissions benefit in California, 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 California can be further parsed by air 

district. Figure 19, below examines the net 32 percent of generation displaced within the state, 

parsed by air district region (see WECC and Air District Region Definitions on page 17 for air 

district regions). In this case, the largest single beneficiary of new wind in SCE territory are 

generators in the Bay Area, at about 9 percent of total generation, or about 30percent of 

generation within California. 

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

This analysis shows a small negative displaced energy 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 Emissions

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 

NOX, SO2, and CO2 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. As with the energy analysis, the displaced 

emissions rate can be parsed by geography (region or air district) and fuel type. 

The displaced emissions rate (erdisp) is calculated as:

where ebaseline represents the baseline emissions (in lbs of NOX or SO2, or tons of CO2) from a set of 

generators i over time period t, escenario are the emissions from the same generators in the scenario 

of interest, and gEE/RE is the generation (in GWh) from the EE/RE in the same time period t. This 

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

EE/RE, or the estimated 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 Displaced Emissions Rate by WECC Region and Fuel Type: SCE Wind

Similar to the displaced energy fraction in Figure 19, the bar in Figure 20 shows the regional 

displaced emissions rate, parsed by the major WECC regions and fuel types. In this case, the net 

aggregate displaced emissions rate is approximately 1010 lbs NOX displaced for each GWh13 of 

new wind generation 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 60 lbs NOX per GWh of new wind. This low rate is a combined 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. 

However, 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 

reality. Figure 21, below, shows the displaced emissions of NOX subdivided among air district 

regions.14

Figure 21: Example of California Component of Displaced NOX Emissions Rate, by Air District 

Region: SCE Wind

The figure shows that the displaced NOX emissions from a new wind project in the SCE service 

territory are distributed over numerous air districts. The highest displaced emissions rate is 

seen in the Bay Area, at about 13 lbs NOX/GWh.

 

13 Emissions in the power sector are often given in lbs/MWh 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 gigawatthour (GWh) is equal to 1000 megawatt-hours (MWh). Emissions of CO2 are given in tCO2/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 in Figure 22, below. 

Figure 22: Example of California Component of Displaced NOX Emissions Rate, by Fuel Type 

within California: SCE Wind

The figure 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 California (~40 percent), at least for this example EE/RE (wind in SCE). 

This small reduction is because natural gas in California has, by a large fraction, the lowest NOX

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

accounts for another 40 percent of the displaced emissions rate. Generally speaking, the reason 

for this is that displaceable wood generation in California has a far higher emissions rate than 

natural gas (about 2.4 lbs NOX /MWh [240 lbs/GWh] for wood, versus < 0.4 lbs NOX/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 woodfired generation raises 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 displaced 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 reasonable 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 characterize 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 electricity 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 discussion 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 behaviors 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 any 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 change 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-intensive) 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 uncertainty, 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 

individual 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 CO2 emissions from generators in California. In the baseline run, slightly 

more than 60.4 million tons of CO2 are emitted from California generators. In the SCE Wind 

scenario, the EE/RE displaces approximately 380,000 tons of CO2 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: CO2 Emissions, Displaced Emissions, and Iteration Run Emissions in California, All 

Fuel Types. Note, the y-axis does not begin at zero.

To estimate if the displaced emissions are significant, or likely due only to random forced 

outages in this particular run, Synapse calculated the standard deviation of all CO2 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 CO2. 

Synapse estimates that the actual displaced CO2 emissions from this EE/RE cannot be predicted 

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

energy for this particular EE/RE is 2923 GWh per year; therefore, the estimated displaced 

emissions rate is approximately 0.13 tCO2/MWh (380/2923 = 0.13), but the error in that rate is 

0.03 tCO2/MWh (80/2923 = 0.03).

In numerous scenarios 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 regions 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 gstd and estd are the standard deviation of generation (in GWh) and emissions (in tons or 

lbs) over cohort of generators i in time period t, and gbaseline and ebaseline are the six baseline runs a

(one through six) over the same cohort of generators in the same time period.

