NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Comparing Resource Adequacy 

Metrics and Their Influence on 

Capacity Value 

Preprint 

E. Ibanez and M. Milligan

National Renewable Energy Laboratory

To be presented at the 13th International Conference on 

Probabilistic Methods Applied to Power Systems 

Durham, United Kingdom

July 7–10, 2014

Conference Paper

NREL/CP-5D00-61017

April 2014 
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US 

Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications. 

Available electronically at http://www.osti.gov/scitech

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
Comparing Resource Adequacy Metrics 

and Their Influence on Capacity Value 

Eduardo Ibanez and Michael Milligan

Transmission and Grid Integration Group

National Renewable Energy Laboratory

Golden, CO USA

eduardo.ibanez@nrel.gov, michael.milligan@nrel.gov

Abstract—Traditional probabilistic methods have been used to 

evaluate resource adequacy. The increasing presence of variable 

renewable generation in power systems presents a challenge to 

these methods because, unlike thermal units, variable renewable

generation levels change over time because they are driven by 

meteorological events. Thus, capacity value calculations for 

these resources are often performed to simple rules of thumb. 

This paper follows the recommendations of the North American 

Electric Reliability Corporation’s Integration of Variable 

Generation Task Force to include variable generation in the 

calculation of resource adequacy and compares different 

reliability metrics. Examples are provided using the Western 

Interconnection footprint under different variable generation 

penetrations. 

Index Terms—Capacity planning, power system reliability, 

probability, solar energy, wind energy

I. INTRODUCTION

The increasing amount of electrical load served by variable 

generation (VG) such as wind and solar photovoltaic energy in 

the United States and many other countries has stimulated an 

interesting line of research to better quantify the capacity 

value of these resources. Traditional methods applied to 

thermal units based on their nominal capacity and average 

outage rates do not apply to VG because of their variable, 

uncertain, and nondispatchable nature. The North American 

Electric Reliability Corporation’s Integration of Variable 

Generation Task Force recently released a report that 

highlighted the need to develop and benchmark metrics that 

reasonably and fairly calculate the capacity value of solar and 

wind power [1]. As the fraction of generation coming from 

VG becomes more relevant, the estimated capacity value of 

VG will have an impact on system planning [2]. However, 

many system operators ignore VG capacity value or base their 

estimations on simple rules of thumb, such as a nominal and 

predetermined value or an average of certain hours of the 

year [3]. 

In this paper, we provide a method to include VG in 

traditional, probabilistic-based, adequacy methods. This 

method has been implemented in the Renewable Energy 

Probabilistic Resource Adequacy (REPRA) tool. The tool is 

used to compare different metrics to quantify system risk. The 

methodology is applied to different renewable penetrations in 

the U.S. Western Interconnection.

The remainder of the paper is organized as follows: 

Section II introduces the concept of effective load-carrying 

capability; Section III describes the proposed modeling 

framework; Section IV describes the numerical example; 

Section V summarizes results and findings; and, finally, 

Section VI concludes.

II. RESOURCE ADEQUACY AND PROBABILISTIC METRICS

Generation system adequacy is the subset of electrical 

system adequacy/reliability that ensures that available capacity 

is sufficient to meet expected demand within an acceptable 

risk threshold [4] at some future date in the planning horizon. 

The metrics most commonly used to assess system adequacy 

revolve around the loss-of-load probability (LOLP). The lossof-load expectation (LOLE) is a measurement of the expected 

days in a year that could face a generation shortfall. Similarly, 

the loss-of-load hours (LOLH) measures the expected number 

of hours in a year with insufficient generation. Expected 

unserved energy (EUE) provides an estimation of the energy 

not likely to be served with the proposed generation portfolio. 

The current standard practice in the industry is to consider a 

LOLE level of 1 day in 10 years as an acceptable target. This 

paper compares this metric and level to other alternatives.

