NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Capacity Value of Wind Plants and 

Overview of U.S. Experience

PSCC

Michael Milligan, Ph.D.

National Renewable Energy 

Laboratory

Stockholm, Sweden

Aug 22, 2011

NREL/PR-5000-52856

Composite photo created by NREL
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Integration Issues in the U.S.
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U.S. Experience Overview

• Overview of U.S. Wind status and the 

interconnected system;

• Integration studies and key results;

• Selected interesting wind events;

• Selected future directions.
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d3amEF59
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There are 3 interconnections in the U.S.

1 2 

3 
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There are many control areas (balancing 

areas) in the West
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Markets cover part of the U.S.

NPPD is 

Joining SPP
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Power System Basics

•Portfolio of different type of 

generators are managed 

so that the sum of all 

output = load at each 

moment.

•Base-load generators run 

at constant output.

•Intermediate/cycling units 

pick up daily load swings.

•Peaking units are seldom 

run but provide peak 

capacity when needed.
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Power System Basics (cont)

•Extra generation – reserves – available in case 

of generator or transmission outage: 

Contingency reserves.

•Some generators can change output and are 

used to manage variability in load (demand).

•The demand for power is not known with 

certainty so may influence the level of reserves 

for managing this uncertainty.

•Wind increases the level of variability and 

uncertainty that the power system operator 

must manage.

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Load-less-wind = net load

National Renewable 

Energy Laboratory 
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Wind output is smoothed with geographical 

dispersion

(Approximately 8 hours)
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Large-Scale Wind Integration Studies

•Sponsored by U.S. DOE, managed by NREL.

•Eastern Wind Integration and Transmission Study, 

released Jan 20, 2010. www.nrel.gov/ewits

•Western Wind and Solar Integration Study, 

released in Mar 2010. www.nrel.gov/wwsis. 

•These studies show that up to 30% (and 5% solar 

in the west) can be integrated reliability and 

economically if operational practices can provide 

additional flexibility thru institutional changes.

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Integration Studies

Detailed power system simulations follow 

operational practice.

Data from power system industry

Wind data.

• Actual wind plant data.

• Simulated wind data for future wind build-out.

Data requirements are stringent so that the 

variability of wind plants is accurately represented 

in the power system operations modeling.

Other power system data must be consistent, 

robust, accurate.

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Atmospheric models

National Renewable Energy Laboratory 

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Colorado: Xcel

Mesoscale 

meteorological 

modeling that can “re- create” the weather at 

any space and time.

Maximum wind power 

at a single point ~ 30 

MW to capture 

geographic 

smoothing.

Model is run for the 

period of study and 

must match load time 

period.

Wind plant output 

simulation and fit to 

actual production of 

existing plants.

See 

www.nrel.gov/wwsis

for details and 

validation
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Integration Study Results

Study results show that wind energy can be 

integrated into power systems reliably and 

economically; in some cases operational 

practice must change.

Most studies have rigorous technical review 

teams, comprised of power system industry 

experts.

Utility Wind Integration Group: Industry 

Exchange for wind integration challenges and 

solutions. 

www.uwig.org contains most integration study 

results.
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Wind reduces emissions, including carbon

At high prices natural gas is displaced by renewable generation, leaving 

coal plants to handle variability at lower emissions reductions. When 

coal is displaced instead, greater emission reductions are observed.

Western Wind and Solar Integration Study, 

www.nrel.gov/wwsis. 

Every 3 wind-generated MW reduces thermal 

commitment by 2 MW.

Also see Impact of Frequency Responsive Wind 

Plant Controls on Grid Performance, Miller, 

Clark, and Shao. 9th International Workshop on 

Integration of Wind Power into Power Systems, 

Quebec, Canada, October 2010.

Scenarios 1-3 are for 20% wind power penetration, with various combinations of 

new transmission and offshore wind farms, while Scenario 4 is for 30% wind 

power penetration. Scenario 2 Carbon Sensitivity includes the results if a $100/ 

metric ton carbon tax were imposed. 