The error rate in generation (gfstd) and the emissions rate (erstd) 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-orminus 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, 

NOX, SO2, and CO2, shown regionally and 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 Energy Fraction (MWh Displaced Energy per MWh of EE/RE) by WECC 

Region and Fuel Type

The first four bars represent the displaced energy fraction of EE/RE implemented in the San 

Diego Gas & Electric (SDG&E) service territory, followed by EE/RE implemented in Southern 

California Edison (SCE), the Los Angeles Department of Water and Power (LADWP), and 

Pacific Gas and Electric (PG&E). Each of the four EE/RE are ordered by onshore wind, utilityscale 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 displaced 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 appear 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 territories). Displaced energy outside of California is a mix of coal 

and natural gas, with some “other” (primarily petroleum and oil outside of California), 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 the 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 off-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 data in the chart above are represented in Table 3 and 4, below (with eight 

scenarios in each table).

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)

Displaced Energy Fraction (%)

SDGE, WindSDGE, Solar (PV)SDGE, EE BaseloadSDGE, EE PeakSCE, WindSCE, Solar (PV)SCE, EE BaseloadSCE, EE Peak

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 For 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 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)

Displaced Energy Fraction (%)

LADWP, WindLADWP, Solar (PV)LADWP, EE BaseloadLADWP, EE PeakPG&E, WindPG&E, Solar (PV)PG&E, EE BaseloadPG&E, EE Peak

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 fraction (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 the 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 these values indicates 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 large 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 Energy 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, the 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 Energy Fraction (MWh Displaced Energy Per MWh of EE/RE) in California, 

Parsed by Air District Region

It is notable that, similarly to the WECC-wide 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/RE 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 targets 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 region (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 scenarios 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 contribution in displaced energy from 

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

emissions.
41

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

Parsed by Fuel Type

Displaced NOX Emissions, by Region and Fuel Type

Oxides of nitrogen (NOX) 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 controlled for NOX emissions, and these controls are largely represented in the model 

with lower NOX rate inputs at controlled generators. As described in Appendix A, NOX

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 NOX emissions, and the state 

contributes a relatively small fraction of NOX in the Western Interconnect (< 5 percent of the 

NOX in this analysis, see Figure 27). Emissions out-of-state are much higher, mostly due to 

uncontrolled coal generators. Therefore, it is expected that significant NOX reductions will 

mostly be realized out-of-state if a program displaces non-California generation. 
42

Figure 27: Total Modeled NOX Emissions by Month in Each WECC Region in 2016

Figure 28, below shows the displaced NOX emissions rate for all of WECC, in pounds of NOX

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 NOX Emissions Rate (in lbs NOX per GWh of EE/RE Generation), by WECC 

Region and Fuel in 2016

A vast majority of the NOX reduction benefit is realized out-of state for most EE/RE. 

Nonetheless, the displacement is highly distributed across WECC. The net rate 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 NOX Emissions, by Air District Region

The small component of NOX displaced emissions rate that is realized in California air district 

regions is graphed in Figure 29, below.

Figure 29: Displaced NOX Emissions Rate (in lbs NOX/GWh) in California Air District Region

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 rates 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 peaking 

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 in the 

Negative Displacement Patterns section of Chapter 5.

Table 5 and Table 6, below, show the NOX 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 NOX Rate (in lbs NOX per GWh of EE/RE) for Eight Scenarios 

by California Air District Region

Displaced lbs of NOx 

per GWh of Energy 

Displaced, by Air 

District

SDGE, WindSDGE, Solar (PV)SDGE, EE BaseloadSDGE, EE PeakSCE, WindSCE, Solar (PV)SCE, EE BaseloadSCE, 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 NOX Rate (in lbs NOX per GWh of EE/RE) for Eight Scenarios 

by California Air District Region

Displaced lbs of NOx 

per GWh of Energy 

Displaced, by Air 

District

LADWP, WindLADWP, Solar (PV)LADWP, EE BaseloadLADWP, EE PeakPG&E, WindPG&E, Solar (PV)PG&E, EE BaseloadPG&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 NOX is derived from offset natural gas-fired units in California (see 

Figure 30, below), although displaced petroleum and biomass are also significant fractions in 

some EE/RE programs. Overall, the absolute magnitude of displaced NOX emissions is small in 

California. However, it is useful to note that the biomass and petroleum fractions of displaced 

NOX are a significantly larger fraction of displaced emissions than of generation because of their 

high absolute emissions rate.