The literature review in [5] and more-recent examples in 

[1] and [6] present the effective load-carrying capability 

(ELCC) as an emerging suitable metric to evaluate the 

capacity value of VG. Given a reliability target, ELCC is 

defined as the maximum load that could be served by a system 

while meeting said reliability target. The ELCC can also be 

defined for a single generation unit as the increase in the 

system ELCC when that unit is added to the system [7]. Fig. 1 

shows a graphical representation of this definition. The red 

horizontal line represents the reliability target of 1 day in 10 

This work was supported by the U.S. Department of Energy under 

Contract No. DE-AC36-08-GO28308 with the National Renewable Energy 

Laboratory. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 1
years, which is a common target used in the industry. The blue 

line represents the reliability curve for the units already in the 

system, which has an ELCC of 10 GW. When a new 

generation unit is added, the reliability curve shifts to the 

right. The horizontal difference between the system curves, 

400 MW, represents the new unit’s ELCC. The example 

shows the addition of wind generation, but the principle 

applies to any type of resource. The fraction of ELCC to 

installed capacity is generally close to one for conventional 

generation and significantly less for wind generation. Solar 

power capacity values calculated by this method are generally 

higher than wind resources but lower than conventional 

generation.

Figure 1. The unit ELCC is the horizontal distance between the reliability 

curves, measured at the target reliability level (400 MW at 1 day/10 years). 

This value is also referred to as the capacity value (or 

credit) of the unit, because it measures the portion of the unit’s 

nominal capacity that contributes to the system during the 

most critical hours. Although the choice of reliability target 

can change the ELCC and capacity value results (by 

performing the calculations in different areas along the 

reliability curve), the results section suggests that this effect is 

moderate over a wide range of targets.

This methodology can also be used to estimate the 

contribution of higher levels of transmission in interconnected 

systems. Analytical examples available in the literature are 

limited to two or three interconnected areas [4,8]. Alternative 

methodologies include the use of Monte Carlo simulations, 

e.g., in GE’s Multi-Area Reliability Simulation program [9] or 

combined with decomposition methods [10].

III. THE REPRA TOOL

The REPRA tool is being developed at the National 

Renewable Energy Laboratory to better understand how 

different generation types can contribute to the resource 

adequacy of the power system from a reliability point of view. 

The different types include renewable generation, which are 

usually nondispatchable sources of power. At the core of the 

model resides a fast-convolution algorithm that combines the 

probability distribution of the traditional generators. These are 

represented by a finite number of states. The simplest case is 

modeled with a Bernoulli distribution, with a probability that 

each unit is not available equal to its equivalent forced outage 

rate, that is: 

P(x) = �

EFOR, x = 0

(1 − EFOR), 0 < x ≤ C

0, x > C

(1) 

where C is the unit’s capacity, x is the generation level, and 

P(x) is the probability associated with that generation level.

The probability of multiple units is combined in a process 

known as convolution [3]. A very simple method to examine 

the combined probability is through the use of a capacity 

outage probability table, which indicates the LOLP for all 

levels of load the system can serve.

Unlike traditional generators, VG production is not solely 

limited by the nameplate capacity but also by the available 

driving resources such as wind speed or solar irradiance, 

which are governed by weather patterns. These patterns 

usually fluctuate and follow daily and seasonal trends. To 

preserve this variation, we make use of a sliding-window 

technique [11] for all hours of the year. Fig. 2 shows a 

graphical representation of a sliding window, which includes 

the current and adjacent hours. The width is predetermined but 

adjustable; in this case, it includes a total of five hours, but can 

also be modified to include adjacent days. Power outputs in 

the window are combined with probabilities (in this case, they 

are equal) to create a probability distribution similar to that 

showed in (1). For each hour of the year, this distribution can 

be convolved with the thermal outage table.

