Clean energy programs and policies can help states 

achieve their goal of providing a less polluting, more 

reliable and affordable electric system that addresses 

multiple challenges, including:

■ Lowering energy costs for customers and utilities 

alike, particularly during periods of peak electricity 

demand;1

■ Improving the reliability of the electricity system 

and averting blackouts at a lower cost;

■Reducing the need for new construction of generating, transmission, and distribution capacity; and 

■ Providing targeted reductions in load (i.e., the 

amount of electric power or the amount of power 

demanded by consumers at a given time) in gridcongested areas, such as southwestern Connecticut 

and San Francisco, California. 

Many states are evaluating the electric system benefits 

of clean energy. These benefits, as described above, go 

beyond the direct energy savings and renewable energy 

generation impacts discussed in Chapter 2, Assessing 

the Potential Energy Impacts of Clean Energy Initiatives. 

This chapter provides an overview of methods that can 

be used to undertake broad assessments of the impacts 

1 Just as energy efficiency program economics can be evaluated from a variety 

of perspectives (total resource costs, program administration costs, ratepayer, 

participant, and society) so can the benefits of clean energy programs. For each 

perspective, the benefits of clean energy are defined differently. In this guide, we 

are examining the equivalent of the total resource cost perspective, considering 

benefits (and costs) to the participants and the utility. While other perspectives 

including the utility costs are important, we focus on those perspectives most 

important to policymakers and clean energy program administrators. For more 

information about the different perspectives used to evaluate the economics of 

programs, see Understanding Cost-Effectiveness of Energy Efficiency Programs: 

Best Practices, Technical Methods, and Emerging Issues for Policy-Makers: A 

Resource of the National Action Plan for Energy Efficiency, November 2008. 

www.epa.gov/cleanenergy/documents/cost-effectiveness.pdf.

DOCUMENT MAP•	 CHAPTER ONE

Introduction

•	 CHAPTER TWO

Potential Energy Impacts of Clean Energy

•	 CHAPTER THREE

Electric System Benefits of Clean Energy

•	 CHAPTER FOUR

Air Quality Benefits of Clean Energy

•	 CHAPTER FIVE

Economic Benefits of Clean Energy

•	 APPENDIX A

Catalogue of Clean Energy Case Studies

•	 APPENDIX B

Tools and Models Referenced in Each Chapter

CHAPTER THREE CONTENTS

3.1 How Clean Energy Can Achieve Electric System 

Benefits ...............................................................................................53

3.1.1 The Structure of the U.S. Energy System.....................53

3.1.2 Primary and Secondary Benefits of Clean Energy....54

3.2 How States Can Estimate the Electric System Benefits 

of Clean Energy................................................................................56

3.2.1 How to Estimate the Primary Electric System 

Benefits of Clean Energy Resources..............................61

3.2.2 How to Estimate the Secondary Electric System 

Benefits of Clean Energy Resources.............................. 77

3.3 Case Studies ......................................................................................83

3.3.1 California Utilities’ Energy Efficiency Programs ........83

3.3.2 Energy Efficiency and Distributed Generation in 

Massachusetts......................................................................85

Information Resources ...........................................................................86

References .................................................................................................89

CHAPTER THREE

Assessing the Electric System

Benefits of Clean Energy

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 51
■ Planning and regulating air quality, water quality, 

and land use;

■ Obtaining support for specific initiatives; and 

■ Policy and program design.

Although quantifying electric system benefits can be 

challenging—particularly when analyzing long-term 

effects in a complex, interconnected electricity grid—it 

is important to consider these benefits when evaluating 

clean energy resources. This chapter presents detailed 

information about the energy system, specifically electricity benefits of clean energy, to help policy makers 

understand how to identify and assess these benefits 

based upon their needs and resources.

■ Section 3.1, How Clean Energy Can Achieve Electric 

System Benefits, describes the energy system in the 

United States and explains the multiple ways that 

clean energy policies and programs can positively 

affect the electric system and electricity markets, 

thereby benefiting consumers, utilities, and society. 

■ Section 3.2, How States Can Estimate the Electric 

System Benefits of Clean Energy, presents an overview of the methods for estimating the primary 

and secondary electric system benefits of different 

types of clean energy resources. 

ӹ Section 3.2.1, How to Estimate the Primary 

Electric System Benefits of Clean Energy Resources, describes the specific basic and sophisticated modeling approaches and associated 

tools that can be used to quantify a set of typically recognized (i.e., “primary”) benefits. 

ӹ Section 3.2.2, How to Estimate the Secondary Electric System Benefits of Clean Energy 

Resources, describes approaches and tools for 

estimating other electric system benefits (i.e., 

“secondary” benefits) that are less frequently 

assessed and often more difficult to quantify. 

■ Section 3.3, Case Studies, presents examples of how 

two states, California and Massachusetts, are estimating the electric system benefits of their clean 

energy programs.

of clean energy on the overall electric system, including 

effects on electricity generation, capacity, transmission, 

distribution, power costs, and peak demand. 

State legislatures, energy and environmental agencies, 

regulators, utilities, and other stakeholders (e.g., ratepayer advocates, environmental groups) can quantify 

and compare the electric system benefits of clean 

energy resources [e.g., energy efficiency, including 

some demand response programs such as load control 

programs, renewable energy, combined heat and 

power (CHP), and clean distributed generation (DG)] 

to traditional grid electricity. This information can 

then be used in many planning and decision-making 

contexts, including:

■ Developing state energy plans and establishing 

clean energy goals;

■Conducting resource planning (by PUCs or 

utilities);

■ Developing demand-side management (DSM) 

programs;

■Conducting electric system planning, including 

new resource additions (e.g., power plants), transmission and distribution capacity, and interconnection policies;

STATES ARE QUANTIFYING THE ENERGY SYSTEM BENEFITS

OF CLEAN ENERGY POLICIES

Several states have quantified the energy system benefits from 

their clean energy measures and determined that the measures 

are providing multiple benefits, including avoiding the costs of 

electricity generation, reducing peak demand, and improving 

energy system reliability.

Georgia conducted an assessment of the benefits of achieving 

energy efficiency improvements in the state and found it could 

reduce demand for electricity by 3,339 GWh–12,547 GWh in 

2010. 

In addition to these energy savings, the analysis showed that the 

improvements could benefit the overall electricity system and:

■ Avoid generation in Georgia of 1,207 GWh–4,749 GWh in 

2010, 

■ Reduce regional wholesale electricity cost by 0.5–3.9 

percent by 2015, and 

■ Lower peak demand by 1.7–6.1 percent by 2015 and 

achieve a number of environmental and economic 

benefits.

(Jensen and Lounsbury, 2005).

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 52
thus may be considerably different from one location 

to another. 

Electricity supply is matched to demand using a portfolio of production technologies. To meet the demand, 

some power plants operate almost continuously, serving as baseload units (e.g., coal and nuclear plants are 

examples of baseload units). Each baseload unit has 

relatively high capital costs, but operational costs are 

low. Also, startup and shutdown at these plants takes 

time, is expensive, and causes additional wear on generating units. Other generation sources are operated 

only during the times of highest demand, serving as 

“peaking” units. The output of these generators rises 

and falls throughout the day, responding to changing 

electricity demand. Natural gas turbines are often used 

for this purpose. These technologies are expensive to 

run for long periods but can be started up and shut 

down quickly. Because electricity must be generated at 

the same time it is used, meeting peak demand and the 

related price volatility are key issues.

The source of the electricity supply can also vary. A 

group of system operators across the region decides 

when, how, and in what order to dispatch electricity 

from each power plant in response to the demand at 

that moment and based on the cost or bid price. In regulated electricity markets, dispatch is based on “merit 

order” or the variable costs of running the plants. In 

restructured markets or wholesale capacity markets, 

3.1	 HOW CLEAN ENERGY CAN

ACHIEVE ELECTRIC SYSTEM BENEFITS

Energy is crucial to all aspects of the U.S. economy. 

This section presents background information on how 

the U.S. energy system is structured (see Section 3.1.1), 

and describes the wide range of benefits that clean 

energy can bring to the electricity component of this 

system (see Section 3.1.2). 

3.1.1	 THE STRUCTURE OF THE U.S. ENERGY

SYSTEM

The energy system in the United States includes all 

the steps, fuels, and technologies from the import or 

extraction of energy resources, to their conversion to 

useful forms, to their use in meeting end-use energy 

demands (e.g., by the transportation, industrial, 

residential, and commercial sectors). Components of 

the energy supply system include transportation fuels, 

electricity, and other forms of energy for use in homes, 

manufacturing, and business. This chapter focuses on 

several components of the larger electric system: electricity production, transmission, distribution, and the 

markets by which electricity is bought and sold. These 

components are hereinafter referred to together as the 

electric system. 

The North American electric system acts essentially 

like four separate systems of supply and demand 

because it is divided into four interconnected grids in 

the continental United States and Canada: the Eastern, 

Western, Quebec, and Electric Reliability Council of 

Texas (ERCOT) Interconnections. These alternating 

current (AC) power grids are depicted in Figure 3.1.1, 

NERC Interconnections. Electricity can be imported or 

exported relatively easily among the numerous power 

control areas within each interconnection system. 

However, for reliability purposes, the interconnections 

have limited connections between them and are connected by direct current (DC) lines. 

Balancing the supply of and demand for electricity 

in an economically efficient manner is complicated 

by a number of factors. For example, the demand for 

electricity varies significantly hour by hour, and cyclically by time of day and season. Residential electricity 

demand peaks in the morning and at night, when more 

residents are at home and operating heating and air 

conditioning units, washers, dryers, and other products 

that use electricity. Commercial and industrial electricity demand varies by type of company or industry, and 

FIGURE 3.1.1 NERC INTERCONNECTIONS

Source: NERC, 2008.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 53
3.1.2	 PRIMARY AND SECONDARY BENEFITS

OF CLEAN ENERGY

Clean energy initiatives can result in numerous benefits to the electric system, predominantly through 

the avoidance of costs associated with generating, 

transmitting, and distributing electricity. Clean energy 

is often cheaper than or just as cost-effective as other 

energy options, while delivering important electric 

system, environmental, and/or economic benefits to 

the state. For example, in California, energy efficiency 

programs have cost the state 2¢–3¢ per kWh on 

average—much less than the cost of new generation, 

which can be more than 6¢ per kWh for new natural 

gas combined cycle plants—while reducing the need 

for new power plants and increasing reliability (NRDC, 

2006). Consequently, quantifying the electric system 

benefits of clean energy options is central to sound 

policy planning, contributes to public confidence in 

clean energy policies, and helps policy makers choose 

among different approaches to delivering clean energy. 

The benefits of clean energy initiatives are categorized 

in this document as primary and secondary benefits. 

Primary benefits are those electric system benefits that 

are conventionally recognized for their ability to reduce 

the overall cost of electric service over time. These 

benefits can occur over the long run, the short run, or 

both. Some of these benefits are significant and most 

can be quantified using well-tested methods. Secondary 

benefits of clean energy are less frequently recognized 

than primary benefits, and tend to be smaller and/or 

harder to quantify. Nevertheless, it is useful to identify 

dispatch is based on the generator’s bid price into the 

market. Electricity from the power plants that are least 

expensive to operate (i.e., the baseload plants) is dispatched first. The power plants that are most expensive 

to operate (i.e., the peaking units) are dispatched last. 

The merit order or bid stack is based on fuel costs and 

plant efficiency, as well as other factors such as emissions allowance prices.

Other conditions also affect electricity supply. Transmission constraints (i.e., when transmission lines 

become congested) can make it difficult to dispatch 

electric generators located away from load centers and 

move their power into areas of high demand, or may 

require certain units to operate to improve system reliability. Extreme weather events can decrease the ability 

to import or export power from neighboring areas. 

“Forced outages,” when certain generators or transmission lines are temporarily unavailable, can also shift 

dispatch to other generators. System operators must 

keep all these issues in mind when dispatching power 

plants. States can also take these issues into consideration by using dispatch models or other approaches to 

estimate which generators would likely reduce their 

output and their emissions in response to the introduction of clean energy resources. 

The electric power transmission system connects 

power plants to consumers. Figure 3.1.2 depicts the 

flow of power from the generating station, or power 

plant, to the transformer and the transmission lines, 

through the substation transformer (which reduces the 

voltage) to the distribution lines, and finally, through 

the pole transformer to the consumer’s service box. 

Electricity transmission is typically between the power 

plant and a substation, and electricity distribution is the 

delivery from the substation to consumers. Electricity 

is usually transmitted through overhead transmission 

and distribution lines, although sometimes underground distribution lines are used in densely populated 

areas. Overlapping lines are provided in the grid so 

that power can be routed from any power plant to any 

load center (e.g., populated areas), through a variety 

of routes. Transmission companies conduct detailed 

analyses to determine the maximum reliable capacity 

of each line. 

The process of generating, transmitting, and distributing electricity is quite complex and involves many 

costs. Clean energy provides opportunities for states to 

reduce many of those costs. 

HOW ELECTRIC GENERATORS ARE DISPATCHED

The operation of electric systems is determined by a set of 

physical constraints and economic objectives, through a 

process referred to as “economic dispatch.” The electric system 

operator dispatches generating units (i.e., signals generators to 

start or increase production) in economic merit order—that is, 

in order of increasing operating costs (starting with the lowest 

costs adjusted for transmission losses), subject to reliability 

considerations including transmission constraints. The highestcost unit dispatched at any point in time is said to be “on the 

margin” and is known as the “marginal unit.” For example, 

high-cost combustion turbines and gas/oil peaking units are 

on the margin for many hours of the week. During off-peak 

times, plants with lower operating costs (e.g., combined cycle 

gas turbines and coal-fired steam units) can be on the margin. 

In some regions the cost used for dispatch is the variable cost 

of running each plant (mainly fuel cost), but in others the 

criterion for dispatch is a bid price submitted by the owners of 

the generators.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 54
gy loss during delivery of electricity to consumers 

through the T&D system.2

■ Deferred or avoided costs of T&D capacity. Clean 

energy resources that are located close to where 

energy is consumed can delay or avoid the need 

to build or upgrade T&D systems or reduce the 

size of needed additions. These savings can occur 

over the long run, the short run, or both. Typical 

components are similar to those for avoided power 

plant capacity. 

Examples of secondary benefits include: 

■Avoided ancillary service costs. Clean energy 

resources that reduce load, that are located close 

to where energy is consumed, or that can support 

smooth operation of the power grid can reduce 

some ancillary services requirements. Ancillary 

services are those electric generator functions 

needed to ensure reliability, as opposed to providing power. Examples include operating reserves 

(e.g., generators that are up and running to take 

over if a load-serving generator fails or load spikes) 

and voltage support (e.g., generators that are 

running and can tune their output to keep voltage 

stable). Clean energy resources that reduce the 

need for ancillary services save fuel and reduce 

2 It is important to note that clean central-station generation incurs the same 

T&D losses as fossil-fueled sources.

these benefits and quantify them, when possible, in 

order to most accurately reflect both the costs and benefits of clean energy. 

The primary electricity system benefits of clean energy 

include:

■Avoided costs of electricity generation or wholesale 

electricity purchases. Clean energy policies and 

programs can displace electricity generated from 

fossil fuels (e.g., natural gas, oil, and coal-fired 

power plants). Savings typically appear as avoided 

fuel costs and reduced cost for purchased power or 

transmission service. 