Figure 30: Displaced NOX (in lbs NOX / GWh) by Fuel Type in California

Displaced SO2 Emissions, by Region and Fuel Type

Sulfur dioxide (SO2) 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 SO2 benefit in the state. However, EE/RE 

implemented in California appear to affect coal generators throughout the West, and result in 

reduced emissions across the WECC region. Figure 31, below, shows displaced SO2 emissions 

throughout the WECC region, again in pounds of SO2 per GWh, due to the fairly small values, 

particularly in California.
46

Figure 31: Displaced SO2 Emissions Rate (in lbs SO2/GWh) in by WECC Region and Fuel Type

The displaced SO2 pattern differs from NOX 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 petroleum coke) and petroleum consumption. The negative 

net displaced emissions of SO2 in the peaking EE/RE in SDG&E and LADWP are due to 

increased coal generation in the Southwest; with little SO2 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 SO2 Emissions, by Air District Region

Displaced SO2 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 petcoke displacement opportunities. Overall, the total displaced emissions 

rate for SO2 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 18–45 lbs 

SO2/GWh; in no circumstances are any negative displacements (i.e., increase in emissions) 

significant at a rate of more than 1 lb SO2/GWh. Tables showing the actual values and 

significance can be found in Appendix C.
47

Figure 32: Displaced SO2 Emissions Rate (in lbs SO2/GWh) by Air District Region

Displaced CO2 Emissions, by Region and Fuel Type

All combustion sources release carbon dioxide (CO2), a greenhouse gas pollutant. As a climate 

change pollutant, CO2 does not impose damages on populations near the generation source as 

do criteria pollutants of NOX and SO2. Displaced CO2 emissions are evaluated across all 

generators in the WECC region. From the perspective of attempting to reduce overall CO2

emissions from the U.S. generating 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 CO2 emissions are highly 

related to net displaced energy, with the important caveat that natural gas generation has a CO2

rate approximately half that of coal generation.17 It is expected that the overall displaced 

emissions rate for CO2 will range between natural gas and coal emissions rates. The regional 

displaced CO2 rates for all EE/RE are given in Figure 33, below.

 

17 In this model, the WECC-wide average gas CO2 rate is approximately 0.5 tCO2/MWh, while the coal 

rate is about 1.1 tCO2/MWh.
48

Figure 33: Displaced CO2 Emissions (tCO2 / MWh of EE/RE) by Region and Fuel Type

In regions where the displaced energy is primarily coal, the displaced CO2 emissions rate is 

higher (although not markedly so) than regions where the displaced fuel is primarily natural 

gas. 

Detailed results for the displaced CO2 rate by region and air district region are given in 

Appendix C.
49

CHAPTER 5:

Discussion and Conclusions

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 

region.

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 factor wind near the SCE service 

territory, the expected displaced NOX 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 NOX 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 districts, displacing approximately 6, 4, and 4 tons of NOX in each, respectively. The 

impact of this project on CO2 is realized primarily outside of California; overall, the project 

would be estimated to displace 494,000 tons of CO2, 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 utility 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, below).

Figure 34: Screenshot of Example Project in Selected Region

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 Screenshot from Data Annex of Displaced Emissions in California Air District 

Regions from a 250 MW Wind Project in SCE Service Territory

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. 

However, 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 resources 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 EE/RE in California and out of state will be natural gas-fired (and will therefore 

have a fairly low emissions displacement for SO2 and NOX), and that these displaced generators 

will be distributed across the WECC region.

Generally, this pattern holds; however, 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.

• An 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 renewable 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 generation and absolute displaced energy in SDG&E appears to confirm 

that coal is displaced in response to additional wind generation (Figure 36). This graph shows 

the differences in generation, by fuel type, in the SDG&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 output by fuel type.

