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Methods to Model and Calculate Capacity 

Contributions of Variable Generation for Resource 

Adequacy Planning (IVGTF1-2): Additional Discussion

NERC Loss of Load 

Expectation Working 

Group

Michael Milligan, Ph.D.

National Renewable Energy 

Laboratory

Atlanta, GA

Jan. 20, 2011

NREL/PR-5500-50355 Composite photo created by NREL
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Report Link and Comment Instructions

The IVGTF Team 1-2 report Methods to Model and 

Calculate Capacity Contributions of Variable 

Generation for Resource Adequacy Planning is at 

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

2_DRAFT_11.22.pdf.

Provide feedback by January 30, 2010 to 

michael.milligan@nrel.gov.
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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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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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Report Outline

•Introduction

•Traditional Resource Adequacy Planning

•Data Limitations

•Approximation Methods

•Ongoing Variable Generation Actions

•Conclusion and Recommendation
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Traditional Resource Adequacy Planning

Loss of Load Probability, 

LOLP

– LOLP analysis is 

typically performed, 

calculations can be 

done hourly or daily on 

a system to determine 

the amount of capacity 

that needs to be 

installed to meet the 

desired reliability 

target. Commonly 

expressed as an 

expected value (loss of 

load expectation, 

LOLE) of 0.1 days/year 

or similar target.
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Traditional Resource Adequacy Planning

Loss of Load Hours, LOLH

LOLH by contrast, is concerned only with the number 

of hours of shortfall, and does not include any 

dimension for persistence of an outage event and 

therefore there is no quantification about how many 

days the outage is spread over.
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Traditional Resource Adequacy Planning

(continued)

Effective Load Carrying 

Capability, ELCC

ELCC essentially 

decomposes the 

contribution that an 

individual generator (or 

group of generators) makes 

to overall resource 

adequacy. A generator 

contributes to resource 

adequacy if it reduces the 

LOLP in some or all hours 

or days. Conventional 

generators’ contribution to 

adequacy is typically a 

function of the unit’s 

capacity and forced outage 

rate.

At the same load level, LOLE is reduced

Additional Load that can be served to 

bring the LOLE back to the target

0.12

0.11

0.10

0.09

0.08

0.07

Loss of Load 

Expectations 

days/years 

11.0x103 8.0 8.5 9.0 9.5 10.0 10.5

Load (GW)

Original Reliability Curve

Reliability Curve after Adding New Generation

Target Reliability Level

<-150 MW->
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ELCC Variation

Wind ELCC declines with penetration

Inter-annual variation implies need for many years 

of data (as with conventional generation)

10.0%

11.0%

12.0%

13.0%

14.0%

15.0%

10% Wind 20% Wind 30% Wind

Capacity Value (%)

Penetration

Western Interconnect 2004-2006 2005

2004

2006

Linear 

(Average)
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Approximation Methods

Approximations are less than ideal and often do 

not take LOLP or risk into account

Approximation to Reliability Analysis:

R’ = Exp{-[(P-L)/m]}

Where:

P = annual peak load, 

L = load for the hour in question, 

R = the risk approximation (LOLP), measured in relative terms (peak hour

risk = 1)

Time Period Methods

– Define the relevant time period to use

– Calculate the mean output of the variable generation over that period; or 

alternatively calculate a percentile or exceedence level of the variable 

generation over the period
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Data Limitations

Data

– Thermal Generation does exist

• Long-term forced outage rates

• GADS

– Wind and Hydro does not have sufficient long term 

data

Need for data from variable generation

– Collected by NERC’s GADS

– Currently does not satisfy requirements for capacity 

valuation of variable generation
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Conclusions and Recommendations

•Additional research to equate traditional reliability targets (such 

as 0.1day/year) to alternative metrics is recommended. As 

adequacy studies are performed, we also recommend 

comparisons of results based on these alternative metrics.

•Alternative approaches and assumptions regarding the 

treatment of interconnected systems should be transparent to 

the analysis, and the development one or more common 

approaches for handling the impact of interconnected systems in 

the reliability assessments will be useful.

•Planning Reserve Margin levels should be benchmarked with, 

or derived from, an LOLP or related approach to resource 

adequacy. This should be done periodically to ensure that any 

correlation between a 0.1days/year target (or other adopted 

target) and a given Planning Reserve Margin do not change as a 

result of an evolving resource mix. 
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Conclusions and Recommendations

(continued)

Simplified approaches should be benchmarked 

and calibrated to the rigorous ELCC calculations to 

ensure the validity of the approximation.

NERC should design and implement a way to 

collect high-quality variable generation data that 

would help inform calculations of capacity value. 

The development of such a database should 

consider defining relevant time periods for the 

variable generation data.
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Conclusions and Recommendations

(continued)

NERC should request that government agencies like 

the DOE, working with NOAA/NCAR develop annual 

high-resolution, modeled wind power and solar power 

data on 10-minute time scales (or faster, as technology 

allows) and 2 km (or smaller) geographic grids. NERC 

should consider collecting 10-minute load data to 

support reliability and other analyses.

NERC should facilitate the dissemination of 

information on how LOLP-related reliability and 

adequacy calculations perform and what they 

measure.
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End of NERC PC Presentation
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Additional Topics

ELCC vs. PRM

Alternative LOLP-related metrics: LOLP, LOLH, LOLE, 

EUE...

Calculation Approaches

Data and LOLP techniques

Note: most work has been done on wind but other 

forms of VG can be analyzed using these methods
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Effective load carrying capability

At the same load level, LOLE is reduced

Additional Load that can be served to 

bring the LOLE back to the target

0.12

0.11

0.10

0.09

0.08

0.07

Loss of Load 

Expectations 

days/years 

11.0x103 8.0 8.5 9.0 9.5 10.0 10.5

Load (GW)

Original Reliability Curve

Reliability Curve after Adding New Generation

Target Reliability Level

<-150 MW->
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Another view of ELCC
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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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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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Reserve Margins Don’t Directly Address 

System Adequacy
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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 

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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

10.0

15.0

20.0

25.0

30.0

35.0

1 2 3 4

Wind Only

2004

2005

2006

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

1 2 3 4

Wind with Overlay

2004

2005

2006

0.0

2.0

4.0

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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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
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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 reliabilitybased metric

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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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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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Other references

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?

46

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