### Indirect Emissions Analysis ▪ 1 

## EPA/600/R-08-087 July 2008



# ANALYSIS OF INDIRECT EMISSIONS BENEFITS OF WIND, LANDFILL GAS, AND MUNICIPAL SOLID WASTE GENERATION

By

Ezra D. Hausman, Jeremy Fisher, and Bruce Biewald Synapse Energy

22 Pearl Street Cambridge, MA 02139

Order No.: EP07C000079

Project Officer

Joseph F. DeCarolis Air Pollution Prevention and Control Division National Risk Management Research Laboratory Research Triangle Park, NC 27711

# Notice

The U.S. Environmental Protection Agency, through its Office of Research and Development, funded and managed the research described here under Order Number EP07C000079 to Synapse Energy Economics. It has been subject ed to the Agency‘s peer and administrative
review and has been approved for publication as an EPA document.



# Foreword

The U. S. Environmental Protection Agency (EPA) is charged by Congress with protecting the Nation's land, air, and water resources. Under a mandate of national environmental laws, the Agency strives to formulate and implement actions leading to a compatible balance between human activities and the ability of natural systems to support and nurture life. To meet this mandate, EPA’s research program is provid ing data and technical support for solving environmental problems today and building a science knowledge base necessary to manage our ecological resources wisely, understand how pollutant s affect our health, and prevent or reduce
environmental risks in the future.

The National Risk Management Research Laboratory (NRMRL) is the Agency's center for investigation of technological and management approaches for preventi ng and reducing risks from pollution that threaten human health and the environment. The focus of the Laboratory's research program is on methods and their cost -effectiveness for prevention and control of pollution to air, land, water, and subsurface resources; protection of water quality in public water systems; remediation of contam inated sites, sediments and ground water; prevention and control of indoor air pollution; and restoration of ecosys tems. NRMRL collaborates with both public and private sector partners to foster technologies that reduce the cost of compliance and to anticipate emerging problems. NRMRL's research provid es solutions to environmental problems by: developing and promoting technologies that protect and improve the environment; advancing scientific and engineering information to sup port regulatory and policy decisions; and providing the technical support and information transfe r to ensure implementation of environmental regulations and strategies at the nat ional, state, and community levels.

This publication has been produced as part of the Laboratory's strategic long-term research plan. It is published and made available by EPA's Office of Research and Development to assist the user community and to link researchers with their clients.

Sally Gutierrez , Director National Risk Management Research Laboratory

Indirect Emissions Analysis ▪ i

# Abstract

A number of techniques are introduced to calculate the hourly indirect emissions benefits of three types of renewable resources: wind energy, Municipal Solid Waste (MSW) combustion, and Landfill Gas (LFG) combustion. These techniques are applied to each of the EPA’s eGRID subregions in t he continental United States in order to derive hourly, seasonal, or annual (as appropriate) coeffi cients for use in evaluating the indirect emissions benefits of such projects in each region.

For wind power impacts, simulated wind proj ect power profiles are derived using publicly available wind data scaled to a typical turbine height for new wind projects and transformed through a power curve for a proxy turbine. The results for one region are compared to the output of an existing projec t, although the limitations of this type of comparison, as well as the limitations of re presenting large areas of the country with single proxy curves, are discussed. LFG and MSW are found to have flat, “always on” profiles based on the limited data available.

The regional, hourly power profiles for each type of resource are combined with the hourly indirect emissions coefficients to yiel d annual indirect emissions benefits for each type of resource for each eGRID subregion.

For each GWh of renewable energy produced each year, the indirect CO 2 

## emissions
benefit is found to be between 600 and 1100 tons of CO 2 

## depending on the region, with coal-dependent regions having the highest i ndirect emissions benefit. The indirect NOx
and SO 2 

## emissions benefit also depends on the regional fuel mix, as well as the stringency of environmental regulation in eac h region. These indirect emissions benefits vary between 500 and 5,000 pounds of NOx per GWh, and between 200 and 1,300 pounds of SO 2 

## per GWh. With some exceptions, thes e results are robust regardless of
whether the renewable resource has a base load profile (such as MSW and LFG) of output which varies diurnally and seasonally (s uch as wind). However, as the calculated benefits do vary in some cases depending on the analytical approach, care must be used in selecting an appropriate calculation methodology for each application.

This report was submitted in fulfillment of Order Number EP07C000079 under the sponsorship of the EPA.

Indirect Emissions Analysis ▪ ii

# Table of Contents

Notice

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

Foreword

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

Abstract

............................................................................................................................... ......................... ii

List of Tables

............................................................................................................................... .............. iv

List of Figures

............................................................................................................................... ............. v
Acronyms and Abbreviations

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

List of Appendices

............................................................................................................................... .. viii

1. Introduction and Summary of Conclusions

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

2. Operating Data on Renewable Energy Technologies

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

A. Wind Generation: Existing and Synthetic Projects

........................................................ 6 Data Sources for Synthetic Generation

........................................................................ 7 Generation of Synthetic Wind Power Data

.................................................................. 8 Comparing Empirical and Simulated Wind Generation

............................................ 10 B. Landfill Gas

....................................................................................................................... 14 C. Municipal Solid Waste

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

3. Hourly generation and emissions data

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

Characterization of emission rates

.............................................................................. 16 Data filtering

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

4. Emissions Displacement Methods

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

A. Limiting Cases

.................................................................................................................. 18 B. Hourly Average Emissions Rate (HAER)

..................................................................... 19 C. Emissions Slope Factor

.................................................................................................. 22 D. Empirical Incremental Emissions Rate (EIER)

............................................................ 29 E. Load-Following Incremental Emissions Rate (LFIR)

.................................................. 30 F. Flexibility-Weighted Hourly Av erage Emissions Rate (FW-HAER)

........................... 31 G. Comparison of Indirect Emissions Rates

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

5. Indirect Emissions Results

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

6. Conclusions

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

Appendix A :

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

Appendix B :

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

Appendix C :

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

Appendix D :

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

Indirect Emissions Analysis ▪ iii

# List of Tables

Table 1. Seasonal and annual emissi ons slope factors and R-squared values for CO 2



## . .................................................................................. 24

Table 2. Seasonal and annual emissi ons slope factors and R-squared values for SO 2



## .................................................................................... 25

Table 3. Ozone season and non-ozone season emission s rate slopes for NOx

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

Table 4. Guide to t he summary fi gures

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

Table 5. Annual indirect emissions reduction rates associated with an incremental GWh of wind energy in each of the eGRID subregions fo r 2005

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

Table 6. Annual indirect emissions reduction rates associated with an incremental GWh of landfill gas of MSW generation in each of the eGRID subregi ons for 2005

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

Table 7. “Shape impact” for indirect emissions calculations for each pollutant in each eGrid r egion

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

Indirect Emissions Analysis ▪ iv

# List of Figures

Figure 1. Indirect emissions results for CO 2 

## , NOx, and SO 2 

## for wind, municipal solid waste and landf ill gas generati on projects

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

Figure 2. Wind power curve fo r a GE 1.5se wi nd turbine

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

Figure 3. Proxy wind turbine power curve plotted against histograms of raw and scaled wind speeds at a samp le site in the Midwest

......... 10

Figure 4. Comparison of power output distribution at sample wind power site in the Midwest with predicted output based on scaled wind data from a nearby meteorological station

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

Figure 5. Comparison of hourly synthetic and observed wind power time series at nearby loca tions in the Midwest

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

Figure 6. Cumulative distributio n of the hourly deviation between observed and synthetic gener ation time series

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

Figure 7. Distribution of CO 2 

## emission rates for fossil generating plants by reported f uel ty pe

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

Figure 8. Representations of CO 2 

## hourly average emissions rate (HAER) in New England

................................................................. 21

Figure 9. Determination of the regi onal emissions slope factor in the RFCE (Reliability Firs t/Central) region

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

Figure 10. Bifurcation of the NOx emissions slope factor for the RFCE region

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

Figure 11. Regional slope factors for CO 2



## ....................................................... 27

Figure 12. Seasonal and regional slope factors for SO 2



## .................................. 27

Figure 13. Regional sl ope factors fo r NOx

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

Figure 14. Flexibility coefficient vs. generator size in New England and Californ ia

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

Figure 15. Hourly dispatch and flexib ility coefficient for three plants of differing capacities in the FRCC s ubregion

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

Figure 16. Comparison of the di stribution of indirect emissions coefficients for CO 2 

## , SO 2, 

## and NOx based on methods described in the te xt

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

Indirect Emissions Analysis ▪ v

## Figure 17. Annual indirect CO 2 

## emissions impact associated with one incremental GWh of wind energy in each of the eGRID subregions for 2005. .......................................................................41

Figure 18. Annual indirect NOx emissions impact associated with one incremental GWh of wind energy in each of the eGRID subregions for 2005. .......................................................................41

Figure 19. Annual indirect SO 2 

## emissions impact associated with one incremental GWh of wind energy in each of the eGRID subregions for 2005. .......................................................................42

Figure 20. Annual indirect CO 2 

## emissions impact associated with one incremental GWh of landfill gas or municipal solid waste energy in each of the eG RID subregions for 2005..........................42

Figure 21. Annual indirect NOx emissions impact associated with an incremental GWh of landfill gas or municipal solid waste energy in each of the eG RID subregions for 2005..........................43

Figure 22. Annual indirect SO 2 

## emissions impact associated with an incremental GWh of landfill gas or municipal solid waste energy in each of the eG RID subregions for 2005..........................43

Indirect Emissions Analysis ▪ vi

## AWEA American Wind Energy Association

BTU British Thermal Unit

CO 2 

## Carbon Dioxide

DOE U.S. Department of Energy

eGRID Emissions & Generation Resource Integrated Database

EIER empirical incremental emission rate (defined in this study)

EPA United States Environmental Protection Agency

FW-HAER flexibility-weighted hourly averag e emission rate (defined in this study)

GE General Electric

GW gigawatt

GWh gigawatt-hour

HAER hourly average emission rate (defined in this study)

kW kilowatt

kWh kilowatt-hour

LFG landfill gas (generating resource)

LFIR load-following incremental emissi ons rate (defined in this study)

mmBTU million British thermal units

MSW municipal solid waste (generating resource)

MW megawatt

MWh megawatt-hours

NCDC National Climatic Data Center

NERC North American Electric Reliability Corporation

NOAA National Oceanic and Atmospheric Administration

NOx mono-nitrogen oxides (NO and NO 2 

## )

NREL National Renewable Energy Laboratory

SO 2 

## sulfur dioxide

WBAN Weather Bureau Army Navy 

# Acronyms and Abbreviations

Indirect Emissions Analysis ▪ vii

# List of Appendices

Appendix A

Identification and map of eGRID subregions

Appendix B

Synthetic wind power time series (hourly per cent of capacity) for each eGRID subregion, represented as color intensity plots

Appendix C

Summary tables of raw and scaled wind speed and synthetic wind power time series

Appendix D

Color intensity maps of hourly indirect CO 2 

## , NOx and SO 2 

## emissions rates for each eGRID subregion, using several alternative calculation methodologies

Indirect Emissions Analysis ▪ viii

# 1. Introduction and Summary of Conclusions

The purpose of this report is to develop, analyze, and report upon methods of quantifying
the indirect emissions benefit associated with renewable energy gener ation resources. In particular, the goal is to conduct a national assessment of indirect emissions reductions from wind, LFG and MSW by NERC subregion. 1 

## Because conventional electricity generation resources can differ widely in their emissions characteristics, the determination of exactly which conventional re source or resources would be running but
for the contribution of the renewable resource would be the ideal first step in calculating indirect emissions benefits. If we knew which resources were “displaced” by the renewable resource, we could simply multiply the emission rate of those resources by the total number of megawatt-hours (MWh) displa ced, accounting for line losses, and an exact answer would be obtained. The answer thus obtained would vary from region to region, and from hour-to-hour within any given region, depending on current and expected market conditions.

Unfortunately, in large, inte rconnected, security-constrained 2 

## electricity markets, determining which units would be displaced in any given hour would require perfect information and a complex dispatch model. Even under such ideal circumstances, the analysis would be prohibitively time-consuming and complex for each hour. Thus our goal in the current study is to develop and apply proxy methods that, while imperfect, are applicable to and useful for the general estimati on of the indirect emissions benefits for renewable energy projects anywhere in the co ntinental United States. Over the course of this report, we develop and apply these methods to estimate the indirect emissions
benefits of three kinds of renewable ener gy projects, for carbon dioxide (CO 2 

## ), sulfur dioxide (SO 2 

## ) and oxides of nitrogen (NOx), in each of the 22 eGRID 3 

## subregions of the
continental United States.

An “indirect emissions benefit” may be a real reduction in total emissions in a region or, under cap-and-trade regulation such as that in much of the United States for the pollutants SO 2 

## and NOx, it may be an opportunity to release emissions allowances for some other use. The calculation methods explor ed in this report are appropriate for either application, but the impacts of these scenarios ar e quite different in terms of their ultimate effect on pollutant emissions. In the case of cap-and-trade regulation, displacing air emissions provides an opportunity for the allowance holder to sell or bank valuable emissions allowances. There may also be a so cietal benefit in terms of reducing the market price of emissions allowances, ther eby reducing this component of the cost of

1 EPA RFQ # RFQ-OH-07-00015, as posted at:

https://www.fbo.gov/?s=opportunity&mode=for m&id=556118954b3728ae31326b103396bcf6&tab=core&_cview=0.

2 “Security constrained” refers to the need for system operators to dispatch generating units to meet load while respecting

the loading on transmission lines and interfaces within thei r operating limits. Because of these security constraints, generating units cannot simply be dispatc hed in merit order, from least expensive to most expensive running costs. In many cases more expensive units must be run out of merit order due to transmission transfer limits.

3 The Emissions & Generation Resource Integrated Databas e (eGRID) is a comprehensive inventory of environmental

attributes of electric power systems. ( http://www.epa.gov/cleanenergy/ene rgy-resources/egrid/index.html .) Definitions and a map of the eGRID subregions are shown in Appendix A. eG RID subregions are roughly coincident with NERC (North American Electric Reliability Corporation) subregions.



### Indirect Emissions Analysis ▪ 1

## electricity. Finally, there may ultimately be a total emissions reduction if the lower cost and greater availability of allowances permit s regulators to tighten the total emissions

cap.