Results show decline from 2008, also eliminating any 

increase in carbon from 2008-2024.

www.nrel.gov/ewits. Overall reduction in emissions in 

study year is estimated to be approximately 33-47%, 

depending on wind energy penetration scenario.
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18

Study Area Dispatch - Week of April 10th - 10%R

0

10,000

20,000

30,000

40,000

50,000

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16

MW

Study Area Dispatch - Week of April 10th - No Wind

0

10,000

20,000

30,000

40,000

50,000

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16

MW

Nuclear Steam Coal Wind

Solar CSP w/ Storage Solar PV Combined Cycle

Gas Turbine Pumped Storage Hydro Hydro

Study Area Dispatch - Week of April 10th - 20%R

0

10,000

20,000

30,000

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50,000

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16

MW

Study Area Dispatch - Week of April 10th - 30%R

0

10,000

20,000

30,000

40,000

50,000

MON APR 10 TUE APR 11 WED APR 12 THU APR 13 FRI APR 14 SAT APR 15 SUN APR 16

MW
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Selected Events
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Example 

week, utility 

in the 

Western U.S.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1 9 17 25 33 41 49 57 65 73 81 89 97 105 113 121 129 137 145 153 161

MW

Hour

The effect of Wind Generation Variability on Utility Load

Utility Load Profile Net Load (Utility Load - Wind) Wind Generation

0

5

10

15

20

25

30

35

40

17

131925313743495561677379859197

103109115121127133139145151157163

Wind power as percent of load

Hour

35% instantaneous 

penetration
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Texas events show wind is slower than contingency

Source: 

NREL/TP-500-43373, ERCOT, 

and WindLogics
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The Western Interconnection is proposing an 

Energy Imbalance Market
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NREL is calculating reserve deployment 

impacts

0

2000

4000

6000

8000

10000

12000

14000

16000

Total Reg Spin Non-spin Total

BAU 2440 2096 4192 8729

EIM 1198 969 1938 4105

Reserve Requirement (MW)

Reserve Savings for Footprint EIM

(Average, Max and Min) 

BAU

EIM
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Reliability Organization Task Force

Not a question of “if.”

It is a question of “how.”

Numerous NERC Task 

Force reports can be 

found at: 

http://www.nerc.com/filez/i

vgtf_Interconnection.html 

(click on individual topical 

area to find reports).
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In Process...

• NREL’s EWITS and WWSIS Phase 2

– Focus on thermal cycling impacts

– Demand response

– More solar energy in the West
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Additional efforts underway

• Energy Imbalance Market in the West;

• Western Electricity Coordinating Committee’s 

Variable Generation Subcommittee;

• Seams issues; inter-BA deliveries;

• Concern regarding insufficient revenue from 

energy-only markets;

• Wind-provided frequency regulation;

• Reserves analysis;

• Impact on balancing costs. 

Milligan, M.; Ela, E.; Hodge, B. M.; Kirby, B.; Lew, D.; Clark, C.; DeCesaro, J.; Lynn, K. 

(2011). Cost-Causation and Integration Cost Analysis for Variable Generation. 37 pp.; 

NREL Report No. TP-5500-51860.
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Ability to integrate wind/solar is a function of 

many factors

Large BA

 Geographically Dispersed Wind and Solar

 Wind/Solar Forecasting Effectively Integrated Into System Operations

 Sub-Hourly Energy Markets

 Fast Access to Neighboring Markets

 NonSpinning and 30 Minute Reserves for Wind/Solar Event Response

 Regional Transmission Planning For Economics and Reliability

 Robust Electrical Grid

 More Flexible Transmission Service

 Flexibility in Generation

 Responsive Load

 Overall

 Example Utility Structures

10 8 7 10 7 2 7 6 7 7 3 7 Large RTO with spot markets

6 6 6 3 3 2 6 4 7 2 2 4 Smaller ISO

1 3 2 1 2 1 2 3 2 2 2 2 Interior west & upper Midwest (non-MISO) 