■ Deferred or avoided costs of power plant capacity. 

Clean energy policies and programs can delay or 

avoid the need to build or upgrade power plants 

or reduce the size of needed additions. Typical 

components are the capital investments and annual 

fixed costs (e.g., labor, maintenance, taxes, and 

insurance) not incurred as a result of clean energy 

initiatives. 

■Avoided electric loss in transmission and distribution

(T&D). The delivery of electricity results in some 

losses due to the resistance of wires, transformers, 

and other equipment. For every unit of energy 

consumption that a clean energy resource avoids at 

the end-use site, it also avoids the associated enerFIGURE 3.1.2	 FLOW OF ELECTRICITY FROM POWER PLANTS TO CONSUMERS

Source: US-Canada Power System Outage Task Force, 2004 https://reports.energy.gov/BlackoutFinal-Web.pdf.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 55
■ Reduced risk from deferring investment in traditional, centralized resources until environmental and 

climate change policies take shape. Clean energy 

policies and programs may reduce the cost of 

future compliance with air pollution control requirements. In addition, clean energy policies and 

programs may limit exposure to costs from any 

future carbon regulations. 

■ Improved fuel diversity and energy security.

Portfolios that rely heavily on a few energy 

resources are highly affected by the unique risks 

associated with any single fuel source (e.g., coal, 

oil, gas). In contrast, the costs of some clean energy 

resources are relatively unaffected by fossil fuel 

prices and thus provide a hedge against fossil-fuel 

price spikes. Other clean energy resources can be 

affected by fossil fuel prices. For example, biomass 

renewables may require fertilizer and/or processing via technologies that use petroleum, natural 

gas, and/or coal, and because wind provides 

intermittent power that may not be available at 

peak demand times, it can require backup peaking 

units (e.g., natural gas turbines). Overall, however, 

the greater the diversity in technology the less 

likelihood of supply interruptions and reliability 

problems. In addition, using diverse domestic 

clean energy resources provides energy security by 

reducing the vulnerability of the electric system to 

attack and reducing dependence on foreign fuel 

sources, such as imported petroleum, which may 

yield political and economic benefits by protecting 

consumers from supply shortages and price shocks

Table 3.1.1 summarizes the traditional costs of generating, transmitting, and distributing electricity, and 

describes the primary and secondary clean energy 

benefits associated with each type of cost. 

3.2	 HOW STATES CAN ESTIMATE

THE ELECTRIC SYSTEM BENEFITS OF

CLEAN ENERGY

The rigor with which states can or may want to analyze 

the electric system benefits of clean energy depends 

on the type of benefit being analyzed, the clean energy 

proposal’s status in the development and design process, the level of investment under consideration, regulatory and system operator requirements, resources 

(e.g., computers, staff) available for the analysis and, 

for some benefits, the utility or region. 

emissions by allowing some units to shut down 

and may delay or avoid the need for investment in 

new generation to provide ancillary services. These 

include stationary energy storage resources such as 

batteries and pumped hydro storage. Other clean 

energy resources, especially demand response 

resources—such as controls on air conditioning or 

water heater load control programs—can free up 

reserves that are needed to respond in the event 

of a system outage. In some regions, clean energy 

resources that operate during peak times reduce 

the required level of operating resources. 

■ Reduced wholesale market clearing prices. Clean en Clean energy policies and programs can lower the demand 

for electricity or increase the supply of electricity, 

causing wholesale markets to clear at lower prices. 

This benefit can be dramatic during peak hours.

■ Increased reliability and power quality. An electric 

grid is more reliable if the loads are lower, especially during peak hours and in areas where transmission is constrained. Integration of clean energy 

resources can increase the reliability of the electricity system since power outages are less likely to 

occur when the system is smaller and not strained; 

more dispersed resources make the system less 

vulnerable to outages. In addition, power quality—

which is important for the operation of some 

electrical equipment—can be enhanced by some 

forms of clean energy resources (e.g., fuel cells). 

■Avoided risks associated with long lead-time 

investments. While clean energy resources certainly 

have some risk (e.g., of underperformance of 

energy efficiency or renewable energy measures), 

these resources offer greater flexibility due to their 

modular, segmented nature, and relatively quick 

installation and disconnection time compared 

with traditional resources. As a result, clean energy 

options increase flexibility to deal with uncertainty 

(relative to large, traditional fossil fuel resources) 

by reducing dependence on conventional fuels 

and allowing planners to be more responsive to 

deviations from load forecasts. The size of the 

potential for some clean energy options, such as 

energy efficiency, is correlated with load, making 

it especially responsive to changes in the planning 

environment. In addition, reducing or delaying 

the need for large utility investments for transmission or generation reduces both the need for large 

amounts of financing and the chance of failed or 

unnecessary investments.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 56
effects. These two approaches are not mutually exclusive, but may be used in a complementary way. Table 

3.2.1 describes the advantages and disadvantages of 

each method and when they are appropriate to use. 

SELECTING BENEFITS TO EVALUATE

Some states may not be interested in estimating all 

types of electric system benefits, or states may be 

considering programs that deliver benefits in only 

some areas. It is generally common practice to evaluate 

A range of basic and sophisticated methods is available 

to allow analysts to estimate how the electric system 

will be affected by clean energy measures, including 

when and where electricity generation may be offset. 

Basic methods typically include spreadsheet-based 

analyses or the adaptation of existing studies or information. Sophisticated methods typically use dynamic 

electric system models that (a) predict the response of 

energy generation to actions that influence the level of 

clean energy resources and (b) calculate the resulting 

TABLE 3.1.1	 ELECTRIC SYSTEM COSTS AND THE PRIMARY AND SECONDARY BENEFITS OF

CLEAN ENERGY

Traditional

Costs

PrimaryBenefits of

Clean Energy Secondary Benefits of Clean Energy Description of Benefit Section

Generation

■ Fuel

■ Variable operation 

and maintenance

■ Emissions Allowances

■ Avoided costs 

of electricity 

generation or 

wholesale electricity 

purchases.

■ Reduced risk from investment in 

traditional, centralized resources 

before environmental and climate 

change policies take shape.

■ Improved fuel and energy security.

■ Clean energy policies and 

programs can displace 

traditional electric energy 

generation.

3.2.1a

■ Avoided ancillary services.

■ Reductions in wholesale market 

clearing prices.

■ Increased reliability and power 

quality.

■ Avoided risks associated with long 

lead-time investments (e.g., risk of 

overbuilding the electric system).

■ Clean energy policies and 

programs can lower the 

demand for electricity 

or increase the supply of 

electricity, causing wholesale 

markets to clear at lower 

prices.

■ Capital and operating 

costs of upgrades

■ Fixed operation and 

maintenance

■ New construction to 

increase capacity

■ Avoided costs 

of power plant 

capacity.

■ Clean energy policies and 

programs can delay or avoid 

the need to build or upgrade 

power plants.

3.2.1b

Transmission & Distribution

■ Capital and operating 

costs of maintenance 

■ Upgrades

■ New construction

■ Deferred or 

avoided costs of 

transmission & 

distribution (T&D) 

capacity.

■ Increased reliability and power 

quality.

■ Clean energy policies and 

programs that are located close 

to where energy is consumed 

can delay or avoid the need to 

build or upgrade T&D systems.

3.2.1c

■ Energy losses ■ Avoided electric 

loss in T&D lines.

■ Clean energy policies and 

programs that avoid energy 

consumption also avoid losses 

associated with transmission 

and distribution.

3.2.1d

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 57
market price effects. Similarly, quantification of 

ancillary service benefits can be difficult in areas 

without regional transmission organizations (RTOs) 

that routinely report market prices, even if the clean 

energy resource has the capability of delivering these 

ancillary service benefits. In this case, analysts may 

decide to devote their limited staff and computing 

power to quantifying benefits that are likely to yield 

the most reliable and meaningful results, and address 

other benefits qualitatively. 

There are a number of considerations in selecting 

which benefits to estimate. As indicated earlier, primary electric system benefits tend to be easier to quantify 

and the methods to quantify them tend to be mature. 

The methods to evaluate the secondary electric system 

benefits are more limited and can be subject to debate. 

Tables 3.2.2 and 3.2.3 outline some of the factors that 

states can consider when deciding which electric system benefits to analyze, including available methods 

and examples, advantages, disadvantages, and purpose 

of analysis. Section 3.2.1, How to Estimate the Primary 

Electric System Benefits of Clean Energy Resources, and 

Section 3.2.2, How to Estimate the Secondary Electric 

System Benefits of Clean Energy Resources, review each 

type of benefit and explain the approaches generally 

used to analyze each benefit.

all the primary benefits for clean energy projects or 

programs. For secondary benefits, however, the need 

for detailed estimation can vary depending on several 

factors, including: 

■The type of clean energy resource being considered, 

■Regulatory or system operator study requirements, 

■Available resources (e.g., computers, staff, and data), 

and 

■Whether certain needs or deficiencies have been 

identified for the existing electric system. 

For example, suppose a state is considering demand 

response resources such as direct load control (i.e., 

programs that enable electric providers to reduce the 

demand of consumer sites at peak times, sometimes by 

directly curtailing major energy-intensive equipment 

such as air conditioners and water heaters). For these 

types of measures, it is increasingly common to consider wholesale market price effects because the benefit 

to consumers from price reductions during peak hours 

can be substantial. On the other hand, if a state energy 

efficiency policy is expected to produce significant 

savings only during off-peak hours or seasons, which 

would result in a smaller impact on the wholesale market, it may not be worthwhile to estimate the wholesale 

TABLE 3.2.1	 ADVANTAGES AND DISADVANTAGES OF BASIC VS. SOPHISTICATED METHODS OF

ESTIMATING ELECTRIC SYSTEM BENEFITS

Advantages Disadvantages When to Use

Basic Estimation

■ Relatively low cost.

■ Requires minimal input data and time.

■ Less robust.

■ Provides approximate estimates.

■ For preliminary studies.

■ When time and/or budget are limited.

■ When limited data resources are available.

Sophisticated Simulation

■ Robust representation of electric system 

dispatch and, in some cases, capacity 

expansion.

■ Provides high level of analytic rigor and 

detailed results.

■ May be available from utility resource 

planners.

■ May allow sensitivities to a wide range of 

assumptions.

■ Time- and resource-intensive.

■ Relatively high cost.

■ Requires significant input data. 

■ Complex.

■ Not transparent in stakeholder process.

■ When a high degree of precision and 

analytic rigor is required.

■ When sufficient data resources are 

available.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 58
TABLE 3.2.2	 PRIMARY ELECTRIC SYSTEM BENEFITS FROM CLEAN ENERGY MEASURES

Applicable Clean

Energy Resources

Considerations for Determining

Whether to Analyze Who Usually Conducts Analysis?

When is Analysis

Usually Conducted or

Made Available?

BENEFIT: Avoided electricity generation or wholesale electricity purchases

■ All resources.

■ Resources that 

operate during peak 

hours.

■ Traditionally analyzed in cost-benefit 

analysis.

■ Widely accepted methods.

■ Data generally available but expensive.

■ Models available but are complex, not 

transparent, and are often expensive 

to use.

■ Many assumptions about technology, 

costs, and operation needed.

■ Long term fuel price forecasts must be 

purchased or developed.

■ Utilities conduct in-depth 

modeling.

■ PUCs and other stakeholders review 

utility’s results and/or conduct own 

analysis.

■ RTO/ISO and the Independent 

Market Monitor.

■ US EIA and private consultancies 

provide electric dispatch and 

capacity expansion forecasts.

■ Resource planning and 

released regulatory 

proceedings.

■ Area-specific DSM 

program development.

■ RTO/ISO avoided cost 

estimates may be 

published on regular 

schedules.

BENEFIT: Avoided power plant capacity additions

■ All resources.

■ Resources that 

operate during peak 

hours.

■ Traditionally analyzed in cost-benefit 

analysis.

■ Generally accepted methods for both 

estimation and simulation.

■ Some assumptions about technology, 

costs and operation needed.

■ Data generally available.

■ Utilities conduct in-depth 

modeling.

■ PUCs and other stakeholders review 

utility’s results and/or conduct own 

analysis.

■ In some regions, RTO/ISO publishes 

capacity clearing prices.

■ Resource planning and 

proceedings.

■ Area-specific DSM 

program development.

■ RTO/ISO avoided cost 

estimates may be 

published on regular 

schedules.

BENEFIT: Deferred or avoided T&D capacity

■ Resources that 

are close to load, 

especially those that 

operate during peak 

hours. 

■ Traditionally analyzed in cost-benefit 

analysis.

■ Load flow forecast availability.

■ Unit cost of T&D upgrades can be 

estimated but may be controversial.

■ T&D capacity savings reasonably 

practical, but site-specific savings 

difficult to generalize.

■ Utilities conduct in-depth 

modeling.

■ PUCs and other stakeholders review 

utility’s results and/or conduct own 

analysis.

■ RTO/ISO.

■ T&D build planning.

■ Area-specific DSM 

program development.

■ RTO/ISO costs 

estimates may be 

published on regular 

schedules.

BENEFIT: Avoided energy loss during T&D

■ Resources that 

are close to load, 

especially those that 

operate during peak 

hours .

■ Traditionally analyzed in cost-benefit 

analysis.

■ Straightforward; easy to estimate 

once avoided energy has been 

calculated

■ Loss factor for peak savings may 

need to be estimated.

■ Utilities collect loss data regularly 

and may conduct in-depth 

modeling.

■ PUCs and other stakeholders review 

utility’s results and/or conduct own 

analysis.

■ Resource planning and 

proceedings.

■ Area-specific DSM 

program development.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 59
TABLE 3.2.3	 SECONDARY ELECTRIC SYSTEM BENEFITS FROM CLEAN ENERGY MEASURES

Applicable Clean

Energy Resources

Considerations for Determining

Whether to Analyze Who Usually Conducts Analysis?

When is Analysis

Usually Conducted?

BENEFIT: Avoided Ancillary Services

■ Resources that can 

start during blackout, 

ramp up quickly, 

or provide reactive 

power. 

■ Resources closer to 

loads.

■ Usually smaller benefits than 

traditionally analyzed benefits .

■ Market price data available for some 

services in some markets (e.g., PJM).

■ Ancillary service savings from clean 

resources often site-specific and 

difficult to estimate.

■ Separating ancillary service value 

from capacity value in long run 

analysis may be difficult.

■ Utilities conduct in-depth modeling.

■ PUCs and other stakeholders review 

utility’s results and/or conduct own 

analysis.

■ Resource planning and 

proceedings.

■ Area-specific DSM 

program development.

BENEFIT: Wholesale Market Price Effects

■ All clean resources .

■ Resources that 

operate during peak 

hours. 

■ Benefits depend on market/pricing 

structure and peaking resources and 

forecasted reserve margins.

■ Actual market price data generally 

available.

■ Studies to estimate benefits may be 

complex.

■ ISOs and utilities conduct in-depth 

modeling.

■ PUCs, other stakeholders review 

utility’s results and/or conduct own 

analysis.

■ Resource planning and 

proceedings.

■ Area-specific DSM 

program development.

