National Renewable Energy Laboratory 

Innovation for Our Energy Future 

A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 

Conference Paper 

NREL/CP-500-38062 

May 2005 

Determining the Capacity 

Value of Wind: A Survey of 

Methods and 

Implementation 

Preprint 

M. Milligan, Consultant 

National Renewable Energy Laboratory 

K. Porter 

Exeter Associates, Inc. 

To be presented at WINDPOWER 2005 

Denver, Colorado 

May 15–18, 2005 
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1

DETERMINING THE CAPACITY VALUE OF WIND: 

A SURVEY OF METHODS AND IMPLEMENTATION 

Michael Milligan 

Consultant 

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401 

303-384-6927 

303-384-6901 (fax) 

michael_milligan@nrel.gov 

www.nrel.gov/wind

Kevin Porter 

Senior Analyst 

Exeter Associates, Inc. 

5565 Sterrett Place 

Suite 310 

Columbia, MD 21044 

410-992-7500 

410-992-3445 fax 

porter@exeterassociates.com 

www.exeterassociates.com

Abstract 

Regional transmission organizations, state utility regulatory commissions, the North 

American Electric Reliability Council, regional reliability councils, and increasingly, the 

Federal Energy Regulatory Commission all advocate, call for, or in some instances, 

require that electric utilities and competitive power suppliers not only have enough 

generating capacity to meet customer demand but also have generating capacity in 

reserve in case customer demand is higher than expected or if a generator or transmission 

line goes out of service. Although the basic concept is the same across the country—

ensuring enough generating capacity to meet customer demand for electricity—how it is 

implemented is strikingly different from region to region. 

Related to this question is whether wind energy qualifies as a capacity resource. Wind’s 

variability makes this a matter of great debate in some regions. However, many regions 

accept that wind energy has some capacity value, albeit at a lower value than other 

energy technologies. Recently, studies have been published in California, Minnesota, 
2

and New York that document that wind energy has some capacity value. These studies 

join other initiatives in PJM, Colorado, and in other states and regions. 

This paper focuses on methodologies for determining the capacity value of generating 

resources, including wind energy. It summarizes several important state and regional 

studies that examine the capacity value of wind energy, how different regions define and 

implement capacity reserve requirements across the country, and how wind energy is 

defined as a capacity resource in those regions. 

Introduction and Overview 

An interesting feature of electric power markets is the near unitary elasticity of demand. 

There is little customer response to increasing electric prices as customers either have 

little incentive to respond to higher electric prices (because of rate freezes or flat pricing 

structures) or do not have the technical ability to respond (because of older meter 

technology). 1 

In recognition of this problem, load-serving entities (LSEs) such as electric utilities 

generally maintain some percentage reserve margin of capacity over and above their load 

requirements to maintain reliable electric service. State regulators, and even state statutes, 

may also require LSEs to maintain a certain reserve margin. The North American Electric 

Reliability Council (NERC) also requires regional reliability councils to meet certain 

reliability standards, and as part of that, regional reliability councils will require LSEs to 

have reserve capacity (generators that can respond quickly) and planning reserve capacity 

(generators that do not have to respond quickly). The regional capacity standards are 

voluntary, differ by region, and depend in part on how each region determines the 

capacity value of a generator. Regional transmission organizations also may require LSEs 

to have a capacity reserve margin. 

Although the source of the capacity reserve requirements may differ, common elements 

are present in all of them. For instance, the nameplate capacity of a generating plant is 

discounted to reflect the probability of the plant going off-line for scheduled or 

unscheduled maintenance. A time differentiation for capacity may also be applied, as it is 

generally (but not always) recognized that available capacity is more valuable at times of 

peak electric demand than at other times. 

An important trade-off is involved with capacity reserve requirements. Almost by 

definition, capacity reserve requirements involve making financial investments in 

generating capacity that will not be used or not used often. One could “overpay” for 

reliability by having too much reserve capacity. Therefore, the trade-off is enough reserve 

capacity to ensure reliable electric service while minimizing the costs of having reserve 

capacity. 

 

1

 In some regions, regulators, utilities, and market participants are working on better enabling customer 

response to higher electric prices, otherwise known as demand response. Because this article is focused on 

wind energy as a capacity resource, demand response will not be discussed further. 
3

A central element of preserving electric reliability is to ensure that more generating 

capacity is available than is necessary to serve demand for electricity, or load. During the 

height of electric power restructuring initiatives, however, capacity requirements were 

sometimes viewed as a vestige of monopoly regulation, to be replaced by competitive 

markets where a multitude of market participants compete to provide energy to 

customers. Reserve capacity requirements were therefore not seen as necessary. The 

California electricity crisis of 2000 and 2001, when California did not have a reserve 

capacity requirement, prompted a reconsideration of reserve capacity requirements. 

With nearly 7 GW of installed wind capacity in the United States at the end of 2004 and 

another 2.5 GW expected to come on-line in 2005, the question of whether wind energy 

is a capacity resource is gaining more attention. Wind’s low cost and environmental 

benefits, and the higher cost of competing fuels such as natural gas, mean that system 

planners will need to grapple with how to determine the capacity value of wind energy. 

Wind generators occupy a unique place in the determination of capacity and effective 

load carrying capability (ELCC). Wind generators have typically very high mechanical 

availability, exceeding 95% in many instances (i.e., the forced outage rate is often below 

5%). However, because wind generators only generate electricity when the wind blows, a 

wind generator arguably has a forced outage when the wind does not blow. Therefore, the 

effective forced outage rate for wind generators may be much higher, from 50% to 80%, 

when recognizing the intermittent availability of wind. In addition, wind’s value to the 

electric system may also vary. The output from some wind generators may have a high 

correlation with load and thereby can be seen as supplying capacity when it is most 

needed. In this situation, a wind generating plant should have a relatively high capacity 

credit. The output from other wind generating plants may not be as highly correlated with 

system load, and therefore would have a lower value to the electric system and should 

receive a lower capacity credit (Milligan and Parsons 1999). The correlation of wind 

generation with system load, along with the wind generator’s outage rate, will determine 

how much capacity credit a wind generator will receive. 

This article discusses how capacity is valued for reliability purposes, compares how 

different regions around the country determine capacity reserve margins, and illustrates 

how the capacity value of wind is calculated. The notion of wind energy as a capacity 

resource is not a universally accepted one, and this article will discuss that as well. After 

all, wind energy is a variable resource, with production only occurring when the wind 

blows. 
4

Methods for Calculating Capacity Credit 

This section examines some methods that can be used to calculate the capacity value of 

an intermittent generator such as wind. The intermittency of wind transforms this 

problem into one of additional complexity. Capacity value methods for wind plants that 

are in use today differ in some respects. In part, this is because of different definitions of 

capacity value, but it is also because capacity value is used in several different contexts, 

as discussed. 

To begin, it is useful to discuss the properties that a desirable capacity credit metric 

should possess. A capacity metric should be capable of assessing all types of generators, 

whether baseload, conventional, or intermittent. Because all generators have some 

probability of failure during critical times, the metric should recognize this potential, and 

plants that experience more outages should have a capacity value less than a plant that 

experiences fewer outages. An intermittent (or conventional) generator that never 

delivers during high-risk (peak) periods should have a low or zero capacity value, 

whereas an intermittent (or conventional) generator that consistently delivers on peak 

should have a relatively high capacity value. A generator that sometimes delivers during 

peak periods should have a capacity value somewhere in between these extreme values. 