This report is organized as follows:

• In Section 2, we develop and present operating char acteristics of wind, landfill gas, and municipal solid waste energy resources in each of the eGRID subregions of the United States. We develop hourly operating profiles for a “typical” such resource in each region, normalized to produce one GWh 4 

## of electricity per year.

• In Section 3, we collect and present data on conventional power plants in each eGRID region based on the EPA’s Acid Rain database. 5 

## We characterize the data and discuss our approach to data filterin g and error handling, in preparation for characterizing the indirect emissions benef its associated with changes in dispatch.

• In Section 4, we implement and evaluate a number of approaches to applying the filtered and cleaned EPA data for the purpos es of calculating hourly avoidable
emissions factors. These factors can then be applied to renewable resource production profiles to calculate annual indi rect emissions benefits. In addition, we present hourly indirect emissions result s for each pollutant, region, and calculation approach under consideration, and we di scuss which approaches make sense for which kinds of analyses.

• In Section 5 we apply the indirect emissions calculation methods to determine
indirect emissions benefits associated with each resource type under consideration for each region and pollutant.

• Finally, in Section 6 , we discuss our results and present suggestions for further
research in this area.
Fundamental to the analysis in this report, all of which is based on retrospective data, is the primary focus on short-term, operational response of the electric system to the presence of new renewable energy resources. This analysis is thus most appropriate for analyzing the impact of a relatively small quantity of new resources during the first few years of operation, during which the displaced resources are likely to be the most flexible, load-following units on the system. Over the long term, it is likely that the capacity mix of an electric system will be altered as a result of the addition of new renewable energy resources. Specifically, new generating capacity investments may be avoided or deferred, or existing capacity may be retired, until the system returns to equilibrium in terms of the balance of base load and load-following resources. Ultimately, it may be expected that the proportion of flexible units will be restored to what it would have been without the new resource. In this sense, as it matures and is incorporated in larger quantities, renewable energy technology will ultima tely displace more base load capacity

4 A GWh (gigawatt-hour) is one thousand megawatt-hours, approximately the amount of power that could be produced by

one of the largest power plants in the United States in one hour. For perspective, the largest new single wind turbines are about 3 MW in capacity but produce, on average, about one MWh of electricity per hour. Thus one of the largest wind turbines would be expected to produce about ni ne GWh of electricity each year. In general, wind projects have more than one wind turbine , and can have up to 200 or more.

5 Available from the EPA Clean Air Markets data website, at http://camddataandmaps.epa.gov/gdm/



### Indirect Emissions Analysis ▪ 2

## on the system. While we touch on this important issue in some parts of this report, it is not a primary focus. 6



## The indirect emissions benefit calculated for any kind of renewable energy resource depends on a number of questions:

• Where is the resource located?

• What is the pollutant of interest?

• Is the time period of interest historical, in the near future, or several years in the
future?

• Is the resource base load, dispatchable, or intermittent and nondispatchable?

• If intermittent and nondispatchable, what is the expected hourly and seasonal profile
of the resource?
Because of the wide range of applications re flected in the possible answers to these questions, the best method to use in calculating indirect emissions benefits can vary with the application. In this report we present a number of methods, and apply them to three types of renewable resources and three pollu tants in 22 regions of the United States.
Even this level of detail is almost certainl y insufficient for precise analysis of the emissions benefits of individual projects. Ho wever, it leads to illuminating and useful
results that will allow for a far more detailed understanding of the issues and a better first approximation of the indirect emissions benefits than has previously been available.

We find:

• The indire ct emissions benefits of r enewable energy for all pollutants vary significantly by region, and that these di fferences can be quantified and applied in calculating the indirect emissions reductions.

• For each GWh of renewable energy produced each year, the indirect CO 2 

## emissions
benefit is found to be between 600 and 1000 tons of CO 2 

## , with coal-dependent regions having the highest indirect emissions benefit.

• For each GWh of renewable energy produc ed each year, the indirect NOx emissions benefit varies between 500 and 5,000 pounds of NOx.

• For each GWh of renewable energy produced each year, the indirect SO 2 

## emissions benefit varies between 200 and 1,300 pounds of SO 2


## .

• These results are robust regardless of whether the renewable resource has a base load profile (such as MSW and LFG) of output which varies diurnally and seasonally (such as wind).

• Different calculation approaches are appr opriate for different types of resources and different applications.

6 For a thorough treatment of this issue, see 2005 Synapse report Using Electric System Operating Margins and Build

Margins: Quantification of Carbon Emission Reducti ons Attributable to Grid Connected CDM Projects, available at http://www.synapse-energy.com/



### Indirect Emissions Analysis ▪ 3

## • More research is needed to establish which methods of calculating indirect emissions changes most accurately reflect real-world dispatch over a range of timescales and system conditions.

Of the methods introduced here, we find t he flexibility-weighted h ourly average emission rate (FW-HAER) to be the most appropriate for near-term (<3 year) projections of indirect emissions impact from wind resources, because it best represents the response of the electric system to rapid fluctuations in system load. We find the seasonal slope factor approach to be most appropriate for calculating the indirect emissions impact from base load resources such as municipal solid wa ste and landfill gas electricity generation.

The annual indirect emissions results for these resources based on 2005 data, assuming one GWh of energy was produced by each over the cou r se of the year, are shown in Figure 1 . With some notable exceptions, the indirect CO 2 

## and NOx emissions benefits of the different renewable energy resources is gener ally similar; this benefit diverges most dramatically by resource type for SO 2 

## . This divergence probably reflects the fact that in many regions, base load resources like MSW and LFG can displace coal, which emits
SO 2 

## , while wind initially primarily replaces gas, which does not emit SO 2 

## . However, with increasing penetration and geographic diversity of wind generation it is likely that this resource will begin to displace more base load coal, and the indirect emissions impact of wind generation will be closer to that of MSW and LFG.Displaced Tons of CO2per GWh 1,200

1,000

800

600 

### (a)

400

200

0AZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVC

Wind MSW/LFG


### Figure 1. Indirect emissions results for (a) CO 2 

### ; (b) NOx; and (c) SO 2 

### for wind, municipal solid waste (MSW) and landfill gas (LFG) generation projects. (Continued on next page)

Indirect Emissions Analysis ▪ 4

### (c) (b)

Wind MSW/LFGDisplaced Pounds of SO2per GWhDisplaced Pounds of NOx per GWh

4,000

2,000

0 Wind MSW/LFG

14,000

12,000

10,000

8,000

6,000 6,000

5,000

4,000

3,000

2,000

1,000

0AZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWAZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYLINYUPNYUPRFCERFCERFCMRFCWRMPARFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVCSPNOSPSOSRMVSRMWSRSOSRTVSRVC



### Figure 1 (Continued)

Indirect Emissions Analysis ▪ 5

# 2. Operating Data on Renewable Energy Technologies

In order to determine the indirect emission s reduction impacts of electricity production from renewable energy sources, it is nece ssary to first establish what kinds of conventional resources they are displacing. As a first step, in this section of the report we explore the operational characteristics of three types of renewable energy resources: wind power generation, municipal solid wa ste combustion, and landfill gas. We develop profiles for these resources representative of each eGRID subregion of the continental
United States 7 

## based on historic data and operating characteristics of these resources. (The eGRID subregions are identified and mapped in Appendix A.) Once we have determined their hourly output profiles over the course of a historic year, we can begin an assessment of which resources they would have been likely to displace.

In the section that follows, we will explore corresponding operational and dispatch
characteristics of conventional resources. Following that we combine the results of these research tasks, characterizing renewable energy output and assessing displaceable conventional resources, to make a final asse ssment of which fossil resources would have been likely to be displaced by renewables in each eGRID subregion. Finally, we use the results of this combined analysis to determi ne factors representing the indirect emissions benefit for each subregion.



# A. Wind Generation: Existing and Synthetic Projects

The American Wind Energy Association (AWEA) estimates that there are approximately
8.3 gigawatts (GW) of installed wind capac ity in the United States as of the 4 th 

## quarter of 2007, with another 4.1 GW under construction. 8 

## Texas and California dominate the current market, but there are underutilized regions throughout the Midwest. Some states with the greatest wind potential, such as t he corridor stretching from North Dakota south through Kansas, have yet to significantly tap this resource. In general, there are extensive opportunities to expand the rapid gr owth in wind generation, resulting in the further displacement of fossil fuel gener ation and decreased emissions of greenhouse gases and other pollutants. This large resource potential motivates the need to
understand in more detail the emissions benefits associated with renewable energy projects in each region.

Ideally, to characterize the output profiles for new wind generation projects, we would draw empirical wind power output datasets from a broad sampling of existing projects around the country. Unfortunately, wind generat ion projects are not required to publicly report hourly generation, and thus there are limited opportunities to calculate expected emissions reductions from existing resources. In addition, “real” wind projects present numerous hurdles that make them problematic as a source of research data for a broadbased study of operational characteristics. For example, there is generally no uniformity, and often no available information, on the tec hnical details of existing resources. With

7 eGRID subregions are almost coincident with NERC subregi ons. We chose to use eGRID subregions for this analysis

as this is the system used by EPA for emissions reporting.

8 American Wind Energy Association, 2007. Updated information can be found at http://www.awea.org/projects/.



### Indirect Emissions Analysis ▪ 6

## operational data available on few projects, what may appear to be regional differences in operating characteristics would be heavily influenced by unknown differences in technology, turbine size, turbine height, vint age, and specific local conditions associated with each individual project. In addition, the geographical coverage of available wind power output data is extremely limited. In fa ct, we were only able to find one source for such data, from the U.S. Department of E nergy’s National Renewa ble Energy Laboratory (NREL). This dataset covered only a small number of projects with limited geographic
distribution. Even with this source we were asked not to present specific, identifiable details or operational data to avoid disclosure of proprietary information, limiting its usefulness for our study.

Our solution to these limitations is to simulate wind power projects based on meteorological data gathered at locations wher e wind projects are likely to be built in each region, determined based on the quality of the wind resource. While wind power data are scarce, wind speed data are abundant and readily available at reasonably high resolution throughout the United States. Near-surface hourly wind speed time series data can be scaled to reflect a typical turbine height, and then converted with the help of a “power curve” 9 

## into corresponding simulated wind power output time series. We have attempted to perform a comparison of one of our simulated wind power time series with the output of a nearby physical wind-power proj ect; however, the results are difficult to interpret. The wind power and the wind data are not from exactly the same location or height, and even such small differences can be extremely significant in determining the output of a resource. The technology and vint age represented by our power curve is also unlikely to be well matched to the actual te chnology and vintage of the specific wind turbines for which we have data. Even so, th is approach provides a useful means of evaluating the potential impacts of wind ener gy production on emissions. The data sources and comparison results are described in more detail below.



## Data Sources for Synthetic Generation
Over 1700 meteorological stations collect hourly data throughout the United States and territories for the National Climate Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). Our primary source of wind data for generating synthetic wind power time series was NCDC’s Weather Bureau Army Navy (WBAN) stations, distributed throughout the Unit ed States. To estimate the temporal characteristics of potential wind power generat ion, and thus expected indirect emissions reductions for each eGRID subregion, we needed to determine likely locations for new wind generation projects. We did this by reviewing the highest resolution available 50­ meter wind speed estimates in each eGRID subregion, as cataloged by the NREL, 10 

## to determine where favorable wind resources were likely to be located based on average wind speeds at 50-meter hub heights. We assumed that the minimum economic wind speed threshold for a wind power project would be at Class 4 wind speeds, or winds of

9 A “power curve” is a graph which relates wind speed to ex pected power production for a spec ific wind turbine technology

and configuration. Actual power output data from wi nd turbines generally exhibit considerable scatter around the expected power curve, but this remains the bes t means of characterizing this relationship.

10 USDOE NREL Wind and Hydropower Electricity Program, 2007. Available online at:

http://www.eere.energy.gov/windandhydro/windpoweringamerica/wind_maps.asp



### Indirect Emissions Analysis ▪ 70 

## 16.25 WS = WS ∗ i

, j i

, j 

## WS j 

## 15.7-16.8 miles per hour (mph). Once the area was selected in this manner, the closest high-quality NCDC station wind data locati on was selected for each subregion. Twentytwo NCDC meteorological stations near these locations were selected as proxies to represent the temporal pattern of wind gener ation expected at these sites. The locations of the sites and summary wind characteristi cs from the selected NCDC WBAN stations may be found in Appendix C

.
It must be noted at the outset that the geographic granularity of the wind data is far from satisfactory. Wind conditions can change signifi cantly over spatial scales as small as a few kilometers or less, so r epresenting each eGRID subregion with a single site entails a considerable simplification. We have tried to choose sites which are reasonably representative of promising wind power locations for each region, understanding that considerably more research and analysis would be needed to pick a truly representative site, if indeed a single site can stand in for a large region. The same can be said for solid waste and landfill gas projects considered in our analysis: there is simply not enough information available to confidently predict the behavior of all potential future sites. This is an inevitable shortcoming of attempting to r epresent site-specific characteristics on a national scale, and it should be kept in mind by the reader in interpreting our results.



## Generation of Synthetic Wind Power Data
WBAN meteorological stations are typically lo cated at local and regional airports, or near rural towns, at 10 meters (33 ft) above ground le vel. Due to surface friction, wind speeds near the ground are significantly lower t han speeds at expected 50-100 m windmill hub heights. In addition, the WBAN stations collect meteorological data in irregular intervals, with increments of approximately one hour. To create hourly synthetic time series, we used the simple average of the two measurements surrounding each hour. Next we normalized wind speeds to Class 4 speeds by scaling the time series for each station to an average speed of 16.25 mph, as follows:

(1)

Wher e:

WS i,j



## = Adjusted wind speed during hour i and station j

WS 0 i,j 

## = Unadjusted wind speed during hour i and station j

and 

## WS j 

## = Average unadjusted wind speed at station j over all hours.

Wind scales non-linearly with height, increasing in speed as the influence of ground-level friction decreases. However, we assume t hat a proximal Class 4 wind site would have similar temporal characteristics to the met eorological station, and thus our approximation is to scale the WBAN wind data linearly to re flect Class 4 characteristics. This approach encompasses a host of decisions, design param eters, and operational characteristics into a single scalar that represents, roughly, the decisions made by the builder of a wind power facility to optimally exploit a local resource.