■ Policy studies.

BENEFIT: Increased reliability and power quality

■ Distributed resources.

■ Resources close to 

load or with high 

power quality. 

■ All resources that 

operate as baseload 

units.

■ All load reducing 

resources that 

increase surplus 

generating and T&D 

capacity in region.

■ Historical reliability data often 

available.

■ Historical power quality data rare.

■ Studies for converting to dollar value 

complex and controversial.

■ Benefits are especially valuable 

for manufacturing processes that 

are sensitive to power quality or 

regions where reliability is significant 

concern.

■ Utilities conduct in-depth modeling 

.

■ PUCs and other stakeholders review 

utility’s results and/or conduct own 

analysis.

■ Usually ad hoc studies.

BENEFIT: Avoided or reduced risks of overbuilding (associated with long lead-time investments, such as the risk of overbuilding the

electric system)

■ Distributed resources 

with short lead times.

■ Resources close to 

load

■ All clean resources.

■ Historical load and load variability 

data often available.

■ Modeling varies from simple to 

complex.

■ Utilities conduct in-depth modeling.

■ PUCs and other stakeholders review 

utility’s results and/or conduct own 

analysis.

■ Policy and risk management 

analysts.

■ Resource planning and 

regulatory review of 

planning.

■ Policy studies.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 60
high-efficiency transformers in a new substation 

can be more expensive than standard equipment in 

terms of up-front costs, but will waste less electricity 

over time, thereby reducing variable operating and 

maintenance costs. Likewise, replacing a chiller in a 

food-processing factory with a more efficient unit incurs a higher capital cost up-front, but reduces annual 

electricity costs for the customer.3 The basic concept 

is to compare the net impact on the cost of power over 

the lifetimes of each alternative that is technically 

capable of meeting the need. The alternative with the 

smallest net impact is typically the preferred choice, all 

other things being equal. 

As indicated above, methods to quantify primary electric system benefits are mature and states can choose 

from a range of basic and sophisticated methods as 

described below.

3 Some states have competition in retail electricity service, others do not, 

and some are in a transitional state. These examples apply to both traditional, 

vertically integrated utilities and to distribution-only utilities. However, the 

existence of retail competition changes some of the details in important ways. 

One such difference is that under retail competition, a portion of the cost 

savings from lowering electric consumption accrues to the distribution utility 

(e.g., reduced need to expand T&D lines) and a portion becomes a reduction 

in the revenues of competitive wholesale generators. The policy implications 

of that split need to be considered, but the important point is that the entire 

savings accrues to the retail customers and to society as a whole.

3.2.1	 HOW TO ESTIMATE THE PRIMARY

ELECTRIC SYSTEM BENEFITS OF CLEAN

ENERGY RESOURCES

Implementing clean energy policies and programs 

results in reduced demand for electricity. As described 

earlier, the primary electric system benefits resulting 

from this reduced demand include: 

■Avoided cost of energy generation or wholesale 

energy purchases,

■Avoided cost of power plant capacity,

■ Deferred or avoided T&D capacity costs, and

■Avoided energy loss during T&D. 

States can compare different electric resources, including clean energy resources such as energy efficiency, 

renewable energy, clean distributed generation, or 

combined heat and power, by examining the net 

present value of the revenue requirements over the 

life of the resource. This enables comparison of 

various options on an equal basis, combining capital 

investments—accounting for carrying costs over the 

book life of the investment—with the discounted 

value of their annual fuel and operating costs over the 

investment’s operating life. For example, installing 

Applicable Clean

Energy Resources

Considerations for Determining

Whether to Analyze Who Usually Conducts Analysis?

When is Analysis

Usually Conducted?

BENEFIT: Avoided or reduced risks of stranded costs (from deferring investment in traditional, centralized resources until

environmental and climate change policies are implemented)

■ All clean energy 

resources.

■ Modeling varies from simple to 

complex.

■ Studies to estimate benefits may be 

complex.

■ Regulatory uncertainty adds to 

complexity of analysis.

■ Policy and risk management 

analysts.

■ Resource planning and 

regulatory review of 

planning.

■ Policy studies.

Fuel and technology diversification

■ All clean energy 

resources.

■ Diversity metrics computable from 

generally available data

■ Portfolio analysis of costs vs. risks 

adds complexity.

■ Must consider existing supply 

resources, not just incremental new 

resources.

■ States.

■ PUCs.

■ Utilities.

■ State energy plans.

■ Resource planning.

TABLE 3.2.3	 SECONDARY ELECTRIC SYSTEM BENEFITS FROM CLEAN ENERGY MEASURES (cont.)

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 61
grid will change with the adoption of a clean energy 

resource, based on engineering and economic fundamentals. Some models can predict energy prices, emissions, and other market conditions as well. 

These models are complex to set up and can be costly. 

Developing a detailed representation of the electric 

system can involve many individual input assumptions, and it is important to validate, benchmark, or 

calibrate complex models against actual data. Access 

to confidential system data can also pose a challenge 

to conducting rigorous avoided cost analysis. However, in many cases datasets already exist for regional 

and utility planning analyses. Furthermore, existing 

sector models have the benefit of being well understood and mature.

While developing a full input data set for a dispatch 

simulation model can be a daunting task, it can provide 

a higher level of analytic rigor than basic estimation 

methods, which simplify complex systems and can 

result in errors in estimated costs. It is important to consider whether existing utility models can be relied on 

and are acceptable to stakeholders in a stakeholder process. If they can be relied on, the incremental work of 

estimating clean energy benefits will be greatly reduced. 

Simulations of clean energy programs using sophisticated models can be done on an individual basis (e.g., 

modeling the impact of wind turbines) or the analysis 

can be used to assess multiple clean energy strategies. 

A single analysis of an affected system can provide a 

basis for analyses of a large number of clean energy 

programs simultaneously. For example, a sophisticated 

model may have the ability to assess the impact of an 

energy efficiency program and a renewable portfolio 

standard, capturing any interactions between the two. 

One of the benefits of more sophisticated approaches is 

their ability to capture these kinds of interactions. 

The remainder of this section provides details about 

the methods available to assess the four primary electric systems benefits of clean energy. 

3.2.1.a Avoided Costs of Electricity Generation 

or Wholesale Electricity Purchases

New clean energy resources (on the demand and supply side) avoid electricity and capacity costs in both 

the short run (e.g., three years or less) and in the long 

run (e.g., typically five to 20 years). In the short run, 

avoided costs consist of avoided fuel, variable operation 

and maintenance (O&M), and emissions allowances 

Basic Methods

Basic methods span a broad range of possibilities, but 

generally rely on relatively simple relationships and 

analytic structures. Many are conceptually similar to 

sophisticated methods, but they use simplifying assumptions (proxy plants, system averages) rather than 

using detailed models to develop the impacts or parameters to estimate impacts (e.g., emissions factors). 

For example, in order to estimate impacts of a clean 

energy resource, the goal is to match impacts (in terms 

of reduced demand for electricity) to the generation 

resource that will be displaced. However, instead of 

running a dispatch model to make these estimates, 

simple proxies—for generating units displaced, or 

emissions rates at the time of displacement—are used 

instead. A dispatch model would identify specifically 

those units on the margin in each time period, but with 

a basic method it may be sufficient to pair impacts (i.e., 

changes in generation requirements due to energy efficiency or other clean energy resources) to the general 

type of unit expected to be on the margin. For example, 

for all impacts during the peak period, a natural-gasfired combustion turbine could be used to estimate 

impacts. During baseload periods, a coal plant could 

be used; while in shoulder periods an oil/gas steam 

might be used. The details would depend on the system 

being analyzed. 

Estimation methods can be used for preliminary 

assessments or screening exercises, such as comparing the cost of a clean energy option with a previous 

projection of avoided costs or the cost of a proxy plant. 

Proxy plant assessments are typically done using cost 

assumptions for the expected next addition; for example, a natural gas combined cycle plant. Although they 

are less robust than modeling methods, basic methods 

require less data, time, and resources, so they can be 

useful when time, budget, and data are limited. 

Sophisticated Methods

State-of-the-art power sector models for simulating 

and projecting power plant operations and costs (or 

T&D system adequacy) represent one type of sophisticated model. The sophisticated models have more 

complex structures and interactions than the basic 

approaches, and are designed to capture fundamental 

behavior of the sector using engineering-economic 

relationships or econometric approaches. They require 

additional input assumptions compared with basic 

methods, but add the ability to evaluate how the 

operations and capacity needs of the existing electric 

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 62
Consequently, both cases involve projections of future 

conditions and are subject to many uncertainties that 

influence electricity markets (e.g., fuel prices, construction costs, environmental regulations, and market 

responsiveness to prices). Since avoided costs are calculated as the difference between these two cases, they 

can be very sensitive to the underlying assumptions for 

either or both cases. This uncertainty is characteristic 

of long-run avoided cost calculations which require 

projections far out into an uncertain future. Therefore, 

states may want to consider performing sensitivity or 

scenario analyses on both the underlying base case 

(e.g., on demand growth, fuel prices) and on the key 

drivers of the case with the new resources (e.g., on the 

cost or timing of new resources) to gauge the potential 

range of results. 

that can be saved at those generating units that would 

operate less frequently as a result of new clean energy 

resource additions. Methods to estimate these shortrun avoided costs are described in this section. 

In the long run, however, avoided costs consist largely 

of the capital and operating costs associated with new 

generation capacity and T&D capacity that are displaced or deferred by clean energy resources.4

 Methods to estimate these long-run costs are described in 

Section 3.2.1.b, Avoided Costs of Power Plant Capacity,

and Section 3.2.1.c, Avoided Transmission and Distribution Capacity. 

Key Considerations

A number of challenges arise when calculating shortand long-run avoided costs. Avoided cost estimates 

generally depend upon the comparison of two cases: 

■A baseline or reference case without the new resource, and 

■A case with the new resource, which in the case of 

a demand-side resource includes a reduction in the 

load or load decrement.

4 Sometimes the short-run and long-run effects of clean energy measures are 

referred to as “operating margin” and “build margin,” respectively (Biewald, 2005).

Short-run avoided costs of electricity generation are the 

operating costs of marginal units. Operating costs include 

fuel, variable O&M, and marginal emission costs. In a 

competitive market, wholesale energy prices will reflect 

the generator’s actual costs for operating marginal units 

in the bids they submit.

TABLE 3.2.4	 COMPARISON OF BASIC AND SOPHISTICATED APPROACHES FOR QUANTIFYING AVOIDED

COST OF ELECTRICITY GENERATION OR WHOLESALE ELECTRICITY PURCHASES

Example Advantages Drawbacks

When to Use

This Method

Basic Method

■ Proxy unit

■ Futures prices

■ Previously estimated 

cost projections

■ Simple.

■ May already be 

available.

■ Combines energy & capacity.

■ Not always relevant to a given policy 

if timing or costs are different.

■ Limited horizon (futures).

■ May miss interactive effects (fuel 

and emissions markets) and leakage 

effects for significant clean energy 

investments over time.

■ When time, budget and data are 

limited.

■ Rough estimates.

■ Preliminary assessment.

■ Overview-type policy assessment.

Sophisticated Method (Dispatch Modeling)

■ ProMod

■ Market Analytics

■ MAPS

■ IPM

■ Robust representation 

of electrical system 

dispatch.

■ Cost.

■ Data- and time-intensive.

■ Not transparent.

■ When clean energy resource use 

will change system operations (e.g., 

clean energy resources change the 

marginal generating resource in a 

large number of hours).

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 63
estimation methods, such as using a previously estimated avoided cost projection, may be more appropriate when time, budget, and access to data are limited, 

but they result in an approximation of the costs of 

avoided energy generation. Consequently, it is important for analysts to consider whether the estimation 

method is an acceptable representation of the actual 

system. For example, already-available avoided costs 

may be out of date or may not match the timing of the 

impacts of the clean energy resource being considered. 

The general steps involved in conducting these methods are described in more detail below. 

Step 1: Estimate clean energy operating characteristics. 

The first part of estimating avoided costs of clean energy is to estimate the amount of energy (in kWh) the 

clean energy measure is expected to generate or save 

over the course of a year and its lifetime. Methods for 

estimating this were described in Chapter 2. 

In addition to estimating annual impacts, it may be desirable to estimate the timing of impacts within a year, 

either hourly or on some less frequent interval. Clean 

energy resources that reduce generation requirements 

at the time of peak, when combustion turbines may be 

operating, will differ from those that affect the system 

during periods of low demand when oil/gas steam 

plants or coal plants may be operating. 

In the case of energy efficiency measures, load impact 

profiles describe the hourly changes in end-use demand resulting from the program or measure. In the 

case of energy resources, the generation profiles (for 

wind or PV, for example) are required. The time period 

can range from 8,760 hourly intervals to two or three 

intervals, such as peak, off-peak, and shoulder periods. 

Similarly, a wind turbine can be expected to produce 

differing quantities of electricity across the day and 

year. These data are used to identify more precisely 

what specific generation or generation types are displaced by the clean energy resources. 

Several sources are available to help predict the load 

profiles of different kinds of renewable energy and 

energy efficiency projects:

■ Performance data for renewable technologies are 

available from the National Renewable Energy 

Laboratory (NREL), as well as universities and other organizations that promote or conduct research 

on the applications of renewable energy. For example, the Massachusetts Institute of Technology’s 

Analysis Group for Regional Energy Alternatives 

Methods for Estimating Short-Run Avoided Costs 

of Electricity Generation or Wholesale Electricity 

Purchases

Two types of methods for quantifying short-run avoided costs of electricity generation or wholesale electricity purchases—basic and sophisticated—are described 

below. Both have advantages and limitations that are 

dictated by individual circumstances (see Table 3.2.4), 

and involve these steps as presented in Figure 3.2.1.

1. Estimate clean energy operating characteristics.

Using the total energy impacts estimates (as 

described in Chapter 2), estimate the load impact 

or energy generation profile of the clean energy 

measure—an estimate of when the energy would 

be available—either on an hourly basis, or some 

other more aggregate time scale. 

2. Identify the marginal units to be displaced. Identify 

the generation resources that would be displaced 

as a result of the clean energy resource, either due 

to reduced demand or increased supply of clean 

energy.

3. Identify the characteristics of the marginal units 

displaced. This specifically includes the avoided 

energy costs (and as described later, avoided 

emissions).

4. Map the energy impacts to the displaced unit information. This is done to calculate the short-run 

avoided costs of electricity generation. For basic 

methods, the estimated energy impacts (reduction in load or energy supplied) are mapped to 

the displaced energy information. For example, if 

hourly impacts are estimated, hourly kWh savings 

are multiplied by hourly avoided costs estimates. 

The summation of these hourly values represents 

the impact of the clean energy resource on costs. 

For sophisticated methods, this calculation may be 

a direct output of the modeling exercise. 

The various approaches are described further below.

Basic Methods for Estimating Short-Run Avoided 

Costs

Short-run avoided costs of energy generation can be 

estimated using simplified methods, such as spreadsheet analysis of market prices, marginal cost data, or 

inspection of regional dispatch information (i.e., fuel 

mix and capacity factor by fuel type). Non-modeling 

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 64
several research organizations that have published 

information on wind resources in specific locations. 

All data will likely require some extrapolation or 

transposition for the intended use. 