In this case, a “sometimes-available” generator cannot be counted on with a high degree 

of certainty, but it does reduce the risk of insufficient generation when it is available. The 

capacity value must therefore be a probabilistic-based metric that can take these generator 

characteristics into account and rank the relative contribution of the generator fleet to the 

reduction of system risk. 

To properly value generators with different capacity contributions, it is useful to adopt 

the principle of vertical consistency. This concept, borrowed and adapted from public 

economics, says that power plants with a high capacity contribution should be ranked 

above power plants that have a lower capacity contribution. Plants that are equivalent in 

their reliability (and delivery) should receive the same ranking. This latter approach is 

horizontal consistency. These fundamental principles, while very simple, can help guide 

us toward a suitable metric. 

Effective Load Carrying Capability 

Fortunately, such a metric does exist that possesses these properties, and it has been used 

for several decades. It is based on well-established reliability theory and practice and can 

be applied to all generators. This metric is based on one of several reliability metrics, 

such as loss of load probability (LOLP), loss of load expectation (LOLE), or expected 

unserved energy (EUE). The metric itself is ELCC and can be carried out with a power 

system reliability model, with appropriate tweaking to properly account for the stochastic 

and variable nature of wind generation. ELCC can discriminate among generators with 

differing levels of reliability, size, and on-peak vs. off-peak delivery. It effectively 

rewards plants that are consistently able to deliver during periods of high demand, and it 

ranks less reliable plants by calculating a lower capacity credit. For intermittent 
5

generators such as wind, the method can discriminate between wind regimes that 

consistently deliver during high-risk periods, sometimes deliver during high-risk periods, 

or never deliver during high-risk periods. In fact, ELCC can provide for a continuum of 

capacity values over these potential outcomes. 

To calculate ELCC, a database is required that contains hourly load requirements and 

generator characteristics. For conventional generators, rated capacity, forced outage rates, 

and specific maintenance schedules are the primary requirements. For an intermittent 

resource such as wind, at least 1 year of hourly power output is required, but more data is 

always better. Over the decades that ELCC has been widely applied, it has been used 

with a number of different reference units. Some early work (for example, Garver 1966) 

measured the capacity value of a generator against a perfectly reliable unit. Because such 

a unit does not exist, we prefer the alternative of measuring capacity value relative to a 

benchmark unit. Although we would prefer a widely adopted benchmark value (for 

example, a gas unit with a forced outage rate of 5%) to allow for easier comparison 

among studies, it is important that the benchmark unit is clearly identified, and all units 

in a given region, such as a balancing authority, should be measured against the same 

benchmark. 

Although there are some variations in the approach, ELCC is calculated in several steps. 

Most commonly, the system is modeled without the generator of interest. For this 

discussion, we assume that the generator of interest is a renewable generator, but this 

does not need to be the case. The loads are adjusted to achieve a given level of reliability. 

This reliability level is often equated to a loss of load expectation (LOLE) of 1 day per 10 

years. This LOLE can be calculated by taking the LOLP (a probability is between zero 

and one and cannot by definition exceed 1) multiplied by the number of days in a year. 

Thus LOLE indicates an expected value and can be expressed in hours/year, days/year, or 

other unit of time. 

Once the desired LOLE target is achieved, the renewable generator is added to the system 

and the model is re-run. The new, lower LOLE (higher reliability) is noted, and the 

generator is removed from the system. Then the benchmark unit is added to the system in 

small incremental capacities until the LOLE with the benchmark unit matches the LOLE 

that was achieved with the renewable generator. The capacity of the benchmark unit is 

then noted, and that becomes the ELCC of the renewable generator. It is important to 

note that the ELCC documents the capacity that achieves the same risk level as would be 

achieved without the renewable generator. 

One concern is what happens if the generator does not generate at the level that was 

estimated by a prior ELCC calculation. For example, a conventional base load unit may 

go out of service during the peak period. Although this can put stress on the grid (and the 

system operator), proper planning usually alleviates the problem because the system is 

planned, built, and operated to account for such risks. This is why planning reserves are 

often calculated to be 15% to 20% of projected peak load, allowing for the possibility that 

some units may not be available when needed. Planning processes in the United States 

often do not perform risk-based analyses of the system but instead rely on deterministic 
6

approaches for capacity planning, such as adding the installed capacity of all the 

individual generators and applying a planning reserve margin on top. Many of these 

analyses use rules of thumb that were originally derived from probabilistic methods, and 

in some regions there is a slow return to probabilistic methods. The potential difficulty of 

deterministic approaches is that two hypothetical systems that are identical in almost 

every way could face significantly different risks. This can happen because units with 

high forced outrage rates (FORs) impose a higher risk of not meeting load than otherwise 

identical units with low FORs. If one system is characterized by generating units with 

high FORs and the other by low FORs, the system LOLP/LOLE will be different. 

Clearly, the objective is to carefully plan for contingencies and to quantify risks 

whenever possible. Using probabilistic approaches such as ELCC allows these risks to be 

quantified and calculated in a systematic, data-driven way. 

It is useful to examine how a conventional unit would fare under an ELCC evaluation. A 

generator’s ELCC is driven by several factors, the most important one being the plant’s 

capacity and forced outage rate. As part of the work assessing the costs of renewable 

energy integration for the California renewable portfolio standard, a hypothetical 

conventional unit was modeled at several alternative FORs. An ELCC value was 

calculated at each FOR so that the impact of the FOR could be seen on ELCC. The 

benchmark unit is a gas-combined cycle with a FOR of 4% and maintenance outage rate 

of 7.6%. Figure 1 illustrates the results and shows that the ELCC of this unit declines as a 

function of the FOR. For example, a unit with a FOR of 60% would have approximately 

40% ELCC relative to its rated capacity and the benchmark unit. For conventional 

generation, the ELCC value often tracks the unforced outage rate (1 – FOR). This is not 

always true, however, and depends in part on the level of system risk that is used for the 

base case. 
7

Figure 1. Comparing a Generic 100-MW 

Conventional Plant to a Gas Benchmark Unit

To derive the ELCC of wind, one ideally would have access to several years of wind 

generation data, load data, and other generation data. The wind ELCC could then be 

calculated using multiple years of data, which would provide confidence that inter-annual 

variability has been captured. But because a long wind generation record often does not 

exist, it is reasonable to expect that wind’s capacity value could vary from year to year. 

As wind projects come on-line, wind generation data will become available and a 

database can be created and updated to calculate some type of moving average of the 

wind capacity value. Examples of how some power pools and regional transmission 

organizations (RTOs) handle multiple years of data is discussed later in this paper. 

One way to help solve the problem of the year-to-year variability of the capacity value 

for wind is to create wind generation scenarios using meso-scale meteorological models. 