Indirect Emissions Analysis ▪ 8

## To convert the scaled wind data into synthet ic wind power data, it was necessary to select a proxy technology with an appropriate wind power conversion function. We used a wind power curve based on the General Electric (GE) 1.5se (1.5 MW nameplate capacity) wind turbine to estimate potential generation associated with the wind time series at each selected meteorological station. The power curve, shown in Figure 2 , is determined by a number of factors including t he area swept by the bl ades, the “pitch” or angle of the blades (which can be variable in different wind conditions), and characteristics of the turbine gearbox. Wind tu rbines require a minimum amount of wind to begin generating power, known as the cut-in speed, and are designed to stall, or even brake, at higher wind velocities (the cut-out speed) to avoid damage. 11 

## Once again, we do not know a priori what technology or set of parameters would be most appropriate for a wind turbine in each area, but by scaling the winds to match a proxy turbine we generate a series that preserves temporal va riability while representing a project which is optimized to local conditions.

Figure 2. Wind power curve for a GE 1.5se (1.5 MW) wind turbine. Source: Http://www.gepower.com/prod_serv/products /wind_turbines/en/down loads/ge_15_brochure.pdf



## The GE 1.5se proxy turbine is a common mid-sized turbine used on-shore; larger turbines are generally used offshore due to high wind conditions. The design tower height for these turbines is 54.7 meters (m), with 35.25 m blades sweeping an area of 3,904 m 2



## . The cut-in speed is 4 meters per second, and the cut-out speed is 25 meters per second. To generate the wind power time series, we relied upon a GE-supplied power curve which was imported as a look-up table for our analysis; the look-up table indexes the power curve and returns an estimated generation value for each hour given the calculated wind speed at turbine height.

11 Tony Burton, David Sharpe, Nick Jenkins, Ervin Bossanyi, Wind Energy Handbook , John Wiley & Sons, 2001.



### Indirect Emissions Analysis ▪ 9

### 20% 1800

2% 4% 6% 8% 10% 12% 14% 16% 18%Percent of hours



### Raw Wind Speed Scaled Wind Speed Power Curve

200 400 600 800 1000 1200 1400 1600Power output from 1.5 MW generator(kW)



### 0%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Wind speed (mph) 0 

# Q = a

b

− cWS 

# 1 + e For purposes of our analysis, we approximat ed the GE wind power curve with a sigmoid
function:

(2)

Where Q i s output (kW) and WS is wi nd speed (m·s -1 

## ). The best fit to the GE curve in these units was obtained with parameter valu es a=1500, b=8.1, and c=0.9. The curve was truncated to zero at the cut-out speed of 25 m·s -1 

## The resulting curve is shown in Figure 3 , along with reported and scaled wind data.

Figure 3. Proxy wind turbine power curve plotted against histograms of raw and scaled wind speeds at a sample site in the Midwest. “Scaled” wind speeds are adjusted to reflect class 4 winds at a turbine height of 50-100 meters using Equation 1.



## Comparing Empirical and Simulated Wind Generation
As noted earlier, we were able to find one source of wind power data for comparison with our synthetic power profiles. The wind power ti me series that we were able to obtain represent twelve operational projects associated with the NREL wind farm monitoring program. The NREL data cover twelve sites including two in Minnesota, one in Iowa, one in Oklahoma, four in Texas, and four in Or egon. Total capacities ranged from 25 MW to 230 MW, and the turbines ranged from 0.6 to1.6 MW each. While NREL was able to make 2005 production records from these projec ts available for research purposes, siteidentifying information remains confidential. The turbines represent primarily plain and low-ridge wind farms; the farms in Iowa and Minnesota are distributed throughout active agricultural land, while the Oregon, Oklahoma, and Texas sites partially overlap grazing
lands.

Indirect Emissions Analysis ▪ 10

## To test whether our simulated wind generation time series were realistic and representative of the output profile for a physical wind project at the same location, we selected an NREL wind project from a midwestern U.S. location and compared the recorded output with the simulated production time series generated using wind data from a nearby location. The test project is in a region with Class 4 and Class 5 wind speeds, running 145 turbines of 750 KW each, for a total capacity of 108 MW. A nearby WBAN meteorological station was located, and wind records for this location extracted from the NCDC data. The wind data were transformed through the power curve (Equation 2) to generate hourly power output.

Such a comparison has a number of complications, including:

• Gaps in wind data. In the case of the selected series, there are 84 gaps larger than 2 hours in duration, and 9 gaps longer than 24 hours in duration. The longest gap is nearly a week in duration. We have no wa y of knowing whether these were neglected because they were zero, or they were very high, or they simply represent random data errors, Thus it is impossible to deter mine what effect their omission may have on our analysis.

• Gaps in power output data. The output time series for the installed project has 552 missing hours in the sample year (6% of all hours) which were removed from the analysis. As with the missing wind data, it is impossible to determine what effect their omission may have on our analysis.

• Granularity of wind data. Wind speed data are taken approximately once per hour, meaning that variations in wind speed within each hour are unknown. In addition, the wind data show recording artifacts, such as certain values that rarely appear in the record including any measurements between approximately 0.5 and 2.75 mph. We have not attempted to perform any smoothing to compensate for this.

• Representativeness of wind site. While the wind data are taken from a meteorological station in proximity to the tu rbine, the station is clearly not measuring
winds identical to those that drive the turb ine. This lack of correspondence to actual values is exemplified by the average wind speed in the meteorological record of only
9.71 mph; to scale to the Class 4 average of 16.25 mph winds, we multiply all values by 1.67, consistent with Equation 1. In addi tion, a wind power installation can contain hundreds of turbines, each responding to highly localized conditions and each subject to maintenance schedules and other operational constraints which are not captured in the power curve.

• Idealization of power curve transformation. Each hour’s scaled wind speed is transformed into an hour of power output assuming a perfect generator response to steady winds, neglecting any effects such as generator inertia which may dampen the response. Wind speeds are implicitly assumed to hold steady for exactly one hour before instantly changing to the next hour’s value, again with an instantaneous response from the turbine.
The comparison of simulated and observed power output is shown in Figure 4 . In this analysis we take the maximum recorded output for the year as the maximum output for the station, although this value is only about 90% of the nameplate capacity of the

Indirect Emissions Analysis ▪ 11

## installation. This assumption presumably co rrects for scheduled maintenance and forced outages which would affect some subset of turbines at all times.

The simulated output curve shows an exaggerated peak at 5%-10% of capacity relative to the recorded power output series, and a peak at the 100% output level which is absent in the observed data. These peaks likely refl ect the granularity issues identified above for the wind records, which can reflect just one or two observations for each hour while the measured power output reflects inevitable va riations in wind speed and output over the course of each hour. For the simulated project t he overall capacity factor over the course of the study period is 32.8%, while for the obse rved project the overall capacity factor is
30.3%.

0% 5% 10% 15% 20% 25% 30%

0 % 5% 10 % 15 % 20 % 2 5% 3 0% 35% 40% 45 % 50 % 55 % 60 % 6 5% 7 0% 75% 80% 85 % 90 % 95 %

1 0 0%

Percent of maximum outputPercent of hours Simulated Generation

Observed Generation



### Figure 4. Comparison of power output distribution at sample wind power site in the Midwest with predicted output based on scaled wind data from a nearby meteorological station. Output levels for observed generation are relative to the maximum recorded output for the year.



## The simulated and observed generation, as well as the difference between the two, are
shown on color charts in Figure 5 . These charts represent each hour of the year as a cell on a 24-hour by 365-day matrix, with each cell color-coded according the scheme shown. Figure 6 shows a cumulative distribution of t he hourly deviation between the two, in percentage points. The simulated output exhibi ts a pronounced diurnal cycle which is not present in the project data. This discrepa ncy could be due to any number of factors, including light winds which blow through the evening at hub-heights which do not appear in the NCDC dataset (NCDC does not record wind speeds less than 3 mph at ground level) or more sensitive wind turbines with lower stalling sp eeds than the proxy turbine, which would produce a generally flatter dist ribution of power generation throughout the day. This discrepancy may also simply reflect the difference in the location of the two
sites. The two time series are within 20 percentage points of each other during approximately 65% of the 6,434 hour s recorded in both datasets.

Indirect Emissions Analysis ▪ 12

### Figure 5. Comparison of hourly synthetic and observed wind power time series at nearby locations in the Midwest. Each plot shows 8,760 hours in 2005, with hour of the day on the vertical axis and day of the year on the horizontal. Missing data shown in black. Top: Observed wind power output (% of maximum); Middle: Synthetic wind power output (% of capacity) based on scaled wind speed data; Bottom: Hourly deviation of synthetic from observed time series.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Difference in Percent of Maximum Output

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Non-Exceedence Hours (%)


### Figure 6. Cumulative distribution of the hourly deviation (in absolute percentage points relative to maximum output) between observed and synthetic generation time series.

Indirect Emissions Analysis ▪ 13

## While there are clearly significant discrepanci es between these two datasets, we feel that the NCDC meteorological dataset is a reasona ble, if by no means ideal, resource for estimating temporal patterns and potential offsets from projects which might be built in each eGRID region.

The hourly synthetic output generated for each of the eGRID subregions is shown in Appendix B as 24-hour by 365-day color intensit y plots. Monthly summary tables of raw and scaled wind speeds, as well as synthetic wind power, may be found in Appendix C. The hourly data are available in electronic format upon request.



# B. Landfill Gas

To determine the operational and dispatch char acteristics of landfill-gas-fired generating facilities, we contacted several landfill gas (LFG) project developers. 12 

## We received hourly generation data for several landfill gas projects from Granger Electric Company and Granger Energy, LLC, (“Granger”) which together own and operate six landfill gas projects in Michigan, and 10 LFG projects in Indiana, Ohio, and Pennsylvania.

According to Granger, the generation profile of LFG projects generally does not differ significantly by time or season. 13 

## The primary factors that impact the energy output of LFG projects have to do with ambient condition s. The warmer and the wetter the climate, the higher the methane content of the landfill gas , such that more power can be produced
from this resource.

High temperatures also affect the plant’s output by generally reducing the temperature gradient that drives the turbine. This temper ature effect is minimal for the reciprocating engines which are used by Granger. Gas turbines appear to be the predominant choice for LFG projects by other companies. Gas turbine outputs are more susceptible to variation in temperature. This characterist ic largely mirrors the behavior of base load generating plants, and system operators ca n reasonably expect a constant and predictable stream of energy output from these facilities. Thus we conclude that the fossil-fuel-fired generation displaced by landf ill gas in any region of the country will be similar in operational and emissions characterist ics to typical base load resources in that same region.



# C. Municipal Solid Waste

Municipal Solid Waste (MSW) generators, also known as waste-to-energy facilities, are currently operating in 27 states and burning roughly 95,000 tons of garbage each day, generating roughly 2.5 GW of electricity. 14,15 

## Even with this large volume of output, the

12 Landfill gas project operators were identified by Rachel Goldstein of the EPA Landfill Methane Outreach Program:

http://www.epa.gov/lmop/.

13 Granger does have one project which oper ates as peaking generation, but this appears to be an anomaly and does not

affect our conclusions about general LFG generation characteristics.

14 New York State Department of Environmental Conservation, http://www.dec.ny.gov/chemical/8979.html

. 15 Because some portion of the fuel supply for MSW generators is generally fossil-based, there is some dispute over

whether this resource should be considered “renewable” or no t. However, as a number of state programs include MSW as a renewable energy source (see dsireusa.org for updated details), we treat it as a renewable resource for the purposes of this report.



### Indirect Emissions Analysis ▪ 14

## facilities are not fuel-limited; our research and discussions with facility operators reveals that waste-to-energy plants r un at high capacity factors of 85% and above. As with landfill gas projects, these characteristics suggest that they serve as base load capacity, and that the fossil generation they are likely to displace is also base load capacity. Thus we conclude that the fossil generat ion displaced by MSW in any region of the country will be similar in operational and emissions characterist ics to typical base load resources in that same region.



# 3. Hourly generation and emissions data

In this section we characterize the operati onal, dispatch, and emissions characteristics of conventional power plants in order to predi ct the change in dispatch and emissions resulting from the presence of new renewable energy resources.

Electricity grids are often characterized as having a range of resource types including base load, intermediate, and peaking resource s. These resources respond differently to
variations in load over different timescales : put simply, base load resources run most hours regardless of load levels, intermediate resources ramp up and down frequently in response to changes in system load, while peaking resources run only during the highestload periods. However, real-world electric grids are more complicated than this, and realworld resources have complex operating cons traints and practices depending on factors including heat rate, ramping capability, demand and price in the energy and ancillary service markets, availability of competing resources, local transmission constraints, environmental constraints, operator or di spatcher discretion, and even the warrantee and/or service contracts on specific pieces of generating equipment. It is thus impossible to predict with specificity which resource s would respond to perturbations in load or available low-cost energy. Howe ver, historical data on unit operations can lend insight into which units are more or less likely to be displaceable at any point in time. 16 

## To the extent that these operational data are coupled with emissions characteristics, they can be used to estimate the likely displaceable emis sions at any point during a historical year. Such an approach would be more refined than using a simple average emission rate at any point in time to represent displaceable emissions, because it would be more sensitive to the actual dispatch characterist ics of individual generating plants.

To implement this approach, we obtained generation and emissions data for United States fossil-fuel powered generating plant s for 2005 from the Hourly Emissions database of the EPA Clean Air Markets Programs. 17 

## The database consists of hourly reported generation, heat rate, and total emissions of CO 2 

## , SO 2 

## , NOx, and mercury for all fossil fuel-fired generators with nameplate capacities above 15 MW. These data form the basis of all of the displaceable emissions analysis reported here. We use the hourly

16 It is common to think of generating resources as responding only to changes in electricity demand; however, certain

intermittent renewable resources, such as wind generation, change the load on fossil resources in a very similar manner to variations in load. Thus we treat the availability of ener gy from such resources in the same manner that we would a reduction in energy demand from consumers.

17 EPA, 2007. Data Sets and Published Reports: Emissions Data Prepackaged Data Sets. Online at

http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=prepackaged.select



### Indirect Emissions Analysis ▪ 15

## generation data to characterize the operational characteristics of generating units in each NERC subregion, and we use the associated emis sions data to characterize the indirect
emissions benefit that would be associated with displacing the output of each generating
unit.



## Characterization of emission rates
Hourly emissions are reported in t he Clean Air Markets database for CO 2 

## , NOx, and SO 2


## . Within the database, the data records include fields to indicate whether the reported
emissions are measured or calculated. Calculated emissions are based on the composition of the fuel, the unit’s heat ra te, and the output of the unit. In general, it appears that most of the emissions data in the database for 2005 are measured.