In the absence of specific data on the load impact or 

energy profile of the clean energy resource, analysts 

will need to use their judgment to assess the timing of 

that resource’s impacts. 

Step 2: Identify the marginal units to be displaced. 

The next step is to identify the units and their associated costs that are likely to be displaced by the clean 

energy resources. While this section discusses the 

process of estimating avoided cost benefits, these same 

methods support the estimation of emissions benefits 

of clean energy. 

In each hour, electric generating resources are dispatched from least to most expensive, on a variable cost 

basis, until demand is satisfied. There are a host of complexities involved in dispatching the generating system, 

including generator start-up and shut-down operating 

constraints and costs, and transmission and reliability 

considerations, among other factors. However, in 

concept, the unit that is displaced is the last unit to be 

dispatched. Estimating the benefits of clean energy 

resources requires identifying this “marginal” unit and 

its avoided costs. Because reported or modeled avoided 

costs may not reflect some of the other complexities 

identified above, simply looking at variable fuel and 

O&M may be misleading. However, basic approaches 

using system averages, time-dependent methods, displacement curves, and load dispatch curve analysis can 

give reasonable estimates of the impacts of clean energy. 

System Averages

The simplest approach to estimating the impacts of the 

displaced unit, absent any detailed information on the 

system, is to use the average generating unit as a proxy. 

Some studies have used this approach. The average system costs and the average emissions characteristics can 

be used to estimate impacts; however, most analysts 

recognize that some types of generating units are almost never on the margin and therefore should not be 

included in the characterization of the marginal unit. 

For example, nuclear units, hydropower, and renewable 

resources are very rarely on the margin and unlikely 

to be displaced by clean energy sources in the short 

run. Moreover, the average cost of generation can differ 

greatly from the marginal source of generation.

and Laboratory For Energy and the Environment 

published a report in 2004 entitled Assessment of 

Emissions Reductions from Photovoltaic Power 

Systems (http://web.mit.edu/agrea/docs/MITLFEE_2004-003a_ES.pdf). Another useful source 

is the Connecticut Energy Conservation Management Board (http://www.ctsavesenergy.org/ecmb/

index.php). 

■The California Database for Energy Efficient Resources (DEER) provides estimates of energy and 

peak demand savings values, measure costs, and 

effective useful life of efficiency measures (http://

www.energy.ca.gov/deer/).

■ Some states or regions have technology production profiles in their efficiency and renewable 

energy potential studies (e.g., NYSERDA’s report, 

Energy Efficiency and Renewable Energy Resource Development Potential in New York State, 

2003, available at http://www.nyserda.org/sep/

EE&ERpotentialVolume1.pdf). 

■ Load impact profile data for energy efficiency measures may be available for purchase from various 

vendors, but typically is not publicly available in 

any comprehensive manner.

■Wind profiles can be obtained from a number of 

sources, including the Department of Energy’s 

NEMS model (http://www.eia.doe.gov/oiaf/aeo/

overview/), NREL (www.nrel.gov), the American 

Wind Energy Association (www.awea.org), and 

FIGURE 3.2.1	 STEPS FOR ESTIMATING

AVOIDED COST

STEP 1

Estimate Clean Energy Operating Characteristics

STEP 2

Identify the marginal units to be displaced

STEP 3

Identify the operating costs of marginal units to be displaced

STEP 4

Calculate the short-run avoided costs of electricity generation

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 65
based on a proxy for its place in the dispatch order. A 

reasonable proxy for the likelihood of a generating unit 

to be displaced by a clean energy measure is the unit’s 

capacity factor. Figure 3.2.2 illustrates this concept 

using capacity factor as a proxy. Baseload plants on 

the right side of the curve, such as nuclear units, are 

assumed to be very unlikely to be displaced; peak load 

plants on the left, such as combustion turbines, are 

much more likely to be displaced. These capacity factor 

estimates can be based on an analysis of actual dispatch 

data, modeling results, or judgment. Historic data on, 

or estimates of, capacity factors for individual plants are 

available from EPA’s eGRID database (http://www.epa.

gov/cleanenergy/energy-resources/egrid/index.html). 

It is important to note that a displacement curve may 

not capture some aspects of electric system operations. 

For example, an extended outage at a baseload unit 

(for scheduled maintenance or unanticipated repairs) 

would increase the use of load-following and peaking 

units, affecting the change in net emissions from the 

clean energy project. According to the displacement 

curve, this plant would be more likely to be displaced, 

even though it would rarely if ever be on the margin. 

The relationship between capacity factor and percent of 

time it will be displaced could be determined analytically (e.g., examining historical data on the relationship 

between a unit’s capacity factor and the time it is on the 

margin. More likely a judgment could be made about 

this relationship. Other proxies could serve to develop this curve, including unit type (e.g., coal steam, 

In response to this observation, one approach sometimes used is to characterize the remaining units—

specifically, the fossil units—as a representation of the 

average marginal unit. This is an improvement over the 

system average, but still does not capture the potential 

impact of a variety of clean energy resources, each with 

differing impact patterns. For example, in many regions of the country coal units are on the margin only 

a small number of hours during the year. Thus, using 

a fossil average may understate cost savings and overstate emissions impacts of the clean energy resource. 

Despite these limitations, absent any detailed information on the impact of the resource or the nature of the 

marginal generation, this approach is an option.

Time Dependent Methods

Another method to estimate the impacts of clean energy resources, including effects on costs and emissions, 

is to identify those resources that are expected to be 

displaced depending on the time the clean energy impacts occur. The most detailed approach is to identify 

the marginal generating unit on an hourly basis. Clean 

energy impacts (in kWh) can then be mapped (using 

the time of impact estimates described above) to the 

appropriate marginal generation source. Costs savings 

(and emissions impacts) can then be estimated. 

Time-dependent methods do not need to be on an 

hourly basis; several less data-intensive basic approaches (displacement curves and load curve analysis) are 

available and described below: 

Displacement Curves

Another approach to estimating what will be displaced 

by clean energy involves displacement curves. Baseload 

plants operate all of the time throughout the year 

because their operating costs are low and because they 

are typically not suitable for responding to the many 

fluctuations in load that occur throughout the day. As a 

result, they would not be expected to be displaced with 

any frequency. These plants would have high capacity 

factors (e.g., greater than 0.8). Capacity factor is the 

ratio of how much electricity a plant produces to how 

much it could produce, running at full capacity, over a 

given time period. Load-following plants, in contrast to 

baseload plants, can quickly change output, have much 

lower capacity factors (e.g., less than 0.3) and are more 

likely to be displaced. 

A displacement curve can be developed to identify 

what generation is likely to be displaced. The curve 

would reflect the likelihood of a unit being displaced, 

FIGURE 3.2.2	 DISPLACEMENT CURVE BASED

ON CAPACITY FACTOR

Source: Keith and Biewald, 2005. 

Sample curve for relating displacement to capacity factor

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 66
nuclear, combustion turbine), heat rate, or pollution 

control equipment in place.

Load Curve Analysis 

In general, generating units are dispatched in a predictable order that reflects the demand on the system and 

the cost and operational characteristics of each unit. 

These plant data can be assembled into a generation 

“stack,” with lowest marginal cost units on the bottom and highest on the top. A dispatch curve analysis 

matches each load level with the corresponding marginal supply (or type of marginal supply). Table 3.2.5, 

Hypothetical Load for One-Week Period, and Figure 

3.2.3, a hypothetical dispatch curve representing 168 

hours by generation unit, ranked by load level, provide 

a combined example of a dispatch curve that represents 

168 hours (a one-week period) during which a hypothetical clean energy resource would be operating. 

Table 3.2.5 illustrates this process for a one-week period. There are 10 generating units in this hypothetical 

power system, labeled 1 through 10. Column [3] shows 

the number of hours that each unit is on the margin. 

In many cases, dispatch curves are available from the 

local power authorities and Load Balancing Authorities 

[e.g., a regional Independent System Operator (ISO)]. 

If this information is not available, states can attempt to 

construct their own analysis. 

Constructing a dispatch curve requires data on:

■ Historical utilization of all generating units in the 

region of interest;

■ Operating costs and emission rates (to support 

emissions estimation, as described in Chapter 4) of 

the specific generating units, for the most disaggregate time frame available (e.g., seasonally, monthly); 

■ Energy transfers between the control areas of the 

region and outside the region of interest (because 

the marginal resource may be coming from outside 

the region); and 

■ Hourly regional loads.

Operating cost and historical utilization data can 

typically be obtained from the EIA (http://www.eia.

doe.gov/cneaf/electricity/page/data.html) or the local 

Load Balancing Authority.5 When generator cost data 

5 Often these sources can also provide generator-specific emission rates for 

estimating potential emission reductions from clean energy. 

FIGURE 3.2.3	 A HYPOTHETICAL LOAD

DURATION/DISPATCH CURVE REPRESENTING

168 HOURS (shown in half-day increments)

by generation unit, ranked by load level

Source: Developed by Synapse Energy, unpublished, 2007. 

0

1,000

2,000

3,000

4,000

5,000

6,000

1 13 25 37 49 61 73 85 97 109 121 133 145 157

Hour

Load (MW)

Oil Combustion Turbine, Old

Gas Combustion Turbine

Oil Combustion Turbine, New

Gas Steam

Oil Steam

Gas Combined Cycle, Typical

Gas Combined Cycle, New

Coal, Typical

Coal, New

Nuclear

TABLE 3.2.5	 HYPOTHETICAL LOAD FOR ONEWEEK PERIOD: HOURS ON MARGIN AND

EMISSION RATE

[1] [2] [3] Unit

Unit nameHours onmargin

1 Oil Combustion Turbine, Old 5

2 Gas Combustion Turbine 10

3 Oil Combustion Turbine, New 9

4 Gas Steam 21

5 Oil Steam 40

6 Gas Combined Cycle, Typical 32

7 Gas Combined Cycle, New 17

8 Coal, Typical 34

9 Coal, New 0

10 Nuclear 0

Weighted average, SO2 emissions (lbs/MWh): 5.59

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 67
In regulated markets, short-run avoided energy costs 

typically include fuel costs, a variable O&M cost, and 

marginal emissions costs for the highest-cost generator 

in a given hour. Data sources for control area hourly 

marginal costs include the U.S. Federal Regulatory 

Commission (FERC) form 714 (http://www.ferc.gov/

docs-filing/eforms/form-714/overview.asp). 

In restructured markets, where RTOs administer regional wholesale power markets, economic dispatch 

is conducted on the basis of bid prices rather than 

generators’ marginal costs (theoretically equivalent to 

the marginal cost). This information is available at each 

ISO’s Web site (see Information Resources at the end of 

this chapter for the Web sites of individual ISOs). 

For longer-term analysis it is necessary to forecast 

cost increases. Historical hourly operating costs for 

the marginal unit (i.e., regulated markets) or market 

prices (i.e., restructured markets) can be escalated 

using forward market electricity prices, though the 

forecast time frame is limited. Forward electricity 

prices are available from energy traders and industry 

journals such as Platt’s MegaWatt Daily (http://www.

platts.com/Electric%20Power/Newsletters%20&%20/

Megawatt%20Daily/).

Step 4: Calculate the short-run avoided costs of electricity generation. For each hour or time of use period, 

multiply the cost of the marginal unit or hourly energy 

market price by the reduction in load (for demandside resources) or the increase in generation (for 

supply-side resources), as estimated using techniques 

described in Chapter 2. Typically, avoided costs are 

expressed as the annual sum of these avoided costs for 

each hour or other time period. 

The Estimating Short-Run Avoided Cost text box illustrates how all four steps can be used to estimate shortrun avoided costs.

Key Considerations

These basic methods have some limitations that should 

be considered when choosing an approach: 

■ Methods that rely on historical data are limited to 

replicating what occurred in the past. Substantial 

changes in costs or performance of generation, or 

other restrictions on their operations (e.g., climate 

legislation, requirements for a renewable portfolio 

standard) could fundamentally change the operation of the system and the implied dispatch curve. 

are not available, capacity factors (from the eGRID 

database, for example, as described above) for traditional generating units can be used to approximate the 

relative cost of the unit (those with the highest capacity 

factors are assumed to have the lowest cost). As an 

exception, variable power resources such as wind and 

hydropower are assumed to have lower costs than fossil 

fuel or nuclear units. 

Operational data (or simplifying assumptions) regarding energy transfers between the control areas of the 

region and hourly regional loads can be obtained from 

the ISO or other Load Balancing Authority within the 

state’s region.

Dispatch curve analysis is commonly used in planning and regulatory studies. It has the advantage of 

incorporating elements of how generation is actually 

dispatched while retaining the simplicity and transparency associated with non-modeling methods. However, 

this method can become labor-intensive relative to 

other non-modeling methods for estimating displaced 

emissions if data for constructing the dispatch curve 

are not readily available. Another disadvantage is that 

it is based on the assumption that only one unit will be 

on the margin at any given time; this generally is not 

true in most regions.

Methods described earlier, such as displacement 

curves, can support the development of a simplified 

dispatch curve. For example, capacity factors can be 

used to “fill” the horizontal segments on the curve as 

shown in Figure 3.2.3. One can assume that units with 

capacity factors greater than 80 percent can fill the 

baseload segments and that peaking units, with the 

lowest capacity factors, would fill the peak segments. 

Units with capacity factors between 80 and 60 percent 

would fill the next slice of the dispatch curve, and so 

on. The resolution would reflect available data or the 

ability to develop meaningful assumptions. The hope 

is that the level of aggregation is such that the units’ 

characteristics are generally similar and as such the 

marginal unit would be approximated by the group average. If data allows, it is possible to take into account 

differences in units that drive their costs and emissions 

(e.g., general unit type and burner type, the presence of 

pollution control equipment, unit size, fuel type). 

Step 3: Identify the operating costs of marginal units 

to be displaced. This process varies depending on 

whether the market is regulated or restructured. 

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 68
especially for clean energy resources with more availability at certain times and for projections of clean 

energy impacts in the future. Dispatch models can also 

be employed to develop parameters that can be used 

to estimate the impacts of a large range of clean energy 

resources. For example, multiple model runs can be 

performed estimating impacts of changes in generation requirements at certain seasons and times of day 

(e.g., winter peak, summer peak, winter base, etc.). 

These parameters, such as the marginal emission rate 

and avoided costs, can be applied to estimates of the 

impacts of clean energy resources at those same times. 

Dispatch models simulate the dynamic operation of 

the electric system given the characteristics of specific 

generating units and system transmission constraints. 

They typically do not predict how the electric system 

will evolve but instead can indicate how the existing 

electric sector will respond to a particular clean energy 

policy or measure. This is appropriate in the short run 

when the electric system is more likely to react than to 

evolve due to clean energy measures. Dispatch models 

specifically replicate least-cost system dispatch and can 

be used to determine which generating units are disӹEven without such fundamental changes, the 

system changes over time as new units are 

added, existing units are retired, and units shift 

in dispatch order. Analyses based on historical data do not capture these shifts, so to the 

extent that estimates are being developed for 

the future these types of basic methods must 

be used with caution. 

■These methods may not adequately address the issue of leakage—in which increases in clean energy 

result in reductions in generation outside the region of interest (e.g., in another state or region)—if 

these transactions are not explicitly accounted for 

in the analysis.