Conceptually there are many variations on this approach. For the Minnesota Department 

of Commerce (MN/DOC), for instance, Enernex and WindLogics developed a 3-year 

wind data record by re-creating the actual weather and normalizing to the long-term 

trend. A variation of this approach may involve the re-creation of several additional years 

of weather data, then running the reliability model for each of these several years to 

capture a longer time period. Other approaches have been used that involve Sequential 

Monte Carlo simulation, discussed further below. 

ELCC as a Function of FOR

0

20

40

60

80

100

120

10 20 30 40 50 60 70 80 90

Forced Outage Rate (FOR) %

ELCC as % Rated Capacity

Can be approximated with unforced capacity (1-FOR)*Cap 
8

Factors that Influence the ELCC of Wind

Regardless of the method used to calculate wind ELCC, a number of factors can 

influence the results. The key influence is the interaction of the system LOLP curve, such 

as the one displayed in Figure 1, and the timing of the wind delivery. Wind that delivers 

significant capacity during the times of relatively high system risk achieves a high 

capacity value. Conversely, wind that generates little or no output during these high-risk 

periods will have a low or zero capacity value. Good siting practices, technology 

characteristics, and geographic dispersion of the wind plant can all affect the potential 

delivery and timing of wind generation to the grid, and therefore the ELCC of the wind 

project. 

The LOLP curve is subject to several influences. The mix of other generation units, their 

capacity, and forced outage rates can play a key role. The way that these parameters 

interact with the load shape has an important influence on the shape of the LOLP curve. 

In a system with significant hydro generation, there can be two distinct influences on the 

LOLP curve. The first is from the non-controllable hydro (run of river) that has arbitrary 

influences on the LOLP curve. This will vary from year to year as a function of the hydro 

flow and changing load shape. Controllable hydro is generally operated so that it benefits 

the system in some optimal way. Generally, controllable hydro is used to mitigate high 

risk and therefore will lower LOLP during peak periods. This has the effect of altering 

the shape of the LOLP curve and can perhaps shift the highest risk hours to near-peak 

hours from peak hours. 

Off-system purchases can also influence the risk profile. Because system operators want 

to ensure sufficient resources during peak periods, it is not uncommon to schedule 

purchases during peak periods. Of course, that will influence the risk profile and the 

ELCC of wind. 

Maintenance on generators is normally deferred to off-peak months in the spring or fall. 

This is done for obvious reasons: the system operator wants to ensure that all generation 

is available during the peak periods when the system is most constrained and at highest 

risk. However, it is not uncommon for the spring or fall maintenance periods to drive up 

the system risk to levels at or near those found during peak periods. This significantly 

alters the risk profile and therefore can play a large role in determining the ELCC of a 

wind plant. 

Wind and the System LOLP Curve 

For a system with a reliability target of 2.4 hours/year LOLE (1 day per 10 years), the 

system risk identified by significant LOLP is generally confined to a relatively small 

number of hours. The number of hours will vary based on several system characteristics, 

the most important of which is the load profile. Figure 2 is a LOLP duration graph that is 
9

based on work performed as part of the California RPS Integration Cost study. The graph 

shows LOLP on the vertical axis and the top risk hours on the horizontal axis. The hours 

in the graph are not necessarily contiguous and generally consist of hours of high 

demand. For the California work, a policy decision was made to eliminate scheduled 

maintenance from the modeling so that renewable capacity values would be independent 

of these schedules. In reality, maintenance scheduling of conventional units can have a 

profound influence on hourly LOLP, and therefore on capacity value for renewable 

energy. In Figure 2 the area under the curve can be integrated and is 2.4 hours/year. Any 

generation that is unable to deliver during these hours will not receive any capacity value. 

Conversely, a unit (or units) that are able to fill the LOLP curve will receive a perfect 

capacity value. In general, the ELCC calculation finds the area under this LOLP curve 

that is covered by the benchmark unit. Then the capacity value of the renewable 

generator is the fraction of the risk reduction achieved by the benchmark unit. 

Figure 2. Loss of Load Probability by Top Risk Hours

This helps us see the impact of two otherwise identical wind plants with alternative 

chronological delivery profiles. Assume for our discussion that wind plant A averages 

30% of rated output during high-risk periods (generally high load) and wind plant B 

averages 5% of rated during the same periods. The annual energy for the two plants is the 

same. It should be clear that plant B does little to alleviate the risk of insufficient 

generation, whereas A does reduce this risk. However, it is important to realize that the 

capacity value of A and B may not be the same as their output during system-critical 

periods, although in some cases we have found that the capacity factor during peak 

periods can do a passable job of estimating the capacity value of the wind plant. We 

discuss this further below. 

Visualizing the curve helps us understand why generators’ ELCC declines as the unit size 

increases. In the simplest case for illustration, suppose that the addition of a 500-MW unit 

Sample Hourly LOLP

1.00E-10

1.00E-08

1.00E-06

1.00E-04

1.00E-02

1.00E+00

0 200 400 600 800

Top LOLP Hours

LOLP (Log Scale)
10

were to reduce all the risk under the curve. At that point, LOLP would essentially be zero 

for all hours of the year. If the 500-MW unit were replaced by a 1000-MW unit, the 

capacity value of the 1000-MW unit would be approximately 500 MW because there 

would be no risk-reduction benefit of the second 500 MW. 

Representing Wind in Reliability Models 

Wind can be represented in reliability models using several approaches. Studies that 

investigate the capacity value of wind can be either prospective or retrospective. A 

retrospective analysis might focus on historical performance of a wind generator or 

generators to analyze the impact on reliability. A retrospective analysis can best be 

accomplished by using hourly wind generation data along with actual load data in the 

reliability model. This provides the most accurate fidelity for a backward-looking 

evaluation, and is relatively simple to accomplish in most modeling frameworks. For this 

approach, the hourly wind generation is subtracted from the hourly load, and the 

reliability calculation then proceeds to determine the LOLP by applying this net 

equivalent load to the hourly outage probability table. As additional wind production data 

becomes available over time, a multi-year analysis that pairs actual wind and load data is 

possible, and it can provide significant insights into inter-annual variability and ELCC 

over time. 

A prospective analysis of how wind may affect future system reliability may involve 

modeling wind in a probabilistic way. The details of this approach will depend on the 

capabilities of the reliability model. However, the approach generally involves modeling 

wind as a multi-block conventional generator. Several levels of wind output are 

calculated and matched with the probability of obtaining that output. These values are 

then converted into the form that is acceptable by the reliability model so that these 

capacities and probabilities look like forced outage rates at different output levels. It is 

critical that these probabilities represent the diurnal and seasonal characteristics of the 

wind resource. This approach is discussed further in Milligan (Wind Energy Journal Part 

2). 

A prospective analysis might estimate the reliability impacts of alternative future wind 

scenarios. Because of the stochastic nature of the wind resource, it is sometimes desirable 

to apply Monte Carlo techniques in the modeling process so that a range of potential 

outcomes can be considered. Work at NREL in the 1990s applied a Sequential Monte 

Carlo approach. The basis of the method was a Markov process that used a separate state 

transition matrix (STM) for each month. This method generated a large number of 

hypothetical wind time series based on wind speed, which was then converted to hourly 

power output estimates. Each of these replications was then run through a reliability 

model, and the results from each case were then captured and summarized. 