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

Wind Scenario, by Fuel Type. The impacts shown result from the addition of 1000 MW of onshore 

wind in the SDG&E service territory

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 generation 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 almost 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 petroleum 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 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.

Figure 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.

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 assumptions 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. Another 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 resources 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 redispatched 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 analysis 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 increase 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. They 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 the 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 model 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 responses 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 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 outages, which have a large effect relative to the modest energy-saving impact of 

a peaking EE program.

Again, examining 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 gas 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 high level of noise.

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.
55

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 peak demand program targeting the 90th percentile highest load 

hours in SDG&E service territory.

The poor signal-to-noise given in the EE peaking scenarios suggests 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 considered 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 transmission 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 checks, 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 operating 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 might choose to dispatch local versus nonlocal 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-orpay 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 

LADWP, 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 service 

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-take 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 Los Angeles basin.

Model Assumptions and Caveats

Assumptions and model 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 additional 

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:

• Model 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 node, 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 effect 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 affected 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 indicates 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 SO2 emissions, effectively establishing a market price for the pollutant. 

This price has fluctuated between under $100/tSO2 to several hundred dollars per ton, 

spiking at over $1,500/tSO2 in 2005 (nominal) (U.S. EPA 2010b). At higher prices, these 

SO2 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 CO2 (CARB 2010), which will similarly establish a market price for CO2

emissions from power produced in or entering California. 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 triggered only by these fuel 

prices, changes in fuel costs could significantly change the marginal resource. 

21 Author’s estimate: Assuming SO2 emissions rate of 4 lbs SO2/MWh, an example price of $500/tSO2

results in a cost adder of $1/MWh, or 5 percent of a $20/MWh running cost. 
57

promoting low-CO2 generation. However, a price reflecting this program is also absent 

from the model construct.

• Generation retirements: The model, 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 

(Oregon) (Reuters 2010), TransAlta Corporation (Washington) (Reuters 2011), Arizona 

Public Service Co. (Arizona) (Navajo Nation Council 2011), and Xcel (Colorado) (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 wind dispatch: In the model structure, the stochastic nature 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 farm 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 foresight” 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 default 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 between 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 an analysis of real, enforceable EE and RE 

programs, there are a variety of steps which should be taken, including:

Physical Build-Out Assumptions: 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 CO2 emissions reduction program, 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 in 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, and 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 created) 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 uncertainty due 

only to forced outage rates. However, for forward-looking models, uncertainty in prices for 

fuels and emissions, as well as uncertainty in hydrological conditions, can affect the emissions 

benefit of EE/RE at the margin. A preferred approach might 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, rather than simply stating that some results are “nonmeaningful.”

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 

MW). 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 standards 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 

quantify the efficacy of a particular emissions-reduction 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 estimate 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) implemented within the 

State. The research resulted in several key findings:

Dispersed Criteria Emissions Benefits 

As shown in this research and elsewhere, the Western grid is highly interconnected, and 

therefore changes in load, generation, or resource availability 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 California 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 greenhouse 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, which shows displaced pounds of NOX 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 generating 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 benefit 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 Gas Benefits

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 higher 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 tCO2/MWh) relative to displacing in-state natural gas only (such as in the LADWP 

baseload EE scenario, 0.4 tCO2/MWh).

Verify Emissions from Existing Resources

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 was crucial to the precision of the modeling results. 

The importance of correcting emissions cannot be overstated, particularly in an already lowemissions state like California. Prior to the emissions correction undertaken in this analysis, the 

estimated NOX emissions from California EGU were 230 percent higher than values reported by 

the U.S. EPA, and SO2 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 

emissions 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 

that verifying, reporting, and correcting the emissions from these and other units in California 

is of paramount importance for correct emissions modeling in the State.

Peak Reduction Uncertainty

In nearly all of the examples of peak energy reduction EE programs, 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 California and an increase 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 counterproductive 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 Emissions Benefits

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 emissions 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 larger 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 outof-state due to the integrated nature 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 clear that 

individual air quality 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 planning 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 commitment 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

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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.”