Pollutant emission rates are reported in the hourly emissions database as tons per million British thermal units (mmBtu) for CO 2 

## , and pounds per mmBtu for NOx and SO 2 

## . For the purposes of this analysis it is necessary to consider emissions in units of mass per MWh output. However, while emission rates per unit of heat input may be fairly constant, emission rates per unit of energy output can vary considerably—this variation is due to changes in the efficiency (or “heat rate”) of a generating unit with different output levels. For purposes of this analysis, we will use the overall average emissions rate for each unit as derived from the EPA hourly emissi ons database, unless otherwise noted.



## Data filtering

The EPA emissions data contain anomalous feat ures which are indicative of either reporting or calculation errors. While the hour ly generation data are used to characterize generator behavior and identify times at which the generator is operational, emission rates could be misleading if anomalous data were included. Therefore, data are removed from the emission rate analysis according to the following rules:

• Generation, CO 2 

## , or heat rate is reported as zero.

• The reported heat rate is in the top 99.5 th 

## or bottom 0.5 th 

## percentile (for a record with 8,760 non-zero hourly values, this would remove the 44 highest and 44 lowest values).

• CO 2 

## emission rate is above 2.5 tons of CO 2 

## per megawatt-hour (tCO 2 

## MWh -1 

## ) or less
than zero.

We did not filter on the basis of NOx or SO 2 

## measurements as we had no basis for judging reasonable emission rate limits.

Indirect Emissions Analysis ▪ 16

# 4. Emissions Displacement Methods

In order to calculate the indirect emission s benefits associated with power production from renewable resources, it is necessary to determine whic h power plants will have their output curtailed as a result of the availability of the new re source. This need arises from the recognition that the emission rates of pow er plants which will be running in either case (with and without the new resource) do not affect the calculation; nor do those of power plants that would not be running in either case. In fact, it is only a small number of plants, as few as one marginal resource in any given dispatch interval, that will determine the relevant emission rate. The difficulty come s in identifying which plant or plants to consider, and thus in identifying the appropriate emissions rate or rates for each interval.

There are numerous factors that affect wh ich power plants operate, are committed to operate, or are held in rese rve at any point in time. These factors include:

• Offer price or short-run marginal cost;

• Constraints such as ramp rates, minimum up and down times, and other operating restrictions;

• Transmission or other dispatch constraints;

• Maintenance schedules;

• Unplanned outages and/or deratings; and

• Environmental constraints.
Unfortunately, most of this information is not available from public sources for most resources. Using only the information whic h we have available from the EPA database (i.e., hourly generation, heat rate, and emissions) , it is impossible to reconstruct all of the factors that guide dispat ch and to determine the precise marginal emissions rate in effect for any particular hour.

Our solution is to infer operational characterist ics from the suite of units in each region based on their patterns of operation and emissi ons over the course of the study year. There is a broad range of possi ble approaches to interpreting the data in this manner. Some of these approaches use emissions characteristics aggregated over a season or year, and some are more directly focused on hourly analysis. 18 

## Each approach carries its own implicit model about system operati on and response to new resources, and each may be more appropriate than others to address the impact of certain kinds of resources or over specific time scales. In this anal ysis, we present five methods, ranging in simplicity, design, and conceptual model, and explore the implications of each. These
methods are:

18 Earlier investigations based on extracting hourly performance information from the same EPA database used here:

• Connors, S., K. Martin, M. Adams, E. Kern, and B Asiamah-Adjei. 2005. “Emissions Reductions from Solar Photovoltaic (PV) Systems” Publication MIT LFEE 2004-003 Report.

• Berlinski, M. and S. Connors, “Economic and Environmental Performance of Potential Northeast Offshore Wind Energy Resources: Final Report”, Report to the Offshore Wind Collaborative Pilot Research Projects, January 2006.



### Indirect Emissions Analysis ▪ 17

## • Hourly Average Emissions Rate (HAER);

• Annual (and seasonal) emissions slope factor (Slope Factor);

• Empirical Incremental Emissions Rate (EIER);

• Load-Following Rate Incremental Emissions Rate (LFIR); and

• Flexibility-Weighted Hourly Aver age Emissions Rate (FW-HAER).



# A. Limiting Cases

Before delving into each method in detail, it is useful to characterize the most extreme emission scenarios to bound the results of our analysis, particularly with respect to CO 2


## . In producing a single MWh of electricity, pow er plants fueled by coal (the most carbonintensive fossil fuel) typically produce somewhat over one ton of CO 2 

## ; modern plants fueled by natural gas produce about 0.6 tons of CO 2 

## per MWh. 19 

## The exact emission rate for each plant depends on technology, age, maintenance, and specific fuel (i.e., some coals have a higher carbon cont ent than others for the same energy output.) The ranges

rates by fuel type.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Tons per MWh 0% 5% 10% 15% 20% 25% 30% 35%Percent of plants Gas
Oil
Coal



### Figure 7. Distribution of CO 2 

### emission rates for fossil generating plants by reported fuel type. Note that reported primary fuel is not a precise categorization, as some units are capable of switching fuels and some plants may actually have units which burn different primary fuels.



## The difference between emission rates from coal and gas essentially bounds the range of outcomes in accounting for the displace d emissions associated with renewable

http://www.eia.doe.gov/cneaf/electri city/page/co2_report/co2emiss.pdf ;. Values in the EPA database confirm these numbers: the median emission rate for a coal plant is 1.024 tCO 2 MW -1 , while for a gas plant it is 0.697 tCO 2 MW -1 

## of reported hourly emission rates for gas, coal , and oil-fired plants in the EPA database
are shown in Figure 7 . While this figure highlights some of the anomalous data in the database, such as coal plants which report unrealistically low CO 2 

## emissions, it does show that the reported data are generally c onsistent with the ex pected range of emission

Indirect Emissions Analysis ▪ 18 19

# ∑ CO 2 

## ( t ) HAER
t 

## = 

# ∑ MW ( t ) generation. Renewable resources cannot di splace more carbon emissions than the emission rate of an inefficient coal plant on a per-MWh basis; nor is it likely that they will displace less CO 2 

## for each MWh generated than the amount emitted by an efficient combined cycle gas plant. This is because other low-carbon sources of power (such as nuclear and hydro) have low running costs and are unlikely to be displaced. 20,21 

## In general, the result will lie somewhere between these two extremes.

For NOx, the story is somewhat more comp licated. NOx emissions tend to be bifurcated into ozone-season emissions (May through September), when emissions are regulated and many power plants are operating em issions control technology; and non-ozone season emissions, when they are not. As will be shown below, in some areas this bifurcation is more pronounced than in others. However, NO x 

## emission rates generally range from zero to perhaps five or six pounds per MWh, with ozone season rates generally one or two pounds below non-ozone season rates.

SO 2 

## emission rates depend upon the fuel used for generation, with natural gas having no sulfur (and therefore producing no SO 2 

## ) and certain types of coal having the most. Thus if only gas generation is displaced, there is no displacement of SO 2 

## emissions. For this reason, we would expect SO 2 

## to have perhaps the largest regional displaceable emissions differences of all of the pollutants considered, with ranges from zero up to about eight pounds per MWh for regions that rely on coal resources for load following capacity.

To narrow these ranges, it is necessary to find a means to identify which types of plants are more likely to be displaced in any given r egion. All of the methods described in this section are designed to refine the estima te of displaced emissions based on the observed operational characteristics of the system as deduced from the EPA database.

For clarity, we will develop the displacement methodologies with primary reference to carbon dioxide emissions since it is the pr imary anthropogenic cause of global warming. However, results will be presented for all th ree of the pollutants considered here.



# B. Hourly Average Emissions Rate (HAER)

One of the most conceptually straightforwar d methods for estimating displaced emissions is to use a simple hourly average. This method calls for adding the aggregate emissions from an eGRID subregion (for example, in tCO 2 

## ) for each hour of the study period and dividing this by aggregate fossil generation (MWh) for the same hour:

(3)

20 As noted earlier, displaced emissions are not necessarily equivalent to avoided emissions, as emis sions policies and

other factors can come into play in determining total emissions in any given area.

21 There are exceptions to this statement – in some cases storage hydropower may be the most flexible resource

available to follow load. However, storage hydro is energy-limit ed resource, so total generation from such a facility will not be affected by the presence of a new renewable resource.



### Indirect Emissions Analysis ▪ 19

## Where t denotes a particular hour, and the su mmations are over all of the generating units in the region of interest.

Equation 3 produces the average emissions rate across all fossil units for a specific region and hour, with the implicit assumption that every fossil plant running during that hour has an equal probability of being displace d. This is clearly not the case on an hourto-hour basis: certain units have limited ramping capability and other operational and
economic constraints which dictate that t hey only be used as base load resources. However, this method is fairly straightfo rward to understand and apply, and as noted earlier we cannot know for sure which units would be displaced in any hour. In addition, as the market matures with higher levels of renewables penetration, it is reasonable to imagine that units throughout the supply curve may ultimately be displaced. Finally, this method may be the most appropriate for base load renewables such as landfill gas or municipal solid waste combustion, for which ou tput is stable and predictable. Thus it may be reasonable to quantify displaced emissions using this approach if more of a long-term or base load impact is under consideration for any of these reasons.

Figure 8 shows selected representations of the variations in HAER for CO 2 

## in the New England (NEWE) eGRID subregion. The first representation is a timeline of hourly variations, demonstrating that the CO 2 

## emissions rate fluctuates between about 0.65 and
0.95 tCO 2 

## per MWh, with an average of 0.73 tCO 2 

## /MWh and a standard deviation of 0.05
tCO 2 

## /MWh. the second panel from the top shows a three-week period in the summer in more detail. The second representation (third from top) is a histogram of hourly HAER values. The final representation (bottom panel) recasts the dataset as a color intensity image of HAER, with day of year (DOY) in 2005 along the horizontal and the hour of the day on the vertical. The green / yellow band across the top and bottom of the image indicates that the evening hours have higher av erage emission rates, probably reflecting the lower contribution of natural gas units during these hours.

We can see from these graphs t hat there is significant diurnal variability to the data over a fairly narrow range, with higher hourly aver age emission rates at night. This reflects the low position in merit order for coal plants with higher CO 2 

## rates, which dominate the lower-usage hours at night, but also the fact that these same large plants strongly influence the overall average, in combinati on with lower-emitting gas plants, even during peak times.

Indirect Emissions Analysis ▪ 200 .62 0.64 0.66 0. 68 0. 7 0. 72 0. 74 0.76 0.78 0. 8 0.8 2 0. 84 0. 86 0. 88 0.9 0.92 0.94 0.9 6 0.9 8 1 Hourly Average Emissions Rate (HAER) in New England (NEWE)

0.6 0.7 0.8 0.9

1HAER (T CO2/MWh)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

0.6 0.7 0.8 0.9

1HAER (T CO2/MWh)

07/18 07/20 07/22 07/24 07/26 07/28 07/30 08/01 08/03 08/05 08/07



### 0 200 400 600 800 1000 1200 1400 1600 1800Number of Hours in 2005



### Emissions Rate Bin (tCO 2 

### / MWh)

Hourly Average Emissions Rate (HAER) in New England (NEWE)Hour 5
10
15
20

50 100 150 200 250 300 350 Day of Year (2005)

0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1



### Figure 8. Representations of CO 2 

### Hourly Average Emissions Rate (HAER) in New England. Top: full record, with detail shown for three weeks in the summer of 2005; Middle: Histogram of hourly emissions rates showing distribution throughout the year; Bottom: 24x365 color representation of emission rates, showing diurnal cycle and seasonal variations.

Indirect Emissions Analysis ▪ 21

# C. Emissions Slope Factor

The emissions slope factor is an empirical estimate of the marginal emissions rate based on the assumption that the obs erved linear relationship between emissions and electricity output from fossil fuel units is the best indica tor of the system’s response to a change in
load. This factor can be calculated as a si mple regression relationship over an entire year, or on a seasonal basis. Because of seasonal differences in dispatch and operating constraints, there may be additional value in performing this analysis on a seasonal basis. For the time period under consideration, total emissions and total fossil generation for each subregion are summ ed for each hour, based on the EPA database. When the hourly pairs of emissions/output data points for a subregion are plotted on a scatter plot, the slope of the regression line represents an empirical measure of the dependence of emissions on total electricity output in units of mass of pollutant emitted per MWh produced. If the correlation is high, this slope provides a reasonable estimate of the regional, seasonal avoidable emissions factor for a change in load or for the addition of emissions-free energy. This metric makes no specific assumptions about which generator will be displaced by a change in system load, but instead relies on a robust diagnostic of the overall system response to variations in load.

One interesting feature of this approach is that the line of best fit rarely has a y-intercept of zero. This may seem counterintuitive, as zero generation would surely be associated with zero emissions. However, zero generation is clearly a domain which is not of interest for this analysis, and the very lowest generat ing units on the supply curve are likely to
have emissions characteri stics that diverge from those of units that are more likely to be load-following. Were we to force the regression lines to cross through the origin, we would obtain a much poorer estimate of the emission factor in each region.

Figure 9 illustrates the application of this method for emissions in the Reliability First/Central (RFCE) subregion. All thre e pollutants of interest are shown. CO 2 

## and SO 2 

## emissions can be characterized as having a linear dependence on total MW output, with
R 2 

## values of 0.98 and 0.81, respectively.

The NOx emissions data ( Figure 10 ) do not lend themselves to this simple relationship. Readily apparent from the Figure are two di fferent trends. These trends represent the emissions in the ozone season, when most generating units are required to operate ozone control technology, and the non- ozone season when they are not. 22 

## Finally, there appears to be an up-tick in NOx emissions during very high load hours during the ozone season. This up-tick may represent units that disengage their NOx controls to increase output during very high load hours.

22 Complicating this analysis is the fact that many units were not required to report NOx output during the non-ozone

season in 2005.



### Indirect Emissions Analysis ▪ 22450,000

400,000 tCO2/MWh y = 8.5754x + 89172

R 2 = 0.8079Emissions (see legend for units) lbs NOx/MWh 350,000 lbs SOx/MWh

300,000

250,000

200,000

150,000 y = 1.5361x + 23825

R 2 = 0.2525 100,000

50,000 y = 0.7615x + 1731

R 2 = 0.9842 0 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
MW Output 

### Figure 9. Determination of the regional Emissions Slope Fact or in the RFCE (Reliability First/Central) region. Total emissions of CO 2 

### , NOx and SO 2 

### vs. MW output for each hour of 2005 are shown. A linear line of best fit is calculated for each pollutant, and the slope of this fit determines the annual slope factor for the region. The bifurcation of the NOx data reflects the differential operations of pollution control equipment during the ozone (summer) vs. non-ozone seasons.