7 6 6 2 2 2 5 4 2 5 2 4 Large vertically integrated utility

1 3 2 1 2 1 2 4 2 2 2 2 Smaller Vertically Integrated Local Utility

8 Unconstrained hydro system

3 Heavily fish constrained hydro system

1 1 1 1 1 1 1 1 1 1 1 11 Weightings Factors

Accommodating Wind and Solar Integration

Adapted from Milligan, M.; Kirby, B.; Gramlich, R.; Goggin, M. (2009). Impact of Electric Industry Structure on 

High Wind Penetration Potential. 31 pp.; NREL Report No. TP-550-46273. 

http://www.nrel.gov/docs/fy09osti/46273.pdf
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Capacity Value ( = Capacity Credit)
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Topics 

• Operational vs. Planning/System Adequacy;

• Capacity Value as Contribution to Adequacy: 

Effective Load Carrying Capabilit;y

• LOLP, LOLE;

• Wind ELCC and modeling approaches;

• Recent selected U.S. results and methods;

• Recommended approaches.
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Two Views of Capacity Value

• Operational: how much capacity will wind 

produce at a given date/time?

• Resource adequacy;

• Is there enough installed capacity in year X to 

reliably serve load? How does wind contribute?

• These are two very different questions.

Photo by Warren Gretz, 

NREL/PIX 10930 

Photo by Warren Gretz, 

NREL/PIX 00070 

Photo from Moorhead Public 

Service, NREL/PIX 11602
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Resource Adequacy

• Often measured based on installed capacity, 

peak load, and a planning reserve;

• A fixed planning reserve margin (15%) does not 

in itself provide a measure of adequacy;

• No system can be perfectly adequate;

• How adequate is adequate enough?

• Quantify the number of times system will be 

inadequate – often measured as hours/year; 

days/year (1d/10y ≈ 99.97%).
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Planning reserve as % of peak is not a good metric

Which System is Most Reliable?

14.6

16.1

20.6

24.3

29.7

18.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

A B C D E F

Reserve Margin (% Peak)
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Reserve Margins Don’t Directly Address System 

Adequacy
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WECC data

0

10

20

30

40

50

60

0 200 400 600 800 1000 1200 1400 1600

Summer Capability (MW)

Forced Outage Rate (%)
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Are There Metrics For Resource Adequacy?

• Loss of load probability:

– Probability of insufficient generation to cover load;

– Not necessarily load shedding; covers the probability 

of unforeseen/spot imports.

• Loss of load expectation = probability x time.

• Expected unserved energy:

– Measures the amount of potential shortfall, not just 

the likelihood.

• All of these measures capture varying levels of 

risk – something that is missing from fixed 

planning reserve margin approaches (15%) 

unless they have been ‘trued up’ with reliability 

results.
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Individual Generators’ Contribution to 

Adequacy can be Measured

• Effective load carrying capability (ELCC);

• Applies to all generators, not just wind;

• De-composes each individual generator’s 

contribution to resource adequacy.
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Effective Load Carrying Capability (ELCC)

Each generator added to the system 

helps increase the load that can be 

supplied at all reliability levels

Gi Gi+1 Gi+2

Added Generators

Combined 

Resources

With 

Wind

Gn-2
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What is ELCC?

• What ELCC is Not: 

– a minimum generation value.

– a schedule or forecast for wind.

• ELCC is:

– Measure of wind (or other resource’s) contribution to 

overall system adequacy.

– Decomposition of the generator’s contribution to 

adequacy (planning margins).
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Capacity Credit (ELCC) Properties

• Not unique to wind, and can be adapted to wind 

generators.

• Wind capacity credit depends on output profile 

(hourly for at least one year):

– Low when wind contributes small amount to reliability;

– High when wind contributes large amount to reliability;

– Depends on system and wind characteristics;

– Values can range from approximately 10%-40%, 

depending on system and wind characteristics;

– Capacity credit outside this range are possible;

– Use multiple years of data if available.
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How Does ELCC Work?