Conventional utility reliability models often have a built-in procedure for Monte Carlo 

analysis. This is often implemented with an STM, which quantifies the probability of 

moving from one state (wind power output level) to another. This general approach is 
11

well suited to wind because it can capture the significant persistence pattern that is 

commonly found in wind speed and wind power time series data. This method was used 

in the MN/DOC study and in the recently completed PacifiCorp Integrated Resource 

Plan. Because of an aggressive schedule in the MN/DOC study, the scope of the 

investigation into some of the modeling options could not be investigated. However, the 

approach has value, and there is interest in examining this further. At the same time, there 

may be ways to enhance the capability of reliability models to better capture the unique 

characteristics of wind generation. For example, in the MN/DOC and PacifiCorp studies, 

the seasonal and diurnal nature of the wind resource may not have been fully captured. 

Conceptually, it is obvious that these patterns should be retained in the simulated time 

series to the extent that they exist in the actual data. However, the details for such an 

algorithm are not clear, and the relatively limited number of data points available to 

populate segregated STMs may further complicate the problem. Some of the NREL work 

used a modified Markov approach that was based on both the previous state and the time 

of day. But when a single year of data is used to populate the monthly STM, 

approximately 30 data points must be used to populate several bins of power output that 

span the output of the wind plant (or the range of relevant wind speeds, depending on 

how the Monte Carlo algorithm is applied). Although this approach appears to have 

merit, the relative lack of data to populate the matrices suggests that an alternative 

method might be more appropriate. 

An additional issue is whether any underlying systematic relationship exists between 

wind and load. The existence and extent of such a relationship will depend on the 

situation. For example, if large wind plants in Montana are used to export power to the 

Northwest markets, any systematic relationship may be weak at best. Conversely, there 

may be a relationship between wind and load, at least during high-risk load hours, in 

cases such as those analyzed in New York by General Electric and the New York State 

Energy Research Development Authority (GE/NYSERDA), as discussed later in this 

paper. Consequently, a one-size-fits-all approach is probably not appropriate. 

There are other modeling approaches that are not Monte Carlo-based techniques. One 

example is the sliding window approach (Milligan 2001). This approach retains the 

diurnal and seasonal characteristics of the wind generation and explicitly convolves 

alternative wind power output levels and their probabilities into the LOLP calculation. 

Based on these points, there is still work that can be done to improve the way that wind 

generation is captured in reliability models. 

Approximation Methods 

Because of the potential difficulty of assembling the appropriate database to use for the 

ELCC calculation, interest in simpler methods has emerged over the past several years. 

To evaluate the capacity value of a wind plant, it would be desirable to have the ability to 

carry out the calculation using only the relevant wind data and whatever minimal 

auxiliary data set. Although several methods can be used to approximate ELCC, an 

unfortunate aspect of all of these methods is that they are indeed approximations. 
12

However, in cases in which ELCC can’t be calculated because of data or other 

limitations, these methods can be useful. In this section we examine several techniques 

that we are familiar with. Other methods may exist or may be developed in the future. 

Broadly speaking, the approximation techniques fall into two categories: risk-based or 

time-period-based. Risk-based categories develop an approximation to the utility’s LOLP 

curve throughout the year. Time-period-based methods attempt to capture risk indirectly, 

by assuming a high correlation between hourly demand and LOLP. Although this 

relationship generally holds, it can be compromised by scheduled maintenance of other 

units and hydro conditions. A further limitation of time-period-based methods is that all 

hours considered by the method are generally weighted evenly, whereas ELCC and other 

risk-based methods place higher weight on high-risk hours and less weight on low-risk 

hours. However, time-period-based methods are much simpler and are easy to explain in 

regulatory and other public proceedings. 

Risk-Based Simple Methods 

Risk-based methods utilize hourly LOLP information either from an actual reliability 

model run or as an approximation. The first technique summarized here can be carried 

out in a spreadsheet, and although it is not terribly difficult, the details are beyond the 

scope of this paper. 

1. California RPS Method 

California’s Renewables Portfolio Standard (RPS, Senate Bill 1078) requires the 

investor-owned utilities to acquire 20% of their energy mix from renewable sources by 

2017. In addition to price, prospective renewable energy generators compete in utility 

renewable energy solicitations on the basis of a “least-cost, best-fit” metric. A part of this 

metric includes capacity value. The California Energy Commission (CEC) adopted 

ELCC as the method to calculate capacity value for all renewable generators and 

renewable technologies under the RPS requirements. A team was assembled to 

investigate the capacity contribution of renewable generators and to assess the integration 

cost of the various renewable energy technologies. Although one goal of the work 

(CWEC 2004) was to develop a simplified approximation to ELCC, methods investigated 

did not provide the required accuracy. 

The RPS method contains several steps and is predicated on the output of a reliability 

model execution that does not consider the renewable generator of interest. The approach 

proceeds as follows. First, data for hourly LOLP, system load, and wind generation are 

collected for the top 10% of load hours for the year. A logarithmic risk metric that is a 

variation of LOLE over this discontinuous period is calculated and normalized. This 

provides us with a measure of risk over the top 10% of load hours that is normalized to 

one. This new metric is called the risk share, and for each hour, represents the fraction of 

risk that occurs in that hour. For each of these hours, the wind generator output is 

multiplied by the risk share, and the total of all of these hourly values represents the 
13

relative risk contribution of the wind plant. This is converted to a percentage of the rated 

capacity of the wind plant. 

The second step proceeds independently and is subsequently combined with the results of 

the first step. This approach develops a set of load-ratio shares over the top 10% of load 

hours. These hourly share values are also normalized and multiplied by the hourly wind 

generation in the appropriate hour. This can be converted to a percentage of the rated 

capacity of the wind plant. 

The third step calculates the ratio of the standard deviation to the mean for the wind 

plant, also known as the coefficient of variation (COV), over the top 10% of load hours. 

For this method, the COV was expressed as a percentage. 

For the California RPS study, this procedure was carried out for each renewable 

generator aggregate, and the results were used to estimate a regression equation. This 

equation used the results from each of the steps above to fit a set of coefficients that 

provided a best fit to ELCC. Although the specific regression equation would not be 

expected to provide accurate results for all generators in all areas, a similar approach 

could be used to develop a set of weights for a wind generator in another region. When 

applied to wind generation at the three resource areas in California (Altamont, San 

Gorgonio, Tehachapi), selected solar and selected geothermal resources, the method had 

a mean absolute percentage error of 1.2% of the actual ELCC value. Because these 

results could not be obtained when the hydro system was included, the method may only 

be applicable to systems in which hydro generation is not significant. 

2. Risk-Based Method 2: Garver’s Approximation 

Garver’s 1966 paper is indeed a classic in the power system reliability literature. The 

Garver technique to estimating ELCC was applied to conventional generators and was 

developed to overcome the limited computational capabilities that were available at the 

time. The technique is based on the development of a risk-approximation function, and in 

some respects it is similar to the CA RPS method. 