Figure 2. Example of a sliding window for wind power generation 

IV. NUMERICAL EXAMPLE

In this section, we apply the reliability tool introduced in 

the previous section to the U.S. Western Interconnection

footprint. The representation of the generation fleet is based 

on the National Renewable Energy Laboratory’s Western 

Wind and Solar Integration Study Phase 2 (WWSIS-2) [12]. 

This data is consistent with other studies performed by the 

Western Electricity Coordinating Council’s Transmission 

Expansion Planning Policy Committee [13].

Table I contains the list of balancing authority areas 

(BAAs) that are considered in this example. BAAs are 

grouped into eight subregions, following the suggested zones 

in [14]. Fig. 3 presents a map of the different BAAs and the 

subregions to which they belong, which are differentiated by 

shades. Throughout this paper, WAUW is merged with 

NWMT because of the small size of the former.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 2
Figure 3. Western Electricity Coordinating Council BAAs and subregions 

TABLE I. BAAS AND SUBREGIONS IN THE WESTERN ELECTRICITY 

COORDINATING COUNCIL

Subregion Code Balancing Authority Area

Northwest AVA Avista

BPA Bonneville Power Administration

CHPD PUD No. 1 of Chelan County

DODP PUD No. 1 of Douglas County

GCPD PUD No. 1 of Grant County

NWMT Northwest Energy

PGN Portland General Electric

PSE Puget Sound Energy

SCL Seattle City Light

TPWR Tacoma Power

WAUW WAPA – Upper Great Plains West

Northern PACW PacifiCorp West

California PG&E Pacific Gas and Electric

SMUD Sacramento Municipal Utility District

TID Turlock Irrigation District

Southern IID Imperial Irrigation District

California LDWP LA Dpt. of Water and Power

SCE Southern California Edison

SDGE San Diego Gas and Electric

Basin FAR EAST Far East

Basin FAR EAST Far East

MAGIC VLY Magic Valley

PACE ID PacifiCorp East (Idaho)

PACE UT PacifiCorp East (Utah)

PACE WY PacifiCorp East (Wyoming)

SPP Sierra Pacific Power (NV Energy)

TREAS VLY Treasure Valley

Rockies PSC Public Service Company of Colorado

WACM WAPA – Colorado Missouri Region

Desert APS Arizona Public Service

Southwest EPE El Paso Electric

NEVP Nevada Power

PNM Public Service Company of New Mexico

SRP Salt River Project

TEP Tucson Electric Power

WALC WAPA – Lower Colorado Region

Canada AESO Alberta

BCTC British Columbia Transmission Corp.

Mexico CFE Comisión Federal de Electricidad

WWSIS-2 examined the effect of integrating large 

amounts of renewable energy into the Western 

Interconnection by studying three scenarios with 33% 

renewable energy penetration and a reference scenario based 

on [13]. Table II summarizes the breakdown of wind and solar 

power for these scenarios. Even though the Base and High 

Solar scenarios have the same wind power penetration, the 

siting of the wind is different, thus the necessary capacity to 

achieve the 8% energy penetration leads to slightly different 

amounts of installed capacity. Although solar power refers to 

both photovoltaic and concentrating solar power, only 

photovoltaic was considered in this analysis. This is because 

concentrating solar power in WWSIS-2 was assumed to have

several hours of storage built-in, thus making it dispatchable

to a certain degree. Calculation of capacity value with storage 

is another current field of research [15].

