# Roadmap for Incorporating Energy Efficiency/Renewable Energy Policies and Programs into State and Tribal Implementation Plans

Appendix I: Methods for Quantifying Energy Efficiency and Renewable Energy Emission
ReductionsI

- 1 EPA

- 456/D

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Ju ly 201 2

Roadmap for Incorporating Energy Efficiency/Renewable Energy Policies and Programs into State and Tribal Implementation Plans

Appendix I: Methods for Quantifying Energy Efficiency and Renewable Energy Emission
Reductions

By:
U.S. Environmental Protection Agency Office of Air Quality Planning and Standards
Outreach and Information Division Research Triangle Park, North Carolina

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards
Outreach and Information Division Research Triangle Park, North CarolinaI

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### ACKNOWLEDGMENTS
We would like to acknowledge substantial contributions from members of an inter

- office EPA team that included the Office of Atmospheric Programs, the Office of Policy Analysis and

Review

, the Office of General Counsel and Regions 1 and 6. Th i s document also reflects

comments received from a number of stakeholders, including state a n d l o c a l air quality agencies.I

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### Contents

FIGURES

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TABLES

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SECTION I.1: PURPOSE

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SECTION I. 2: GETTING STARTED: IMPORTANT FACTORS

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Availability of Staff and Budgetary Resources

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Energy Data Availability and Relationship between Air and Energy Agencies

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Potential Magnitude of Emission Reductio ns

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Multi

- Jurisdictional Collaboration

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SECTION I.3: STEPS FOR QUANTIFYING EMISSIONS OF ENERGY EFFICIENCY AND RENEWABLE ENERGY POLICIES

AND PROGRAMS

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Step 1: Develop a Future Projected Baseline Emissions Inventory

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Step 2a: Estimate the Energy Savings of Energy Efficiency and Combined Heat and Power Policies and Programs I

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Energy Efficiency

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Combined Heat and Power

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Sources of Information for Energy Efficiency and Combined Heat and Power Policies and Programs

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Step 2b: Estimate the Energy Impacts of Demand Response Policies and Programs

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Step 2c: Estimate the Renewable Energy Generated from Policies

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Important Considerations

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Step 3: Understand How EE/RE Policies and Programs Impact Emissions in a Nonattainment Area

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Determining the Geographic Boundary of Emissions Analysis

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Steps to Analyze Energy Efficiency/Renewable Energy Policy and Program Impacts

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Step 4: Choose an Approach to Quantify Avoided or Displaced Electric Generating Unit Emissions

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Four Emission Quantification Approaches

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Suggested Quantification Approaches for Each State and Tribal Implementation Plan Pathway

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Important Considerations for Emission Quantification Approaches

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Ma naging Uncertainty

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References

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### FIGURES
Figure 1: Steps for Quantifying Emissions of Energy Efficiency and Renewable Energy Polici es and Programs

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- 8 Figure 2: Resources for Emissions Inventory Development

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- 9 Figure 3: Resource for Emissions Projections

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- 10 Figure 4: Emissions Quantification Approaches

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- 18 Figure 5: eGRID2010 Subregion Representational Map

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- 20 Figure 6: Emissions Quantification Using an eGRID Approach

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- 21 Figure 7: Sample Curv e for Relating Displacement to Capacity Factors

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- 22 Figure 8: Steps for Historical Hourly Emissions Rate Approach

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### TABLES
Table 1: How to Choose an Emissions Quantification Approach

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- 19 Table 2: Allocating Displaced Energy Using the Capacity Factor Approach

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- 24 Table 3: Suggested Emissions Quantification Approaches for Each Pathway

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### SECTION I .1: PURPOSE The purpose of this appendix is to help jurisdictions determine the best emissions quantification approach when accounting for energy efficiency/renewable energy (EE/RE) policies and programs in state implementation plans/tribal implementation plans ( SIP s /TIP s )

. This appendix starts with an overview of the important factors to consider prior to quantifying emission of EE/RE policies and programs. Then

, the emission quantification process is
described with links to resourc es, tools and key information to help state, tribal and local agencies select an appropriate emissions quantification approach

. After reviewing this appendix, jurisdictions should understand t he methods and options for quantifying EE/RE policies and programs in their SIP/TIP.



### S ECTION I

. 2 : GETTING STARTED: IMPORTANT FACTORS

I mportant factors to consider before quantifying emissions of EE/RE policies and programs
include :

Availability of staff

, budgetary resources , and e nergy data ; Energy data availability and a ir agency’s relationship with state, tribal and local energy experts ;
P otential magnitude of emission reductions for the electric generating unit ( EGU ) sector associated with EE/RE polic ies and programs ; and

W hether to engage in multi

- jurisdictional collaboration.

Understanding these factors upfront will aid the decision making process for how to account for

emis sion reductions in a SIP /TIP

. 

### Availability of Staff and Budgetary Resources Understanding the range and capability of the emission quantification approaches will help a jurisdiction judge the level of resources needed to address analytical questions on the emissions impact of EE/RE policies and programs. It is possible to estimate the emission impacts of EE/RE policies and programs with low staff resources. Basic approaches are easy to

em ploy and are often useful in justifying more effort if there is a significant emission reduction potential. Methods that are more sophisticated could mean increased staff time, air

- energy trainings and monetary resources devoted to paying for fees associa ted with proprietary energy
models. Reviewing the existing tools and resources that complement each emission quantification approach described in this appendix is a good first step to determining the level of effort needed to quantify the emission impacts of EE/RE policies and programs



### Energy Data Availability and Relationship b etween Air and Energy Agencies A significant piece of information necessary for quantifying the emissions impact of EE/RE policies and programs is data on energy impacts from state energy office s , utility commission s , or other data source s

. The strength of relationship between a state’s air agency and its energy agencies may influence whether the appropriate type of data is available for a SIP/TIP emissions analysis. For example, s ome approaches will require hourly energy impacts information

, whileI

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others only need annual or season energy impact information. It is possible that

, even in cases where relationships between air and energy agencies are strong

, the type s of data needed f or specific emissions analysis may not be available at the level of detail compatible with the desired emissions quantification approach. In these cases, air agencies may need to rearrange the data to use it with a specific approach or choose the emission s quantification approach that is compatible with the available energy impacts information. Reviewing th is appendix will help determine what energy impacts information will be needed depending upon the state, tribal or local air agency’s policy objectives and analytical questions

. 

### Potential Magnitude of Emission Reductions One of the most important objectives in understanding the potential magnitude of emission reductions of EE/RE policies and programs is comparing the potential emission reductions for a particular pollutant resulting from an EE/RE policy or program to existing emissions for that pollutant :

1) F rom the EGU sector in the state, 2) From all sources in that state, and 3) I n a nonattainment area for which the SIP/TIP is intended.

This comparative analysis will help illuminate how large an impact the EE/RE policies and

program may potentially have in an area relative to other contributors. Generally, state, tribal
and local air agencies inventory emissions on an annual basis to review the emission reduction potential of different control strategies

. However, EE/RE policies affect EGUs that operate margin ally at shorter time scales. Therefore, a typical annual emissions profile of the EGU
sector will not be completely indicative of the emission re duction potential of EE/RE policies and programs within a jurisdiction. Some emission s quantification approaches provided in this appendix

, such as the capacity factor and hourly emissions approaches, can be used at the same time as the emissions inventory development process to help determine the emission reduction potential of EE/RE policies and programs over shorter time periods without expending large resources.