Sophisticated Methods for Estimating Short-Run 

Avoided Costs: Dispatch Modeling 

Sophisticated simulation modeling, such as electric 

dispatch modeling, requires developing a detailed representation of the electric system with many individual 

input assumptions. While developing a full input data 

set for a dispatch simulation model can be a resourceintensive task, the output from a simulation model can 

provide more valid estimates than a basic approach, 

Estimating Short-Run

Avoided Cost

To illustrate the described approach 

for estimating short-run avoided costs, 

consider the case of a state that wishes 

to evaluate the potential benefits of 

an energy efficiency program. Sample 

calculations are illustrated in the accompanying table.

Step 1: The state estimates that the energy efficiency program would reduce 

electricity demand as shown in the 

Avoided Electricity column (based on 

an analysis of annual savings from the 

typical system and a typical load shape). 

Step 2: Using a load curve analysis, 

the state estimates that natural gas 

combustion turbines are typically on 

the margin during peak periods for both 

summer and winter, a mix of natural gas 

combined cycle units and natural gas-fired 

steam units (about 50% of each) are on the 

margin during shoulder periods, and existing coal-fired generators (pulverized coal) 

are typically on the margin during the offpeak periods.

Step 3: The avoided costs associated with 

each of these marginal generating technologies are estimated based on typical 

variable operating and fuel costs for those 

types of units estimated to be on the margin. The results are show in the Avoided 

Energy Cost for Time Period column.

Step 4: The Total Avoided Energy Cost

column shows the result of multiplying the 

Avoided Electricity column by the Avoided 

Energy Cost for Time Period column. Summing across all periods yields the expected 

avoided costs for one year.

SAMPLE CALCULATION OF SHORT-RUN ENERGY AVOIDED COSTS

Time Period

Avoided

Electricity (MWh)

Avoided Energy

Cost for Time

Period ($/kWh)

Total Avoided

Energy Cost ($)

Summer Peak (912 hours) 123,120 0.08 9,234,000

Summer Shoulder (1368 hours) 153,900 0.06 8,772,300

Summer Off-Peak (1368 hours) 20,520 0.03 513,000

Winter Peak (1278 hours) 115,020 0.07 8,051,400

Winter Shoulder (1917 hours) 143,775 0.06 8,195,175

Winter Off-Peak (1917 hours) 19,170 0.03 479,250

Total 575,505 35,245,125

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 69
seasonally, with multiple load segments). These models 

are applied similarly to models that strictly address 

dispatch, but offer the ability to capture the differing 

marginal resources over load levels and time. 

Tools

There are several dispatch models available for states 

to use:

■ EnerPrise Market Analytics (powered by PROSYM) 

supported by Ventyx®. 

A chronological electric power production costing 

simulation computer software package, PROSYM 

is designed for performing planning and operational studies. As a result of its chronological 

nature, PROSYM accommodates detailed hourby-hour investigation of the operations of electric 

utilities. Inputs into the model are fuel costs, variable operation and maintenance costs, and startup 

costs. Output is available by regions, by plants, 

and by plant types. The model includes a pollution 

emission subroutine that estimates emissions with 

each scenario. http://www1.ventyx.com/analytics/

market-analytics.asp

■ Multi-Area Production Simulation (MAPSTM) developed and supported by GE Energy and supported 

by other contractors.

A chronological model that contains detailed 

representation of generation and transmission 

systems, MAPS can be used to study the impact on 

total system emissions that result from the addition of new generation. MAPS software integrates 

highly detailed representations of a system’s load, 

generation, and transmission into a single simulation. This enables calculation of hourly production 

costs in light of the constraints imposed by the 

transmission system on the economic dispatch of 

generation. http://www.gepower.com/prod_serv/

products/utility_software/en/ge_maps/index.htm

■ Plexos for Power SystemsTM owned by Energy 

Exemplar.

A simulation tool that uses LP/MIP (Linear 

Programming/Mixed Integer Programming) optimization technology to analyze the power market, 

Plexos contains production cost and emissions 

modeling, transmission modeling, pricing modeling, and competitiveness modeling. The tool can be 

used to evaluate a single plant or the entire power 

system. http://www.energyexemplar.com

placed and when they are displaced based on economic 

and operating constraints. 

Hourly dispatch modeling is generally used for nearterm, highly detailed estimations. This approach is appropriate for financial evaluations of specific projects, 

short-term planning, and regulatory proceedings. Sensitivity cases can be run to explore the range of possible 

impact values. While this type of modeling is generally 

seen as very credible in these contexts, it often lacks 

transparency. For example, dispatch models vary in 

terms of how they treat outage rates, heat rates, bidding 

strategies, transmission constraints, and reserve margins. Underlying assumptions about these factors may 

not be apparent to the user. Moreover, labor and data 

needs are extensive. Software license and labor costs 

can be prohibitively high for many agencies and stakeholders, who often must rely on the results of dispatch 

modeling conducted by utilities and their consultants 

for regulatory proceedings.

Generally, this method involves modeling electricity 

dispatch with and without the new resource, on an 

hourly basis, for one to three years into the future. As 

with basic estimation methods, it is essential to establish the specific operational profile of the clean energy 

resource. Alternatively, an hourly dispatch model can 

be used to determine hourly marginal costs and emission rates (lbs/kWh), which can then be aggregated 

by time period and applied to a range of clean energy 

resources according to their production characteristics. 

Some models, described later in this chapter, simulate 

both capacity planning and dispatch, although they 

may have a simpler representation of dispatch (e.g., 

NEW YORK ENERGY $MARTSM PROGRAM COST

EFFECTIVENESS ASSESSMENT

The New York State Energy Research and Development 

Authority (NYSERDA) periodically evaluates the costeffectiveness (using a benefit-cost ratio) of New York Energy 

$mart energy efficiency programs. NYSERDA uses a production 

costing model, MAPS, to forecast the avoided energy and 

capacity benefits of the programs for several years. Avoided 

energy costs are forecasted by applying MAPS escalation rates 

to the weighted average energy price by location and time 

period. The weighted average energy prices are based on 

historical hourly NYISO day-ahead market data for January 

2000 through December 2004. The avoided capacity costs 

are forecasted by applying the same escalation rates to NYISO 

monthly capacity data by location and time period. 

Source: Heschong Mahone Group, Inc., 2005. 

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 70
capital costs of peaking capacity (e.g., a combustion 

turbine) or on the market price for peaking capacity. This is a critical factor in competitive wholesale 

markets. Over the long run, however, new clean energy 

initiatives typically avoid or defer both the cost of 

building new power plants and the cost of operating 

them. These are the avoided costs of power plant capacity that can be estimated using either basic estimation 

or sophisticated simulation approaches.8 Both have 

advantages and limitations, as described in Table 3.2.6.

Basic Methods for Estimating Avoided Costs of 

Power Plant Capacity

Basic estimation methods involve the use of tools such 

as spreadsheets to estimate any long-run avoided costs 

of power plant capacity that may result due to a clean 

energy measure under consideration. One method 

commonly used is the proxy plant approach. This approach involves estimating the avoided cost of a power 

plant that might be built in the future. Energy cost 

estimates (as described above) would reflect this plant’s 

dispatch costs for future estimates and the capital costs. 

Depending on future expectations of capital costs, 

fuel prices, and environmental requirements, either a 

8 For information about how utilities estimate avoided costs, see The Guide 

to Resource Planning with Energy Efficiency: A Resource of the National Action Plan for Energy Efficiency, November 2007, www.epa.gov/cleanenergy/

documents/resource_planning.pdf, or Costing Energy Resource Options: An 

Avoided Cost Handbook for Electric Utilities (Tellus Institute, 1995).

■ PowerBase SuiteTM (including PROMOD IV®) supported by Ventyx. 

A detailed generator and portfolio modeling 

system, with nodal locational marginal pricing 

forecasting and transmission analysis, PROMOD 

IV can incorporate extensive details in generating 

unit operating characteristics and constraints, 

transmission constraints, generation analysis, unit 

commitment/operation conditions, and market 

system operations. http://www1.ventyx.com/

analytics/promod.asp

3.2.1.b Avoided Costs of Power Plant 

Capacity

While the avoided cost of energy generation is the 

major short-run benefit, avoided costs of power plant 

capacity in the long run can be significant and should 

be included in resource decisions.6 For example, in the 

short run, surplus centralized generation capacity that 

is freed up by clean energy policies and programs can 

be sold to other utilities in the region for meeting their 

capacity needs. These costs are based on the levelized7

6 For more information about establishing energy efficiency as a high priority 

resource in long run planning, see National Action Plan for Energy Efficiency 

Vision for 2025: A Framework for Change, November 2008. http://www.epa.

gov/cleanenergy/energy-programs/napee/resources/vision2025.html.

7 The present value of capital costs, levelized in real dollars to remove the 

effect of inflation.

TABLE 3.2.6.	COMPARISON OF BASIC AND SOPHISTICATED APPROACHES FOR QUANTIFYING AVOIDED

COSTS OF POWER PLANT CAPACITY

Example Advantages Drawbacks When To Use This Method

Basic approach

■ Peaker construction cost.

■ See also above for combined 

capacity & energy estimate.

■ Simple.

■ May already be available.

■ Peaker methodology does 

not reflect opportunities to 

displace baseload in the long 

run. 

■ Rough estimates.

■ Preliminary screening of 

demand response resources.

■ Overview-type policy 

assessments.

Sophisticated approach

■ Capacity Expansion/Ventyx.

■ PowerBase Suite.

■ IPM.

■ Robust representation of 

electrical system operation.

■ Cost.

■ Data- and time-intensive.

■ Not transparent.

■ When clean energy resource 

use will change system 

operations (e.g., clean 

energy resources change the 

marginal generating resource 

in a large number of hours).

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 71
■The Bureau of Economic Analysis (BEA). The BEA 

provides information on economic forecasts. The 

BEA releases measures of inflation (e.g., the Gross 

Domestic Product Implicit Price Deflator), which 

are available on its Web site http://www.bea.gov/

national/index.htm#gdp 

■The Securities and Exchange Commission (SEC) 

and the Federal Energy Regulatory Commission 

(FERC). Individual utility historical financial data 

are available in annual reports and other utility 

filings with the SEC and FERC. Utilities file annual 

10-K and quarterly 10-Q company reports with 

the SEC. These data are available from the SEC 

EDGAR system at http://www.sec.gov/edgar.shtml. 

Utilities also file FERC Form 1, which is available 

from FERC at http://www.ferc.gov/docs-filing/

eforms/form-1/viewer-instruct.asp. They can also 

be retrieved from the eLibrary at http://www.ferc.

gov/docs-filing/elibrary.asp. 

Using data on initial construction costs, fixed and variable operating costs, and financial data, a discounted 

cash flow analysis can be conducted. Once estimated, 

the net present value of the cost of owning the unit that 

reflects the full carrying costs of the new unit (including interest during construction, debt servicing, property taxes, insurance, depreciation, and return to equity 

holders) can be converted to annualized costs (in $/

kW-year). The annual capital costs ($/kW-year) can 

be multiplied by the annual capacity savings from the 

technology to estimate the avoided capital costs. The 

load profile information (reductions in demand at peak 

hours), discussed earlier would provide an estimate of 

displaced capacity, or simpler estimates can be used.

combined cycle combustion turbine or a new advanced 

coal plant may be used as the proxy plant to represent 

the long-run avoided costs of energy and capacity of 

clean energy initiatives. 

Data required for this method include:

■Cost and performance information for the proxy 

plant; and

■Capital cost escalation rates, a discount rate, and 

other financial data.

Utilities are one possible source of these data and often 

provide this information to public utility commissions 

in resource planning and plant acquisition proceedings. Other data sources include:

■ Regional transmission organizations, independent 

system operators, and power pools. These sources 

maintain supply and demand projections by region 

and often sub-region. 

■The U.S. Energy Information Administration (EIA) 

Annual Energy Outlook. This resource provides 

long-term projections of fuel prices and electricity 

supply and demand. In addition, some states and 

regions develop their own forecasts of electricity 

demand, fuel prices, and other variables. http://

www.eia.doe.gov/oiaf/aeo/

■ Regional reliability organizations. These organizations can provide information on required reserve 

margins.

ELECTRIC ENERGY EFFICIENCY AND RENEWABLE ENERGY IN

NEW ENGLAND: THE OTC WORKBOOK

An analysis conducted by the Regulatory Assistance Project 

(RAP) explains how energy efficiency and renewable energy 

have led to many positive effects on the general economy, 

the environment, and energy security in New England while 

also quantifying these effects in several new ways. The report 

assesses the air quality effects of efficiency and renewable 

investments using the OTC Workbook tool. The analysis finds 

that there is clear progress in reducing CO2 emissions from the 

deployment of energy efficiency and renewable energy. The 

projections by the OTC Workbook indicate that due to current 

energy efficiency programs, 22.5 million tons of CO2 emissions 

are avoided from 2000–2010.

Source: The Regulatory Assistance Project. http://www.raponline.org/

Pubs/RSWS-EEandREinNE.pdf

A RESOURCE FOR CALCULATED AVOIDED EMISSIONS:

THE MODEL ENERGY EFFICIENCY PROGRAM IMPACT

EVALUATION GUIDE

The Model Energy Efficiency Program Impact Evaluation Guide 

provides guidance on model approaches for calculating energy, 

demand, and emissions savings resulting from energy efficiency 

programs. The Guide is provided to assist in the implementation 

of the National Action Plan for Energy Efficiency’s five key 

policy recommendations and its Vision of achieving all costeffective energy efficiency by 2025. Chapter 6 of the report 

presents several methods for calculating both direct onsite 

avoided emissions and reductions from grid-connected electric 

generating units. The chapter also discusses considerations for 

selecting a calculation approach (NAPEE, 2007). 

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 72
impact of energy efficiency resources, based on the 

program design and estimates of its energy and 

capacity savings, or add renewable resources as an 

available supply. This method would capture the 

unique load shape of the clean energy resource.

■ For a less rigorous estimate (e.g., to use in screening candidate clean energy policies and programs 

during program design), reduce the load forecast 

by a fixed amount in each year, proportionally 

to load level. This method does not capture the 

unique load shape or generation supply of the 

clean energy resource.

■ For renewable resources, add the resource to 

the supply mix (or for some models and nondispatchable resources, renewable energy could be 

netted from load in the same manner as is done for 

energy efficiency).

In both the precise and less rigorous methods described above, the difference in the projected capital 

and operating cost over the planning period of the 

two cases is the avoided capacity cost to use in analyzing the clean energy resource. If a per unit avoided 

cost, such as the avoided cost per MWh, is needed for 

screening clean energy resources or other purposes, it 

may be computed by taking the avoided cost (i.e., the 

difference between the cost in the two cases) for the 

relevant time period (e.g., a given year) and dividing 

that by the difference in load between the two cases. 

Step 3: Calculate the avoided costs of power plant 

capacity. The difference between the costs in the two 

projections above represents the annualized or net 

present value costs that would be avoided by the clean 

energy resource.