The approach approximates the declining exponential risk function (LOLP in each hour, 

LOLE over a high-risk period). It requires a single reliability model run to collect data to 

estimate Garver’s constant, known as m. Once this is done, the relative risk for an hour is 

calculated by 

R’ = Exp{-[(P-L)/m]} 

where P = annual peak load, L = load for the hour in question, R’ is the risk 

approximation (LOLP), measured in relative terms (peak hour risk = 1). A spreadsheet 

can be constructed that calculates R’ for the top loads. Then modify the values of L by 

subtracting the wind generation in that hour. 
14

Calculate LOLE approximation for (a) no-wind case and (b) wind case by summing the 

hours. Use all hours for which no-wind risk exceeds some tolerance – probably around 

500 hours. Compare to gas plant or other benchmark, de-rated by its forced outage rate. 

Time-Period Methods 

To avoid using a reliability model altogether, it is possible to collect only hourly load and 

wind data for at least 1 year and use these data to calculate an approximation to ELCC. 

This approach is appealing in its simplicity, but it does not capture the potential system 

risks that are part of the other methods discussed above. Milligan and Parsons (1999) 

compared the ELCC with a series of calculations for hypothetical wind generation to 

determine whether these simpler approaches are useful. Although several alternative 

methods were compared, the most straightforward approach was to calculate the wind 

capacity factor (ratio of the mean to the maximum) over several times of high system 

demand. The calculations were carried out for the top 1% to 30% of loads, using an 

increment of 1%. Figure 3 is taken from that study. Although an ideal match was not 

achieved, the results show that at approximately 10% or more of the top load hours, the 

capacity factor is within a few percentage points of the ELCC. 

Figure 3. Comparing Capacity Credit Versus Capacity Factor 

Capacity Credit vs. Capacity Factor (Year 1)

0

0.1

0.2

0.3

0.4

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Top Loads (%)

Capacity Factor/Credit

Capacity Factor Capacity Credit
15

Methods to Assess Wind Capacity Credit in the United States 

In this section we survey some of the approaches that are in use today to evaluate wind 

capacity credit. These methods come from a variety of entities, ranging from RTOs, 

Public Utility Commissions, utilities, or studies carried out on behalf of these 

organizations. 

Pennsylvania-New Jersey-Maryland Regional Transmission 

Organization 

PJM is an RTO that encompasses all or parts of Delaware, Illinois, Indiana, Kentucky, 

Maryland, Michigan, New Jersey, Ohio, Pennsylvania, Tennessee, Virginia, West 

Virginia, and the District of Columbia. PJM includes more than 56,000 miles of 

transmission lines and more than 1,000 generating units. PJM has more than 163,000 

MW of capacity, and it serves about 131,000 MW of peak demand (PJM 2005). 

In general terms and on an annual basis, PJM requires LSEs to have a reserve margin of 

capacity above what is required to serve load. To meet that requirement, LSEs can selfsupply capacity, enter into bilateral arrangements with generators for capacity, or 

purchase capacity through a PJM-administered capacity market. In both PJM East and 

PJM West, the capacity market consists of daily, monthly, interval, and multi-month 

markets. PJM’s current required reserve margin is 15%. 

The capacity credit for wind in PJM is based on the wind generator’s capacity factor 

during the hours from 3 p.m. to 7 p.m., from June 1 through August 31. The capacity 

credit is a rolling 3-year average, with the most recent year’s data replacing the oldest 

year’s data. Because of insufficient wind generation data, PJM has applied a capacity 

credit of 20% for new wind projects, to be replaced by the wind generator’s capacity 

credit as noted earlier once the wind project is in operation for at least a year. As an 

example, a new wind generator will receive a capacity credit of 20% the first year; the 

average of 20% and the wind generator’s capacity factor during the hours from 3 p.m. to 

7 p.m. from June 1 through August 31 in the second year; and the average of 20% and the 

wind generator’s capacity factor during the hours from 3 p.m. to 7 p.m. for June 1 

through August 31 for years two and three, and so on. In addition, wind generators are 

also required to bid into PJM’s day-ahead energy market, along with other generators 

receiving capacity credit in PM. 

 

If PJM receives enough wind generation data, PJM will replace the 20% capacity credit 

for new wind projects with the average capacity factor during the 3 p.m. to 7 p.m. hours 

from June through August for all wind generators that have been in operation for 3 years 

or more in PJM. 
16

New York ISO 

The New York ISO (NYISO) consists of the transmission assets of eight transmission 

owners located in New York and a small part of New Jersey. The NYISO includes about 

36,500 MW of generating capacity and serves about 31,000 MW of load (Lampi 2004). 

The NYISO also requires LSEs to have capacity reserves over their load requirements. 

Like PJM, the NYISO has a financial market for capacity, but the NYISO has three 

auctions for capacity: a 6-month strip auction held twice a year, prior to the summer and 

winter capability periods; a series of monthly auctions; and a monthly spot auction for 

LSEs that have not met their reserve obligations. 

Unlike PJM, the NYISO values capacity for other months besides summer, since the 

NYISO winter peak is close to the summer peak. The NYISO allows wind projects over 1 

MW in capacity to qualify for capacity credit. Wind generators can submit the results of a 

4-hour sustained maximum output test, for both summer (June 1 through September 15) 

and winter (November 1 through April 15).2

 The results of the tests are the wind 

generator’s initial capacity credit in the NYISO. The NYISO adjusts the capacity credit 

monthly based on data submitted by the generator on actual generation and maintenance 

hours the previous month. Intermittent generators such as wind are exempt from having 

to bid into the day-ahead energy market in the NYISO, a requirement for other 

non-intermittent generators. 

As noted later in this paper, the GE/NYSERDA wind integration study found that 

onshore wind projects had a lower capacity value (9%) than is currently provided to wind 

by the NYISO. The NYISO will likely investigate changing the methodology for 

determining the capacity credit of wind. 

ISO New England 

ISO New England operates in six states and includes more than 32,000 MW of capacity 

(ISO New England 2005) and serves about 25,000 MW of load (LaPlante 2004). ISO 

New England changed its energy market to incorporate a day-ahead energy market along 

with a real-time energy market in 2003, similar to PJM. 

Three wind generators are registered with ISO New England, at a total capacity of about 

1.5 MW. These wind generators participate in the ISO New England energy market as 

“settlement only” resources, a category for generating resources under 5 MW. 

Settlement-only resources sell electricity into the grid at real time and receive the real 

time market clearing price. These resources are not assessed any operating charges for 

schedule deviations or imbalances and receive a capacity credit equal to the unit’s 

capacity, multiplied by 1 minus its forced outage rate. 

 

2

 PJM does similar tests to determine the eligible capacity credit for non-wind generators, although PJM 

only considers the results from the summer test, even though tests are done in both summer and winter. 
17

Wind generators over 5 MW would be classified as intermittent power resources and can 

schedule into the ISO New England’s day-ahead market. If intermittent power resources 

do not submit bids into the day-ahead market, then before the next operating day, these 

resources must self-scheduled the capacity amount for each hour. If in real time the 

capacity amount is different than the self-schedule amount, the intermittent power 

resource must contact the ISO and re-declare its schedule. As with settlement-only 

resources, intermittent power resources are not assessed operating charges for scheduled 

deviation or imbalances and receive a capacity credit equal to the unit’s capacity, 

multiplied by 1 minus its forced outage rate. 