### Multi

- Jurisdictional Collaboration

Since powe r plants are interconnected through the larger electric power system , to fully
understand the emissions impact of an EE/RE policy

, it i s often important to consider collaborating with neighboring states to develop joint EGU analyses . Understanding th e goa ls
and intentions of neighboring state, tribal and local air agencies could be an important factor in deciding the most appropriate emissions quantification approach. For example, a highly resource intensive approach for one state may make sense if multip le states are contributing resources. Including collective EE/RE policy goals may also increase the emissions reduction potential within the region. In many cases, a collective quantification effort would enhance efficiencies in costs and would better capture cross state benefits in the air quality planning

process.I

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### S ECTION I

. 3 : STEPS FOR QUANTIFYING EMISSIONS OF ENERGY
EFFICIENCY AND RENEWABLE ENERGY POLICIES AND PR OGRAMS

There are four general steps state, tribal and local agencies should cons ider when quantifying emission reductions of new EE/RE policies and programs (see Figure 1). ( If all of the EE/RE policies and programs sought for inclusion in the SIP /TIP pro cess are included in baseline emission projections, then steps 2 ,3 and 4 are not needed.)



### Step 1: Develop a Future Projected Baseline Emissions Inventory As a part of the SIP/TIP process, a jurisdiction must prepare an emissions inventory that documents the emissions in a selected base year for the EGUs within the a rea of analysis ( e .g.,

s tate

- level or multi

- state level emission inventories)

. The information necessary to develop an Figure 1 : Steps for Quantifying Emissions of Energy Efficiency and Renewable Energy Policies and ProgramsI

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emission inventory for the EGU sector includes electricity generation data , and emission factors to convert estimates of energy use into emissions or measured report s from continuous emissions monitoring (CEMS) equipment. For both SIPs /TIPs and developing a future projected baseline emission inventory , a bottom

- up emissions inventory is the most appropriate and involves collecting activity data, emissions profiles an d pollution control device information for
each EGU in the state, tribal area, locality, or region

. Developing a future projected baseline emissions inventory provides a comprehensive assessment of emissions and details regarding spatial and temporal attributes that are requi red for air quality modeling. (See Figure 2 for resources on emissions inventory development.)

Emissions relevant to SIPs/TIPs include all six criteria air pollutants, including precursors to

ozone (n itrogen oxides (NO x ) and volatile organic compounds (VOC) ) , as well as ca rbon
monoxide (CO) , sulfur dioxide (SO 2 ), lead (Pb)

, and coarse and fine particulate matter (PM)

.

Figure 2 : Resources for Emissions Inventory Development

A baseline emissions projection forecasts what emissions will occur in the future, in the absence of additional policies. This baseline projection is a reference case that includ es “on the books” EE/RE policies or programs , against which the impacts of new policies and programs are measured

. When state, tribal or local agencies take into account certain EE/RE policies and programs in developing a baseline emissions projection

, then such activities are considered already included in the SIP /TIP , as part of the SIP /TIP baseline pathway, and addition al emission reductions cannot be granted through an other SIP /TIP pathway

. Only those policies and programs not included in the future baseline may be included in the subsequent steps. In other words, if a state, tribal or local agency incorporates the EE/RE policies and programs in the SIP baseline emissions projections

, then any emission reductions from those EE/RE policies and Emissions Collection and Monitoring Plan

System (ECMPS)

• The EPA system for reporting of emissions data, monitoring plans, and certification data.

• It replaces the Emission Tracking System that previously served as a repository of
SO 2 , NO
x , and cabon dioxide (CO 2 ) emissions data from the utility industry.

• The EPA collects data annually in five

minute, hourly, daily, monthly and annual intervals from continuous emissions monitors at all large U.S. power plants.

• Available at: http://camddataandmaps.epa.gov/gdm/ Emissions & Generation Resource Integrated Database ( eGRID )

• This free, publicly available software from EPA has data on annual SO 2 , NO
x , and CO 2 , and mercury (Hg) emissions for most power plants in the United States.

• It also provides annual average non

base load emission rates, which may better characterize the emissions of load

- following resources.

• Available at: http://www.epa.gov/egridI

- 10 EPA Emissions Inventory Improvement Program Technical Report

Series, Volume X: Emissions Projections

.

• This is guidance that provides information and procedures to state and local agencies for projecting future air pollution emissions for the point, area, and mobile source sectors.

• It describes data sources and tools states can use for emissions projections.

• Available at: http://www.epa.gov/ttn/chief/eiip/techreport/volume10/x01.pdf programs are already counted and cannot be “double counted” in other parts of the SIP/TIP. ( Refer to Figure 3 for resources for emissions projections and Appendix E for more information
on the SIP /TIP baseli ne emission projections pathway

. )

State, tribal and local agencie s can project future emissions based on historic trends and expectations about the impact of numerous factors, including projections of population growth and migration, economic growth, fuel availability and prices, and technological progress. Some of these factors will rely on
data about electricity imports and exports, power plant construction or retirement, transmission
constraints and environmental regulations.

The underlying assumptions for the
baseline emissions projection include an electricity demand forecast

. The forecast documents the historical, current

, and projected pattern of energy demand in the

state or region

, as well as how future generation projections will meet forecasted demand

. 

### Step 2 a : Es timate the E nergy S avings of Energy Efficiency and Combined Heat
and Power Polic i es and Programs

Energy Efficiency The purpose of this step is to determine the energy savings from the specific EE policy/program, which can then be used to estimate any associated emission reductions from the power sector. 1
Energy savings refer to the expected reduction in energy demand that are the result of a specific EE policy and/or program. For SIP/TIP purposes, e nergy savings can be a r eduction in the base year energy demand, and /or a reduction in the projection of future energy demand

. The impacts of EE policies or programs are typically measured in kilowatt

- hours (kwh) or gigawatt

- hours (GWhs) and are generally reported on an annual basis, but some EE programs may be available in smaller time intervals, such as a s easonal or hourly scale.

A general approach f or determining the amount of energy saved for EE policies and programs, although each EE policy and/or program will have individual factors to be considered . T he general approach is:

1 Estimated savings from EE policies and programs can be calculated in many different ways. State energy regulators typically have methods for estimating energy savings in your jurisdiction. Methods may vary in precision and rigor. E nergy regulators can provide the estimates of the energy savings in your jurisdiction from the EE policies and programs they oversee. Figure 3 : Resource for Emissions ProjectionsI

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Step A : D etermine the baseline energy us age subject to the EE policy and/or program. 2

S tep B : D etermine the projected energy use after implementation of the EE policy and/or program.
Step C : S ubtract the result of Step A from the result from Step B. The result yields the projected energy savings due to the EE policy and/or program.

For later verification purposes, state, tribal and local agencies may need to collect and compare
the evaluated (e.g., ex

- post) 3 estimate of energy savings to initial forecast impacts. This added step can result in more accurate estimates of energy savings and associated emission
reductions. The appropriate level of energy savings verification depends primarily on the SIP pathway selected. Other factors to co nsider are the magnitude of projected energy impact and the nature and type of efficiency program under consideration (for more information, consult the appendices on each SIP pathway)

.

Combined Heat and Power Combined heat and power (CHP) is an efficie nt method of providing energy services (electricity and thermal energy

, including cooling) to the end user. Instead of purchasing electricity from the grid and simultaneously burning fuel in an on

- site boiler or furnace to produce needed thermal energy, a n industrial or commercial user can use a CHP plant on site to provide both services in one energy efficient step. CHP’s inherent efficiency and the elimination of demand for electricity from the power system with associated transmission and distribution losses can provide significant energy savings and lower emissions that can be accounted for in a SIP/TIP

. Calculating the net e nergy savings from CHP requires estimating the expected reduction in the
amount of electricity gen e rated by an existing utility system as a result of the specific CHP policy and/or program and subtracting from that the incremental fuel used by CHP systems over and above the existing boilers and/or furnaces that were replaced

. The resultant savings

can reduce current energy demand, future demand, or both. T he purpose of this step is to determine the energy saving impacts of a specific CHP policy/program.