Reuters. March 5, 2011. “TransAlta to phase out coal boilers in Washington 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 Electricity Coordinating Council’s Transmission Expansion 

Planning Policy Committee: Part 3, Western Interconnection Transmission Path Utilization 

Study. April.

WECC. Accessed 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

Appendix 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) projects 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 reflect 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 from 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 recently 

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 largest emitting sources. 

Findings

Emissions by Unit Type

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 Energy 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 differences 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 changes 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 (NOX) and 82 percent of sulfur dioxide (SO2) in the state. These units 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 gas-fired units which did report emissions to the CEMS system account for a large 

fraction of remaining generation (25 percent) yet only emitted 5 percent of total NOX from 

stationary sources, according to the model output for 2012.

Table A-1, below, shows a breakdown of the fraction of generation and emissions 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 this 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, CO2, NOX, and SO2 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 

Generation

(%)

Fraction of 

NOx

(%)

Fraction of 

SO2

(%)

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 Emissions Rates

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 reporting 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 NOX 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 NOX 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 SO2

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 units, 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 SO2

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 

best 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, Synapse used and cross referenced three sources of emissions data:

• the Ventyx Market Analytics25 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 and 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 Information 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 SO2 and NOX emissions rates on a unitby-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. Available online at 

http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2007_Version1-1_xls_only.zip. 

25 The actual results are from the Market Analytics interface for the PROSYM model, part of a Ventyx 

software suite.

26 More information on eGRID can be found here: http://www.epa.gov/cleanenergy/energyresources/egrid/faq.html. 
A-4

U.S. EPA data. Further, the SO2 and NOX emissions rates of units built in the future were 

adjusted to match the lowest 10th percentile of emissions in the California today.

Existing Units

Emissions rates for existing units in the model were adjusted if the unit could be positively 

identified (i.e., correlated with 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 were 

adjusted. 

• SO2 emissions rates were adjusted using CEMS data for eight (8) natural gas-fired units, 

reducing estimated emissions by 1.7 tons of SO2 in California in 2012 (Table A-3, below).

• SO2 emissions rates were adjusted dramatically using eGRID data for 11 petroleum and 

coal-fired units (almost all non-dispatchable industrial users), reducing estimated 

emissions by 61,892 tons of SO2 in 2012. (Table A-4, below)

• NOX 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 NOX in 

2012. (Table A-5 below)

• NOX emissions rates were adjusted using eGRID data for 75 natural gas, wood, and 

petroleum-fired units, reducing estimated emissions by 27,012 tons of NOX 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 SO2 emissions were underestimated in the model could generally 

not be adjusted upwards (17 units).

Future Units

Emissions rates for future units in the model were adjusted if the unit emitted SO2 or NOX 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 adjusted.

• SO2 emissions rates for future units were adjusted using the lowest 10th percentile of 

emissions for nine (9) biomass and natural gas-fired units, reducing estimated emissions 

by 1.1 tons of SO2 in 2012 (Table A-9, below).

• NOX emissions rates for future units were adjusted using the lowest 10th percentile of 

emissions for 36 biomass, wood and natural gas-fired units, reducing estimated 

emissions by 461.1 tons of NOX in 2012 (Table A-10 and Table A-11, below).

Testing Adjustments

To test if total emissions of NOX and SO2 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.

NOX and SO2 rates were altered markedly for coal, petroleum, and wood-fired units, as well as 

natural gas units not in the CEMS database. Overall, NOX 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), SO2 emissions dropped in the model from 70.8 to 8.9 thousand tons. This 

reduction brings the overall SO2 emissions in the model below U.S. EPA estimated SO2 emissions 

in 2012. Seventeen (17) units, accounting for 2.3 percent of generation, reported SO2 emissions 

rates above those estimated by the U.S. EPA. Due to the way SO2 is estimated in the model 
A-5

construct, it was deemed restrictively difficult to alter SO2 emissions in an upwards direction for 

most units.27

 

27 Emissions of SO2 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 SO2 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 SO2 emissions rate. Therefore, Synapse only altered SO2 capture rates; 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 NOX and SO2 by Unit Categorization, in California Only, 

Before and After Adjustment (2012) 

NOx (tons) SO2 (tons)

Generation (GWh)

PreAdjustmentPostAdjustmentPreAdjustmentPostAdjustment

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 CA 199,925 22,302 13,577

No Changes in Emissions Due to New Regulations

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 how these rules might change 

emissions, heat rate, or dispatch dynamics of generators in or outside of California.