• Holds the system at constant annual risk level 

with/without wind;

• Can be measured relative to a perfect unit or 

selected benchmark unit; 

• Utilizes reliability/production simulation model:

– Hourly/daily loads;

– Generator characteristics;

– Wind generation pattern (hourly for >= 1 year);

– Calculates hourly/daily LOLP (loss of load 

probability).

• Conventional units ELCC is primarily a function 

of FOR and unit size.
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Another view of ELCC

Available margin as a percentage of peak demand

0

Probability

0.02

10 20

Area under curve = 0.1 days/year
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Representations of wind in reliability models

• Hourly wind production (real or simulated).

• Time-synchronized with load (more later).

• This captures the actual variability in wind 

output.
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Capacity Table: Simple Convolution Example

Assume 6-50 MW units, each with FOR=.08

MW-Out MW-In Probability LOLP

0 0.0000 300.0000 0.60635500 1.00000000

1 50.0000 250.0000 0.31635913 0.39364500

2 100.0000 200.0000 0.06877372 0.07728587

3 150.0000 150.0000 0.00797377 0.00851214

4 200.0000 100.0000 0.00052003 0.00053838

5 250.0000 50.0000 0.00001809 0.00001835

6 300.0000 0.0000 0.00000026 0.00000026

LOLP is the cumulative probability function
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Assume 6-50 MW units, each with FOR=.08

MW-Out MW-In Probability LOLP

0 0.0000 300.0000 0.60635500 1.00000000

1 50.0000 250.0000 0.31635913 0.39364500

2 100.0000 200.0000 0.06877372 0.07728587

3 150.0000 150.0000 0.00797377 0.00851214

4 200.0000 100.0000 0.00052003 0.00053838

5 250.0000 50.0000 0.00001809 0.00001835

6 300.0000 0.0000 0.00000026 0.00000026

LOLP is the cumulative probability function
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LOLP Calculation with 50-MW Wind Plant

Assume 6-50 MW units, each with FOR=.08

MW-Out MW-In Probability LOLP

0 0.0000 300.0000 0.60635500 1.00000000

1 50.0000 250.0000 0.31635913 0.39364500

2 100.0000 200.0000 0.06877372 0.07728587

3 150.0000 150.0000 0.00797377 0.00851214

4 200.0000 100.0000 0.00052003 0.00053838

5 250.0000 50.0000 0.00001809 0.00001835

6 300.0000 0.0000 0.00000026 0.00000026

Outage table with no wind

Step 1
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LOLP Calculation with 50-MW Wind Plant

Assume 6-50 MW units, each with FOR=.08

MW-Out MW-In Probability LOLP

0 0.0000 300.0000 0.60635500 1.00000000

1 50.0000 250.0000 0.31635913 0.39364500

2 100.0000 200.0000 0.06877372 0.07728587

3 150.0000 150.0000 0.00797377 0.00851214

4 200.0000 100.0000 0.00052003 0.00053838

5 250.0000 50.0000 0.00001809 0.00001835

6 300.0000 0.0000 0.00000026 0.00000026

Move to 150 MW load less 50 MW wind in outage table

Step 2
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LOLP Calculation with 50-MW Wind Plant

Assume 6-50 MW units, each with FOR=.08

MW-Out MW-In Probability LOLP

0 0.0000 300.0000 0.60635500 1.00000000

1 50.0000 250.0000 0.31635913 0.39364500

2 100.0000 200.0000 0.06877372 0.07728587

3 150.0000 150.0000 0.00797377 0.00851214

4 200.0000 100.0000 0.00052003 0.00053838

5 250.0000 50.0000 0.00001809 0.00001835

6 300.0000 0.0000 0.00000026 0.00000026

Move to 150 MW load less 50 MW wind in outage table

Step 2
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Alternative underlying reliability metrics

• ELCC can be based on:

• LOLP/LOLE – daily;

• LOLH (hourly LOLP);

• EUE – expected unserved energy.

• LOLP/LOLE measures count time periods but 

ignore the energy risk.

• EUE does not count, but aggregates energy risk.
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Types of assessments

• Frequency 

distribution/duration curves 

for load and wind (fewer 

computation requirements 

but can’t be easily justified.