For determining the amount of energy saved for CHP policies and programs, although each policy and/or program will have individual factors to be taken into account, the general approach is as follows:

Step A : Determine the baseline forecast of energy use for the activity subject to the CHP policy and/or program. Step B : Determine the projected energy use after implementation of the CHP policy and/or program. The projected energy use must reflect :

o T he reduction in energy use at the utility power plant due to the reduction in demand caused by the generati on of power on

- site

.

2 EPA ( 2010c ), Chapter 2.

3 Evaluations of ratepayer funded energy

- efficiency programs

– sometimes referred to as evaluation, measurement, and verification (EM&V)

– provide retrospective estimates of energy savings. They are typically conducted on an annual basis by state Public Ut ility Commissions (PUCs). It is important to note that energy

efficiency forecast data

– not ex

- post EM&V data

– are used to calculate avoided

- emissions for SIP purposes.I

- 12 Sources of Information on Energy Impacts of EE/CHP Policies

• State Public Utility Commissions

• State or Local Energy Offices

• Energy Information Administration

• Electric Grid Operators

• Regional Transmission Organizations

• Independent Service Organizations

• Utilities o T he incremental increase in fuel use at the industrial or commercial facilities that the CHP system uses over and above the boilers and/or furnaces that CHP replaced. Step C : S ubtract the result of Step A from the result from Step B. The result yields the projected energy savings due to the CHP policy and/or program.

For later verification purposes, state, tribal and local agencies may need to collect and compare
the evaluated (e.g., ex

- post) estimate of energy and fuel savings to initial forecast impacts. This added step can result in more accurate estimates of energy and fuel savings as well as
associated emission reductions. The appropriate level of verification depends primarily on the SIP pathway selected. Other factors to consider are the magnitude of projected energy impact and the nature and type of CHP program under consideration. ( See the SIP Pathway

a ppendices for more information.)

Operational and p erformance data for CHP technologies are also important factors in this analysis. This information is available from the EPA Combined Heat and Power Partnership, 4 National Renewable Energy Laboratory (NREL), the D epar tment of E nergy ’s (DOE) eight Regional Clean Energy Application Centers 5 a nd universities and other energy associations that promote or conduct research on the applications of CHP

.

Sources o f I nformation for E nergy E fficiency and C ombined Heat and Power P olicies
and P rograms Energy experts within a jurisdiction can help state, tribal and local air agencies understand
current and f uture policy opportunities

, as well as obtain estimates of the kilowatt

- hour (KWh) impacts (projected and historical)
from the EE/CHP policy/ program of

interest. The EPA recommends starting with the p ublic u t ility

c ommission staff and e nergy o ffices. These agencies are typically responsible for the evaluation,
measurement and verification (EM&V) of EE programs , and the tracking of RE generation. If jurisdictions need further information, the Energy Information
Administration (EIA) and electric grid operators can also be good sources. For example, a large utility that controls the dispatch of resources within th e area of analysis could be an electric grid operator and could provide electricity d emand and supply forecasts . If a state, tribal or local jurisdiction is located within a regional transmission organization (RTO) or an independent

s ystem operator (ISO)

, these entities can also be helpful resources. These organizations can help develop the energy impacts information or serve as a re source for assistance in developing

4 For more information, go to: http://www.epa.gov/chp/

. 5 For more information, go to: http://www1.eere.energy.gov/industry/distributedenergy/racs.html

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the energy savings or generation e stimates for particular EE/CHP policies or programs. Refer to Appendix B, for more information on the roles and responsibi lities of these organizations.

When gathering information from energy organizations in a state or region, air agencies will want to ask specific questions about the policy or program design characteristics.

. 6 The questions below are intended to help air agencies ask questions up front to avoid confusion and
ensure the energy savings information is provided in the proper SIP/TIP context.

Program period: What year does the policy/program producing the energy savings start and e nd? Anticipated compliance or penetration rate: How many utilities will achieve the target
or standard called for? How many consumers will invest in new EE/CHP equipment
based on the initiat ive? How will this rate change over the time period? Compliance assurances: Are there compliance penalties , monetary incentives or other

assurances included in the policy or program design? Annual degradation factor: How quickly will the performance of the EE measure installed become less efficient?
Transmission and distribution loss (T&D) : Is there an increase or decrease in T&D losses that would require adjustment of the energy savings estimate? Past Performance: W hat is the success rate of existing or past EE/RE policies and programs? Evaluation, Measurement and Verification (EM&V) : Which programs have EM&V procedures in place, and how will energy savings be measured and verified over time?



### Step 2b: Estimate the Energy I mpacts of D emand Response P olicies and Programs “Demand response” is a broad term encompassing a range of program s designed to reduce electricity demand during peak periods of electric ity use. Demand response initiatives range from pr ograms that provide customer incentives for voluntary or mandatory load curtailment based on contractual arrangements, to dynamic pricing structures that charge higher rates for energy consumed during peak periods.

Demand response policies and p rograms can effective ly reduce emissions during “h igh e lectric
d emand d ay s

. ” 7 For example, hot summer days are conducive to ground

- level o zone formation, and air conditioning loads on such days are often major contributors to electricity demand spikes. At the same time, some EGUs called “peaking units” only operate during periods of peak demand when the electric grid requires maximum generating capacity, and could be high

- emitting sources. Peaking units may lack NO x controls because they have low

6 EPA (2010c), p. 42

. 7 On the hot, hazy days of sum mer, higher demand for electricity can result in a dramatic increase in ozone

forming air pollution. These are called “high electric demand days” or HEDD.I

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emissions on a seasonal basis, even if hourly emissions are high during periods when they are in

use.

Since demand response programs target peak demand hours, air agencies will want to collect hourly energy savings data , during peak hours, to translate the energy savings from these programs into emission reductions. Demand response programs may not always provide emission reduction benefits. For instance, some demand response programs re lieve demand
from the electric grid by replac ing tha t generation with back

- up generators that emit emissions. (e.g., diesel generators) To properly account for the emission impact of demand response

programs in a SIP/TIP , emission calculations should account for the net emissions (e.g.,
emission reductions from the electricity grid and any emission increases from back

- up generation)

.

To avoid a net emissions increase th at can result from the use of emissions

- intensive sources of backup power generation

, state, tribal and local agencies should c ombin e demand response efforts that focus on passive approaches

, such as cycling down equipment to curtail electricity
demand

, with efforts to promote clean backup power that can be an effective strategy for achieving this objective. Some program administrators have addressed this issue by including requirements for the types of load reductions that are eligible for demand response incentives.



### Step 2 c : Estimate the Renewable Energy Generated from Policies This step determines how much energy would be displaced by the RE policy and/or program

, including less polluting sources of new energy, such as cogeneration and fuel cells. In general, for renewable sources, state, tribal and local agencies will need data showing the total amount of energy provided to the gr id by the RE source. Keep in mind, for later verification purposes,
data on the amount of renewable energy generated may need to be collected and compared to original estimates.

Operational and p erformance data for renewable technologies are also importa nt factors in this analysis. This information is available from National Renewable Energy Lab (NREL) , as well as
universities and other RE associations that promote or conduct research on the applications of

RE

. In addition, generation

- related data and RE potential information can be obtained from many sources, including:

State energy offices Utility Int egrated Resource Planning filings Public utility commissions

ISOs North American Electric Reliability Corporation (NERC)
EPA’s Emissions & Generation R esource Integrated Database (eGRID) 8

DOE’s EIA
DOE’s NREL

8 For more information, go to: www.epa.gov/egrid

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Important Considerations

T he jurisdiction’ s analysis of RE policy and program impacts needs to account for how various RE resources generate energy at different times of the day and year

. For example, w ind power facilities may be unable to provide energy during high ozone episodes due to stagnation conditions, as compared to solar power facilities. Land

- based w ind energy facilities have to be

s ited in locations where winds speeds are suff iciently strong to produce power on as many days as possible, which can be in locations outside of the urban corridor where air quality tends to be the poorest. Nevertheless, these remote RE facilities may affect emissions levels at fossil

fired EGUs that are positioned upwind of jurisdictions with poor air quality. Any air

- quality analysis of RE policy impacts should account for these emissions impacts. O ff

- shore wind generation

– which may be close r to coastal load centers and areas of poor air quality

– should likewise be analyzed to determine how generation and emissions from surrounding EGUs are affected, including the magnitude of such impacts.