Capacity expansion or system planning models can 

examine potential long-term impacts on the electric 

sector or upon the entire energy system—in contrast 

to the dispatch models used to assess the avoided costs 

Sophisticated Methods for Estimating Avoided 

Costs of Power Plant Capacity: Capacity 

Expansion Models 

Sophisticated simulation methods, such as capacity expansion models (also called system planning models), 

can be used to quantify the long-run avoided capacity 

costs that result from implementing clean energy 

measures. Capacity expansion models predict how the 

electric system will evolve over time, including what 

capacity will be added through the construction of new 

generating units and what units will be retired, in response to changes in demand and prices. This method 

involves allowing the model to predict what will likely 

happen to the 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 resource. Capacity expansion models are 

typically used 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. 

Using capacity expansion models to estimate the 

avoided costs of power plant capacity typically involves 

the steps described below.

Step 1: Generate a business-as-usual forecast of load 

and how it will be met. Some capacity expansion models use existing generating plants and purchase contracts to serve the load over the forecast period, and the 

model (or the modeler) adds new generic plants when 

those resources do not meet the load forecast. The type 

of plants added depends on their capital and operating 

costs, as well as the daily and seasonal time-pattern of 

the need for power determined using discounted cash 

flow analysis as described earlier. The model repeats 

this process until the load is served through the end of 

the forecast period and a least-cost solution is found. 

This base case contains a detailed schedule of resource 

additions that becomes the benchmark capital and 

operating costs over the planning period for later use 

in the long-run avoided cost calculation. 

Step 2: Include the clean energy resource over the planning period and create an alternate forecast. The following two approaches can be used to incorporate the 

clean energy resource into the second projection:

■ For a more precise estimate of the savings from 

a clean energy program, reduce the load forecast 

year by year and hour by hour to capture the 

Capacity Expansion Modeling involves three steps:

1. Generate a BAU forecast of load, and how load will be met 

without the clean energy resources;

2. Create an alternate forecast that includes the clean energy 

resources over the planning period to show how load is 

expected to be met.

3. Calculate the avoided costs of power plant capacity.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 73
resources; optimization of future decisions; nonproduction-related cost recovery (e.g., construction 

expenditures, AFUDC, and property taxes); full 

pro-forma financial statements; and rate design. 

http://www1.ventyx.com/analytics/strategist.asp

Tools: Whole Energy–Economy System Planning 

Models

Energy system-wide models with electricity sector 

capacity expansion capability include:

■ U.S. DOE National Energy Modeling System 

(NEMS) is a system-wide energy model that represents the behavior of energy markets and their 

interactions with the U.S. economy. The model 

achieves a supply/demand balance in the enduse demand regions, defined as the nine Census 

divisions, by solving for the prices of each energy 

product that will balance the quantities producers 

are willing to supply with the quantities consumers wish to consume. The system reflects market 

economics, industry structure, and existing energy 

policies and regulations that influence market 

behavior. The Electric Market Model, a module 

within NEMS, forecasts the actions of the electric 

power sector over a 25 year time frame and is an 

optimization framework. NEMS is used to produce 

the Energy Information Administration’s Annual 

Energy Outlook, which projects the U.S. energy 

system through 2030 and is used as a benchmark 

against which other energy models are assessed. 

http://www.eia.doe.gov/oiaf/aeo/overview/

■ MARKet ALlocation (MARKAL) Model was created by the DOE Brookhaven National Laboratory 

in the late 1970s, and is now supported by a large 

international users group. MARKAL quantifies the 

system-wide effects of changes in resource supply 

and use, technology availability, and environmental policy. The MARKAL model determines the 

least-cost pattern of technology investment and 

utilization required to meet specified demands and 

constraints, and tracks the resulting changes in 

criteria pollutant and CO2 emissions. This model is 

a generic framework that is tailored to a particular 

application through the development of energy 

system-specific data. MARKAL databases have 

been developed by various groups for national, 

regional, and even metropolitan-scale applications. 

For example, EPA has developed national and 

Census-division level databases (http://www.epa.

gov/appcdwww/apb/globalchange/markal.htm). 

of energy generation, which focus on only the electric 

sector. Capacity expansion models that can examine 

the potential impacts of programs upon the entire energy system are generally used for projecting scenarios 

of how the energy system will adapt to changes in supply and demand or to new policies including emissions 

controls. They take into account the complex interactions and feedbacks that occur within the entire energy 

system (e.g., fuels and emissions markets), rather than 

focusing solely upon the electric sector impacts. This 

is important because there are tradeoffs at the system 

level in the technological and economic feasibility of 

fuels and technologies that may not be captured by a 

model that focuses solely on a particular aspect of the 

electric system. In addition to capturing the numerous interactions, energy system capacity expansion 

models can also model dispatch, although often not in 

a chronologic, 8760-hour dispatch.9

Tools: Electric Sector-only Capacity Expansion 

Models

Commonly used electric sector-only capacity expansion models for calculating long-run avoided costs of 

power plant capacity include:

■ IPM® developed and supported by ICF International.

This model simultaneously models electric power, 

fuel, and environmental markets associated with 

electric production. It is a capacity expansion and 

system dispatch model. Dispatch is based on seasonal, segmented load duration curves, as defined 

by the user. IPM also has the capability to model environmental market mechanisms such as emission 

caps, trading, and banking. System dispatch and 

boiler and fuel-specific emission factors determine 

projected emissions. IPM can be used to model the 

impacts of clean energy resources on the electric 

sector in the short and long term. http://www.icfi.

com/Markets/Energy/energy-modeling.asp#2

■ PowerBase Suite (including Strategist®) supported 

by Ventyx.

Strategist is composed of multiple application modules incorporating all aspects of utility planning and 

operations. This includes forecasted load modeling; 

marketing and conservation programs; production 

cost calculations including the dispatch of energy 

9 For more information about using capacity expansion models to estimate air 

and GHG emissions from clean energy initiatives, please see Section 4.2.2, Step 

2: Quantify Air and GHG Emission Reductions from Clean Energy Measures.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 74
■Avoid or delay costly T&D upgrades, construction, 

and associated O&M costs, including cost of capital, taxes and insurance; and 

■Reduce the frequency of maintenance, because 

frequent peak loads at or near design capacity will 

reduce the life of some types of T&D equipment.

Deferral of T&D investments can have significant economic value. The value of the deferral is calculated by 

looking at the present value difference in costs between 

the transmission project as originally scheduled and 

the deferred project. Most often, the deferred project 

will have a slightly higher cost due to inflation and cost 

escalations (e.g., in raw materials), but can have a lower 

present value cost when the utility discount rate is considered (which affects the utility’s cost of capital). The 

difference in these two factors determines the value of 

deferring the project. 

The avoided costs of T&D capacity vary considerably 

across a state depending on geographic region and 

other factors. Figure 3.2.4, California T&D Avoided 

Costs by Planning Area in 2003, was developed for 

the California Public Utilities Commission in 2003. 

It illustrates how avoided costs of T&D capacity vary 

in California (in $/kW-year) by planning area, utility, 

climate zone, and time of day. Using avoided cost estimates based on these differences, rather than on statewide system averages, enables states to better target the 

design, funding, and marketing of their clean energy 

actions (E3 and RMI, 2004; Baskette et al., 2006). 

The benefit of avoided T&D costs is often overlooked 

or addressed qualitatively in resource planning, because 

estimating the magnitude of these costs is typically 

more challenging than estimating the avoided costs of 

energy generation and plant capacity. For example, the 

avoided T&D investment costs resulting from a clean 

energy program are highly location-specific and depend 

on many factors, including the current system status, 

the program’s geographical distribution, and trends in 

customer load growth and load patterns. It is also difficult to estimate the extent to which clean energy measures would avoid or delay expensive T&D upgrades, 

reduce maintenance, and/or postpone system-wide 

upgrades, due to the complexity of the system. 

MARKAL requires seconds to an hour to run on 

a desktop computer, depending on the size of the 

database and the options selected. http://www.

etsap.org/markal/main.html

■ Energy 2020 is a simulation model that includes 

all fuel, demand, and supply sectors and simulates 

energy consumers and suppliers. This model can 

be used to capture the economic, energy, and environmental impacts of national, regional or state 

policies. Energy 2020 models the impacts of a clean 

energy measure on the entire energy system. User 

inputs include new technologies and economic 

activities such as tax breaks, rebates, and subsidies. 

Energy 2020 uses emission rates for NOX, CO2, 

SO2, and PM for nine plant types included in the 

model. It is available at the national, regional and 

state levels. http://www.energy2020.com/ 

Key Considerations

While capacity expansion or system planning modeling 

is generally seen as very credible in long-run contexts, it:

■ is more resource-intensive than the estimation 

methods and 

■ often lacks transparency due to its complexity and 

proprietary nature. 

It is important to carefully consider key assumptions, 

such as fuel price forecasts and retirements, and the 

ability to accurately model the complex factors affecting 

the system including environmental and other regulatory requirements (e.g., renewable portfolio standards). 

These assumptions point to the need for model validation or calibration against actual data or another 

projection model. 

Most of the models are supported by their developers 

or other consultants who have available data sets. Some 

studies calibrate against the NEMS-generated Annual 

Energy Outlook produced by DOE’s Energy Information Administration. 

3.2.1.c Avoided Transmission and 

Distribution Capacity Costs

Clean energy policies and programs—such as customer-sited renewables and clean DG, including CHP—

that are sited on or near a constrained portion of the 

T&D system, can potentially: 

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 75
of the original T&D investment projects and the present 

value of deferred T&D projects.11

Another factor affecting location-specific T&D project 

cost estimates is system congestion and reliability. 

During periods of high congestion, interconnected 

resources that can be dispatched at these specific times 

are credited at time-differentiated avoided costs. This 

approach is used by the California PUC to estimate 

long-term avoided costs to support analyses of the 

cost-effectiveness of energy efficiency measures. [See 

Section 3.5, Case Studies (E3 and RMI, 2004)]. Reliability considerations are reflected in avoided cost 

calculations through consideration of the Loss of Load 

Probability (LOLP), which is an indicator of the probability of failure to serve loads (NARUC, 1992).

12

Tools

Specialized proprietary models of the T&D system’s 

operation may be used to identify the location and 

timing of system stresses. Examples of such models 

include the following:

PowerWorld Corporation offers an interactive power 

systems simulation package designed to simulate high 

voltage power systems operation on a variable time 

frame. http://www.powerworld.com/ 

Siemens (PSS®E) offers probabilistic analyses and 

dynamics modeling capabilities for transmission planning and operations. https://www.energy.siemens.

com/cms/00000031/en/ueberuns/organizati/services/

siemenspti/softwareso/Pages/psse_1439533.aspx

3.2.1.d Avoided Energy Loss During 

Transmission & Distribution 

In addition to avoiding electricity generation, power 

plant capacity additions, and T&D capacity additions, 

clean energy policies and programs can avoid energy 

losses during T&D when these resources are located 

near the electricity consumer. Avoided energy losses 

during T&D can be estimated by multiplying the estimated energy and capacity savings from clean energy 

11 The investment in nominal costs is based on revenue requirements that 

include cost of capital, insurance, taxes, depreciation, and O&M expenses 

associated with T&D investment. (Feinstein et al., 1997; Orans et al., 2001; 

Lovins et al., 2002)

12 LOLP can be used to allocate the marginal capacity costs to time periods 

(NARUC, 1992, 118). A LOLP of 0.01 means there is a one percent probability 

that the utility might not be able to serve some or all of customer load. Because 

LOLP increases as customer usage increases, a LOLP-weighted marginal 

capacity cost will be high during high LOLP periods.

The most appropriate approach for estimating avoided 

T&D costs is the system planning approach.

10 The 

system planning approach uses projections and thus 

can consider future developments, whether conducted 

via a modeling or non-modeling approach. Generally, 

it is difficult to be precise when calculating the avoided 

cost of T&D capacity because these costs are very 

site-specific and their quantification involves detailed 

engineering and load flow analyses. 

The system planning approach uses projected costs and 

projected load growth for specific T&D projects based 

on the results from a system planning study—a rigorous 

engineering study of the electric system to identify sitespecific system upgrade needs. Other data requirements 

include site-specific investment and load data. This approach assesses the difference between the present value 

10 A projected embedded analysis approach based on historic data also exists, but is considered appropriate for cost allocation during ratemaking. For 

estimating avoided costs due to energy efficiency measures it is important 

to consider future capital investment plans, making the system planning approach preferable. 

FIGURE 3.2.4	 CALIFORNIA T&D AVOIDED

COSTS BY PLANNING AREA IN 2003

Source: Baskette et al., 2006. 

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 76
during peak hours simply because line losses are higher 

at peak times.

The significance of losses in high load periods is further increased by the high marginal energy costs and 

energy prices experienced at those times. Due to the 

variation in loads over the course of the year, T&D loss 

estimates are more precise when developed for short 

time periods (e.g., less than one year). 

Utilities routinely collect average annual energy loss 

data by voltage level (as a percentage of total sales at 

that level). RTOs and ISOs also provide loss data. Note 

that transmission loss, which is smaller than distribution loss, may be included in wholesale energy prices 

in restructured markets.

Estimates of line loss can be applied to the energy 

impacts estimated as described in Chapter 2. If load 

profile information is available, then estimates can 

reflect the higher on-peak loss rate.

3.2.2	 HOW TO ESTIMATE THE SECONDARY

ELECTRIC SYSTEM BENEFITS OF CLEAN

ENERGY RESOURCES

Clean energy policies and programs result in many additional electric system benefits that affect the efficiency 

of electric systems and energy markets. These secondary 

benefits have associated cost reductions, but the methodologies for assessing them are sometimes diverse, 

qualitative, and subject to rigorous debate. As described 

in Section 3.1, some of the key secondary benefits of 

clean energy to electric systems and markets include: 

■Avoided ancillary service costs; 

■Reductions in wholesale market prices;

■ Increased reliability and improved power quality; 

■Avoided risks associated with long lead-time 

investments, such as the risk of overbuilding the 

electric system;

■Reduced risks from deferring investments in traditional centralized resources until environmental 

and climate change policies take shape; and 

■ Improved fuel diversity and energy security.

The ability to estimate the secondary benefits of clean 

energy policies and programs and the availability 

of methods vary depending on the benefit. These 

policies and programs located near or at a customer 

site by the T&D energy loss percentage. An approach 

for determining the energy loss is described below.

The energy loss factor is the percent difference between 

the total energy supplied to the T&D system and the 

total energy taken off the system for delivery to end-use 

customers during a specified time period, calculated as 

1 minus (delivered electricity/supplied electricity). T&D 

losses in the range of 6 percent to 10 percent are typical, 

which means that for every 1 kWh saved at the customer’s meter, 1.06–1.10 kWh is avoided at the generator.

Line loss is typically higher when load is higher, especially at peak times when it can be as great as twice 

the average value. The line loss reductions from energy 

efficiency, load control, and DG are thus significantly 

higher when the benefits are delivered on peak than 

when they occur at average load levels, which greatly 

enhances the reliability benefits. A clean energy measure that saves 1.0 KWh of power at the customer’s meter may save, for example, 1.2 KWh from the generator 

VERMONT USES SYSTEM PLANNING APPROACH TO

ESTIMATE AVOIDED TRANSMISSION COSTS

The Vermont Electric Company (VELCO) owns and maintains 

the bulk transmission facilities in the state to serve all the 

electric distribution utilities. In 2003, VELCO undertook a study 

of alternatives to a proposed major upgrade in the northwest 

corner of Vermont. The transmission upgrade was reliabilitydriven and urgently needed, which resulted in a very high 

bar for alternatives. VELCO reached an agreement with the 

Vermont Department of Public Service to conduct a thorough 

study of distributed generation, energy efficiency, and new 

central generation as alternatives to the upgrade. 