• Chronological simulation: 

best approach.

• Simplified approaches: use 

with caution; benchmark 

with ELCC required.
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Data required

• Hourly load and wind/solar (VG), synchronized.

• Generation capacities, forced outage rates. 

Photo from Kodiak Electric Association, NREL/PIX 16799 Photo by Warren Gretz, NREL/PIX 02368
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Representations of VG in reliability models

• Hourly VG production (real or simulated).

• Time-synchronized with load.

• This captures the actual variability in VG output.

• Multiple years (just like thermal units).
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Conventional Stochastic Approaches: 

Multiple-block Unit

• Wind as a multiple-block generator.

• For each month, calculate several discrete generation 

levels and frequency of occurrence according to 

reliability model requirements.

• Partition by hour of day.

• Result: 24 distributions per month, each representing a 

given hour of the day.

• Does not preserve underlying “weather” of VG and 

load.
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Sequential Monte Carlo: Use with Care

• Basic Approach:

– Build probabilistic model of wind resource or wind 

generation;

– Repeatedly sample from the family of distributions;

– Run reliability model for each simulated year of wind 

data;

– Collect results.

• Computationally expensive:

– May not adequately capture wind-load synergies;

– Can be difficult to obtain synthetic time series that 

adequately represent the complex correlation and 

auto-correlation structure in the real wind generation 

patterns.
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Characteristics and Overview of Findings
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Capacity value declines with more wind
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ELCC and LOLP depend on electrical footprint

0.0

5.0

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1 2 3 4

Wind Only

2004

2005

2006

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1 2 3 4

Wind with Overlay

2004

2005

2006

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2.0

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6.0

8.0

10.0

12.0

14.0

1 2 3 4

Incremental ELCC from Overlay

2004

2005

2006
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ELCC also depends on

• Relationship of 

wind to load;

• Generator 

maintenance 

schedules;

• Hydro run of river, 

peak shaving;

• Import schedules 

and control area 

boundaries.

Hydro and No-Hydro LOLP

0

10

20

30

40

50

60

70

0 20 40 60 80 100

Top Load Hour

Abs Pct Diff

Abs Pct Diff Mean Abs Pct Dif
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If you use one year of data you may be in trouble

Minnesota 20% Wind Integration Study

Wind Capacity Value (ELCC) by Penetration

0

5

10

15

20

25

2003 2004 2005

Year

Percent of Wind 

Rated Capacity

15%

20%

25%
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8 years of data appears safe (so far)
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Observations 

• Concern regarding inter-annual variability of 

capacity value of wind.

• Range of wind variation less than thermal units.

Available margin as a percentage of peak demand

0

Probability

0.02

10 20

Area under curve = 0.1 days/year
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0.0

20.0

40.0

60.0

80.0

100.0

120.0

Wind1 Wind2 Wind3 Wind4 Thermal

Percent of rated capacity

Wind Scenario

Avg

Min

Max

Range of capacity value (ELCC), EWITS

MW size also important! Minimum = 0
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Hourly data (or faster) is required
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For large penetrations you need data from multiple locations
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ELCC is not just a function of capacity factor
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Other (non-reliability) Approaches

• Many entities use an 

approximation method.

• Common approximation is 

wind capacity factor over 

some defined peak period.

• Most have not compared 

the approximation to a 

reliability-based metric.

Photo from Tennessee Valley Infrastructure 

Group Inc., NREL/PIX 12087
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Approximations and different methods

• Approximations to ELCC:

– Approximations may be needed due to lack of data;

– Computational time is not a reason;

– Approximations will have errors;

– Important to establish reliability target. 

• Different methods:

– Not calculating capacity value;

– Should not be compared (except to benchmark 

simple methods).
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Peak period capacity factor simple approaches have 

not been benchmarked with ELCC

12:00 PM 1:00 PM 2:00 PM 3:00 PM 4:00 PM 5:00 PM 6:00 PM 7:00 PM 8:00 PM

CPUC

PJM

NY ISO

NY ISO

Idaho Power

PNM

ISO New England

ISO New England

October-May

June-September

July

July

December -February

June - August

June - August

May -September

Peak Period Methods
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International Comparisons
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Country values have wide range

Wind capacity value decreases at larger penetrations, faster for 

smaller areas.