TABLE II. WESTERN WIND AND SOLAR INTEGRATION STUDY PHASE 2

SCENARIOS

Scenario Wind

Penetration

Solar

Penetration

Wind

Capacity

(GW)

Solar

Capacity 

(GW)

Base 8% 3% 27.9 11.4

High Wind 25% 8% 66.2 34.6

High Mix 16.5% 16.5% 43.8 54.9

High Solar 8% 25% 23.4 81.7

Load time series data from 2006 was chosen from the 

Ventyx Velocity Suite [16] and were increased to represent 

the load in 2020, the focus year. The wind data set was 

derived from the large wind speed and power database [17] 

developed by 3TIER using a numerical weather prediction 

model applied to the Western United States. Because the 

model allows for the re-creation of the weather at any time and 

space, wind speed data was sampled at representative hub 

heights for modern wind turbines every 10 minutes during a 3-

year period on a 2-km spatial resolution. The resulting data set 

was then used to construct the 2006 time series, which was 

paired with the 2006 load data time series to preserve the 

consistency of common weather impacts. Solar data was 

produced by the National Renewable Energy Laboratory [18] 

based on the satellite-derived irradiance generated by the State 

University of New York/Clean Power Research [19], which is 

available on a 10-km grid at an hourly resolution.

V. RESULTS

A. Metric Comparison 

ELCC values are calculated at the BAA, subregion, and 

interconnection levels following the structure in Table I.

LOLE is used as the driving metric for the calculations for a 

wide range of values (from 1 day in 1 year to 1 day in 100 

years) in this section. The LOLH and EUE metrics are 

calculated for each case. This section summarizes the 

observed relationships.

For all the footprints, we find that the behavior between 

the three metrics resembles a linear relationship, especially 

between LOLE and LOLH. Figs. 4 and 5 show examples of

these relationships.

The slopes shown in Figs. 4 and 5 vary significantly across 

the different regions. To better understand the origin of these 

slope changes for the LOLE and LOLH cases, we utilize 

Fig. 6, which represents the top 25 hours in decreasing LOLP 

order. All the hours contribute to the LOLH metric; whereas 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 3
only a few hours contribute to the LOLE metric (the largest 

value per day, highlighted in red). Fig. 6 reveals that the areas 

with the smallest slope are those that have very sharp decline 

in LOLP value and, as a result, have very few hours that 

contribute mostly to both LOLH and LOLE. Alternatively, 

more evenly distributed LOLP curves result in high LOLH-toLOLE ratios.

Figure 4. LOLH versus LOLE for the selected BAAs, subregions, and the 

Western Interconnection 

Figure 5. EUE versus LOLE for the selected BAs, subregions, and the 

Westner Interconnection 

Figure 6. LOLP for the top 25 hours for 0.1 day/year for the selected 

BAAs, subregions, and the Westner Interconnection

This behavior is driven mainly by two factors: net load 

distributions and the probabilistic convolution of traditional 

generators. Fig. 7 summarizes the 25 top net load hours, which 

coincide with the same hours in Fig. 6. Large drops between 

the first top hours tend to create small LOLH-to-LOLE ratios, 

e.g., Southern California of the Western Interconnection. The 

sharp decrease is not sufficient to guarantee that behavior, as 

is the case for PNM. The translation between demand levels 

and LOLP depends on the convolution of the generating units. 

Fig. 8 shows that these curves can vary significantly, 

especially for smaller footprints with fewer generators. 

Figure 7. Net load duration curves for 1 d/10 years LOLE level for the 

selected BAAs, subregions, and the Western Interconnection

Figure 8. LOLP versus load level for two subregions 

The linear relationship between LOLE and LOLH can be 

studied through the graphs in Figs. 9 and 10 (Interconnect and 

PSC, respectively). Using the same coloring scheme to discern 

the top 25 hours that contribute to LOLE and LOLH, different 

LOLE target cases are represented. In both figures, even 

though the magnitudes of LOLP change, the relative shape of 

the distribution remains fairly stable. Thus, the LOLH-toLOLE ratios remain close to constant for the cases observed.

A similar process can be followed to explain the 

relationship between LOLE and EUE. In this case, one needs 

to compare the LOLP of the top hours by day to the EUE of 

the individual hours. There are instances in which the behavior 

does not adjust as well to a linear relationship because of the 

necessary translation between LOLP and EUE, as shown in 

Fig. 11.