The study identified a range of central generation and 

distributed generation options and estimated their costs. In 

addition, a location-specific study of the available energy 

efficiency potential and the program costs for delivering 

that potential was prepared. Various combinations of energy 

efficiency and generation were assembled as alternatives to the 

proposed transmission project and compared based on total 

present value of cost of service. The study determined the cost 

of the transmission upgrade and the cost of a smaller upgrade 

so that the difference in those two costs could be used to 

assess the cost-effectiveness of the alternative resource 

package. While the alternatives were not adopted, due in part 

to the fact that only the transmission option's costs could be 

spread across the whole ISO region, this study demonstrates 

one way to use the system planning approach to estimate 

avoided transmission costs. 

Source: LaCapra Associates, 2003; Orans, 1989; Orans, 1992.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 77
to estimate the price of voltage support benefits. The 

reactive power provisions in Schedule 2 of the FERC 

pro forma open access transmission tariff, or an RTO’s 

equivalent schedule for reactive support, can be used as 

a proxy for the avoided cost of voltage support. However, the Schedule 2 payments are often uniform across 

a large region. As a result, they may not capture differences in the value of these services in load pockets. Alternately, the difference in reliability with and without 

the clean energy resource can also give some indication 

of voltage support benefits. (See the reliability metrics 

discussion in Section 3.2.2.c Increased Reliability and 

Power Quality.)

Some clean energy measures can have direct beneficial 

effects on avoiding certain voltage support or reactive 

power requirements. Reactive power ancillary services 

are local in nature, and clean energy policies and 

programs that reduce load in a load pocket area can 

minimize the need for local reactive power requirements. On the other hand, solar and wind resources 

may require backup voltage support due to their intermittent nature. 

It is important to note that the avoided costs of reactive 

power and other ancillary services are typically smaller 

than other costs, such as avoided energy, capacity, and 

T&D investment. For example, 2003 reactive power 

payments were only 0.52 percent of the total costs of 

serving load in PJM (Burkhart, 2005). 

methods are less mature than those for primary benefits, and as such tend to rely more upon non-modeling 

estimation approaches than more sophisticated simulation modeling ones. Secondary electric system benefits, 

and methods for estimating them, are described below.

3.2.2.a Avoided Ancillary Services Costs

“Ancillary services” is a catch-all term for electric 

generator functions needed to ensure reliability, as opposed to providing power, and include services such as 

operating reserves and voltage support. 

Operating Reserves

Energy efficiency programs avoid the need for corresponding operating reserves (those generation 

resources available to meet loads quickly in the event a 

generator goes down or some other supply disruption 

occurs) and thus avoid the respective costs. 

RTOs routinely report market prices for ancillary services. In those regions with ancillary service markets, 

such as PJM, NYISO, ISO-NE, ERCOT and the California RTO, services are provided at rates determined 

by the markets and thus are easily valued.13 The market 

value of a given MW of clean energy short-term reserve is equal to the operating reserve price, as posted 

by the RTO or ISO on its Web site.

Voltage Support 

Voltage support is important to ensure the reliable and 

safe operation of electricity-consuming equipment 

and the grid. There are few market metrics available 

13 There can be opportunity costs associated with provision of operating 

reserve. Some regions allow demand response and other clean energy resources 

to bid directly into the energy market. 

ANCILLARY SERVICES THAT CLEAN ENERGY RESOURCES CAN

PROVIDE TO THE SYSTEM

Operating reserve – Spinning: Generation synchronized to the 

grid (i.e., “spinning”) and usually available within 10 minutes 

to respond to a contingency event. For example, 50 MW of 

spinning operating reserve means that a generation unit can 

increase its output by 50 MW within 10 minutes. 

Operating reserve – Supplemental: Generation that is 

available within 30 minutes but is not necessarily synchronized 

to the grid.

Reactive Power/Voltage Support: The ability of a generator 

to “absorb” or “generate” reactive power to meet voltage 

standards on the grid.

DEMAND RESPONSE COULD IMPROVE PLANT UTILIZATION

AND REDUCE EMISSIONS IN NEW ENGLAND

Compared with other regional control areas, New England has 

a small amount of quick-start capacity relative to the regional 

peak load. As such, a number of large oil- and gas-fired steam 

units that do not have the ability to start quickly must run 

constantly to provide reserve capacity. A study conducted for 

the New England Demand Response Initiative (NEDRI) used a 

production costing model (PROSYM/MULTISYM) to evaluate 

how hypothetical aggressive demand response programs 

implemented during the summer of 2006 would affect power 

plant utilization and net emissions when such programs are 

used for reserve capacity. The study found that the demand 

response programs could result in more efficient plant 

utilization, reducing operation of the steam units, and increasing 

operation of efficient combined-cycle units in the region. If no 

diesel generators participate in the demand response programs, 

the study identified the additional potential for reductions in 

NOx, SO2, and CO2 emissions during the summer.

Source: Synapse Energy Economics, 2003.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 78
price reduction benefit (expressed in $/MWh or total 

dollars for the time period). 

This approach for calculating the market price change 

can be applied to the electric energy market and capacity market, if one exists in the region. This benefit can 

be calculated using spreadsheets, an electric system 

dispatch model (e.g., MAPS, ProSym), or an energy 

system model for a more aggregated estimate. Another 

approach, used by the CPUC in California’s avoided 

cost proceeding, is to use historical loads and prices 

(CPUC, 2006).

3.2.2.b Reduction in Wholesale Market 

Clearing Prices

In addition to the benefits of reduced wholesale electricity costs (i.e., avoided energy and capacity costs 

described in Section 3.3), clean energy resources can 

reduce the wholesale market clearing price for electricity as a result of decreased demand for electricity, gas, 

or both. This can directly benefit both utilities and 

consumers.

The methods for estimating short-run wholesale 

market price effects involve relatively well-understood 

data and are reasonably straightforward to apply. In 

contrast, wholesale market price effects over the long 

term involve relatively poorly understood relationships, and estimating these price effects can become 

quite complex. For this reason, this section presents 

the steps involved in estimating the magnitude of the 

price effects of resource additions in the near term using a basic approach. For longer-term forecasts, a more 

sophisticated approach such as a dispatch model may 

be preferred. 

The potential market price decrease attributable to 

a particular clean energy resource can be estimated 

based on a load curve analysis as follows.

Step 1. Determine the time period for which the calculation is to be made.

Step 2. Determine the size of the clean energy resource 

(and the hourly shape if relevant), typically in MW.

(For more information, see Step 1: Estimate Clean Energy Operating Characteristics in Section 3.2.1.a)

Step 3. Develop a dispatch curve that can be based 

upon either generating unit data (i.e., capacity ratings 

and operating costs) or market clearing price data 

(typically available from the ISO or control area operator). (For more information, see Step 2: Identify the 

Marginal Units to be Displaced in Section 3.2.1.a) 

Step 4. Calibrate or validate the calculation for the case 

without the clean energy resource.

Step 5. Analyze a case with the clean energy resource 

by reducing demand or adding supply to represent the 

clean energy resource.

Step 6. Compare the wholesale market price results for 

the two cases. The difference is the wholesale market 

PRICE EFFECTS OF DEMAND RESPONSE IN THE NORTHEAST

IN JULY AND AUGUST, 2006

In all four of the structured, RTO-run eastern spot electricity 

markets, historically high peak load values occurred during a 

week-long heat wave in August 2006. Market coordinators 

from New York (over 1,000 MW of load reduction), PJM (520 

MW of peak reduction) and New England (625 MW of peak 

reduction) all acknowledged the role that demand response 

played in keeping peak load lower than what otherwise would 

have occurred. 

For example, PJM estimated that wholesale prices would have 

been $300/MWh higher without demand response during the 

highest demand hours of the heat wave, corresponding to a 

reported savings of about $650 million for energy purchasers. 

Payments to all demand response providers totaled only 

$5 million; even considering the potential costs of demand 

response programs, such as program administration costs, the 

benefit-cost ratio is favorable.

 Source: PJM, 2006a, PJM, 2006b.

PRICE EFFECTS DUE TO THE NEW YORK ENERGY $MART

PROGRAM

An evaluation of the cost-effectiveness of a portfolio of 

programs under NYSERDA’s New York Energy $mart public 

benefits program estimated the reduction in average wholesale 

electricity prices over the period 2006 (full implementation of 

program) to 2008 (the year after which no currently known 

planned new capacity is assumed to come online). The 

analysis used a production cost model, Multi Area Production 

Simulation Software (MAPS), to compare the average annual 

wholesale electricity commodity prices in two cases: one with 

the New York Energy $martSM Program (the base case), and a 

one without the program benefits (the sensitivity case). The 

study estimated electricity market price reductions of about 

$11.7 million in 2003 to $39.1 million (in 2004 dollars) in 2023 

as a result of the program. 

Source: Heschong Mahone Group, 2005.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 79
3.2.2.c Increased Reliability and Power 

Quality

An expansion in the use of clean energy resources can 

improve both the reliability of the electricity system 

and power quality. For example, California’s investments in energy efficiency, conservation, and demand 

response played a role in averting rolling blackouts in 

the summer of 2001. Power quality problems occur 

when there are deviations in voltage level supplied 

to electrical equipment. Some forms of clean energy 

resources, such as fuel cells, can provide near perfect 

power quality to their hosts.

Reliability Metrics

Although clean energy resources can improve system 

reliability, measuring these benefits can be difficult. The 

most common reliability metrics are indices, which 

are relatively well-established and straightforward to 

calculate (see text box, Reliability Indices). Historical 

reliability data are often available. 

Converting reliability benefits into dollar values is 

complex, however, and the results of studies that have 

attempted to do so are controversial. For this reason, 

their use in support of resource decisions is less common than for other, well-established benefits, such as 

the avoided costs of generation, capacity, and T&D.

Power Quality Metrics

The data needed to assess power quality benefits are 

neither consistently measured nor comprehensively 

collected and reported. Specialized monitoring equipment is typically necessary to measure power defects, 

and acceptable standards for power quality have been 

changing rapidly. 

Power quality improvements produce real economic 

benefits for electricity consumers by avoiding damage 

to equipment and associated loss of business income 

and product, and, in some cases, the need for redundant power supply. At the extreme, some commercial 

and industrial processes, such as silicon chip fabrication and online credit card processing, are so sensitive 

to outages or power quality deviations that customers 

take proactive steps to avoid these concerns, including 

construction of redundant transmission lines or installing diesel or battery backup power. The costs of such 

equipment could also be used to estimate the value of 

increased reliability and power quality.

RELIABILITY CONCEPTS

Reliability refers to the electric system’s availability to 

consistently serve the demanded load. 

Power Quality refers to the consistency of voltage of electricity 

supplied to electrical equipment (usually meaning the voltage 

stays within plus or minus 5 percent).

RELIABILITY INDICES

SAIFI (system average interruption frequency index): the 

average frequency of sustained interruptions per customer 

over a predefined area. It is calculated as the total number 

of customer interruptions divided by the total number of 

customers served.

SAIDI (system average interruption duration index): commonly 

referred to as customer minutes of interruption or customer 

hours, it provides information on the average time customers 

are interrupted. It is calculated as the sum of the restoration 

time for each interruption event times the number of 

interrupted customers for each interruption event divided by 

the total number of customers.

CAIDI (customer average interruption duration index): 

the average time needed to restore service to the average 

customer per sustained interruption. It is calculated as the sum 

of customer interruption durations divided by the total number 

of customer interruptions.

MAIFI (momentary average interruption frequency index): 

considers momentary interruptions resulting from each 

single operation of an interrupting device, such as a recloser. 

It is calculated as the total number of customer momentary 

interruptions divided by the total number of customers served.

RELIABILITY BENEFITS OF CLEAN ENERGY

Clean energy provides reliability benefits because when a 

small clean energy unit fails, the result is less catastrophic 

than when one large, traditional generating unit fails. For 

example, suppose a utility has the choice of installing one 

hundred kilowatts of clean DG around its system or installing a 

single 10 megawatt generator (100 units times 100 kW). In this 

situation, there would likely be a greater probability of the 10 

MW generator being out of service than of finding all 100 of the 

smaller units out of service. Such an effect can either reduce 

the reserve margin required (which benefits both the utility and 

consumers) or, if the reserve margin is fixed, reduce the price of 

reserve capacity (Lovins et al., 2002). 

THE IMPORTANCE OF POWER QUALITY

It is important to maintain consistent power quality; otherwise, 

electrical equipment can be damaged. For example, consumer 

and commercial electrical and electronic equipment is usually 

designed to tolerate extended operation at any line voltage 

within 5 percent nominal, but extended operation at voltages 

far outside that band can damage equipment or cause it to 

operate less efficiently.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 80
3.2.2.e Reduced Risks from Deferring 

Investment in Traditional, Centralized 

Resources Pending Uncertainty in Future 

Environmental Regulations 

Clean energy resources offer planners options for 

mitigating current and future environmental regulation 

risks. Clean energy can reduce the cost of compliance 

with air pollution control requirements. Utilities and 

states also see clean energy as a way to reduce their 

financial risk from future carbon regulations. 

For example, a 2008 study looked at 10 utilities in 

the western U.S. and examined how their respective 

resource plans accounted for future carbon regulations. 

The study found that the majority of the 10 utilities 

included aggressive levels of energy efficiency and 

renewable energy to reduce carbon emissions. The 

study also found that in making these decisions the 

utilities did not consider the indirect impacts of future 

carbon regulations, such as increased wholesale electric 

market price, retirements of conventional generation 

plants, and the impact on transmission and distribution expansion (Barbose et al., 2008). 

When comparing new generation options in the face 

of potential environmental regulations, some states 

and utilities are reducing financial risk by placing a 

higher cost premium on traditional resources relative 

to clean energy. For example, California has adopted 

an $8/ton carbon dioxide greenhouse gas adder to be 

used in comparing resources (Johnston et al., 2005; CA 

PUC, 2004). 

3.2.2.f Improved Fuel Diversity and Energy 

Security

Portfolios that rely heavily on a few energy resources 

are highly affected by the unique risks associated with 

any single fuel source. In contrast, the costs of clean 

3.2.2.d Avoided Risks Associated with Long 

Lead-time Investments Such as the Risk of 

Overbuilding the Electric System 

Clean energy options provide increased flexibility to 

deal with uncertainty and risk related to large, traditional fossil fuel resources, including: 

■Clean energy resources, such as wind and photovoltaics, reduce the impact on electric system costs 

from fuel price uncertainty relative to traditional 

resources, and lower the financial risks and costs 

associated with generation.

■ In terms of resource planning, clean energy options offer great flexibility. If one is unsure that 

long-term forecasts for load growth are 100 

percent accurate, then clean energy resources offer 

greater flexibility due to their modular nature and 

relatively quick installation times relative to traditional resources.14

■Clean energy resource options provide more time 

to develop technologically advanced, less polluting, 

more efficient, large-scale technologies. 