### Step 3 : Understand H ow EE/RE P olicies and P rograms I mpact E missions in a
N onattainment A rea Air quality planners need to determine the d isplaced emissions attribut able to each applicable
EGU in order to understand how those emission reductions will improve the air quality in the

nonattainment area. 9 Emission reductions cannot be assigned to the nonattainme nt area based on where the EE/RE policy is implemented. Rather, a jurisdiction should assign emission
reductions based on where the displacement of electrical generation will likely occur . All emission quantification approaches described below, except fo r the eGRID non

- baseload emissions rate approach, can allocate emission reductions to an individual EGU or power plant. To determine if an EE/RE policy affects a nonattainment area, air agencies should (1) estimate
which EGUs a nd EE/RE policy or program w ill likely affect generation and (2) use the emission
rates at those EGUs.

For example, if the nonattainment area imports a significant amount of electricity from locations outside and downwind of the area, reduced demand from EE could result in less
el ectricity being imported, rather than reduced production (and consequently reduced emissions) within the nonattainment area, or in areas affecting its air quality. Conversely, if the energy savings reduce emissions at upwind sources, then the measure may p roduce some air quality benefits to the area. (For more details, see the section below on determining the geographical area where emissions occur

. )

After state, tribal and local agencies quantify the emission reductions of EE/RE policies/program, as desc ribed in step four of this appendix, t he state should use the appropriate air quality model to evaluate the extent to which reductions will improve air quality

9 The current policy with respect to taking credit for emission reductions outside nonattainment areas for purposes of Reasonable Further Progress in ozone SIPs is as follows: RFP credit can be taken for VOC and NO
x emission
reductions within 100 kilometer s (km) and 200 km, respectively, outside the nonattainment area under certain circumstances. This policy is currently under reconsideration. See “ Reasonable Further Progress Requirements for
the 1997 8

- Hour Ozone National Ambient Air Quality,” 75 Federa l Register 80420

- 80425, 80421, http://www.gpo.gov/fdsys/pkg/FR

- 2010

- 12

- 22/pdf/2010

- 32139.pdf

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in the nonattainment area from the selected EE/RE policy a nd/or program. The EPA has
separate modeling guidance documents to help state, tribal and local agencies choose the best air quality model for SIPs/TIPs. 10

Determining the Geographic Boundary of Emissions Analysis Determining the appropriate geographical boundaries for an emissions quantific ation analysis requires an understanding of the electric power grid relative to the nonattainment area and EE/RE policy of interest. (Refer to Appendix B for more information on the electric grid.) A state, tribal or local agency will need to choose the most appropriate boundary for their analysis based on key factors:

Whether adjacent states are analyzing EE/RE policies within the same NERC region 11

The location of the area in which the EE and/or RE policy is being implemented
Whether there are any transmission constraints where the EE/RE policy is being implemented How energy is transferred within the state and across state boundaries

Determining the location of the emission reduction s that occur at fossil fuel fired generation

units is challenging because electricity from numerous generators is fed into an electrical grid from which many different consumers at various locations will draw power. There typically is no direct connection between a specific facility generating electricity and the end user of that electricity. Understanding how the electric grid operates within a jurisdiction is the first important step in making an approximation a s to which units would be affected by a certain EE/RE policy and program.

In areas of the c ountry where several states in close proximity to one another implement EE and/or RE policies and programs, it may be advantageous for these states to work together in conjunction with their electric grid operation, ISO/RTO and EPA r egional office to ident ify the overall impact of the EE/ RE policy and programs on the electrical grid in the future. Ideally, such a process will yield a technically valid solution that attributes the emission reductions from
decreased reliance on fossil fuel fired EGUs in an e quitable manner between the states, and also ensures that double counting of emission reductions does not occur.

The EPA understands that conducting this type of analysis may be beyond the means of the jurisdictions that implement these EE/ RE polici es a nd programs. Accordingly, EPA encourages

any state, tribal and local agencies that need assistance with this to contact the relevant EPA regional office for assistance.

Steps to Analyze Energy Efficiency/Renewable Ener gy Policy and Program Impacts Determining the location of the fossil fuel fired units that can operate less as EE or RE becomes available can be a complex task, particularly when the efficiency or renewable resources are
located outside the nonattainment area that seeks to use the emis sions reduction from that

10 For more information , go to: http://www.epa.gov/ttn/scram/aqmindex.htm

. 11 For mo re information, go to: http://www.eia.gov/cneaf/electricity/page/prim2/figure7.html

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reduced generation for SIP/TIP purposes. The following three steps can be used to analyze EE/RE policies and programs:

Step 1: Review historical analysis to d etermine the location of the fossil fuel fired EGUs
that could reduce their output as EE or RE resources were made available

. This information should already exist at the balancing authority or reliability organization such as an, ISO or RTO that oversees the electrical grid for the area or the utility that is complying wit h the EE or RE policy. Step 2: Understand how the grid has responded in the past as efficiency or renewable
resources have come on

- line in order to develop planning assumptions for how the grid will respond in the future. Step 3: Obtain and review the results from existing dispatch modeling conducted by the grid operator of the balancing authority, ISO or RTO. The grid operators have the most pressing need to accurately determine the impact that EE or RE resources will have on the future operation of t he electrical network.

The next section will describe four different emission quantification approaches that state

, tribal and local agencies can consider.



### Step 4: Choose an Approach to Q uantify Avoided or Displaced Electric Generating Unit Emissions State, tribal and local agencies will need to choose the most appropriate emission quantification approach based on the agency ’s policy objectives. Each approac h answer s different analytical questions , has varying levels of rigor, assumptions, resource re quirements,
data needs

, and varied temporal and spatial scales of emission outputs. They all are intended to quantify the avoided or displaced emissions from fossil fuel generation as a result of EE/RE policy/program implementation. 12

The level of sophistication of each emission quantification approach is inversely relate d to the degree of uncertainty inherent in each approach. Basic approaches apply simple assumptions and tend to have a higher degree of imprecision compared to more sophisticated a pproaches. Figure 4 shows how the level of sophistication of those approaches increases as you go from straightforward emissions calculations to complex modeling.

B asic approaches , such as eGRID emission rates, are generally more appropriate for preliminary screening analys e s or to be used for analyzing the incorporating of emission reductions in specific SIP/TIP pathways

, such as the weight of evidence (WOE) or e merging /voluntary

measures SIP pathway (Refer to Appendix H for the WOE pathway and Appendix G for the

e merging /voluntary measures pathway)

. More sophisticated approaches , such as energy modeling can simulate how the system of EGUs will change generation and emissions given specific assumed conditions and capture multiple assumptions for multiple factors such as :

12 Lifecycle emissions are not addressed in this appendix and do not apply to quantifying emissions for SIP/TIP

purposes.I

- 18

electricity imports, exports, fuel prices, unit availability, and technology performance. (Refer to Appendix B for more information on how the electric grid operates) Therefore

, more sophist icated appro aches are most appropriate for quantifying emissions for the baseline emissions projectio n and control strategy pathways. ( Refer to Appendix E for information on
the baseline emissions projection pathway and Appendix F for information on the control

strat egy pathway.)

Four Emission Quantification Approaches This section explains four emission quantification approaches, including how each approach works, when to use it, the advantages and limitations, available tools to support each, and an emission quantification example 13

. The approaches are:

B asic approach: e GRID sub region “non

- base load” emission rates

B asic approach: c apacity factor emission rates

M idrange approach: historical hourly emission rates

S ophisticated approach: energy model s

Agencies will have different policy objectives and associated analytical questions associated with incorporating EE/RE policies and programs in their SIP s/TIPs . Some of the policy objectives and analytical questions state, tribal and local agencies may want to address can be satisfied by

one or more of the quantification approach es . Table 1 provides analytical questions commonly
asked during the SIP /TIP process and the corresponding emissions quantification approach.