Southwest Power Pool 

The Southwest Power Pool (SPP) recently adopted a method to calculate wind capacity 

contribution (SPP GWG 2004). The process of developing the method was managed by 

the Generation Working Group of the SPP, and it involved numerous discussions. The 

method that emerged is a monthly method and therefore results in 12 capacity measures 

for the wind plant. The process first examines the highest 10% of load hours in the 

month. Wind generation from those hours is then ranked from high to low. The wind 

capacity value is selected from this ranking, and it is the value that is exceeded 85% of 

the time (the 85th percentile). Up to 10 years of data are used if available. For the wind 

plants studied in the SPP region, the capacity values ranged from 3% to 8% of rated 

capacity. According to SPP’s Generation Working Group (SPP GWG) presentation, this 

method is used for long-term planning. Although it appears counter-intuitive to us, the 

SPP GWG believes that ELCC/LOLP methods are better used to determine the level of 

desired spinning or operating reserves and not to determine the reliability impacts of 

wind. 

Rocky Mountain Area Transmission Study 

The Rocky Mountain Area Transmission Study (RMATS) is a multi-stakeholder, 

regional transmission study in the west. RMATs encompassed Colorado, Idaho, Montana, 

Utah, and Wyoming and was established by the governors of Wyoming and Utah to 

assess the feasibility of investing in new transmission to either access remote coal and 

wind resources or to export generation to other areas in the West (RMATS 2004). 

RMATS used 20% of rated capacity for all wind plants in the region. Although this is 

clearly a simplification and does not take account the significant differences between 

wind delivery profiles and the match to load, the wind capacity contribution is an 

important factor in determining the required capacity of other generation resources to 

meet loads during the study period. Because the RMATS modeling was based on 

local/regional load modeling and respected transmission constraints, it is likely that the 

wind capacity contribution across the RMATS region would vary, perhaps considerably. 
18

GE/New York State Energy Research and Development Authority 

A recently completed study by General Electric for the New York State Energy Research 

and Development Authority examined the impact of 3,300 MW of wind on the New York 

bulk power system (GE Energy Consulting 2005). Although the study focused on 

reliability impacts and operational issues, the team assessed the capacity contribution of 

wind using ELCC. The study used simulated wind data from more than 100 sites 

throughout the state, matched to the year of load data. This important step accounts for 

any underlying systematic correlation that may exist between wind and load. (This 

correlation would be expected to vary by region, and it would likely be nonlinear with a 

potentially complex lag structure). The study found that on-shore wind plants would be 

expected to have approximately 9% capacity value relative to rated capacity, and offshore wind would be approximately 40%. For the on-shore wind scenarios, the modelers 

found that a time-period based approach did a good job of approximating the capacity 

value. For the summer season, calculating the wind capacity factor during the hours from 

1:00 p.m. to 4:00 pm. 

Minnesota Department of Commerce/Xcel 

The Minnesota Department of Commerce (MN/DOC) study examined the impact of 

1,500 MW of wind capacity distributed at various locations in southwest Minnesota. This 

represents approximately 15% wind penetration, based on the ratio of rated wind capacity 

to peak load. One of the tasks of this study was to calculate the capacity contribution of 

wind. The study used a Sequential Monte Carlo method, which performed repeated 

sampling of an annual state transition matrix that was calculated based on the wind data 

used in the study. The intent of this approach is to capture some of the impact of the 

interannual variation of wind so that estimates of ELCC may be more robust. The SMC 

cases found a 26.7% capacity contribution for the prospective wind plants. For 

comparison, the study also used a simple “load-modifier” method that calculates 

reliability based on a simple netting of the wind generation against hourly load. When 

this approach was used, the prospective wind capacity value was 32.9% of rated capacity. 

PacifiCorp 

PacifiCorp recently completed a new Integrated Resource Plan (Pacificorp 2005). Wind 

generation was modeled using the same Sequential Monte Carlo approach used by 

Enernex in the MN DOC study. For the several prospective wind locations analyzed by 

PacifiCorp, the capacity contribution of wind averaged approximately 20% of rated 

capacity. The capacity value from the IRP is used as part of an evaluation to determine 

how much additional capacity is needed to meet future load forecasts. 
19

Electric Reliability Council of Texas (ERCOT) 

ERCOT evaluated the operating wind plants to determine the capacity contribution of 

wind. The analysis was based on wind generation from 4:00 p.m. to 6:00 p.m. during July 

and August, the peak period for ERCOT. During this time period, the average output of 

the wind was 16.8% of rated capacity. Because of the variability of wind generation, the 

ERCOT Generation Adequacy Task Group is developing a confidence factor. Although 

the method of evaluation of this confidence factor is unclear from the document, the 

recommendation under consideration is to use 2% of rated wind capacity as the capacity 

value. 

Mid-Continent Area Power Pool (MAPP) 

The Mid-Continent Area Power Pool (MAPP) approach is a monthly method that 

calculates wind capacity value based on the timing of its delivery relative to peak. Up to 

10 years of data (wind and load) can be used if available. For each month, a 4-hour time 

window surrounding the monthly peak is selected. Any contiguous 4-hour period can be 

selected, as long as the peak hour falls within the window. The wind generation from that 

4-hour period in all days of the month is then sorted, and the median value is calculated. 

The median value is wind’s capacity value for the month. If multiple years of data are 

available, the process is carried out on the multi-year data set. The results of these 

calculations are used in operational planning in the power pool. 

Portland General Electric (PGE) 

Portland General Electric (PGE) assumed a 33% capacity factor in its 2002 Integrated 

Resource Plan as a placeholder and plans to review additional studies and data as they 

become available (Bolinger 2005). PGE’s IRP calls for 195 MW of wind.3

 

Idaho Power 

Idaho Power gives wind a 5% capacity credit, based on a 100-MW wind plant’s projected 

output that would occur 70% or more of the time between 4:00 p.m. and 8:00 p.m. during 

July, Idaho Power’s peak month (Bolinger 2005). Therefore, Idaho Power’s method is 

similar to SPP’s by multiplying a subjective statistical number by actual capacity factor 

values. 

 

3

 Portland General Electric. Final Action Plan: 2002 Integrated Resource Plan, March 2004. Available at 

http://www.portlandgeneral.com/about_pge/regulatory_affairs/pdfs/2002_irp/actionPlan_final.pdf. 
20

Puget Sound Energy (PSE) 

Puget Sound Energy just released its 2005 Integrated Resource Plan that includes a wind 

integration study as an appendix. Although not specified in the plan, a personal 

communication with a PSE representative determined that PSE’s determination of a 

capacity credit for wind is the lesser of 20% of nameplate capacity, or 2/3 of the capacity 

factor of a wind project in January, which is PSE’s peak month. 