Detailed Adjustments

The following sections of this appendix have tables indicating explicit adjustments made to 120 

existing units and 45 future 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 10th 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 primary 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: SO2 Emissions Rate Corrections for California Electric Generating Facilities Based on CEMS (2009) Emissions Rates 

Ventyx Model Name CEMS / eGRID Name

Fuel 

Type City, State

Air District 

Region

Model 

Generation 

in 2012 

(GWh)

PreAdjustment 

SO2

Emissions 

(tons)

Ventyx 

Assumed 

SO2

Removal 

Rate (%)

Ventyx 

Assumed 

SO2

Emissions 

Rate 

(lbs/MWh)

CEMS

Estimated 

SO2

Emission 

Rate 

(lbs/MWh)

Synapse 

Adjusted 

SO2

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

San 

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: SO2 Emissions Rate Corrections for California Electric Generating Facilities Based on eGRID (2007) Emissions Rates

Ventyx Model Name CEMS / eGRID Name Fuel Type City, State

Air 

District 

Region

Model 

Generation 

in 2012 

(GWh)

PreAdjustment 

SO2

Emissions 

(tons)

Ventyx 

Assumed 

SO2

Removal 

Rate (%)

Ventyx 

Assumed 

SO2

Emissions 

Rate 

(lbs/MWh)

eGRID 

Estimated 

SO2

Emission 

Rate 

(lbs/MWh)

Synapse 

Adjusted 

SO2

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 Pittsburg, 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 Pittsburg, 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 Petroleum 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 Carbon 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: NOX Emissions Rate Corrections for California Electric Generating Facilities Based on CEMS (2009) Emissions Rates

Ventyx Model Name CEMS / eGRID Name

Fuel 

Type City, State

Air District 

Region

Model 

Generation 

in 2012 

(GWh)

PreAdjustment 

NOx 

Emissions 

(tons)

Ventyx 

Assumed 

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 0.013 

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

Ventyx Model Name CEMS / eGRID Name Fuel Type City, State

Air District 

Region

Model 

Generation 

in 2012 

(GWh)

PreAdjustment 

NOx 

Emissions 

(tons)

Ventyx 

Assumed 

NOx 

Emissions 

Rate 

(lbs/MWh)

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 Creek 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 Joaquin

 

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

Port 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: NOX Emissions Rate Corrections for California Electric Generating Facilities Based on eGRID (2007) Emissions Rates (2 of 3)

Ventyx Model Name CEMS / eGRID Name Fuel Type City, State

Air District 

Region

Model 

Generation 

in 2012 

(GWh)

PreAdjustment 

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 Kern 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 0.599 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 Kittrick, 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 CA 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 San Joaquin 225 640.0 

 

5.678 0.825 0.069 

Table A-8: NOX Emissions Rate Corrections for California Electric Generating Facilities Based on eGRID (2007) Emissions Rates (3 of 3)

Ventyx Model Name CEMS / eGRID Name Fuel Type City, State

Air District 

Region

Model 

Generation 

in 2012 

(GWh)

PreAdjustment 

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 POSO 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 Wood 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 COGEN 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 North 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 Carson, 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 Cogeneration 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 

WILBUR 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: SO2 Emissions Rate Adjustments for Future California Electric Generating Facilities; Based on Lowest 

10th Percentile Emissions for Fuel Type

Ventyx Model Name Ventyx Model Description

Fuel 

Type

Ventyx Assumed Physical 

Location

Air 

District 

Region

Model 

Generation 

in 2012 

(GWh)

PreAdjustment 

SO2

Emissions 

(tons)

Ventyx 

Assumed 

SO2

Emissions 

Rate 

(lbs/MWh)

10th 

Percentile 

SO2

Emission 

Rate for 

fuel Type 

(lbs/MWh)