120,000

y = 3.5035x + 1414.5
R 2 = 0.9314 100,000 O3 Season
Non-O3 SeasonTotal NOx Emissions (lbs) 80,000

60,000

40,000 y = 1.8629x + 1626.8
R 2 = 0.9592

20,000

0 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
MW Output 

### Figure 10. Bifurcation of the NOx emissions slope factor (pounds of NO x

### per MW output) for the RFCE region. Slopes for the ozone season (May through September) and non-ozone season hours are shown. The difference reflects the required operation of ozone control technology during the ozone season. Also apparent are excess NOx emissions during hi gh-load hours during the ozone season.

Indir ect Emissions Analysis ▪ 23

## CO 2 

## Slope Factor R-Squared

Region Spring Summer Fall Winter Annual Spring Summer Fall Winter Annual
AZNM 

## 0.59 0.53 0.56 0.55 0.58 0.95 0.97 0.94 0.91 0.96
CAMX 

## 0.56 0.50 0.53 0.48 0.52 0.91 0.99 0.95 0.92 0.97
ERCT 

## 0.69 0.59 0.64 0.67 0.66 0.94 0.99 0.99 0.97 0.97
FRCC 

## 0.73 0.69 0.73 0.72 0.74 0.98 0.99 0.99 0.99 0.99
MROE 

## 0.80 0.77 0.83 0.96 0.83 0.94 0.97 0.95 0.94 0.95
MROW 

## 0.97 0.88 0.93 0.91 0.95 0.98 0.99 0.98 0.94 0.98

NEWE 

## 0.63 0.65 0.61 0.68 0.65 0.94 0.98 0.97 0.88 0.95

NWPP 

## 0.82 0.71 0.78 0.70 0.82 0.97 0.95 0.90 0.87 0.95
NYCW 

## 0.66 0.55 0.63 0.64 0.56 0.83 0.97 0.98 0.91 0.93

NYLI 

## 0.55 0.48 0.61 0.51 0.53 0.84 0.90 0.97 0.82 0.92
NYUP 

## 0.69 0.66 0.60 0.76 0.66 0.95 0.97 0.92 0.95 0.95
RFCE 

## 0.78 0.69 0.75 0.81 0.76 0.99 0.99 0.98 0.98 0.98
RFCM 

## 0.81 0.67 0.75 0.93 0.71 0.81 0.97 0.91 0.94 0.93
RFCW 

## 0.95 0.85 0.89 0.92 0.89 0.98 0.99 0.99 0.99 0.99

RMPA 

## 0.74 0.70 0.86 0.89 0.79 0.85 0.92 0.90 0.93 0.90
SPNO 

## 0.93 0.93 0.96 1.08 0.97 0.94 0.97 0.99 0.97 0.97
SPSO 

## 0.70 0.60 0.66 0.65 0.66 0.87 0.98 0.97 0.89 0.94
SRMV 

## 0.66 0.60 0.64 0.64 0.61 0.94 0.97 0.94 0.89 0.96
SRMW 

## 0.93 0.86 0.95 0.99 0.94 0.99 0.99 0.99 0.98 0.99
SRSO 

## 0.83 0.70 0.79 0.80 0.80 0.99 0.98 0.97 0.96 0.98
SRTV 

## 0.81 0.76 0.87 0.88 0.84 0.97 0.97 0.98 0.97 0.97
SRVC 

## 0.94 0.79 0.88 0.90 0.85 0.97 0.99 0.98 0.99 0.98 These figures and statistics s uggest that the empirical slope factor approach is a welldefined and reasonable way to characterize the variation of emissions with load in this region, especially if some care is taken in subdividing the load to isolate meaningful trends. Unfortunately, the regression relationships are not as constrained for all pollutants and all regions. Figure 1 shows the se asonal and annual slope factors for CO 2 

## , and Table


## 2 for SO 2 

## , for each eGRID region. Table 

## 3 shows analogous results for NOx separated into the ozone and non-ozone seasons. Also shown are the corresponding R-squared metrics indicating the strength of the regres sion relationship underlying each slope factor. The R-squared values for CO 2 

## are uniformly quite high, indicating strong confidence in
these calculat ed slopes. For NOx the relationshi ps are also generally quite strong, but for
SO 2 

## the relationships range from very strong to extremely weak. This diagnostic should be taken into account in ap plying this approach.

In most cases, the differences between t he seasons are small, and some of these relationships require significant error bars. As a rule we apply seasonal slopes in performing the emissions analysis, but it is important to keep in mind that we do so with more confidence in some areas and for some pollutants than for others.

Summary results for the Slope Factor me thod for all eGRID subregions, including factors
calculated for CO 2 

## (tons/MWh), SO 2 

## (lbs/MWh), and NOx (lbs/MWh) are displayed in Figure 11

, Figure 12 , and Figure 13 , res pectively.

Table 1. Seasonal and annual emissions slope factors (left) and R-squared values (right) for CO 2 

### . Spring = March-May; Summer = July-August; Fall = September-November; Winter = January, February, and December.

Indirect Emissions Analysis ▪ 24

## SO 2 

## Slope Factor R-Squared

Region Spring Summer Fall Winter Annual Spring Summer Fall Winter Annual
AZNM 

## 0.29 0.19 0.34 0.07 0.06 0.06 0.10 0.11 0.00 0.00
CAMX 

## 0.31 0.03 0.33 0.16 0.18 0.05 0.01 0.10 0.03 0.07
ERCT 

## 1.49 0.51 0.80 1.52 0.86 0.32 0.16 0.26 0.27 0.25
FRCC 

## 5.68 5.08 5.30 5.70 5.19 0.94 0.92 0.93 0.79 0.91
MROE 

## 4.65 3.59 4.18 6.20 4.15 0.70 0.68 0.34 0.79 0.47
MROW 

## 6.59 4.36 5.06 5.22 4.95 0.82 0.82 0.75 0.74 0.80
NEWE 

## 3.04 3.26 1.87 4.33 2.98 0.38 0.78 0.27 0.63 0.40
NWPP 

## 2.15 1.07 1.25 0.97 1.85 0.74 0.28 0.09 0.05 0.47
NYCW 

## 1.51 1.01 1.35 2.07 0.99 0.47 0.70 0.77 0.83 0.51
NYLI 

## 2.94 1.59 3.01 3.05 2.07 0.33 0.54 0.75 0.55 0.50
NYUP 

## 6.11 5.98 4.93 7.00 6.14 0.84 0.88 0.76 0.79 0.85
RFCE 

## 12.99 6.39 8.58 9.73 8.58 0.87 0.79 0.78 0.85 0.81
RFCM 

## 7.40 4.77 6.85 9.68 5.87 0.72 0.78 0.79 0.72 0.72
RFCW 

## 13.66 10.38 12.02 14.73 11.00 0.76 0.90 0.92 0.93 0.84
RMPA 

## 0.06 1.00 2.03 0.82 1.11 0.00 0.19 0.49 0.15 0.19
SPNO 

## 5.91 7.02 7.84 7.26 6.59 0.45 0.85 0.90 0.79 0.79
SPSO 

## 1.52 1.03 1.52 2.13 1.43 0.21 0.43 0.53 0.41 0.38
SRMV 

## 1.41 1.53 1.53 1.91 1.52 0.35 0.62 0.32 0.36 0.47
SRMW 

## 6.69 6.58 6.64 9.02 7.08 0.72 0.83 0.89 0.70 0.79
SRSO 

## 8.75 5.26 7.28 9.14 7.57 0.81 0.64 0.67 0.75 0.77
SRTV 

## 6.56 6.36 7.19 9.23 7.07 0.45 0.70 0.72 0.72 0.66
SRVC 

## 10.84 7.29 9.76 10.24 8.60 0.90 0.89 0.90 0.92 0.89 Table 2. Seasonal and annual emissions slope factors (left) and R-squared values (right) for SO 2



### .

Indirect Emissions Analysis ▪ 25

## NOx Slope Factor R-Squared

Region
AZNM
CAMX
ERCT
FRCC
MORE
MROW
NEWE
NWPP
NYCW
NYLI
NYUP
RFCE
RFCM
RFCW
RMPA
SPNO
SPSO
SRMV
SRMW
SRSO
SRTV
SRVC Ozone

season



## 0.79 0.20 0.83 2.56 1.37 3.26 1.10 2.17 2.08 1.88 1.61 1.86 2.06 2.10 1.73 4.06 2.21 1.68 1.17 1.40 1.80
1.61 Non-Ozone Season 

## 0.92 0.62 0.76 2.63 1.93 4.43 1.34 2.37 1.70 2.24 2.15 3.50 3.29 5.18 2.61 5.56 2.58 1.51 3.55 2.26 4.38
3.60 Ozone

season 

## 0.54 0.21 0.88 0.95 0.77 0.90 0.83 0.88 0.89 0.82 0.93 0.96 0.87 0.95 0.55 0.88 0.94 0.71 0.50 0.80 0.77
0.96 Non-Ozone Season 

## 0.28 0.42 0.59 0.90 0.64 0.85 0.52 0.61 0.52 0.74 0.82 0.93 0.71 0.87 0.67 0.85 0.74 0.66 0.67 0.67 0.73
0.90 Table 3. Ozone season and Non-Ozone season emissions rate slopes for NOx. Ozone season = May through September.

Indirect Emissions Analysis ▪ 26Seasonal CO2Emissions Rate Slope0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 

### Spring (MAM) Summer (JJA) Fall (SON) Winter (DJF) AnnualAZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVCSeasonal SO2Emissions Rate Slope0 2 4 6 8 10 12 14 16AZNMCAMXERCTFRCCMROENYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVC 

### Spring (MAM) Summer (JJA) Fall (SON)
Winter (DJF)

AnnualMROWNEWENWPPNYCWNYLI 

### Figure 11. Regional slope factors for CO 2 

### (tons/MWh)

Figure 12. Seasonal and regional slope factors for SO 2 

### (lbs/MWh)

Indirect Emissions Analysis ▪ 270 1 2 3 4 5 6AZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVCSeasonal NOx Emissions Rate Slope 

### Ozone Season Not Ozone Season



### Figure 13. Regional slope factors for NOx (lbs/MWh)

Indirect Emissions Analysis ▪ 28

# CO 2 

# ( t )

− CO 2 

# ( t

− 1) EIER
t 

# = MW ( t )

− MW ( t

− 1) 

# D. Empirical Incremental Emissions Rate (EIER)

The previously discussed methods for es timating emissions factors rely upon the empirical dependence of total emissions on to tal output over a predefined period, which could be a year, a season, or any other time period. As a result, they produce emissions factors characteristic of the selected time per iod, but not indirect emissions factors at an hourly level. However, it is possible to take this analysis to its limit and assess the hourly slope factor by dividing the change in total emissions from one hour to the next by the change in total output for the same hour. This approach results in the empirical incremental emissions rate (EIER), which ma y be expressed mathematically as follows:

(4)

The implicit model underlying this approach is that un its whi ch are not marginal or displaceable in any hour will have constant out put and emissions, so that the metric will be sensitive only to units which change their output from hour to hour. This theoretically weights the metric by the change in output for these “marginal” plants. In contrast, the hourly average emission rate approach was we ighted by the total output of each unit, even for units whose output is insensitive to changes in load.

While the EIER has the attractive feature that it is a direct estimator of the dependence of change in emissions on change in total generation for each hour, there are certain considerations which must be made in its application. First, the estimator does not distinguish between units that change their out put because they are in a normal ramping cycle, for example in anticipation of daily c hanges in load from those that change their output in response to shorter term load fluctuations. This may be problematic as the former may be large units and exhibit large changes in load, so they are likely to dominate the metric during the hours when their output varies. Next is a numerical stability issue: if the change in total gener ation is small, the ca lculated EIER could be dominated by reporting and round-off errors in the emissions data. Thus we choose (somewhat arbitrarily) to eliminate hours from consideration if the change in total generation in the region from the previous hour is less than 100 MW. In this case we carry forward the EIER from the previous hour.

Finally, the EIER can be less than zero in some hours. This phenomenon occurs when total reported generation output in some regi on rises or falls during an hour by an amount greater than the 100 MW threshold, but tota l reported emissions changes in the other direction. This is not necessarily reporting er ror; it may well be that changes in dispatch, due to transmission constraints or some other consideration, have enough of an impact
on emission rates to offset the effect of changing total output. This instability, and the wide scatter in the hourly results for this approach, cast some doubt on the value of this approach for predicting hour-to-hour indire ct emissions benefits associated with renewable energy resources.

Indirect Emissions Analysis ▪ 29

# E. Load-Following Incrementa l Emissions Rate (LFIR)

The load-following incremental emissions rate is our name for a hybrid of the hourly average emissions rate and the empirical incremental emissions rate introduced by Stephen Connors in a number of studies of avoided emissions in the United States. 23,24


## The implicit model underlying the LFIR is that units which change their output in the same direction that load is changing during any dispatch period can be treated as load following. Thus the LFIR approach is designed to yield the average emission rate of the units that change output in the directi on of changing load during each hour under consideration. However, as Connors notes, 25 

## units which are held for spinning reserves or automatic generation control may actually be load following units, and would be missed by the first criterion. Thus the list of “load following” units must include any unit whose last change in output has been in the dire ction of the change in total load, even if subsequent hours have produced only small cha nges in output (less than 2.5% of their maximum.) Finally, Connors examines the ran ge of unit behavior and finds that any unit whose output is between 55% and 90% of its c apacity is likely to be providing spinning reserve service, and designates these units as load following as well. 26



## Following this logic, we find that a large proportion of generating units on the system most of the time would be designated as “l oad following” in the 2005 EPA database; in many regions, one-third to one-half of all re sources earn this designation more than 50% of the time. This brings the effect of this approach close to that of the HAER approach based on all units running, despite the significa ntly more complicated analysis required.At the same time, because the units (and hence emission rates) may change from hourto-hour even when total load is changing onl y slightly, this method suffers from even greater numerical instability than the EIER approach.

Connors describes the logic for selecting l oad following units as “excessively inclusive” 27 

## in designating too large a proportion of avail able units in each hour. To remedy this, he weights each unit’s contribution for each hour by its MW change in output from the previous hour. This method may serve to mi tigate some of the distortion from large base load resources that happen to make it in to the list of “load-following” units by coincidentally ramping along with load, bu t only for the hours in which they do not happen to be ramping. For the hours when they are, they will clearly dom inate the calculated average, as their changes in load will be mu ch greater than that for smaller, truly loadfollowing units.