The 95% Fallacy: Using X Percentile to Calculate Capacity Credit 

New gas plants are capable of achieving low forced outage rates—high levels of 

reliability. Because gas plants have often been the generator technology of choice in 

recent years, it can be tempting to use this gas plant characteristic in an attempt to 

estimate the capacity value of an intermittent generator such as wind. To carry out this 

approach, one collects wind generation over the relevant high-load period (for example, 

the top 10% of load hours). The next step is to calculate the 95th percentile of wind 

generation—the level of wind generation that is achieved 95% of the time during these 

load hours. A variation of this approach, one that we have encountered, is to then feed 

this 95th percentile generation into a reliability model to calculate the ELCC of the wind 

plant. In both of these variations, the method only values capacity levels that are 

exceeded 95% of the time. All other capacity levels are assigned a value of zero. The use 

of a percentile arbitrarily discounts reliability contributions that are achieved at levels 

below the percentile value. These approaches are based on fallacious use of probability 

theory, and they ignore the statistical independence of outages and the fact that system 

reliability can be achieved at a very high level (such as 1 day in 10 years LOLE) even 

though every unit in the system is somewhat unreliable. 

To illustrate, we set up a series of reliability cases. Rather than specifically calculate 

ELCC, these cases illustrate how increasingly unreliable generators can still achieve a 1-

day-in-10-year level of system reliability. We emphasize that we do not recommend the 

pursuit of unreliable generators, as this would be costly. However, the point is to 

illustrate that even highly unreliable plants contribute something to overall system 

reliability, albeit at a very low rate. Plants that have higher forced outage rates have 

correspondingly lower ELCC values. 

The case study uses hourly load data from the California ISO. Instead of using the 

existing generator fleet, a hypothetical generator mix was developed that consists of 95 

500-MW units, each with a forced outage rate of 9%. The base case also included 54 100-

MW units, each with a forced outage rate of 10%. This mix of generation achieved a 1-

day-in-10-year reliability level. 

To illustrate the impact of less reliable plants, the forced outage rates on the 100-MW 

units was increased in steps of 10% up to 90%. And just for fun, we also included a case 

with all the 100-MW units and a 95% forced outage rate. The purpose of these scenarios 

is to show that the 1d/10y reliability target can be achieved even with the 95% forced 
21

outage fleet. Figure 4 captures the results of the simulation scenarios. The graph shows 

that even with unreliable units, a reliability target of 1 day per 10 year LOLE can be 

achieved. The point of this exercise is not to argue for unreliable generators. The point is 

to show that even unreliable units can contribute to a reliable system, although it would 

take many of these generators to do so! 

Figure 4. 1 Day/10 Years Can be Achieved With Unreliable Generators 

Summary of Study Results 

We have chosen the results from several recent studies to illustrate the range of capacity 

values found to apply to wind. Figure 5 is taken from the California RPS Integration Cost 

Study (phase 3) and shows the capacity value for the three wind resource areas (this study 

was completed prior to completion of High Winds near Sacramento) and other renewable 

generation in California. All of these capacity values were based on ELCC, and the graph 

shows the range of wind capacity values in the mid-20s along with the much higher 

geothermal and solar gas-assist capacity values. 

Number of 100 MW Units to Achieve 

1d/10y Reliability Target @ Different 

Forced Outage Rates

0

200

400

600

800

1,000

1,200

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Forced Outage Rate for 100 MW Units

No. 100 MW Units 
22

Figure 5. Selected California RPS Phase III Results 

Relative to a Gas Reference Unit 

Figure 6 shows other capacity credit values for wind measured by ELCC. Many of the 

results came from the MN/DOC study recently completed by Enernex. The ELCC values 

from that study ranged from the upper 20s to low-mid 30s, depending on the modeling 

technique used for the scenario and whether wind is the currently installed wind or 

prospective wind expected to be developed within the study horizon. 

0

20

40

60

80

100

Altamont San Gorgonio Tehachapi Geothermal Solar

ELCC as % Rated Capacity
23

Figure 6. Other Selected Wind Capacity Values 

A more complete summary of wind capacity value appears in Table 1. Most approaches 

use either ELCC or a time-period basis to calculate wind capacity factor. 

0

5

10

15

20

25

30

35

40

NYSERDA/GE MN/DOC/Xcel

(1)

MN/DOC/Xcel

(2)

MN/DOC/Xcel

(3)

CO Green PacifiCorp

ELCC as % Rated

(1) Existing wind (2) Potential new wind (3) Potential w/Monte Carlo Analysis
24

Table 1. Wind Capacity Value in the United States 

Potential Changes to Financial Capacity Markets in the 

Northeast

The three northeastern RTOs are unique in that they all have separate financial markets 

for capacity to ensure that utilities and other load-serving entities have enough capacity to 

meet electricity demand, plus a pre-determined reserve margin. Although each 

northeastern RTO has separate capacity markets by time (daily, monthly, multi-monthly), 

there typically is not a locational element to the capacity market. In other words, a 

generating plant located in a remote and perhaps unconstrained area may receive the 

same capacity value as a generating plant that is located in a transmission-congested 

region. 

All three northeastern RTOs are considering significant changes or have significantly 

changed their financial capacity markets. Motivations for the changes include concern 

that new entry is not encouraged; that the location of the generator is not valued; that the 

volatile results of capacity prices perpetuate a “boom-bust” generation cycle; and that 

other desirable characteristics of generation (quick response, fast ramping) are not 

valued. 

ERCOT 10% May change to capacity factor, 4:00-6:00pm, Jul (2.8%) 

PSE and Avista Peak Period PSE will revisit the issue (lesser of 20% or 2/3 Jan C.F.)

PGE 33%

Idaho Power Peak Period 4pm-8pm during July (5%)

PacifiCorp ELCC Sequential Monte Carlo (20%)

MAPP Peak Period Monthly 4-hour window, median

Top 10% loads/month; 85th SPP Peak Period percentile

RMATS Rule of thumb 20% all sites in RMATS

PUC decision (30%) and possible followup to current Enernex 

study; Xcel using MAPP approach (10%) in internal work

CO PUC/Xcel ELCC

GE/NYSERDA ELCC Offshore/onshore (40%/10%)

MN/DOC/Xcel ELCC Sequential Monte Carlo (26-34%)

Jun-Aug HE 3pm-7pm, capacity factor using 3-year rolling 

average (20%, fold in actual data when available)

PJM Peak Period

CA/CEC ELCC Rank bid evaluations for RPS (low 20’s)

Region/Utility Method Note

ERCOT 10% May change to capacity factor, 4:00-6:00pm, Jul (2.8%) 

PSE and Avista Peak Period PSE will revisit the issue (lesser of 20% or 2/3 Jan C.F.)

PGE 33%

Idaho Power Peak Period 4pm-8pm during July (5%)

PacifiCorp ELCC Sequential Monte Carlo (20%)

MAPP Peak Period Monthly 4-hour window, median

Top 10% loads/month; 85th SPP Peak Period percentile

RMATS Rule of thumb 20% all sites in RMATS

PUC decision (30%) and possible followup to current Enernex 

study; Xcel using MAPP approach (10%) in internal work

CO PUC/Xcel ELCC

GE/NYSERDA ELCC Offshore/onshore (40%/10%)

MN/DOC/Xcel ELCC Sequential Monte Carlo (26-34%)

Jun-Aug HE 3pm-7pm, capacity factor using 3-year rolling 

average (20%, fold in actual data when available)

PJM Peak Period

CA/CEC ELCC Rank bid evaluations for RPS (low 20’s)

Region/Utility Method Note
25

PJM also plans to revamp its capacity reserve requirements by providing locational 

premiums to generating capacity in transmission-constrained areas and abandoning an 

annual capacity reserve determination in favor of adjusting capacity payments, with 

lower payments when there is higher reserve capacity and the converse when there is a 

lower amount of capacity. 