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 4
Figure 9. LOLP for the top 25 hours for different LOLE targets for the

Western Interconnection

Figure 10. LOLP for the top 25 hours for different LOLE targets for PSC

Figure 11. The relationship between LOLP and EUE

B. Effect on Capacity Value 

After understanding the behavior of the different metrics, 

we proceed to estimate their effect on capacity value. To do 

this, ELCC values are calculated for the system with and 

without VG. The different metrics are used, making sure that 

they are consistent across different reliability levels. For 

instance, after calculating ELCC using an LOLE of 1 day in 

10 years, the observed values for LOLH for that case are 

annotated. Then ELCC calculations are performed with and 

without VG achieving the desired LOLH level. The capacity 

value calculated by both methods is done under equivalent 

levels of reliability.

Fig. 12 shows the reliability curves (first suggested in 

Fig. 1) for different metrics. These curves are depicted for four 

scenarios: conventional generation only, with wind, with 

photovoltaic, and with the addition of both. 

Figure 12. Different metrics versus peak load (or ELCC) for PSC

It is not surprising to see that the curves behave similarly 

given the underlying linear relationship between metrics. As 

mentioned earlier, capacity value can then be calculated by 

measuring the horizontal distance between two curves. These

are calculated for all the areas and different equivalent levels 

of reliability. Fig. 13 shows the results across all deployment 

scenarios for the Rockies and Southern California subregions. 

According to the results, capacity value is rather insensitive to 

metric selection. This is not surprising after understanding the 

tight relationships between the metrics. Further, the reliability

level targeted is very small, especially given that the results 

expand two orders of magnitude.

Figure 13. Wind and photovoltaic capacity value for the Rockies and 

Southern California by metrics and WWSIS-2 scenario 

VI. CONCLUSIONS

The paper presents a methodology to include VG in 

traditional resource adequacy calculations. Different metrics 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 5
based on the concept of ELCC are presented and compared 

through a numerical example that expands a multitude of 

regions in the Western Interconnection and for different levels 

of wind and solar penetration.

The comparison of metrics suggests an underlying linear

relationship between them, and these patterns are explained by 

studying the different cases. VG capacity values are calculated 

with the alternative metrics and compared for equivalent 

reliability levels, and they are found to be rather close. 

Similarly, the reliability level chosen does not have a 

significant impact on the capacity value calculations. Future 

work will examine these findings for other footprints and 

years of data and will consider whether the definition of the

metrics is responsible for the observed results from a 

theoretical point of view.

REFERENCES

[1] Integration of Variable Generation Task Force, “Methods to model and 

calculate capacity contributions of variable generation for resource 

adequacy planning,” North American Electric Reliability Corporation, 

Princeton, NJ, , Rep., 2011 [Online]. Available: 

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

[2] M. Milligan and K. Porter, “Wind capacity credit in the United States,” 

in Proc. 2008 IEEE PES GM, pp. 1–5. 

[3] J. Rogers and K. Porter, “Summary of time period-based and other 

approximation methods for determining the capacity value of wind and 

solar in the United States: September 2010–February 2012,” National 

Renewable Energy Laboratory, Golden, CO, Subcontract Rep. 

NREL/SR-5500-54338, 2012 [Online]. Available: 

http://www.nrel.gov/docs/fy12osti/54338.pdf

[4] R. Billinton and R. N. Allan, Reliability Evaluation of Power Systems, 

New York: Plenum Press, 1996. 

[5] M. Milligan and K. Porter, “Determining the capacity value of wind: 

An updated survey of methods and implementation,” presented at

WindPower, Houston, TX, 2008 [Online]. Available: 

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

[6] A. Keane, M. Milligan, C. J. Dent, B. Hasche, C. D’Annunzio, K. 

Dragoon, H. Holttinen, N. Samaan, L. Soder, and M. A. O’Malley, 

“Capacity value of wind power,” IEEE Trans. on Power Syst., vol. 26, 

no. 2, pp. 564–572, May 2011. 