13 Emission quantification examples are currently under development. For more information on specific SIP/TIP examples , go to Appendix K. Figure 4 : Emissions Quantification ApproachesI

- 19 Table 1 : How to Choose an Emissions Quantification Approach

Analytical Questions Emission Quantification Approaches Energy

D ata N eeds Emissions

O utputs Pathways

What is the relative magnitude of SO 2 or NOx
emission reductions of an EE/RE poli cy or program in my jurisdiction ? eGRID region

non

- base load

emission rates approach A nnual or season

energy impacts ( megawatt hours ( MWh) ) eGRID regional

average non

base load

emissions WOE

Which EGUs in my state are on the margin and
how much will emissions be displaced on a
seasonal or annual basis? C apacity factor

emission rates approach A nnual or seasonal

energy impacts (MWh) EGU emissions

of marginal

units on an

ozone season or annual basis W OE

,

Emerging / Voluntary

Measures , or Control Strategy Pathways How can I quantify hourly
emission reductions?

How much are emissions reduced during peak electricity demand? Historical h ourly

emission rates approach H ourly energy

impacts, peak and/or base load effects (MW or MWh) Quantify EGU

hourly

emission rates and hourly

emission
reductions Baseline, WOE,

or Control Strategy Pathways

How will EGU emissions change in future years?

How can I compare baseline emissions
forecast s and emissions of new EE/RE policies?

How can I simulate emission changes of EE/RE policies when also subject to cap and trade program(s) ? Energy m odels approach H ourly, seasonal or

annual energy impacts depending

upo n model capabilities (MW or MWh) Average

emissions
based on dispatch order, hourly

emissions or
seasonal

emissions WOE,

Emerging / Voluntary
Measures, or
Control Strategy Pathways

Basic Approach: eGRID Sub region “Non

- B ase L oad” Emission Rates
This approach entails a simple calculation

. An agency would multiply the amount of generation or electricity consumption displaced by the EE/RE policy/program by the “non

- base load” emission rate indicated for a specific pollutant in an eGRID subregion. The no n

- base load emission rate for an eGRID subregion represents an average emission rate for the EGUs that are likely to be displaced by an EE/RE policy and program.

What is eGRID?
The EPA’s eGRID is a data source that provides information on the environmental characteristics of almost all electric power generated in the U

. S. The eGRID includesI

- 20

operational data such as total annual emissions, emission rates, generation, resource mix, capacity factors

, and heat input. 14

The eGRID includes emissions of GHG s , NO x , and SO 2 , and Hg

. The eGRID emissions data are associated with the generation of electricity, not with the consumption of electricity; therefore,
the values do not account
for transmission and distribution losses, imports and
exports across eGRID sub regions (or any other

ge ographic area),

transmission
constraints within any geographic area, or life cycle

emissions at EGUs (e.g.

, emissions
from the extraction, processing, and transportation of fuels).

The eGRID
sub regions are identified and defined by EPA, using the NERC regions and power control areas as a guide. An eGRID
sub region is often, but not always, equivalent to an Integrated Planning Model (IPM) 15 sub region

. The 26 eGRID sub regions in eGRID2010 are subsets of the NERC reg ions as show n in Figure 5.

When to use the eGRID approach
The EPA recommends that s tate, tribal and local agencies use this approach as a method for

estimat ing potential emission reductions for the WOE SIP pathway. Alternatively, the approach can be used as a preliminary, screening ana lysis before carrying out a more sophisticated analysis that could be used under another SIP /TIP pathway submission or to justify a more sophisticated analysis under the WOE pathway

.

14 For more information, go to: http://www.epa.gov/cleanenergy/energy

- resources/egrid/index.html

. 15 The IPM is a multi

- regional, dynamic, deterministic linear programming mo del of the U.S. electric power sector. It provides forecasts of least cost capacity expansion, electricity dispatch, and emission control strategies while meeting energy demand and environmental, transmission, dispatch, and reliability constraints. Figure 5 : eGRID2010 Subr egion Repr esentational Map

Source: http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2010V1_1_year07_Summa ryTables.pdf >I

- 21 Non

- base load emission rate (lb/ MWh )

x (1/1

- grid loss factor)

x Reduced consumption or supply in energy of EE policy and

program ( MWh )

x (2,000lbs/1 short ton conversion for criteria pollutants)

= Tons of emissions reduced from EE/RE policy and program J urisdiction s can estimate the emissions reduction potential for SO 2 emissions, ozone season or
annual NO x emissions in their region by apply ing eGRID sub region emission factors to the EE/RE policy energy imp acts

. This method requires minimal resources , simple assumptions, and provide s a rough estimate of average emission s at a regional scale

. This approach is most appropriate when EGUs within the eGRID subregion contribute to air quality in the nonattainment area, either by being in or upwind to the nonattainment area.)

How does this approach work? State, tribal and local agencies can use t he eGRID sub region non

- base load output emission
rates to estimate emission redu ctions of EE/RE policies and programs that reduce consumption of grid

- supplied electricity. In particular, eGRID will help planners f igure out the n on

- base load output emission rates . This type of rate is useful to understand because it is associated with
emissions from the EGUs most likely to be displaced when EE/RE policies and programs are implemented. These emissions data are derived from plant level data and are aggregated up to
the eGRID sub region level. 16

T his basic eGRID method entails a simple calculation where a jurisdiction would multiply the amount of generation or electricity sales displaced by the EE/RE policy/program by the
“non

- base load” emission rate indicated for a specific pollutant

in an eGRID sub region

. 17 The

non

- base load emission rate for

an eGRID sub region represents an average emission rate for the EGUs that are likely to be displaced by an EE/RE policy and

program. This method is recommended to help state, tribal and local agencies estimate
the relative magnitude of emission impacts from a potential EE/RE policy or program by using the equation in Figure 6

.

What are the ad vantages and limitations of the eGRID approach ? Advantages: Easy “ back of the envelope ” calculation

Non

- base load output emission rates gives a basic understanding of how much EGU
emissions could likely be avoided or displaced

16 EPA (2010a).

17 Grid loss factors should be included in this calculation. EPA (2010c). Figure 6 : Emissions Quantification using an eGRID ApproachI

- 22

eGRID data is derived from reported data submitted to EPA and the E nergy I nformation
Administration

Limitations: Future looking EGU capacity is not represented

o Some EGUs in base year may have already or will shut down in future years

. The eGRID approach does not show which EGUs could have its emissions displaced Information is from 2007 and is generally on a three year time lag The eGRID only accounts for generation within a specific area and does not include information about imports/exports of electricity (except for state level net imports)

o A pproach assumes EE/RE policies will affect all non

- baseload plants proportionally to each plant’s non

- base load generation.

What tools and resources are available for this approach?
The eGRID contains information in several formats, including spreadsheets, and summary
tables in Portable Document Format ( PDF ) format

. 18

Basic Approach: C apacity F actor E mission R ates This approach is based on the assumption that a power plant’s capacity factor is an indicator for the amount of generation subject to displacement. This approach makes general assumptions based on the capacity factor of a power plant to
show

, which power plants may reduce generation with additional EE/RE policies or programs. Capacity
factor information can be found n
the eG RID or a state, tribal or local agency can use their own capacity
factors from other sources. T his approach does not account for the complex nature of the electrical grid
such as electricity imports, exports

, and transmission constraints

.

What is a capacity factor?