PJM’s proposal, called the Reliability Pricing Model (RPM), would attempt to 

differentiate among generators for their true reliability or capacity value and provide 

some longer-term price signals. Generators would still receive a base capacity price, but 

some generators could receive additional payments for capacity resources that have 

locational value (i.e., in transmission constrained areas, as determined by PJM’s 

transmission planning process) or provide operational flexibility (such as quick start 

capability, dispatchability, supplemental reserve, and flexible cycling). PJM also would 

abandon its annual setting of the reserve margin in favor of a range of resource 

requirements based on the probability of not having enough capacity to serve load for 1 

day in 10 years. PJM would apply a pricing factor that would increase as reserve levels 

approach the 1-day-in-10-years factor and decrease when reserve levels are higher. 

The NYISO incorporated a “demand curve” into the monthly auction part of their 

capacity market by paying a declining price for capacity over their capacity requirement, 

instead of not paying anything. The NYISO also includes a locational element to their 

capacity market design by requiring that LSEs in New York City and Long Island obtain 

part of their capacity from within those areas. ISO New England also plans to develop a 

demand curve but will subdivide their region-wide capacity market into five zones 

beginning in January 2006: Maine; Northeastern Massachusetts and Boston; Southwest 

Connecticut; the rest of Connecticut; and a combination of Vermont, New Hampshire, 

Rhode Island, and the rest of Massachusetts. Each zone can have its own unique capacity 

price, and regions can be added or subtracted annually. 

 The PJM and ISO New England proposals are controversial, and it is not clear they will 

move forward—Connecticut has sued FERC over its approval of ISO New England’s 

proposal. Should these initiatives be implemented, the implication for wind projects is the 

amount that they get paid for their capacity may depend on where the wind project is 

located. Although it will vary with the particular circumstances, potential offshore wind 

energy projects may see more benefit, as they may have higher capacity factors at times 

of peak demand (as exhibited in the GE/NYSERDA study in New York). In addition, 

offshore wind projects may be located near transmission-constrained areas, perhaps 

further increasing their potential capacity value. In contrast, onshore wind projects in 

remote areas may see lower capacity payments. 

Assessment and Recommendations 

A capacity-based metric is useful in several alternative contexts, from adequacy 

determination to financial settlement markets. Capacity from a generator at some time in 

the future is not guaranteed. Because all generators are subject to outage, even during 
26

system-critical times, a probabilistic approach to calculating capacity value is 

appropriate. This is especially true for intermittent resources such as wind power plants. 

Because of the stochastic nature of the wind, and therefore wind energy, a method that 

can explicitly quantify the risks associated with this resource is critical. Standard power 

system reliability theory exists that can be used for this purpose. 

When a reliability-based approach is used to calculate the capacity credit of wind power 

plants, risk is explicitly embodied in the calculation. The ELCC method is rigorous, datadriven, and can finely distinguish among generators that have different impacts on system 

reliability. However, the method requires datasets that are not always available and is 

influenced by many system characteristics. For these reasons and others, simplified 

methods have been developed. These methods are sometimes based on wind generation 

during a time period that corresponds to high system risk hours. In other cases, methods 

can approximate the system LOLP curve so that high-risk hours receive more weight than 

other hours. We favor experimentation with such methods but suggest that it would be 

helpful to benchmark simple methods against ELCC. This will help eliminate the 

sometimes-arbitrary assumptions that can be introduced by some simple calculations we 

have encountered. 

Interannual variability of wind generation is an important issue, and it can have an effect 

on any capacity metric. We recommend that multiple years of data be used in capacity 

value calculations. If that is not possible, we think that several approaches covered in this 

paper can be useful. Going forward, we expect that the capacity value of wind generating 

plants will continue to be a topic that receives significant attention. We encourage open 

analysis and reporting of the findings that increasing experience with wind will bring. 

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27

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PJM 2005. “At a Glance.” Available at www.pjm.com/about/glance.html

RMATS 2004. Rocky Mountain Area Transmission Study. Available at 

http://psc.state.wy.us/htdocs/subregional/FinalReport/rmatsfinalreport.htm

SPP GWG 2004. Southwest Power Pool 2004, Generation Working Group. Wind Power 

Capacity Accreditation. 
F1147-E(12/2004) 

REPORT DOCUMENTATION PAGE Form Approved 

OMB No. 0704-0188

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PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ORGANIZATION.

1. REPORT DATE (DD-MM-YYYY)

May 2005 

2. REPORT TYPE

Conference Paper 

3. DATES COVERED (From - To)

5a. CONTRACT NUMBER

DE-AC36-99-GO10337 

5b. GRANT NUMBER

4. TITLE AND SUBTITLE

Determining the Capacity Value of Wind: A Survey of Methods and 

Implementation; Preprint 

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

NREL/CP-500-38062 

5e. TASK NUMBER

WER5 5201 

6. AUTHOR(S)

M. Milligan and K. Porter 

5f. WORK UNIT NUMBER

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, CO 80401-3393 

8. PERFORMING ORGANIZATION 

REPORT NUMBER

NREL/CP-500-38062 

10. SPONSOR/MONITOR'S ACRONYM(S)

NREL 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

11. SPONSORING/MONITORING 

AGENCY REPORT NUMBER

12. DISTRIBUTION AVAILABILITY STATEMENT

National Technical Information Service 

U.S. Department of Commerce 

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Springfield, VA 22161 

13. SUPPLEMENTARY NOTES

14. ABSTRACT (Maximum 200 Words)

Regional transmission organizations, state utility regulatory commissions, the North American Electric Reliability 

Council, regional reliability councils, and increasingly, the Federal Energy Regulatory Commission all advocate, call 

for, or in some instances, require that electric utilities and competitive power suppliers not only have enough 

generating capacity to meet customer demand but also have generating capacity in reserve in case customer 

demand is higher than expected, or if a generator or transmission line goes out of service. Although the basic 

concept is the same across the country, how it is implemented is strikingly different from region to region. 

Related to this question is whether wind energy qualifies as a capacity resource. Wind’s variability makes this a 

matter of great debate in some regions. However, many regions accept that wind energy has some capacity value, 

albeit at a lower value than other energy technologies. Recently, studies have been published in California, 

Minnesota and New York that document that wind energy has some capacity value. These studies join other 

initiatives in PJM, Colorado, and in other states and regions. 

This paper focuses on methodologies for determining the capacity value of generating resources, including wind 

energy and summarizes several important state and regional studies. 

15. SUBJECT TERMS

wind energy; wind plant; wind farm; capacity; grid; California; Minnesota; New York 

16. SECURITY CLASSIFICATION OF: 19a. NAME OF RESPONSIBLE PERSON 

a. REPORT 

Unclassified 

b. ABSTRACT 

Unclassified

c. THIS PAGE 

Unclassified

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OF ABSTRACT

UL 

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