[7] L. L. Garver, “Effective Load Carrying Capability of Generating 

Units,” IEEE Trans. Power Syst., vol. PAS-85, no. 8, pp. 910-919, Aug. 

1966. 

[8] L. Goel, C. K. Wong, and G. S. Wee, “And educational software 

package for reliability evaluation of interconnected systems,” IEEE 

Trans. on Power Syst., vol. 10, no. 3, pp. 1,147–1,153, Aug. 1995. 

[9] G. E. Haringa, G. A. Jordan, and L. L. Garver, “Application of Monte 

Carlo simulation to multi-area reliability evaluation,” IEEE Computer 

Applications in Power, vol. 4, pp. 21–25, Jan. 1991. 

[10] B. Hasche, M. Wattiau, K. Grave, and R. Kuwahata, “Generation 

system adequacy in Europe: Application of a multi-area reliability 

calculation,” presented at the 11th Annual Int. Workshop on LargeScale Integration of Wind Power into Power Systems as Well as on 

Transmission Networks for Offshore Wind Power Plants, Lisbon, 

Portugal, Oct. 2012. 

[11] M. Milligan, “A chronological reliability model incorporating wind 

rorecasts to assess wind plant reserve allocation,” presented at 

WindPower, Portland, OR, 2002 [Online]. Available: 

http://www.nrel.gov/docs/fy02osti/32210.pdf

[12] D. Lew, G. Brinkman, E. Ibanez, B. M. Hodge, M. Hummon, A. 

Florita, and M. Heaney, “The western wind and solar integration study 

phase 2,” National Renewable Energy Laboratory, Golden, CO, Tech. 

Rep. NREL/TP-5500-55588, 2013 [Online]. Available:

http://www.nrel.gov/docs/fy13osti/55588.pdf

[13] Transmission Expansion Planning Policy Committee Western 

Electricity Coordinating Council, “TEPPC 2020 study report–2010 

study program: 10-year regional transmission plan,” Western 

Electricity Coordinating Council, Salt Lake City, UT, Rep., Sept. 2011 

[Online]. Available: 

http://www.wecc.biz/library/StudyReport/Documents/2020%20Study%

20Report.pdf 

[14] Western Electricity Coordinating Council, “2010 power supply 

assessment,” Salt Lake City, UT, Rep. [Online]. Available: 

http://www.wecc.biz/Planning/ResourceAdequacy/PSA/ 

[15] S. H. Madaeni, R. Sioshansi, and P. Denholm, “Estimating the Capacity 

value of concentrating solar power plants with thermal energy storage: 

A case study of the southwestern United States,” IEEE Trans. on 

Power Syst., vol. 28, no. 2, pp.1,205–1,215, May 2013. 

[16] Ventyx, “Energy market data,.” Rep., 2010 [Online]. Available: 

http://www.ventyx.com/velocity/energy-market-data.asp

[17] 3TIER, “Development of regional wind resource and wind plant output 

datasets,” National Renewable Energy Laboratory, Golden, CO, 

Subcontract Rep. NREL/SR-550-47676, 2010 [Online]. Available: 

http://www.nrel.gov/docs/fy10osti/47676.pdf

[18] K. Orwig, M. Hummon, B.-M. Hodge, and D. Lew, “Solar data inputs 

for integration and transmission planning studies,” presented at the 1st 

Int. Workshop on Integration of Solar Power into Power Systems,

Aarhus, Denmark, Oct. 2011. 

[19] S. Wilcox, M. Anderberg, R. George, W. Marion, D. Myers, D. Renne, 

N. Lott, T. Whitehurst, W. Beckman, C. Gueymard, R. Perez, P. 

Stackhouse, and F. Vignola, “Completing production of the updated 

National Solar Radiation Database for the United States,” presented at 

the Solar 2007 Conf., Cleveland, OH, July. 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 6