In summary, we find that the LFIR approach suffe rs from many of the same distortions as both the EIER approach, and the HAER approach. The LFIR is numerically unstable, producing emissions rates which are highly and unrealistically variable throughout the year, while at the same time it gives undue weight to large units which are likely to have

23 Connors et al. (2005).

24 Berlinski, and Connors (2006).

25 Ibid., p.1-4.

26 Connors et al., 2005, Table 1.1, p. 1-6.

27 Ibid, p. 1-10.



### Indirect Emissions Analysis ▪ 30

## N ramping

, i 

## F

i 

## = N operating

, i



# ∑ ( F i 

## ER i

, t 

## ) FW-HAER t 

## = 

# ∑ F i

## larger changes in output from hour to hour but are not necessarily likely to be loadfollowin g . Although we have calculated and tabul ated the estimated indirect emissions benefits using the LFIR for comparison, we conclude that it does not produce a useful metric for estimating displaced emission factors on electricity grids.



# F. Flexibility-Weighted Hourly Average Emis sions Rate (FW-HAER)

The final method considered, FW-HAER is al so based on the premise that only certain units are likely to be displaced by variabl e output renewable energy resources. This approach attempts to identify load following units based on their operational behavior over the course of the entire study period, rather than during individual hours as with the LFIR method. The FW-HAER approach assigns to each unit a score based on how readily it appears to shift output; this score is then used as a weighting coefficient in calculating the indirect emissions coefficient for each hour.

While it is impossible to determine exactly which units would be load following at any given time given the complexities of dispatch in real electric systems, it is reasonably straightforward to determine which tend to behave in a more or less flexible manner. Large base load units are generally scored lo wer than smaller units. Units that appear to spend a lot of time ramping in response to load during the year, or that are often found partially dispatched, are ranked as highly displaceable whenever they are running.

To capture these dynamics, the FW-HAER appro ach tracks how often a plant is in the process of ramping relative to the number of hours it operates during the year, as shown in Equation 5:

(5)

Where F is the flexibility coefficient and i refers to an individual unit. A “ramping” hour is
defined a s an y hour in which the change in the unit’s output is greater than or equal to 2.5% of its maximum capacity; by dividi ng the number of ramping hours by the total
number of hours in which the unit operates, we obtain a unit flexibility coefficient which represents the proportion of its operating hour s which appear to be in “ramping” mode.

Once the flexibility coefficient of each unit on the system is calculated, the indirect emissions rate for each hour of operations is calculated as shown in Equation 6:

(6)

Where ER i,t 

## is the emission rate for unit i during hour t and the summations are over all units operating during that hour, and F i 

## is as in Equation 5. Again, this method does not weight by either unit size or by the change in output in any given hour. Only units which appear to be more flexible in their output over the year have incr eased influence on the displaceable emissions rate. This approach avoids giving undue weight to large, inflexible

Indirect Emissions Analysis ▪ 31

## units, and also exhibits substantially less vo latility than methods that are ba sed on hourly changes in output.

The FW-HAER approach has several advantages for estimating the emissions impacts of variable output resources. It does a much better job than any other approach in narrowing the list of candidate units that dom inate the indirect emissions calculation. Unlike the other approaches considered here, the FW-HAER avoids giving undue weight to large, inflexible units. FW -HAER is much more numerically stable than either the EIER approach or the LFIR approach, yielding a much smaller range of displaceable emissions rates through the year. Finally, because of this stability and more refined identification of the most flexible units in any region, it does the best job of differentiating regions by their
true indirect emissions rates. These feat ures are demonstrated in the figures below.

Figure 14 shows the relationships among flexib ility coefficient, generator size, and number of operating hours for (a) New England and (b) California. The size of each circle represents the observed number of operating ho urs in 2005. In both regions, we see that the very highest flexibility coefficients are attributed to small units which run infrequently; when running, these may also be the first units displaced. In California, it is apparent that the largest units are not displaceable; in New England, there does not appear to be much of a relationship between unit size and flexibility.

Figure 15 shows hourly generation profiles for thr ee sample plants in Florida, exhibiting typical profiles for low, medium, and high flexibility coefficients.

Indirect Emissions Analysis ▪ 32(a) (b)Figure 14.Flexibility coefficient vs. generator size in New England (NEWE)and California (CAMX). Each circle represents a single generating plant in the indicatedregion, and the size reflects total operating hours in 2005. Maximum size is 8760 hours.Indirect Emissions Analysis▪33200W)

100Generation (M



## (a) Jan 0

200Generation (MW) Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

100

07/28 0 07/31 08/03 08/06 08/09 08/12

1000W)

500Generation (M



## (b) Jan 0

1000Generation (MW) Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

500

07/28 0 07/31 08/03 08/06 08/09 08/12

100W)

50Generation (M



## (c) Jan 0

100Generation (MW) Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

50

07/28 0 07/31 08/03 08/06 08/09 08/12



### Figure 15. Hourly dispatch and flexibility coefficient (F) for three plants of differing capacities in the FRCC subregi on. Each lower graph is a summer detail of the 2005 profile shown above. (a) Polk power plant, F = 0.08; (b) Manatee gas plant, F = 0.55; (c) Debray peaker, F = 0.86.

Indirect Emissions Analysis ▪ 34

# G. Comparison of Indirect Emissions Rates

It is instructive to compare the implications for indirect emission rates from each estimation approach. Figure 16 displays these results in three panels, for CO 2 

## , SO 2 

## , and NOx, respectively. Each panel is organized vert ically by region, with indirect emission rates on the horizontal axis. For each region, the range (single standard deviation around the mean) of the hourly indirect emission rates may be read and compared for each of the calculation approaches considered. Met hods depicted are the empirical incremental emission rate (EIER, solid line), the hourly average emission rate (HAER, angle brackets), and the flexibility-weighted average emission rate (FW-HAER, shaded rectangle). 28 

## Also shown is the annual (CO 2 

## , SO 2 

## ) or seasonal (NOx) slope factor for each region, which is shown as a point as it is not an hourly value but a metric calculated
from the annual or seasonal data.

The graphs in Figure 16 illustrate the variations among regions, and within each region between the different calculation approaches. For example, the leftmost panel illustrates
that for CO 2 

## , there is general agreement among the different calculation approaches in all regions, although the slope factor tends to suggest a lower indirect emissions rate than other methods. The HAER and the FW-HAER appr oaches both yield values in a fairly tight range and are generally in agreement with each other, while the EIER approach

regions such as California and New England, which rely more heavily on gas, have indirect emissions rates of perhaps 0.6 to 0.8 tCO 2 

## per MWh.

Color intensity plots for each hourly method for each pollutant, and for each region are provided in Appendix D. These plots display the diurnal and seasonal variations in each displacement factor derived using each approach. The data are also available in
electronic format. yields a much broader range of hourly values re flectin g its numerical instability. It is also
clear from Figure 16 that certain regions, such as t he Midwest areas that rely heavily on coal generation, have indirect emissions rates of over 1.0 to 1.2 tCO 2 

## per MWh, while

28 We have not plotted the single-standard-deviation range based on the LFIR approach in Figures 14-16 because it is far

too broad for this scale, reflecting the numerical instability of this approach.



### Indirect Emissions Analysis ▪ 35(a) (b) (c)Figure 16.Comparison of the distribution (mean +/- one standard deviation) of indirect emissions coefficients for (a) CO2, (b) SO2and (c) NOx based on methodsdescribed in the text.Line:empirical incremental emission rate (EIER);Angle brackets:hourly average emission rate (HAER);Shaded rectangles:flexibility-weightedhourly average emission rate (FW-HAER). Also shown as round marks are the annual (for CO2and SO2) or seasonal (for NOx; dark = ozone season) emissions slopefactors for each region.Indirect Emissions Analysis▪36

# 5. Indirect Emissions Results

In this section we present our assessme nt of what the annual indirect emissions reductions of NOx, SO 2 

## , and CO 2 

## would have been in each of the 22 eGRID subregions in 2005, had an incremental renewable energy project (wind, landfill gas, or municipal solid waste) produced one gigawatt-hour (GWh) of energy over the course of the year. As discussed earlier, we find that landfill gas and municipal solid waste have identical base load energy profiles. Thus we have comb ined the analyses for these two resources into a single set of results.

All of the results presented here are bas ed on a one-to-one displacement of fossil generation, assuming emissions-free generation from the renewable resource. While beyond the current scope, it would improve pr ecision to incorporate line losses, as well as consideration of internal energy use at power plants. In general, renewable generation sites are remote from load centers, meaning that more than one MW of generation is required to serve one MW of load. Fossil generation sites may be near to or remote from load. However, all fossil units have some parasitic load—again, more than one MW must be produced in order to deliver one MW onto the system. The extent to which these cancel out in any given region, or the adjustments that should be made to accommodate them, is beyond the scope of this study.

The calculated indirect emissions result s are presented graphically as outlined in Table 4

. The results are also tabulated in Table 5 (wind projects) and Table 6 (landfill gas and municipal solid waste.)

Table 4

. Guide to the summary figures.
Figure Resource Type Pollutant Methods Regions

Figure 17 Wind CO 2 HAER, EIER, FWHAER All eGRID regions

Figure 18 Wind NOx HAER, EIER,

FW-HAER All eGRID regions

Figure 19 Wind SO 2 HAER, EIER,

FW-HAER All eGRID regions

Figure 20 MSW, LFG CO 2 HAER, seasonal and annual slope factors All eGRID regions

Figure 21 MSW, LFG NOx HAER, Seasonal Slope Factor All eGRID regions

Figure 22 MSW, LFG SO 2 HAER, Seasonal and Annual Slope Factors All eGRID regions


## The results show significant regional differences in the indirect emissions of all pollutants,
no matter what calculation method is used. T hey also show that the calculation method applied makes a large difference in some regions, while in other regions these differences are less significant. This lead s to the question of which method is most appropriate to use under what conditions.

In our judgment, the FW-HAER best captures the short-term operational behavior of power plants because it reflects observat ions of short-term changes in output for individual plants which respond flexibly to system perturbations. We recommend this approach in particular for shorter-ter m indirect emissions benefits for CO 2 

## , in response to

Indirect Emissions Analysis ▪ 37

## variable output, non-dispatchable resources such as wind power. The regional FW-HAER values presented in this paper can be app lied for this purpose, where the definition of short-term depends on the application but may be the first two or three years of a new
wind resource lifetime.

Over the longer term, and with greater penetration of wind resources, it is reasonable to assume that the syste m will adjust by disp lacing resources lower down in the dispatch order, ultimately restoring the utilization rate of load-following capacity to accommodate fluctuations in both load and wind resource output. In addition, NOx and SO 2 

## are both regulated under regional or national cap-and-tr ade regimes which make hourly indirect emissions analysis less relevant for these pollutants. Thus for longer-term indirect CO 2 

## analysis, or for NOx and SO 2 

## , we recommend using either HAER or the emissions slope factor. These approaches are based on the l onger-term response to perturbations over the full resource portfolio, as opposed to reflec ting primarily the marginal unit or units. These two approaches (HAER and slope facto r) give generally similar results. The primary difference between them is that the HAER size-weights base load fossil resources running at any given hour, giving the most weight to large units regardless of their flexibility in response to changes in output. Conversely, the slope factor method tends to deemphasize continually operational resources regardless of size. As we find it unlikely that the largest and lowest running cost resources would ever be displaced by renewable energy projects, we prefer the slope factor approach for those regions and pollutants for which the slope factor is well defined.

For landfill gas and municipal solid waste reso urces, the most likely type of generators to be displaced are intermediate units, which are more costly to run than base load but which do not have the flexibility of load-following resources. In this case, the best method would again be either HAER or the slope factor approach, and we would again give slight preference to the slope factor approach if the slope is well-defined.

Finally we note that most of our results, especially those for CO 2 

## , are relatively insensitive to hourly or seasonal variations in wind power generation. Replacing the calculated wind profiles with a flat power output profile (suc h as that characteristic of landfill gas or municipal solid waste) results in small changes in the calculated indirect emissions impact over the course of a year. Table 8 shows this result as the percent change in the total annual indirect emissions impact for wind resources if the hourly profile is replaced by a flat profile, typical of an LFG or MSW facility. The small changes suggest that indirect emission factors and wind output pr ofiles are generally poorly correlated, and in many cases little is gained from the effort to apply realistic profiles.

However, some of these differences are larger than others and probably important—for example, in some regions the impact of us ing a realistic wind-based shape on indirect NOx emissions is as high as 10%. This differ ent probably reflects seasonal differences in wind strength which are correlated with seasonal differences in the operation of NOx control equipment. Whether the effort required to establish realistic wind profiles pays sufficient dividends in this type of analysis to be justified probably depends on the specific application.