A power plant’s capacity factor is a ratio in a given period of time: "the
actual electricity produce d by a generating unit" to "the electricity that could have been produced at continuous full

- power operation." 19

18 For more information on eGRID, go to: http://www.epa.gov/cleanenergy/energy

- resources/eg rid/index.html

. 19 For more information, go to: http://www.eia.doe.gov/electricity/page/prim2/charts.html

. Figure 7 : Sample Curve for Relating Displacement to

Capacity Factors

Source: http://www.synapse

energy.com/Downloads/SynapseReport.2005

- 07.PQA

- EPA.Displaced

Em issions

- Renewables

- and

- Efficiency

- EPA.04

- 55.pdfI

- 23

The capacity factor of a power plant can be used as a proxy for how likely the power plant is to be displaced by a n EE/RE policy or program

. Figure 7 shows an example of a displacement

curve, or a “ rule of thumb

, ” that relat es the likelihood that a power plant or unit’s output would be displaced to its capacity factor. Baseload plants

, such as nuclear units or large coal

- fired plants

, have capacity fa ctors close to 1, so they are represented on the lower right hand side of Figure 7. The capacity factor approach assumes that t hese plants will displace zero percent of their generation from the addition of EE/RE policies and programs . As a power plant’s capacity factor gets closer to zero, the likel i hood that a plant will displace generation increase s

. For example, the capacity factor approach assumes that p eak load plants with low capacity factors

, such as combustion turbines are represented on the upp er left hand side of Figure 7

, and therefore, are much more likely to be displaced.

When to use the capacity factor approach State, tribal and local a gencies could use a capacity factor approach to incorporate EE/RE policies and programs i n their SIP/TIP as a control measure, as long as future generation characteristics, exports, and imports are included in the analysis

. The approach could also be used in an analysis of the benefits of EE/RE policies and programs in support of a WOE
demonstration or volu ntary/emerging measure pathway. This approach is most appropriate to answer the questions such as:

What is the relative dispatch order of the power plants within a state or region? Which power plants are on the margin and what are the emission rates of those plants?

E stimating the emission impacts of energy efficiency policies or policies or solar policies or

programs that generally affect non

- baseload power plants more so than baseload power plants would be the appropriate type of EE/RE policy or program to apply for this approach (e.g., u sing this approach for specific RE technologies , such as on

- shore wind, that displace baseload EGUs would not produce satisfactory results)

.

How does this approach work? This approach is based on the assump tion that a power plant’s capacity factor is an indicator for
the amount of generation and emissions subject to displacement by an EE/RE policy/program.
This approach makes general assumptions about power plant generation and displacement
based on an power plant’s historica l annual or seasonal generation

. By using a general rule of
thumb show n in Figure 7

, the impacts of EE/RE policies an d programs are allocated to each power plant’s or unit’s annual or seasonal emission rate.

For example, t he app roach assumes that power plants with low capacity factors (operating at equal to or less than 20 percent of capacity ) are most likely to be displaced by the EE/RE policy/program and power plants with high capacity fa ctors (operating at equal to or greater
than 80 percent of maximum capacity) would not be displaced by the EE/RE policy/program.
The power plants with capacity factors between 20

- 80 percent are displaceable using a linear
relation ship shown in Figure 7

.I

- 24

Seasonal Variation Considerations While the annual capacity factors described above are helpful in determining which power plants are likely to displac e generation by EE/R E programs/policies

, EPA recommends using seasonable capacity factors when possible for summer

- time ozone because annual capacity factors ignore seasonal weather variations

. For example, many combustion turbines only
operate dur ing summer daytime hours in a typical year. Using an annual capacity factor would incorrectly allocate displaced emissions to these units du ring seasons when they are not operating. If you are assessing the impact of EE/RE programs on ground

- level ozone, t he EPA’s eGRID has ozone season capacity factors available for this type of analysis.

Table 2 illustrates how an example could work in p ractice for an efficiency program projected to save 1,000 MWhs pear year. There are seven generating units in this hypothetical power system, labeled A through G in the first column

. The second column shows the percentage of each unit’s production that could be displaced by the efficiency program, based on the rule of
thumb from Figure 7

. The third c olumn shows each unit’s actual generation in the historical year being used. The fourth c olumn shows th e amount of energy that could be displaced at
each unit , which is the second column multiplied by the first column. The fifth c olumn shows the percentage of the saved energy that is allocated to each unit. This is done by dividing the displaceable energy for each unit by the total available displaced energy ( e .g., Unit A’s displaced energy is 50,000 MWhs, which is 6.5 percent of the total 768,100 MWhs displaceable energy)
and the sixth c olumn shows the MWhs displaced at each generating unit (column five

m ultiplied by 1,000 MW h s). The final step would be to multiply the MWhs displaced in Column 6 with the appropriate emission rates for each unit.

Table 2 : Allocating Displaced Energy Using the Capacity Factor Approach

Unit (1) Percentage Displaceable (2) Historical

Generation (MWh) (3) MWhs

Displaceable (4) Percentage of Energy

Saved Allocated to

Unit (5) MWhs Displaced

(6)

A 100% 50,000 50,000 6.5 % 65

B 82% 65,000 53,000 6.9 % 69

C 79% 120,000 94,800 12% 123

D 48% 500,000 240,000 31% 312

E 22% 1,500,000 330,000 43% 430

F 0% 1,800,000 0 0% 0

G 0% 2,000,000 0 0% 0
Totals 6.035,000 768,100 100% 1,000

Source: http://www.synapse

- energy.com/Downloads/SynapseReport.2005

- 07.PQA

- EPA.Displaced

- Emissions

Renewables

- and

- Efficienc y

- EPA.04

- 55.pdf

What are the advantages and limitations of this approach? Advantages : Emissions can be assigned to each power plant or generating unit

Relative ly easy calculation if infrastructure is set upI

- 25

Simple way to get a relative sense of the marginal unit in area of analysis

Limitations: This is a simplified approach assuming power plant s are operating the same throughout the year or ozone season
Emissions estimates are approximate

, based on annual or seasonal capacity factor s

o Does n o t account for maintenance and outages Imported and e xported power is not considered

Assumes power plant generation characteristics are the same in the base year and future year.

Assumes a general rule of thumb where all energy savings or generation affect a ll peaking units first

, which is not always true with some E E programs or RE technologies (e .g., street lighting programs, w ind power)

What tools and resources are available for this approach?
The EPA is providing a Capacity Factor Emissions Calculator (CFEC) for s tate, tribal and local agencies to use for this approach. The CFEC uses eGRID annual and ozone season capacity
factors for the power plant s represented in this database. Jurisdictions can follow the instructions in this appendix and the CFEC when using this emission quantification approach. 20

Midrange Approach: Historical Hourly Emission Rates
This approach requires technical manipulation of data about historical generation, including load and emission rates to determine EGU dispatch order and marginal emissions rates. By applying this approach

, s tate, tribal and local agencies will understand where EGUs lie within the dispatch order for each hour

, day

, and month of a historical year. Depending upon where geographical boundaries are set, t his approach may not account for electricity imports, exports

, and transmission constraints. And understanding these things will be useful in quantifying emissions in a SIP/TIP becaus e the electricity grid is a large interconnected system that crosses nonattainment area and s tate boundaries. For example, if an EGU in a nonattainment area is exporting it’s generation outside of the nonattainment area, then EE/RE policies implemented

wi thin the nonattainment area will have little effect on the EGU’s emissions.

The EPA has information state, tribal and local agencies can use for this approach. The EPA
collects generation, emissions and heat input data in hourly intervals from CEMS for all large EGUs subject to EPA’s trading programs. 21 State, tribal and local agencies can use r eported hourly generation and emissions data for each EGU to derive hourly emission rate s for one hour or group of hours

. For example, 2 p

. m

. to 6 p

. m

. in the E lectric Reliability Council of Texas (ERCOT) NERC region 22 is a typical group of hours that represents times of peak demand.