Synapse 

Adjusted 

SO2

Removal 

Rate (%)

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

Ventyx Model Name Ventyx Model Description

Fuel 

Type

Ventyx Assumed 

Physical Location

Air 

District 

Region

Model 

Generation 

in 2012 

(GWh)

PreAdjustment 

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)

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 GT4 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 BAY 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 

HUMBOLDT 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 0.004 

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

Ventyx Model 

Name Ventyx Model Description

Fuel 

Type

Ventyx Assumed 

Physical Location

Air 

District 

Region

Model 

Generation 

in 2012 

(GWh)

PreAdjustment 

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 

CA

 

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 Energy 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) scenarios. 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 (IEPR). The 2009 IEPR 

utilizes two “bookend” cases to examine high penetrations of RE build-out, one that examines a wind-dominated build-out (known 

as the “High Wind” scenario) and one that examines a solar-thermal heavy build-out (“High Solar”). 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 

somewhere 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 hours 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 differently 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 pattern, 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 of Name Plate Capacity Adjustments for Hybrid Base 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

RPSPV_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 generation 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 Base 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 Form 923.
B-3

Table B-2: Summary of Generation (GWh) from High Wind, High Solar, and Hybrid Scenarios Uranium Geothermal Coal Biomass Petroleum Hydro Natural Gas Wind Solar Thermal Solar PV

Energy Efficiency% from Renewable

2016

High Wind 37,113 17,873 2,694 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

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

High Solar, and Hybrid Scenarios28

 

28 Historic and modeled data may not represent the same conditions (such as hydrologic conditions or maintenance outages), and 

include changes in generation fleet in California.
C-1

Appendix C: 

Detailed Results

This appendix includes tables and graphical charts showing the displacement generation fraction and the displacement rate for NOx, 

SO2, and CO2. 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 California air district (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)

Displaced Energy Fraction (%)

SDGE, 

Wind

SDGE, 

Solar (PV)

SDGE, EE 

Baseload

SDGE, EE 

Peak

SCE, Wind

SCE, Solar 

(PV)

SCE, EE 

Baseload

SCE, EE 

Peak

LADWP, 

Wind

LADWP, 

Solar (PV)

LADWP, EE 

Baseload

LADWP, EE 

Peak

PG&E, 

Wind

PG&E, 

Solar (PV)

PG&E, EE 

Baseload

PG&E, EE 

Peak

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 NOX Emissions Rate by WECC Region (lbs NOX/GWh)

Displaced Emissions Rate (lbs NOx/GWh)

SDGE, 

Wind

SDGE, 

Solar (PV)

SDGE, EE 

Baseload

SDGE, EE 

Peak

SCE, Wind

SCE, Solar 

(PV)

SCE, EE 

Baseload

SCE, EE 

Peak

LADWP, 

Wind

LADWP, 

Solar (PV)

LADWP, EE 

Baseload

LADWP, EE 

Peak

PG&E, 

Wind

PG&E, 

Solar (PV)

PG&E, EE 

Baseload

PG&E, EE 

Peak

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 95 152 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 SO2 Emissions Rate by WECC Region (lbs SO2/GWh)

DisplacedEmissions Rate (lbs SO2/GWh)

SDGE, 

Wind

SDGE, 

Solar (PV)

SDGE, EE 

Baseload

SDGE, EE 

Peak

SCE, Wind

SCE, Solar 

(PV)

SCE, EE 

Baseload

SCE, EE 

Peak

LADWP, 

Wind

LADWP, 

Solar (PV)

LADWP, EE 

Baseload

LADWP, EE 

Peak

PG&E, 

Wind

PG&E, 

Solar (PV)

PG&E, EE 

Baseload

PG&E, EE 

Peak

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 CO2 Emissions Rate by WECC Region (t CO2/MWh)

Displaced Emissions Rate (tCO2/MWh)

SDGE, 

Wind

SDGE, 

Solar (PV)

SDGE, EE 

Baseload

SDGE, EE 

Peak

SCE, Wind

SCE, Solar 

(PV)

