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Update: Effective Load-Carrying 

Capability of Photovoltaics in 

the United States 

Preprint 

R. Perez 

ASRC, SUNY 

R. Margolis 

National Renewable Energy Laboratory

M. Kmiecik and M. Schwab 

ASRC, University at Albany

M. Perez 

University of Rochester

To be presented at Solar 2006 Conference 

Denver, Colorado 

July 8–13, 2006 

Conference Paper 

NREL/CP-620-40068 

June 2006 
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UPDATE: EFFECTIVE LOAD CARRYING CAPABILITY OF PHOTOVOLTAICS 

 IN THE UNITED STATES 

Richard Perez, 

ASRC, SUNY 

251 Fuller Rd, 

Albany, NY 12203 

Perez@asrc.cestm.albany.edu

Robert Margolis 

NREL 

901 D. St, SW Suite 930 

Washington, DC 22024 

Robert_Margolis@nrel.gov

Marek Kmiecik 

And 

 Michael Schwab 

ASRC, Univ. at Albany 

Marek@asrc.cestm.albany.edu

Marc Perez 

University 

of Rochester 

Rochester, NY 14627 

mp003m@mail.rochester.edu

ABSTRACT

We provide an update on the US distribution of PV’s 

Effective Load Carrying Capability (ELLC) by analyzing 

recent load data from 39 US utilities and time-coincident 

output of PV installations simulated from high resolution, 

time/site-specific satellite data. Results show that overall 

regional trends identified in the early 1990s remain pertinent 

today, while noting a significant increase in PV ELCC the 

Western and Northern US, and a modest decrease in the 

central and eastern US. 

1. BACKGROUND AND RATIONALE

This study is an update and an expansion of the original 

work of Perez et al. (1993, 1996). In this original work, 

selected utility loads from the late 1980s and early 1990s 

were analyzed in conjunction with PV output simulated 

from low resolution 3-hourly satellite data (Justus et al., 

1986). The results from the selected utility sample were 

extrapolated to all US utilities by modeling ELCC from the 

robust relationship observed between ELCC and utility 

summer to winter peak load (SWP) ratio. 

The published results of the original study were limited to 

sun-tracking PV at very low grid penetration. 

Using a higher resolution and more accurate satellite model 

to simulate site/time specific PV output, the emphasis of the 

present work is placed on reporting state-by-state potential 

and on assessing the impact of grid penetration and array 

geometry on ELCC. We consider PV penetrations ranging 

from 2% to 20%. Selected PV geometries include two-axis 

tracking (ideal case), horizontal, south-facing 30o

-tilt and 

southwest-facing 30o

-tilt. 

2. METHODOLOGY 

2. 1 Effective Load Carrying Capability (ELCC)

The ELCC of a power generator represents its ability to 

effectively increase the generating capacity available to a 

utility or a regional power grid without increasing the 

utility’s loss of load risk (Garver, 1966). For instance, a 

utility with a current peaking capability of 2.5 GW could 

increase its capability 2.55 GW with the same reliability by 

adding 100 MW PV, provided the ELCC of the 100 MW 

PV is 50 MW, or in relative terms, 50%. 

Ideally dispatchable generators with no down time have a 

relative ELCC of 100%. Non-dispatchable generators such 

as wind or photovoltaics (PVs) are a priori assumed to have 

no or little ELCC. For PV however, the ELCC can be 

significant because PV generation may be reliably available 

at critical demand times (e.g., Perez et al., 2005) and thus 

may effectively increase the grid’s generating capacity. 

ELCC may be statistically derived from the analysis of 

time-coincident series of load demand and power generation 

data (Garver, 1966). As in the original study, our approach 

here is to experimentally determine ELCC for a 

representative sample of utilities, and to project the results 

to the entire country using observed regional and load shape 

patterns. 

2.2 Experimental Data

Utility Loads: We selected 39 utilities distributed 

throughout the country (see list in Fig. 1), and acquired two 

recent years of hourly load data (2002 and 2003) for each 

(FERC, 2005). Load data obtained from FERC were 

 1
corrected for daylight savings time 

adjustment, as needed in order to be 

synchronous with PV generation 

data. 

0% 20% 40% 60% 80% 100%

Seattle Dept. of Lighting

Bonneville Power Administration 

Minnesota Power and Light 

Portland General 

Tennesee Valley Authority 

Jacksonville Electric Authority 

Central Maine Power 

Hawaiian Electric 

Commonwealth Edison 

Tampa Electric 

Indianapolis Power & Light 

City of Chattanooga 

Florida Power and Light 

First Energy 

Consolidated Edison 

Wisconsin Public Service Corp 

Black Hills Corporation 

Central Illinois Light Co 

Orange and Rockland 

Wisconsin Electric Power Co 

Northern States Power 

Cambridge Electric Light Co 

Boston Edison 

Ameren 

Tucson Electric 

United Illuminating Co 

Illinois Power Company 

Lincoln Electric System 

Salt River Project 

Colorado Public Service Co 

Golden Spread Electric Coop 

Arizona Public Service 

Omaha Public Power District 

Rochester Gas & Electric 

SMUD 

S.Diego Gas & Electric Co 

Pacific Gas & Electric 

Nevada Power 

Idaho Power Company 

2-axis-tracking reference ELCC

2002-03

experimental

values 

.