All other things being equal, a resource or resource 

plan that offers more flexibility to respond to changing 

future conditions is more valuable than a less flexible 

resource or plan. Techniques such as decision tree 

analysis or real option analysis provide a framework 

for assessing this flexibility. These approaches involve 

distinguishing between events within one’s control (i.e., 

decision nodes) and those outside of one’s control (i.e., 

exogenous events) and developing a conceptual model 

for these events as they would occur over time. Specific 

probabilities are generally assigned to the exogenous 

events. The results of this type of analysis can include 

the identification of the best plan on an expected value 

basis (i.e., incorporating the uncertainties and risks) or 

the identification of lower risk plans. 

Above and beyond the expected value of the plan, 

certain resources may have some “option value” if they 

allow (or don’t foreclose) other resource options in the 

future. For example, a plan that involves implementing 

some DSM in the near term can have value above its 

simple short-run avoided cost, in that it develops the 

capability for expanded DSM deployment in the future 

if conditions call for it. 

14 Of course, clean energy resources carry their own risk of non-performance.

THE IMPORTANCE OF LOW PERFORMANCE CORRELATIONS

Similar resources (e.g., fossil fuels such as coal and oil) tend to 

face similar specific risks, and as a result their performances 

tend to be correlated. For example, coal and oil both emit 

CO2 when burned and thus could be associated with future 

climate change regulatory risk, which in turn would likely 

increase costs and affect the performance of oil- or coal-fired 

generation. On the other hand, disparate resources (e.g., coal 

and wind) have lower performance correlations—and hence 

more value for offsetting resource-specific risks within the 

portfolio—than resources that have little disparity.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 81
data required for the indices (annual state electricity generation by fuel type and producer type) are 

readily available from the EIA Form 906 database.16

Use of these indices is appropriate for preliminary 

resource diversity assessment and as a state or 

regional benchmark. Annual state electricity 

generation data by producer type and fuel type are 

available.

A limitation of these indices is that decisions on how 

to classify resources (e.g., calculating the share of 

all coal rather than bituminous and subbituminous 

coals separately) can have a large effect on the results. 

Another shortcoming is that the indices do not differentiate between resources that are correlated with each 

other (e.g., coal and natural gas) and thus can underestimate the portfolio risk when correlated resources 

are included. 

■ Portfolio Variance. The concept of portfolio theory 

suggests that portfolios should be assembled and 

evaluated based on the characteristics of the portfolio, rather than on a collection of individually 

assessed resources. Portfolio theory and portfolio 

variance measures account for risk and uncertainty 

by incorporating correlations between resources 

16 EIA Form 906 has been superseded by EIA Form 923. Both data sets are 

available at http://www.eia.doe.gov/cneaf/electricity/page/eia906_920.html

energy resources are not affected by fossil fuel prices 

and thus can hedge against fossil-fuel price spikes by 

reducing exposure to this volatility. 

Diversity in technology can also reduce the likelihood 

of supply interruptions and reliability problems. For 

example, while geothermal plants can be expensive 

to construct, they offer an almost constant supply of 

energy and are best suited for baseload generation. Gas 

turbines, on the other hand, are relatively inexpensive 

to construct and can start quickly, but have a high 

operating cost and so are best suited for peaking generation. Figure 3.2.5 illustrates the relationship between 

electricity and natural gas prices in New England.

Two approaches for estimating the benefits of fuel and 

technology diversification include market share indices 

and portfolio variance.

■ Market share indices. Market share indices, such as 

the Herfindahl-Hirschmann Index and ShannonWeiner index, identify the level of diversity as a 

function of the market share of each resource.15

These indices are computationally simple and the 

15 For more information about these indices, see U.S. Department of Justice 

and the Federal Trade Commission, Issued April 1992; Shannon, C.E. "A 

mathematical theory of communication." Bell System Technical Journal 27: 

379–423 and 623–656, July and October 1948.

FIGURE 3.2.5.	 NATURAL GAS AND ELECTRICITY PRICES IN NEW ENGLAND

A large portion of New England’s electricity is generated from natural gas. Due to this high dependence on one fuel source, and because fuel 

represents a large portion of the cost to produce electricity, natural gas and electricity prices are highly correlated. 

Sources: EIA; ISO NE, summary of monthly data, 2006. 

0

20

40

60

80

100

120

140

2003 2004 2005 2006 2007 2008 2009

Peak Period Electric Prices ($/MWh)

0

5

10

15

20

25

Elec Gen NG Prices ($/MBtu)

Peak Price

NG Price

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 82
3.3	 CASE STUDIES

The following two case studies illustrate how assessing the electric system benefits associated with clean 

energy can be used in the state energy planning and 

policy decision-making process. 

3.3.1	 CALIFORNIA UTILITIES’ ENERGY

EFFICIENCY PROGRAMS

Benefits Assessed

■Avoided electricity generation costs

■Avoided T&D costs

■Avoided environmental externality costs

■Avoided ancillary services costs

■Reduced wholesale market clearing prices

Clean Energy Program Description

In 2005, the California Public Utilities Commission 

(CPUC) approved a new method for calculating 

avoided costs for use in evaluating 2006–2008 utility 

energy efficiency programs in California. 

when projecting overall portfolio performance, 

as measured by the standard deviation of cost or 

some other measure of performance. The standard 

deviation can be calculated for a number of portfolios, each with a variety of different resources, to 

find portfolios that simultaneously minimize cost 

and risk. It is important to acknowledge this inherent trade-off between cost and risk; there is not a 

single portfolio that lowers both. 

Like market share metrics, portfolio analysis does 

not readily incorporate the non-price and qualitative 

benefits of fuel diversity, such as energy independence, 

which can be a benefit of clean energy. It is safer to 

have many smaller, generating resource units that are 

located in a variety of locations and do not require fuel 

stored on-site than to have one easily targeted large 

unit. Also, using domestic clean energy resources to 

reduce dependence on foreign fuel sources, such as 

imported petroleum, may yield political and economic 

benefits by protecting consumers from supply shortages and price shocks. Care should be taken to consider 

price as well as factors that are not easily quantified 

when choosing among portfolios with different costrisk profiles.

TABLE 3.3.1	 COMPARISON OF OLD AND NEW AVOIDED COST METHODOLOGIES

Avoided Cost

New Methodology Old Methodology

Time Area Time Area

Avoided electricity generation costs Hourly Utility-specific Annual 

Average 

Values

Statewide

Avoided Electric Transmission & 

Distribution Costs

Hourly Utility, planning area and 

climate zone specific

Avoided Natural Gas Procurement Monthly Utility-specific

Avoided Natural Gas Transportation & 

Delivery

Monthly Utility-specific

Environmental externality Adders for 

Electric and Gas

Annual value, applied by 

hour per implied heat rate

System-wide (uniform 

across state)

Reliability adder (Avoided ancillary services 

costs)

Annual value System-wide (uniform 

across state)

None None

Price elasticity of demand adder (Reduced 

wholesale market clearing prices)

Time of use period (onvs. off-peak) by monthSystem-wide (uniform 

across state)

None None

Source: E3 and RMI, 2004

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 83
Results

These results demonstrated the value of estimating 

avoided costs using time- and location-specific data by 

highlighting the importance of reducing demand during peak hours. It found that avoided costs (especially 

T&D avoided costs) were particularly high during peak 

hours and the peak summer season.

Figure 3.3.1 shows the results of avoided cost calculations for three different efficiency resources—air conditioning, outdoor lighting, and refrigeration programs—

using both the new and existing methodologies. The 

largest difference in avoided costs between the new 

and the old methods occurred in the air conditioning 

program ($133/MWh with the new method compared 

with $80/MWh with the old method), illustrating the 

higher value placed on peak hour reductions. Outdoor 

lighting and refrigeration measures had lower avoided 

cost values when estimated with the new method than 

with the old method, because these appliances are used 

off-peak or throughout the day—many hours of which 

have very small avoided costs. Outdoor lighting appliances had the lowest values because they are used offpeak, when there are no avoided values for T&D. Since 

the initial avoided cost values were adopted, the CPUC 

adopted correction factors for residential and commercial air conditioning measures to better account 

for their previously undervalued peak load reduction 

contribution. 17 (CPUC, 2006)

As shown in Table 3.3.2, when applying this new 

methodology, California’s energy efficiency programs 

are estimated to have a total program lifetime benefit of 

17 Hourly avoided costs are averaged over the time-of-use periods for measures whose hourly load data are not available. Because this method did not 

use a load-weighted average, the measures that make a significant contribution to peak load reduction such as air conditioning were undervalued. To 

address this problem, the CPUC adopted correction factors for air conditioning 

measures to increase the averaged avoided cost values.

Method(s) Used

The methodology is described in a detailed report 

issued in October 2004, Methodology and Forecast of 

Long Term Avoided Costs for the Evaluation of California 

Energy Efficiency Programs (E3 and RMI, 2004). The 

new methodology includes five major categories of 

costs that are avoided when demand is reduced through 

installation of energy efficiency resources. It produces 

time- and location-specific cost estimates, whereas the 

previous avoided cost methodology relied more upon 

average statewide values. Table 3.3.1 summarizes the 

differences between the old and new methodologies. 

The key findings of this study were based on the avoided 

costs derived from the new methodology and an avoided costs spreadsheet model that allows ongoing updates 

to account for changes in variables such as fuel prices.

TABLE 3.3.2	 ESTIMATED COST EFFECTIVENESS TEST RESULTS FOR THE CALIFORNIA INVESTOR OWNED

UTILITIES’ 2006–2008 EFFICIENCY PROGRAMS

Costs & Benefits SDG&E SoCalGas SCE PG&E Total

Total costs to billpayers (TRC) $299,443,761 $225,381,390 $857,516,394 $1,341,473,455 $2,723,814,999

Total savings to billpayers (TRC) $579.619,963 $318,003,849 $2,367,984,783 $2,153,115,608 $5,418,724,203

Net Benefits to billpayers $280,176,202 $96,622,459 $1,510,468,390 $811,642,153 $2,694,909,204

Source: CPUC, 2005

FIGURE 3.3.1	 COMPARISON OF AVOIDED

COSTS FOR THREE EXAMPLE MEASURES

Source: E3 and RMI, 2004. 

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 84
The analysis used the PROSYM simulation model to 

determine the potential price and emissions impacts 

of the scenarios. The model was used to simulate the 

average hourly wholesale market clearing prices and 

the regional greenhouse gas emissions (apportioned to 

Massachusetts based on GWh load) in 2020 under a 

reference case and each of the following four scenarios:

$5.4 billion, twice as large as the cost of the programs18

(CPUC, 2005).

For More Information

■ Energy Efficiency Portfolio Plans and Program 

Funding Levels for 2006-2008 - Phase 1 Issues. California Public Utilities Commission. Interim Opinion. September 22, 2005. http://www.cpuc.ca.gov/

PUBLISHED/FINAL_DECISION/49859.htm

3.3.2	 ENERGY EFFICIENCY AND

DISTRIBUTED GENERATION IN

MASSACHUSETTS

Benefit(s) Assessed

■Reduction in wholesale market clearing prices

■Avoided greenhouse gas (CO2

) emissions

Clean Energy Program Description

This study explores the potential price and emissions 

benefits of different options to increase distributed generation and energy efficiency in Massachusetts. The options include the addition of the following new demand 

resources over the baseline scenario through 2020:

■ photovoltaics (PV), 

■ energy efficiency (EE), 

■ combined heat and power (CHP), and 

■ combined EE and CHP.

Method(s)

The analysis required the development of a reference 

case to determine what the wholesale electric prices 

and carbon dioxide emissions would be without the 

additional clean energy resources. It assumed no ratepayer-funded investments in demand side management 

(DSM) programs beginning in 2007 and so it assumed 

energy savings achieved through the end of 2006 remain 

constant in the future. The reference case also assumed 

no new policies to encourage distributed generation. 

18 As a result of the energy efficiency programs, California’s investor-owned 

utilities project savings of about 7,370 GWh of electricity, 1,500 MW of peak 

demand, and 122,000 megatherms of natural gas from 2006 to 2008. Relative 

to a base case without the programs, the utilities expect to reduce carbon 

dioxide emissions by about 6,600,000 tons — the equivalent of the emissions 

of about 1.2 million cars over the same period.

FIGURE 3.3.2	 REDUCTION IN AVERAGE

ANNUAL WHOLESALE ELECTRIC ENERGY

PRICE FOR MASSACHUSETTS PURCHASES IN

2020 UNDER PV, EE, AND CHP+EE CASES

Source: Impacts of Distributed Generation on Wholesale Electric 

Prices and Air Emissions in Massachusetts, Synapse Energy 

Economics, March 31, 2008.

FIGURE 3.3.3	 REDUCTIONS IN REGIONAL

CO2 EMISSIONS IN 2020 UNDER PV, EE, AND

CHP+EE CASES RELATIVE TO REFERENCE

CASE MASSACHUSETTS CO2 EMISSIONS

Source: Impacts of Distributed Generation on Wholesale Electric 

Prices and Air Emissions in Massachusetts, Synapse Energy 

Economics, March 31, 2008.

Chapter 3 | Assessing the Multiple Benefits of Clean Energy 85
These market price changes will affect the wholesale 

energy costs paid by Massachusetts customers. Even 

though it is expected to achieve the lowest reduction in 

market clearing prices, PV is expected to achieve the 

largest wholesale market cost savings to Massachusetts 

consumers: $65 for every MWh generated by PV. EE is 

estimated to reduce costs by $24 for every MWh saved. 

The study estimates a savings of $35 per MWh of CHP 

generation. The values are different due to the different 

load shape profiles for each resource and the timing 

(and costs) for when each is likely to be used. 

For greenhouse gas emissions, each of the alternative 

scenarios would achieve reductions of CO2 emissions 

relative to the reference case. The combined EE and 

CHP scenario is likely to produce the greatest impact, 

with a reduction of 2.4 million short tons CO2 /year in 

2020. The majority of these reductions come from EE. 

For More Information

■ Impacts of Distributed Generation on Wholesale 

Electric Prices and Air Emissions in Massachusetts, 

Synapse Energy Economics, March 31, 2008. 

http://www.masstech.org/dg/2008-03-SynapseDG-Impacts-on-NE.pdf

■ 250 MW of incremental PV;

■ Investment in EE sufficient enough to reduce annual growth of Massachusetts’ energy consumption 

to 0.6 percent;

■ 750 MW of incremental DG from CHP; and

■A combined CHP and EE case.

The scenarios are compared against the reference case 

to determine the impacts.

Results

The study projected that the combined effect of the 

PV, EE, and CHP would be to virtually eliminate load 

growth in Massachusetts. 

In terms of impact on wholesale market prices:

■ the 250MW of PV is expected to displace 356 

GW of purchases from the wholesale market and 

reduce wholesale market prices by $.033/MWh or 

0.4 percent, 

■ EE is expected to reduce prices by 1.6 percent, and 

■ the combined EE and CHP scenario would produce a 5.1 percent reduction in prices. 

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Chapter 3 | Assessing the Multiple Benefits of Clean Energy 86
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Chapter 3 | Assessing the Multiple Benefits of Clean Energy 87
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