20 Capacity factors are located in the eGRID excel file called: eGRID2010V1_ 1_year07_PLANT.xls. EPA (2010b).

21 For more information, go to: http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=emissions.prepackaged_select

22 The Electric Reliability Council of Texas manages the flow of electricity in Texas. For more information , go to : http://www.ercot.com/about/

.I

- 26

Taking into account the emission rates from the EGUs that typically operate during these hours would represent the potential emission reduction during peak hours within ERCOT.

When to use historical hourly emission rate approach By applying this approach state, tribal and local agencies will understand (for every hour or segment of hours of an historical year ) which EGUs are base load (operating all hours of the day), which EGUs are load following (EGUs that ramp up or down depending upon demand)

, and which EGUs are peaking units (EGUs that only operate at high demand periods). This approach is most appropriat e to answer the questions such as:

Ho w much emissions are reduced in blocks of hours, or during periods of peak electricity
demand?

H ow much emissions are reduced for demand response policies or programs ?

Agencie s can use a n historical hourly emissions rate approach to incorporate EE/RE policies and programs i n their SIP /TIP as a control measure, as long as future generation characteristics,
exports

, and imports are included in the analysis

. The approach could also be used in an analysis of th e benefits of EE/RE policies and programs in support of a WOE demonstration or baseline analysis

.

How does this approach work?
The state, tribal or local agency first needs to evaluate how the EE/RE policy reduces load or displaces generation in the area of analysis. Most importantly, the agency needs to identify if the EE/RE policy impacts peak hours and/or base load energy use. From this, air quality planners will be able to d etermine which EGUs within the dispatch order will be affected by t he policies.

It is possible for multiple EE/RE policies/programs to affect both the base load and peak hours of a day. In that case, the agency should add the programs “ bottom up ” to obtain an aggregate
level of energy savings and generation on an hourly basis and then apply their impacts to the

predicted displaced EGUs. 23 Refer to Figure 8 for steps associated with this approach.

23 Synapse (2005). Figure 8 : Steps for Historical Hourly Emissions Rate Approach

Step 1 : Collect EGU hourly information from EPA,
DOE or other Step 2 :

Analyze
EGU dispatch order and hourly
emissions Step 3 :

Identify "hourly segments of EGU
emission rates" Step 4 :

Apply hourly generation or savings profile of EE/RE Step 5 :

Quantify hourly
emission changes of new EE/RE

programI

- 27

What are the advantage s and limitations to the approach?

Advantages: Reported data is easy to find on EPA’s website on an hourly, daily

, and quarterly basis

E GU e mission rates from any group of hours can be derived from the hourly data Emission impacts of different RE technologies can be compared against other RE technologies (e.g., wind vs. solar) and EE policies and programs

Limitations: Setting up hourly emissions database can be resource intensive if infrastructure is not in place

Future EGU capacity changes are not represented

. Only EGUs subject to EPA’s national reporting requirements are represented in EPA’s hourly EGU database 24
Energy i mport and export exchanges and transmission constraints are not captured

What tools and resources are available?
The EPA has available hourly emissions and generation data. 25 EGUs greater than 25 megawatts (MW) must report hourly emissions data to EPA on quarterly basis to comply with EPA’s National Acid Rain Program

. 26

The Mid

- Atlantic Regional Air Management Association (MARAMA) completed an hourly emissions analysis of the states in the Northeast and Mid

- Atlantic region. 27 The purpose of the analyses wa s to a ssist s tates in the N ortheast and m id

- A tlantic region s with address ing policy
relevant questions concerning emissions during periods of peak electricity demand.

The Washington Council of Governments used a time

- matched marginal emissions approach that matches certain EE or RE technologies or measures with historical hourly emissions
information from the EPA hourly database. The emissions tool is applicable for the Virginia/Maryl and / Washington, DC area. 28

24 For more information, go to: http://www.epa.gov/airmarkets/

. 25 For more information, go to: http://www.epa.gov/airmarkets/

. 26 For more information on t he hourly emissions and generation information

, go to: http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=iss.progressresults

27 For more information, go to: http://www.marama.org/RegionalEmissionsInventory/2007hourlypoint/FinalDoc_mar2011_Analysis_of_Hrly_CA MD_Emissions_Data.pdf

. 28 For more information, go to: http://www.mwcog.org/environment/air/EERE/default.asp

.I

- 28

Sophisticated Approach: Energy M odel s This approach employs dynamic simulation models that forecast which EGUs will be displaced
in the future based on inputs and assumptions in the model. Energy models can produce emissions output information at various levels and account for the complex interactions of the grid such as, transmission constraints, import/export dynamics, fuel prices, air pollution control equipment and wide range of energy policies

, and environmental regulations.

What is an energy model? Energy models require extensive underlying data and complex formulation that represents the engineering and economic decisions made by the energy system. They capture the complex interactions within the electricity market an d simulate what might happen given a set of assumptions. These models are equipped to:

Simulate e nergy transfers among different regions

O ptimize system dispatc h f rom EGUs Incorporate transmission constraints, forced outages, environmental regulations, plant retirements, new generation

, and limitations on specific power plants (e.g., ramp rates,

start

- up constraints minimum down time)

Dispatch model

s Dispatch mode ls specifically replicate least

- cost system dispatch, with the lowest cost resources dispatched first and the highest cost last. Dispatch models determine which generating units are displaced and when they are displaced based on economic and operating con straints.

Capacity expansion models Capacity expansion models (also called system

- planning models) can examine the potential long

- term impacts on the electric sector or upon the entire energy system of an EE/RE policy
and program. These models predict how the electric system will evolve over time; including what capacit y will be added through the construction of new generating units and what units
will be retired

, in response to changes in new regulations, demand

, and prices.

When to use an energy mod el approach If a jurisdiction wants to incorporate EE/RE policies and programs in their SIP/TIP in the baseline emissions projection pathway or as a control strategy , using a n energy model, dispatch m odel, capacity expansion model , or comparable model

, is recommended. Either energy model or an alternative emission projection tool can handle multiple iterations of different EE/RE policy/program scenarios, therefore

, state, tribal and local agencies can use a single emission quantification method to compare emissions of a baseline emissions projections and varying policy case s with new EE/RE policies.

Dispatch models are generally good to use for analysis of one to five years into the future especially when the future EGU fleet is not chan ging substantially. An hourly dispatch model can be used to determine hourly emission rates, which can then be aggregated by time periodI

- 29

and applied to a range of EE/RE policies and programs according to their production
characteristics.

Capacity expa nsion models forecast future generation and retirements, as well as the dynamic fluctuation within the electric grid and are generally useful to use for analysis five to 30 years
into the future. This approach is most appropriat e to use when an EE/RE policy or program is at a large enough scale to completely change electric system operations (e .g., EE/RE policy transforms the electric sector market or future EGU operational characteristics)

How does this approach work? Dispatch models Energy mo deling experts or consultants normally operate dispatch models. These models determine which EGUs operate on the “margin” in the electric al power system (typically the most expensive unit needed to meet demand is the “marginal EGU” in a given time period). States can use hourly dispatch or energy models to determine hourly marginal emission rates ( pounds /kWh), which can then be aggregated by time period and applied to a portfolio of programs used to achieve the EE/RE policy requirement. 29

There ar e important considerations when using dynamic simulation models such as dispatch models. Since this method can be less transparent than other methods, jurisdictions should
inform the EPA regional office of important input assumptions for any dispatch or e nergy model
used to measu re displaced emissions.