Indirect Emissions Analysis ▪ 38Table 5.Annual indirect emissions reduction rates associated with an incremental GWh of wind energy in each of the eGRID subregions for2005. Calculationmethods shown are:HAER:Hourly average emission rate;FW-HAER:Flexibility-weighted hourly average emission rate;EIER:Empirical incrementalemissions rate;LFIR:Load-following incremental rate;Slope factor:Indirect emission rate based on seasonal relationship between emissions and outputacross all hours in each season. For CO2and SO2the seasonal slope factors are based on conventional 3-month seasons; for NOx the slopes are defined forthe ozone and non-ozone regulation seasons.eGridRegion2005 Avoided CO2(tons/GWh)2005 Avoided NOx (lbs/GWh)2005 Avoided SO2(lbs/GWh)HAER EIER LFIR FW-HAERSlopeFactorHAER EIER LFIR FW-HAERSlopeFactorHAER EIER LFIR FW-HAERSlopeFactorAZNM844573 591 738 9432,768 1,335 1,260 1,641 9271,782 1,335 1,049 801 218CAMX670497 496 619 8921,238 259 287 411 5081,138 259 202 139 216ERCT845606 596 684 1,0401,058 863 877 785 7753,920 863 1,3001,0741,116FRCC845694 689 757 9942,645 2,811 2,757 1,675 2,5844,857 2,811 5,2543,5195,454MROE1,071803807 1,054 1,1142,981 1,902 2,346 4,380 1,8107,678 1,902 5,1479,3104,738MROW1,094885884 1,060 1,0794,456 3,407 3,388 4,461 4,1016,887 3,407 5,0757,8505,451NEWE728588 637 711 7801,177 1,004 944 913 1,2534,177 1,004 2,6912,7373,126NWPP981716 657 741 1,0763,267 2,400 2,101 2,310 2,5472,546 2,400(1,085)1,4931,350NYCW800616 657 717 7421,821 2,073 957 1,206 1,8461,452 2,073 1,3531,2511,545NYLI662530 621 687 7441,623 2,277 1,160 1,317 2,1584,204 2,277 2,8043,5132,773NYUP731664 698 875 8162,016 1,991 1,864 2,172 1,9676,277 1,991 6,3679,7376,015RFCE854724 731 911 8963,062 2,691 2,485 3,039 3,00913,5962,691 9,117 12,842 9,881RFCM1,059835773 1,072 9593,194 3,277 3,057 2,988 2,9129,363 3,277 6,4487,5117,420RFCW954885 854 948 9063,579 3,852 3,496 3,435 4,21912,2503,852 11,037 13,443 12,994RMPA980661 612 734 1,0562,963 1,675 1,011 1,805 2,3771,848 1,675 708 1,699 895SPNO1,066912906 1,040 1,0744,060 4,019 3,203 3,955 4,9666,949 4,019 6,9108,9536,909SPSO905618 629 735 1,0622,642 2,662 2,610 2,151 2,4663,912 2,662 1,4541,3311,531SRMV765659 653 686 1,0271,638 1,912 1,900 1,407 1,6162,693 1,912 2,1481,4041,611SRMW996905 900 1,001 9662,664 2,415 2,489 2,717 2,8396,739 2,415 6,3487,9417,332SRSO960818 826 914 9972,769 2,300 2,504 2,919 2,08911,3082,300 9,293 10,796 7,909SRTV1,005856882 987 9743,755 3,270 3,252 3,708 3,4999,004 3,270 9,463 12,374 7,469SRVC1,035955837 974 9333,349 3,177 2,685 3,405 3,02911,2893,177 9,302 12,135 9,902Indirect Emissions Analysis▪39Table 6. Annual indirect emissions reduction rates associated with anincremental GWh of landfill gas ofMSW generation in each of the eGRIDsubregions for 2005. Calculation methods shown are:HAER:Hourly averageemission rate;Slope factor:Indirect emission rate based on seasonalrelationship between emissions and output across all hours in each season.For CO2and SO2the seasonal slope factors are based on conventional 3­month seasons; for NOx the slopes are defined for the ozone and non-ozoneregulation seasons.Table 7. “Shape impact” for indirect emissions calculations for each pollutantin each eGrid region. The shape impact isthe change in calculated indirectemissions if an hourly wind profile is replaced with a constant generationprofile with the same total output.eGridRegion2005 AvoidedCO2(tons/GWh)2005 AvoidedNOx (lbs/GWh)2005 AvoidedSO2(lbs/GWh)HAERSlopeFactorHAERSlopeFactorHAERSlopeFactorAZNM8389432,7189011,710223CAMX6678931,2264761,129206ERCT8521,0421,0697753,981 1,080FRCC8599952,632 2,5824,806 5,442MROE1,0861,1172,996 1,7647,834 4,649MROW1,0981,0804,459 4,0226,891 5,307NEWE7327771,138 1,2324,140 3,119NWPP9821,0783,274 2,4722,553 1,362NYCW7957401,739 1,8871,339 1,482NYLI6607401,549 2,1104,103 2,645NYUP7368122,004 1,9176,306 6,003RFCE8628982,904 2,82913,7209,424RFCM1,0589583,103 2,7879,380 7,164RFCW9569053,303 3,90012,18712,688RMPA9801,0552,952 2,2781,830974SPNO1,0691,0754,088 5,0126,949 7,005SPSO9091,0622,653 2,4643,958 1,549SRMV7641,0251,660 1,6282,665 1,593SRMW9999702,458 2,4826,773 7,226SRSO9619972,654 1,97611,2697,601SRTV1,0049753,366 3,1758,927 7,327SRVC1,0329323,096 2,79011,1689,529eGridRegionCO2ShapeImpactNOx ShapeImpactSO2ShapeImpactHAERSlopeFactorHAERSlopeFactorHAERSlopeFactorAZNM-0.7% -0.1%-1.8% -2.8%-4.0% 2.2%CAMX-0.4% 0.0%-0.9% -6.3%-0.8% -4.6%ERCT0.9% 0.2%1.0% -0.1%1.5% -3.2%FRCC1.7% 0.1%-0.5% -0.1%-1.0% -0.2%MROE1.5% 0.3%0.5% -2.5%2.0% -1.9%MROW0.3% 0.1%0.1% -1.9%0.1% -2.6%NEWE0.5% -0.4%-3.3% -1.7%-0.9% -0.2%NWPP0.2% 0.2%0.2% -2.9%0.3% 0.8%NYCW-0.7% -0.2%-4.5% 2.2%-7.8% -4.1%NYLI-0.3% -0.5%-4.6% -2.2%-2.4% -4.6%NYUP0.6% -0.4%-0.6% -2.5%0.5% -0.2%RFCE0.9% 0.2%-5.1% -6.0%0.9% -4.6%RFCM-0.1% -0.1%-2.8% -4.3%0.2% -3.4%RFCW0.1% -0.2%-7.7% -7.6%-0.5% -2.4%RMPA0.0% -0.1%-0.4% -4.2%-1.0% 8.8%SPNO0.3% 0.1%0.7% 0.9%0.0% 1.4%SPSO0.4% 0.1%0.4% -0.1%1.2% 1.1%SRMV-0.1% -0.2%1.3% 0.7%-1.0% -1.1%SRMW0.3% 0.5%-7.7% -12.6%0.5% -1.5%SRSO0.1% 0.0%-4.2% -5.4%-0.3% -3.9%SRTV-0.1% 0.1%-10.4% -9.3%-0.9% -1.9%SRVC-0.3% 0.0%-7.6% -7.9%-1.1% -3.8%Indirect Emissions Analysis▪40

### Indirect Emissions Analysis ▪ 41Tons CO2per GWh

HAER EIER FW-HAERAZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVC 1,200

1,000

800

600

400

200

0Pounds NOx per GWhAZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVC 3,500 4,000 4,500 5,000

3,000

2,500

2,000

1,500

1,000

500

0

HAER EIER FW-HAER 

### Figure 17. Annual indirect CO 2 

### emissions impact associated with one incremental GWh of wind energy in each of the eGRID subregions for 2005.

Figure 18. Annual indirect NOx emissions impact associated with one incremental GWh of wind energy in each of the eGRI D s ubregions for 2005.

### Indirect Emissions Analysis ▪ 42Pounds SO2per GWh

HAER EIER FW-HAERAZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVC 16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0Tons CO2per GWhAZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVC 800 1,000 1,200

600

400

200

0

HAER Seasonal Slope Factor Annual Slope Factor 

### Figure 19. Annual indirect SO 2 

### emissions impact associated with one incremental GWh of wind energy in each of the eGRID subregions for 2005.

Figure 20. Annual indirect CO 2 

### emissions impact associated with one incremental GWh of landfill gas or municipal solid waste energy in each of the eGRID subregions for 2005.Pounds NOx per GWh 6,000

5,000

4,000

3,000

2,000

1,000

0

HAER Seasonal Slope FactorAZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVCPounds SO2per GWh 10,000

8,000

6,000

4,000

2,000

0

HAER Seasonal Slope Factor Annual Slope FactorAZNMCAMXERCTFRCCMROEMROWNEWENWPPNYCWNYLINYUPRFCERFCMRFCWRMPASPNOSPSOSRMVSRMWSRSOSRTVSRVC 12,000 14,000 16,000



### Figure 22. Annual indirect SO 2 

### emissions impact associated with an incremental GWh of landfill gas or municipal solid waste energy in each of the eGRID subregions for 2005.

Indirect Emissions Analysis ▪ 43 Figure 21. Annual indirect NOx emissions impact as so ciated with an incremental GWh of landfill gas or municipal solid waste energy in each of the eGRID subregions for 2005.

# 6. Conclusions

In this analysis we have produced and applied a number of approaches for calculating indirect emissions impacts and provided a general discussion of which might be more appropriate under different circumstances. We have calculated indirect emissions
benefits associated with CO 2 

## , NOx, and SO 2 

## for wind, landfill gas, and municipal solid waste electricity generating resources, in each of the 22 eGRID subregions of the
continental United States.

The indirect emissions benefit calculated for any kind of renewable energy resource depends on a number of questions:

• Where is the resource located?

• What is the pollutant of interest? Is t here a cap and trade system in place for this pollutant?

• Is the time period of interest historical, in the near future, or several years in the
future?

• Is the resource base load, dispatchable, or intermittent and nondispatchable?

• If intermittent and nondispatchable, what is the expected hourly and seasonal profile
of the resource?

• We have found that there are important r egional differences in indirect emissions which can be quantified in ways that will be useful for estimating the emissions benefits of renewable energy pr ojects in each region.

• Because of the wide range of applications reflected in the possible answers to the questions posed above, the best method to use in calculating indirect emissions is almost certainly application-specific. We believe that these methodologies provide useful guidance for many applications, and a better understanding of the issues for approximation of indirect em issions benefits than has previously been available.

• The results presented here could be validated by a modeling analysis, using a full electric system dispatch model to simulate actual indirect emissions under realistic unit commitment and dispatch conditions. The results could then be compared to those that would be derived using each of the methods applied here. This comparison would provide an empirical basis for establishing which approach is most consistent and accurate in predicting indi rect emissions in any particular region or under specific circumstances, at l east in the context of the model.

• In summary, we have found that:

• The indirect emissions benefits of r enewable energy for all pollutants vary significantly by region, and these differences can be quantified and applied in calculating indirect emissions impacts;

• Different calculation approaches are appr opriate for different types of resources and different applications;

Indirect Emissions Analysis ▪ 44

## • In general, hourly profiles of renewable energy resources make only a modest difference in quantifying annual indirect emissions benefits;

• More research is needed to establish which methods of calculating indirect emissions benefits most accurately reflect real-world dispatch over a range of timescales and system conditions.

• Finally, we note again that the geographic co verage of this analysis was by necessity coarse, in the interest of presenting resu lts for the entire cont inental United States, particularly with respect to wind resources. For the analysis to be applied to calculate or predict indirect emissions impacts for specific projects, the tables of hourly avoidable emissions data in Appendix D could be applied directly to site-specific operational and meteorological data. However, given the modest impact of differences in hourly profiles noted abov e, the annual results presented here are likely to be sufficient for most purposes.

Indirect Emissions Analysis ▪ 45

# Appendix A:

Identification and map of eGRID subregions

Indirect Emissions Analysis ▪ 46 Appendix A

### Figure A-1. Map of eGRID subregions. Subregions in Alaska and Hawaii are not considered in this analysis. Acronyms are defined below. Source of figure: https://www.energystar.gov/istar/pm pam/help/eGRID_Subregion_Map.htm

.



## AZNM Arizona and New Mexico RFCE Reliability First - East CAMX California RFCM Reliability First - Michigan ERCT ERCOT (Texas) RFCW Reliability First - West FRCC FRCC (Florida) RMPA Rocky Mountain region MROE Midwest Reliability - East SPNO Southwest Power Pool - North MROW Midwest Reliability - West SPSO Southwest Power Pool - South NEWE New England SRMV SERC - Mississippi Valley NWPP Northwest SRMW SERC - Midwest NYCW New York City/ Westchester SRSO SERC - South NYLI New York - Long Island SRTV SERC - Tennessee Valley NYUP New York - Upstate SRVC SERC - Virginia/Carolina

Indirect Emissions Analysis ▪ 47 Appendix A

# Appendix B:

Color intensity plots of synthetic wind power time series (hourly percent of capacity) for each eGRID subregion. Ea ch grid shows hour of the day on the vertical axis and days of the year on the horizontal axis. Color scale for all charts is shown below and at the bottom of the last page.

These data were obtained by scaling publicly available ground-level meteorological data to a typical tu rbine height and class 4 wind strength (Equation 1 in the text) and then transforming into wind power output using Equation 2.

The hourly data are available in electronic format upon request.

Indirect Emissions Analysis ▪ 48 Appendix B

### Indirect Emissions Analysis ▪ 49 Appendix B

### Indirect Emissions Analysis ▪ 50 Appendix B

### Indirect Emissions Analysis ▪ 51 Appendix B

### 6 8
13

5 11 7 15 14 9,10 19 16 12

17 21 22

2 

### 3 20

1

18

4 

# Appendix C:

Summary tables of raw and scaled wind speed and synthetic wind power time series. Monthly summaries are shown for each eGRID subregion.

The hourly data are available in electronic format upon request

The proxy wind turbine locations are shown below, overlaid on map of all U.S. WBAN stations.