SCE, EE 

Baseload

SCE, EE 

Peak

LADWP, 

Wind

LADWP, 

Solar (PV)

LADWP, EE 

Baseload

LADWP, EE 

Peak

PG&E, 

Wind

PG&E, 

Solar (PV)

PG&E, EE 

Baseload

PG&E, EE 

Peak

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 NOX per GWh of Energy Displaced, by WECC Region and Fuel Type
C-6

Figure C-3: Displaced lbs of SO2 per GWh of Energy Displaced, by WECC Region and Fuel Type
C-7

Figure C-4: Displaced Tons of CO2 per MWh 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 District

SDGE, 

Wind

SDGE, 

Solar (PV)

SDGE, EE 

Baseload

SDGE, EE 

Peak

SCE, Wind

SCE, Solar 

(PV)

SCE, EE

Baseload

SCE, EE 

Peak

LADWP, 

Wind

LADWP, 

Solar (PV)

LADWP, EE 

Baseload

LADWP, EE 

Peak

PG&E, 

Wind

PG&E, 

Solar (PV)

PG&E, EE 

Baseload

PG&E, EE 

Peak

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 Area

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%

-

Central Coast 0.1% 2.2% -4.2% -1.6% -2.9% -4.3% -14.5% 5.8% 11.0% 14.2% 15.6%

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

Table C-6: Displaced NOX Emissions Rate by Air District (lbs NOX/GWh)

Displaced lbs of NOx 

per GWh of Energy 

Displaced, by Air 

District

SDGE, 

Wind

SDGE, 

Solar (PV)

SDGE, EE 

Baseload

SDGE, EE 

Peak

SCE, Wind

SCE, Solar 

(PV)

SCE, EE 

Baseload

SCE, EE 

Peak

LADWP, 

Wind

LADWP, 

Solar (PV)

LADWP, EE 

Baseload

LADWP, EE 

Peak

PG&E, 

Wind

PG&E, 

Solar (PV)

PG&E, EE 

Baseload

PG&E, EE 

Peak

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 SO2 Emissions Rate by Air District (lbs NOX/GWh)

Displaced lbs of SO2

per GWh of Energy 

Displaced, by Air 

District

SDGE, 

Wind

SDGE, 

Solar (PV)

SDGE, EE 

Baseload

SDGE, EE 

Peak

SCE, Wind

SCE, Solar 

(PV)

SCE, EE 

Baseload

SCE, EE 

Peak

LADWP, 

Wind

LADWP, 

Solar (PV)

LADWP, EE 

Baseload

LADWP, EE 

Peak

PG&E, 

Wind

PG&E, 

Solar (PV)

PG&E, EE 

Baseload

PG&E, EE 

Peak

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 North 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 CO2 Emissions Rate by Air District (t CO2/MWh)

Displaced tons of 

CO2 per MWh of 

Energy Displaced, by 

Air District

SDGE, 

Wind

SDGE, 

Solar (PV)

SDGE, EE 

Baseload

SDGE, EE 

Peak

SCE, Wind

SCE, Solar 

(PV)

SCE, EE 

Baseload

SCE, EE 

Peak

LADWP, 

Wind

LADWP, 

Solar (PV)

LADWP, EE 

Baseload

LADWP, EE 

Peak

PG&E, 

Wind

PG&E, 

Solar (PV)

PG&E, EE 

Baseload

PG&E, EE 

Peak

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 0.00 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 NOX per GWh of Energy Displaced, by Air District
C-14

Figure C-7: Displaced lbs of SO2 per GWh of Energy Displaced, by Air District
C-15

Figure C-8: Displaced Tons of CO2 per MWh of Energy Displaced, by Air District
C-16

Figure C-9: Displaced Energy Fraction, by Fuel in California
C-17

Figure C-10: Displaced lbs of NOX per GWh of Energy Displaced, by Fuel in CA
C-18

Figure C-11: Displaced lbs of SO2 per MWh of Energy Displaced, by Fuel in CA
C-19

Figure C-12: Displaced Tons of CO2 per MWh of Energy Displaced, by Fuel in CA