The following information should accompany a state, tribal or local agency’s SIP/TIP submittal under this pathway for any quantification of emission reductions using a dis patch or similar type of model:

Type and amount of energy savings/generation information used

o Specify if peak (MW), annual (MWh), seasonal, and/or hourly load information was applied for EE/RE policy Fuel prices assumed for all fuels and technologies Emission rates for each applicable EGU

What are the advantages and limitations of dispatch models ? Advantages : Electricity transfers are well represented Can provide very detailed estimations about specific plant and plant type effects Highly detailed

, geographically specific hourly emiss ions data at the EGU level Can simulate emission changes for jurisdictions subject to cap and trade programs

Limitations : The model is only as good as the assumptions

29 EPA (2010c)

, pp. 69

- 70

.I

- 30

All dispatch models are proprietary and require more resources than other approaches Expertise in energy modeling is normally recommended Future changes in electric system are not factored into model

Capacity expansion models Energy modeling experts or consultants normally operate capacity expansion models. These models are typically us ed for longer

- term studies (e.g., five to 20 years), where the impacts are dominated by long

- term investment and retirement decisions. They are also typically used to evaluate large geographic areas. This method involves allowing the model to predict wha t will likely happen to the electric energy resource mix based on costs of new technology, growth, existing fleet of generating assets, environmental regulations (current and planned), and considering dispatch both with and without the new clean energy res ource. 30

The following documentation should accompany a state, tribal or local agency’s SIP/TIP submittal under this pathway for any quantification of emission reductions using a c apacity

e xpansion m odel or similar type of model:

Fuel price forecasts, EGU retirements, and EE/RE regulatory requirements (e.g., renewable portfolio standards) Plant type and emission rates of assumed new generation for all applicable future years
Information on whether model outputs were validated or calibrated against actua l data or another projection model

What are the advantages and limitations of capacity expansion models ? Advantages : This is the most sophisticated way to capture how the electrical grid will react to EE/RE policies Future EGU generation and retirements are represented Provides very detailed estimations about specific plant and plant type effects Highly detailed geographically data at EGU or emission unit level Can simulate emission changes for jurisdictions subject to cap and trade programs.

Limitations : The outputs of energy model s are only as good as the assumptions Energy models are proprietary

, require significant resources to run than other approaches

, and can be data intensive Input assumptions for c apacity e xpansion m odels can be difficult to discern due to the proprietary nature of the models Expertise in energy modeling is normally recommended Hourly emissions information is not always available

30 EPA (2010c)

, p. 71

- 72.I

- 31

What tools and resources are available? There are many different energy models available. Dispatch models include : PROSYM

, PROMOD

, and Ventyx Market Analytics. Capacity e xpansion or p lanning models include : NEMS, IPM, and ENERGY 2020.

Suggested Quantification Approaches for Each S tate and Tribal Implementation Plan Pathway
The EPA is providing suggested emission quantification approaches state, tribal and local agencies can use as guidelines when accounting for emission impacts of EE/RE policies and programs within a certa in SIP/TIP pathway (see Table 3 ). These approaches are suggestions only, so a jurisdiction can choose to use an alternative approach, not listed here, that has comparable rigor and emission results. Before getting too deep into any EE/RE emissions analysis, contact the air program in an EPA regional office 31 to discuss options for the emission quantification approach that is appropriate for the EE/RE policies and programs at hand.

Table 3 : Suggested Emissions Quantification Approaches for Each Pathway

Pathway S uggested Quantification Methods 32
Baseline Emissions Projection Pathway Energy model s approach Historical hourly emission rate approach Alternative emissions projection tools or analysis Control Strategy Pathway Energy model s approach

H istorical hourly emission rate approach Capacity factor approach Emerging/ Voluntary Measures Pathway C apacity factor approach

WOE Pathway Energy model s approach

H istorical hourly emission rate approach Capacity factor a pproach

eGRID sub region non

- base load emission rates

Important Considerations for Emission Quantification A pproaches
All emi ssion quantification approaches should address the following key information:

Defining geographic boundaries at the appropriate regional level (s ee section below) Identifying EGU dispatch order

A ccounting for electricity imports/exports Transparency of data sources and analytical steps Recog nition of any transmission constraints Environmental regulations affecting EGUs

If a state, tribal or l ocal agency is not using a n energy modeling approach

, the following information should be specifically addressed in any SIP/TIP submission :

31 For more information, go to: http://www.epa.gov/aboutepa/where.html

. 32 The quantification methods are only suggestions. Please note that the capacity factor and historical hourly emissions approach are under a third

- party peer revie w process as of June 2012

.I

- 32

Future Generation If the projections for EE/RE policies and programs extend out more than five years , then

, for more sophisticated analyses, a state, tribal or local agency should develop assumptions for how future generation will change o ver time. The jurisdiction should examine each nonattainment

area 33 and assign emission rates to new units expected to come online or exclude planned retired plants in the jurisdictio n’s future emission rates. There are multiple organizations that project how EGUs will meet future demand and react to new environmental regulations. The EPA recommends obtaining projections of future EGU growth from EPA, EIA, e lectric g rid

o perators , RT O s , ISO s , or NERC.

It is also important to consider which new resources may be entering an area and whether there are plans for transmission upgrades. Energy efficiency resources can help an area avoid the need for new or upgraded transmission lines. Depending upon the region, upgrades could encourage further development of RE , or may permit greater access by older, high

- emitting sources that may be more likely to run if the new transmission is built.

Energy Exports and Imports
The EGUs located in the area of analysis may import or export significant amounts of energy. The first step in address ing electricity transfers is to determine whether there has been significant movement in recent years between the area of analysis and other areas. The
followi ng data sources are available for electricity import/export information :

Data on total generation and export/import percentages will indicate whether the area is a net importer or exporter

, as well as the magnitude of transfers relative to total

genera tion 34
Most system operators ( b alancing a uthorities, R TOs /I SO s ) release information annually about generation, loads

, a nd interchange on their system

L ong

- term power purchase agreements that underlie exports and import transfer
information

If EGUs collectively in an area of analysis are net exporter s or importer s, then the next step is to determine if the transfer level follows a daily load pattern, a seasonal load pattern or is a consistent source of energy to an outside importer throughout th e year. Once typical energy transfers are characterized, the dispatch order in the area of analysis should be adjusted to
account for these transfers within the relevant time frames. 35

Managing Uncertainty

A ll SIP /TIP emission reduction measures have some lev el of uncertainty, whether it comes from the uncertainty associated with the emissions factors for certain sources, the level of

33 For more information on areas not meeting the National Ambient Air Quality Standards, go to: http://www.epa.gov/oar/oaqps/greenbook/

. 34 F or more information , go to: http://www.epa.gov/cleanenergy/energy

- resources/egrid/index.html

. 35 Synapse (2005).I

- 33

compliance wit h existing SIP/TIP measures, or air quality modeling for an attainment
demonstration. By using conservative assu mptions, appropriate discount factors or verification techniques

, emission reductions from EE/RE policies and programs can be applied for SIP/TIP

purposes. The EPA recognize s that there will likely always be some level of uncertainty regarding the exact q uantity and location of emission reductions resulting from EE or RE

measures. However, in many cases EPA also believe that you can apply existing tools with sufficient rigor to be able to quantify estimated emission reductions with acceptable certainty

to allow the reductions to be credited.I

- 34



### References

EPA (2010a). eGRID Technical Support Document . Available online at

< http://www.epa.gov/cleanenergy/documents/egridzips/eGRID2010TechnicalSupportDocumen t.pdf >

EPA (2010b)

. eGRID Table in: eGRID2010V1_0_STIE_USGC

. Available online at

< http://www. epa.gov/cleanenergy/energy

- resources/egrid/index.html >

EPA (2010 c )

. Assessing the Multiple Benefits of Clean Energy. September 2011. Available online at

< http://www.epa.gov/statelocalclimate/resources/benefits.html >

Synapse Energy Economics, Inc. (2005). Methods for Estimating Emissions Avoided by Renewable Energy and Energy Efficiency. July 8, 2005. Available online at < http://www.synapse

energy.com/Downloads/SynapseReport.2005

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- EPA.Displaced

- Emissions

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and

- Efficiency

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- 55.pdf >35 United States
Environmental Protection Agency Office of Air Quality Planning and Standards
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- 001 j

Ju ly 2012