Differences: wind resource at peak loads, reliability level, 

methodology.

Capacity credit of wind power

0 %

5 %

10 %

15 %

20 %

25 %

30 %

35 %

40 %

45 %

0 % 10 % 20 % 30 % 40 % 50 % 60 %

Wind power penetration as % of peak load

Capacity credit

Germany

UK

Mid Norway

US Minnesota 2004

US Minnesota 2006

US New York on-off-shore

US California

"Ireland ESBNG 5GW"

"Ireland ESBNG 6.5GW"

...but that depends on methods, and real differences in wind-load 

correlations
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Common findings

• Declining marginal contribution to planning 

reserves as a function of penetration;

• Capacity value increases with geographic 

diversity;

• Capacity value is relatively small fraction of wind 

installed capacity;

• Multiple years of data required – just like thermal 

units.
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NERC Integrating Variable Generation Task Force 

1.2: Scope & Objectives

–Consistent and accurate methods are needed to 

calculate capacity values attributable to variable 

generation.

–Technical considerations for integrating variable 

resources into the bulk power system. 
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NERC IVGTF 1.2 Scope & Objectives (continued)

• Specific actions, practices and requirements, 

including enhancements to existing or 

development of new reliability standards:

– Calculations and metrics, including definitions and their 

applications used to determine capacity contribution and reserve 

adequacy.

– Contribution of variable generation to system capacity for highrisk hours, estimating resource contribution using historical data.

– Probabilistic planning techniques and approaches needed to 

support study of bulk system designs to accommodate large 

amounts of variable generation.
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NERC Report Outline

•Introduction;

•Traditional Resource Adequacy Planning;

•Data Limitations;

•Approximation Methods;

•Ongoing Variable Generation Actions;

•Conclusion and Recommendation.
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IEEE Wind Capacity Value Task Force paper in press

• Recommends ELCC.

• Discussion of alternative targets: 0.1 d/y ≠ 

2.4h/y.

• Careful benchmarking of simple approaches 

to ELCC.
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Recommended Approaches (similar to IEEE 

Working Group)

• Adopt a reliability target such as 1d/10y;

• Derive the percentage reserve margin that 

corresponds to the reliability target

• To determine any generator’s contribution, use 

ELCC;

• Benchmark wind ELCC with other performance 

metric such as capacity factor over a peak period;

• Use multiple years of data;

• As more wind plant data become available, re-visit 

and tweak as necessary;

• Interconnection or regional analysis.
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Summary 

• Resource adequacy 

assessment should 

explicitly consider risk.

• ELCC captures each 

generators’ contribution 

to resource adequac.y

• On their own, reserve 

margin targets as a 

percent of peak can’t 

capture risks effectively.

• Recommend 

benchmarking 

reliability-based 

approaches with others.

Photo by Michael Mulligan, NREL/PIX 13371
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Other references

• NERC Report: 

http://www.nerc.com/docs/pc/ivgtf/IVGTF1-2.pdf 

• IEEE Task Force Paper: Keane, Milligan, et al, IEEE 

Transactions on Power Systems, Vol 26, No 2. May 2011. p 564.

• Milligan and Porter, “Determining the Capacity Value of 

Wind: An Updated Survey of Methods and 

Implementation.” Presented at WindPower 2008. 

Available at http://www.nrel.gov/docs/fy08osti/43433.pdf

• Milligan and Porter, “Determining the Capacity Value of 

Wind: A Survey of Methods and Implementation.” 

Presented at WindPower 2005. Available at 

http://www.nrel.gov/docs/fy05osti/38062.pdf

• Billinton and Allan: Reliability Evaluation of Power Systems, 2nd Ed. Springer, 1996.
Questions/ 

Discussion?

79

Photo by

Michael Mulligan, 

NREL/PIX 13367