NERC WBAN Index Subregion Number Location Latitude Longitude

1 AZNM 23055 GUADALUPE PASS, TX 31.5 -104.49
2 CAMX 23187 SANDBERG, CA 34.44 -118.43 3 ERCT 23042 LUBBOCK, TX 33.4 -101.49
4 FRCC 12844 WEST PALM BEACH, FL 26.41 -80.06
5 MROE 14898 GREEN BAY, WI 44.31 -88.07 6 MROW 24012 DICKINSON, ND 46.48 -102.48
7 NEWE 14739 BOSTON, MA 42.22 -71.01 8 NWPP 24150 LIVINGSTON, MT 45.42 -110.27 9 NYCW 94789 NEW YORK, NY 40.4 -73.48 10 NYLI 94789 NEW YORK, NY 40.4 -73.48
11 NYUP 4725 BINGHAMTON, NY 42.13 -75.59
12 RFCE 93730 ATLANTIC CITY, NJ 39.28 -74.28 13 RFCM 94860 GRAND RAPIDS, MI 42.53 -85.31
14 RFCW 14848 SOUTH BEND, IN 41.43 -86.2 15 RMPA 24018 CHEYENNE, WY 41.1 -104.49 16 SPNO 23065 GOODLAND, KS 39.22 -101.41
17 SPSO 23047 AMARILLO, TX 35.13 -101.43 18 SRMV 12916 NEW ORLEANS, LA 29.59 -90.15 19 SRMW 93822 SPRINGFIELD, IL 39.51 -89.41 20 SRSO 13874 ATLANTA, GA 33.38 -84.26
21 SRTV 13893 MEMPHIS, TN 35.04 -89.59
22 SRVC 93729 CAPE HATTERAS, NC 35.14 -75.37



### Indirect Emissions Analysis ▪ 52 Appendix BMonthly average interpolated wind speed (mph)from representative WBAN station in each NERC subregionJanFebMarAprMayJunJulyAugSeptOctNovDecAZNM19.6321.5222.0120.2116.4215.6915.9413.5614.9816.8219.5319.26CAMX14.0813.9913.3714.9114.4314.1612.2311.3811.3913.4315.1913.98ERCT10.6110.3613.2414.1512.4913.8710.749.2310.379.6911.2410.86FRCC10.498.748.9910.338.448.858.977.679.0710.6910.408.48MROE9.238.349.129.599.278.046.986.446.417.3610.587.67MROW11.7810.6613.2714.2312.7410.9510.8310.5610.5211.1413.1413.25NEWE12.3210.8612.2111.2911.539.7510.459.189.6912.6711.0211.49NWPP15.9514.1215.8512.5712.1511.639.3510.3012.5212.3518.2819.86NYCW12.3311.7113.0112.4010.9710.289.429.7510.3112.1013.1112.68NYLI12.3311.7113.0112.4010.9710.289.429.7510.3112.1013.1112.68NYUP8.117.238.378.358.006.727.007.227.367.5810.188.87RFCE8.158.7710.4010.728.308.176.856.266.949.589.368.85RFCM9.498.238.9010.238.687.907.367.328.327.9812.829.93RFCW9.238.409.2610.538.588.087.056.117.196.9412.319.88RMPA11.0311.3613.9913.0311.1411.019.869.809.7010.0714.7913.47SPNO10.209.7313.2614.2412.7612.9112.769.7412.2811.2312.6411.57SPSO11.8712.0413.0114.2911.7114.1012.5010.5513.0111.8613.0210.90SRMV8.948.798.399.937.476.127.085.458.338.578.309.02SRMW9.869.3711.3210.939.267.526.345.746.357.6012.369.58SRSO8.928.7210.088.967.717.665.827.037.618.488.809.33SRTV9.758.6010.399.357.387.037.106.647.046.819.438.46SRVC10.609.5111.2712.029.598.657.447.259.0410.189.699.52Indirect Emissions Analysis▪53Appendix CMonthly average interpolated representative wind speedscaled to Class 4 (16.25 mph average) for each NERC subregionJanFebMarAprMayJunJulyAugSeptOctNovDecAZNM38.6442.3643.3339.7832.3230.8931.3826.7029.4833.1238.4437.92CAMX28.0427.8626.6329.6828.7428.1924.3522.6722.6826.7430.2427.84ERCT21.4920.9826.8228.6525.2928.0921.7618.6921.0019.6222.7621.99FRCC13.7511.4511.7913.5511.0611.6111.7610.0611.9014.0213.6411.12MROE8.367.558.268.688.407.286.335.835.806.669.586.95MROW16.5014.9318.6019.9417.8515.3415.1814.8014.7415.6118.4018.57NEWE14.7913.0314.6613.5513.8311.7012.5411.0211.6315.2013.2313.80NWPP18.8616.7018.7414.8714.3713.7511.0612.1814.8114.6021.6123.48NYCW18.1417.2319.1418.2416.1315.1213.8614.3515.1617.8019.2918.66NYLI23.5122.3224.8123.6420.9119.6017.9518.5919.6423.0724.9924.18NYUP14.2412.6814.6914.6514.0411.8012.2812.6712.9113.3017.8715.56RFCE13.8514.9117.6818.2214.1013.8811.6410.6311.7916.2815.9115.05RFCM18.6916.2317.5420.1617.1115.5814.5114.4316.4015.7325.2719.57RFCW16.9615.4417.0219.3515.7614.8512.9611.2313.2112.7522.6318.16RMPA20.7921.4026.3624.5620.9920.7618.5818.4718.2818.9727.8725.39SPNO13.8813.2318.0319.3617.3617.5517.3613.2416.7015.2617.1915.73SPSO21.6121.9223.6826.0221.3125.6622.7619.2023.6721.5923.7019.83SRMV12.1811.9711.4313.5310.188.349.647.4311.3411.6711.3012.29SRMW20.2219.2223.2322.4219.0015.4213.0111.7813.0315.5925.3619.66SRSO12.6012.3114.2412.6510.8910.838.219.9210.7511.9812.4313.18SRTV13.7712.1414.6813.2110.429.9310.039.389.959.6213.3211.94SRVC15.1013.5516.0617.1313.6712.3310.6110.3312.8914.5113.8113.57Indirect Emissions Analysis▪54Appendix CMonthly average calculated wind power output from1.5 MW proxy turbinein each NERC subregion (dropout at 56 mph)JanFebMarAprMayJunJulyAugSeptOctNovDecAZNM883.49919.73910.01823.61879.141056.201102.16988.58990.65901.21849.34987.33CAMX971.55941.26865.851048.521101.111005.35924.58835.30803.95906.921037.77971.48ERCT680.73670.23929.82902.22899.961121.26769.51577.06692.23604.10675.85672.41FRCC286.16166.17190.93268.04137.00198.17193.28149.79199.06267.63256.74164.98MROE58.6223.1051.4164.8547.9328.7414.3611.5817.2516.27113.4721.29MROW439.92343.82573.12655.90518.31386.22329.69343.77336.63391.38559.79532.01NEWE338.89213.95272.13241.16286.37119.42156.53107.23138.02363.12251.55254.57NWPP572.73479.22574.52402.75358.02323.25185.49229.19368.41354.81786.22880.60NYCW546.35504.41589.69577.93406.64330.33276.92288.96336.61572.02633.20574.40NYLI822.24767.69848.34849.13677.99606.39531.39556.41649.84812.71851.24858.86NYUP335.36324.15368.73377.29294.03158.21171.78197.17225.59261.58491.64368.78RFCE356.54394.50540.16584.68341.90271.12169.22164.24237.97498.45501.62415.26RFCM638.43477.22605.68721.99549.50423.01363.23354.43452.75446.94890.43673.93RFCW506.60413.12549.15606.09457.05350.10281.30208.01304.92264.16780.63548.80RMPA644.99659.34895.00835.90664.56643.43566.71526.45522.83602.56813.64838.43SPNO290.58217.16515.08587.51515.45497.59490.45233.00451.34366.46414.62362.94SPSO752.59766.10815.82878.05735.241004.26859.04625.42912.29697.96848.84601.09SRMV202.22177.89160.16283.73114.1367.34106.2462.86207.91207.15209.46216.39SRMW677.87624.12797.53796.78637.27390.96274.41204.13305.80480.58898.76648.20SRSO253.66227.19310.51235.63127.92139.6091.5892.5197.90210.36218.47209.75SRTV258.66200.36335.47242.6497.7387.1794.7987.37108.31100.74289.18168.19SRVC353.12226.16407.72500.17228.59172.7890.2088.40237.99312.39281.43262.02Indirect Emissions Analysis▪55Appendix C

## Figure Title Page

D-1 Hourly Average CO 2 

## Emissions Rate 56

D-2 Empirical Incremental CO 2 

## Emissions Rate 59

D-3 Flexibility-Weighted Hourly Average CO 2 

## Emissions Rate 62

D-4 Load Following Incremental CO 2 

## Emissions Rate 65

D-5 Hourly Average NOx Emissions Rate 68

D-6 Empirical Incremental NOx Emissions Rate 71

D-7 Flexibility-Weighted Hourly Average NOx Emissions Rate 74

D-8 Load Following Incremental NOx Emissions Rate 77

D-9 Hourly Average SO 2 

## Emissions Rate 80

D-10 Empirical Incremental SO 2 

## Emissions Rate 83

D-11 Flexibility-Weighted Hourly Average SO 2 

## Emissions Rate 86

D-12 Load Following Incremental SO 2 

## Emissions Rate 89 

# Appendix D:

Color intensity maps of hourly indirect CO 2 

## , NOx and SO 2 

## emissions rates for each eGRID subregion, using several alternative calculation methodologies.

Each grid shows hour of the day on the vertical axis and days of the year on the horizontal axis. Color scale is shown at the bottom of each page.

The indirect emissions metrics shown are:

The data are available in electronic format upon request.

Indirect Emissions Analysis ▪ 56 Appendix DHourly Average CO 2 Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-1: Hourly Average CO 2 

## Emissions Rate(tons per MWh)

Indirect Emissions Analysis ▪ 57 Appendix DHourly Average CO 2 Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2



## D-1 (continued): Hourly Average CO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 58 Appendix DHourly Average CO 2 Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-1 (continued): Hourly Average CO 2 

## Emissions Rate (continued)

Indirect Emissions Analysis ▪ 59 Appendix DEmpirical Incremental CO 2 Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-2: Empirical Incremental CO 2 

## Emissions Rate (tons per MWh)

Indirect Emissions Analysis ▪ 60 Appendix DEmpirical Incremental CO 2 Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-2 (continued): Empirical Incremental CO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 61 Appendix DEmpirical Incremental CO 2 Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-2 (continued): Empirical Incremental CO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 62 Appendix DEmpirical Incremental CO 2 Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-2 (continued): Empirical Incremental CO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 63 Appendix DFlexibility Weighted Hourly Aver age CO 2 Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-3: Flexibility-Weighted Hourly Average CO 2 

## Emissions Rate (tons per MWh)

Indirect Emissions Analysis ▪ 64 Appendix DFlexibility Weighted Hourly Aver age CO 2 Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-3 (continued): Flexibility-Weighted Hourly Average CO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 65 Appendix DSPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-3 (continued): Flexibility Weighted Hourly Average CO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 66 Appendix DConnor Incremental CO 2 Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-4: Load-Following Incremental CO 2 

## Emissions Rate (tons per MWh)

Indirect Emissions Analysis ▪ 67 Appendix DConnor Incremental CO 2 Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-4 (continued): Load-Following Incremental CO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 68 Appendix DConnor Incremental CO 2 Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (tons/MWh)

0.6 0.7 0.8 0.9 1 1.1 1.2 

## D-4 (continued): Load-Following Incremental CO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 69 Appendix DHourly Average NO X Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-5: Hourly Average NOx Emissions Rate (pounds per MWh)

Indirect Emissions Analysis ▪ 70 Appendix DHourly Average NO X Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-5 (continued): Hourly Average NOx Emissions Rate

Indirect Emissions Analysis ▪ 71 Appendix DHourly Average NO X Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-5 (continued): Hourly Average NOx Emissions Rate

Indirect Emissions Analysis ▪ 72 Appendix DEmpirical Incremental NO X Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-6: Empirical Incremental NOx Em issions Rate (pounds per MWh)

Indirect Emissions Analysis ▪ 73 Appendix DEmpirical Incremental NO X Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-6 (continued): Empirical Incre mental NOx Emissions Rate

Indirect Emissions Analysis ▪ 74 Appendix DEmpirical Incremental NO X Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-6 (continued): Empirical Incre mental NOx Emissions Rate

Indirect Emissions Analysis ▪ 75 Appendix DFlexibility Weighted Hourly Aver age NO X Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-7: Flexibility-Weighted Hourly Average NOx Emissions Rate (pounds per MWh)

Indirect Emissions Analysis ▪ 76 Appendix DFlexibility Weighted Hourly Aver age NO X Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-7 (continued): Flexibility-Weighted Hourly Average NOx Emissions Rate

Indirect Emissions Analysis ▪ 77 Appendix DFlexibility Weighted Hourly Aver age NO X Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-7 (continued): Flexibility-Weighted Hourly Average NOx Emissions Rate

Indirect Emissions Analysis ▪ 78 Appendix DConnor Incremental NO X Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-8: Load-Following Incremental NOx Emissions Rate (pounds per MWh)

Indirect Emissions Analysis ▪ 79 Appendix DConnor Incremental NO X Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-8 (continued): Load-Following Incremental NOx Emissions Rate

Indirect Emissions Analysis ▪ 80 Appendix DConnor Incremental NO X Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 

## D-8 (continued): Load-Following Incremental NOx Emissions Rate

Indirect Emissions Analysis ▪ 81 Appendix DHourly Average SO X Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 5 10 15 

## D-9: Hourly Average SO 2 

## Emissions Rate (pounds per MWh)

Indirect Emissions Analysis ▪ 82 Appendix DHourly Average SO X Emissions Rate Emissions Rate (lbs/MWh) NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 5 10 15



## D-9 (continued): Hourly Average SO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 83 Appendix DHourly Average SO X Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 5 10 15



## D-9 (continued): Hourly Average SO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 84 Appendix DEmpirical Incremental SO X Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 5 10 15 

## D-10: Empirical Incremental SO 2 

## Emissions Rate (pounds per MWh)

Indirect Emissions Analysis ▪ 85 Appendix DEmpirical Incremental SO X Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 5 10 15 

## D-10 (continued): Empirical Incremental SO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 86 Appendix DEmpirical Incremental SO X Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 5 10 15 

## D-10 (continued): Empirical Incremental SO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 87 Appendix DFlexibility Weighted Hourly Aver age SO X Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

2 4 6 8 10 12 14 

## D-11: Flexibility-Weighted Hourly Average SO 2 

## Emissions Rate (pounds per MWh)

Indirect Emissions Analysis ▪ 88 Appendix DFlexibility Weighted Hourly Aver age SO X Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

2 4 6 8 10 12 14 

## D-11 (continued): Flexibility- Weighted Hourly Average SO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 89 Appendix DFlexibility Weighted Hourly Aver age SO X Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

2 4 6 8 10 12 14 

## D-11 (continued): Flexibility- Weighted Hourly Average SO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 90 Appendix DConnor Incremental SO X Emissions Rate AZNM

5 10 15 20

CAMX

5 10 15 20

ERCT

5 10 15 20

FRCC

5 10 15 20

MROE

5 10 15 20

MROW

5 10 15 20

NEWE

5 10 15 20

NWPP

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 5 10 15 

## D-12: Load-Following Incremental SO 2 

## Emissions Rate (pounds per MWh)

Indirect Emissions Analysis ▪ 91 Appendix DConnor Incremental SO X Emissions Rate NYCW

5 10 15 20

NYLI

5 10 15 20

NYUP

5 10 15 20

RFCE

5 10 15 20

RFCM

5 10 15 20

RFCW

5 10 15 20

RMPA

5 10 15 20

SPNO

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 5 10 15 

## D-12 (continued): Load-Following Incremental SO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 92 Appendix DConnor Incremental SO X Emissions Rate SPSO

5 10 15 20

SRMV

5 10 15 20

SRMW

5 10 15 20

SRSO

5 10 15 20

SRTV

5 10 15 20

SRVC

50 100 150 200 250 300 350 5 10 15 20

Emissions Rate (lbs/MWh)

0 5 10 15 

## D-12 (continued): Load-Following Incremental SO 2 

## Emissions Rate

Indirect Emissions Analysis ▪ 93 Appendix D