1991-estimates

Figure 1: Experimentally derived reference ELCC (2-axis tracking, low grid 

penetration) compared against earlier estimates from the original study (Perez et al., 

1996) 

PV generation: PV output was 

modeled for each selected array 

configuration. Time/site specific 

hourly irradiances used as input to 

the model were obtained from highresolution satellite observations 

(Perez et al., 2002, 2004). 

Time/site-specific wind speed and 

temperature data were obtained 

from the National Climatic Data 

Center (NCDC, 2005). 

The first step was to experimentally 

derive ELCC for the selected utility 

sample for each selected PV 

configuration and grid penetration. 

Utility-specific results are provided 

in Fig. 1 for the 2-axis-tracking, 

low penetration case. The values 

reported are 2002-03 averages and 

contrasted against 1991 values from 

the original study for the same 

utilities. 

 

The second step was to observe and 

to model the relationship between 

the reference ELCC – 2-axis 

tracking at 2% grid penetration – 

and ELCCs for other PV 

geometries and grid penetrations. 

An example of such experimental 

relationship is shown in Figure 2 

between the southwest facing 

ELCC at 5% penetration and the 

reference ELCC. These 

relationships are very well defined 

and can be easily fitted with a linear 

approximation. Such linear 

relationships were defined for each 

case, resulting in modeled ELCC 

standard error of 1-4% (see Table 

1). 

The third step was to investigate 

whether the relationship between 

the reference ELCC and the utility 

SWP Ratio observed in the original 

study remained valid with the new 

data points. Fig. 3 compares the 

original and current trends. These 

 2
results indicate that the SWP ratio remains a 

strong predictor of ELCC, justifying following 

step. 

The fourth step consisted of taking the 500+ utility 

gridded data from the original study and to update 

the map by layering the 2002-2003 interpolated 

map of differences between the earlier period and 

the current period derived for the 39 selected 

utilities. 

Finally, the resulting gridded map was processed 

into statewide averages. Table 2 summarizes 

ELCCs observed for each state, and each PVgeometry/Load-penetration scenario. A subset of 

these results is provided graphically in Fig. 4, 

while figure 5 provides a state-by-state ranking for 

each selected geometry at low grid penetration 

while also providing information on statewide 

ranges for the reference ELCC. 

3. DISCUSSION

The main conclusions reached in the original 

study remain valid: PV’s effective capacity is 

significant – and considerably higher than PV’s 

capacity factor – for much of the United States. 

The utility-specific results presented in Fig. 1 

indicate that utilities in the southwestern US 

exhibit the highest values, followed by Central US 

utilities and mid-Atlantic utilities. The lowest 

ELCCs are found in the North Pacific coast, the 

northern fringes of the Great Lakes and New 

England, and to a lesser extent, Florida. Overall, 

the national trends noted in the original study are 

conserved, but some regional changes are 

noteworthy. The upper northeast and central 

northwest exhibit strong ELCC increases – see for 

instance Idaho Power Company, Rochester Gas 

and Electric, Central Maine Power and Portland 

General -- while some erosion is noted in the large 

eastern metropolitan utilities – such as New 

York’s Consolidated Edison. Although still 

speculative, the reasons for these changes could 

include, on the one hand a tendency for the 

northernmost utilities towards higher summer 

loads fueled by increased air conditioning 

deployment and warmer summers, and on the 

other, the implementation of effective peak load 

mitigation strategies in places like the New York 

metro area, offsetting the highest demands indirectly driven 

by the sun. The use of a more accurate, higher resolution 

hourly solar resource model (instead of an interpolated 3-

hourly model as in the original study) may have also 

contributed to more conservative results in the eastern 

climates by better capturing transient cloudiness at critical 

times. 

10%

30%

50%

70%

90%

10% 30% 50% 70% 90%

ELCC for 2-axis-tracking PV at 2% penetration

ELCC for SW-30 PV array at 5% penetration

Figure 2: Observed ELCC at 5% penetration for a fixed southwestfacing array vs. reference 2-axis tracking, low penetration ELCC 

TABLE 1 

Fitted relationships between reference ELCC (2 % penetration, 2-axis 

tracking) and other ELCCs 

penetration - geometry slope intercept R Std. error

5% - 2axis -0.07 1.00 1.2% 1.05

10% - 2axis -0.13 0.97 2.9% 1.04

15% - 2axis -0.15 0.95 3.9% 0.95

20% - 2axis -0.14 0.92 4.2% 0.83

2% - horizontal -0.08 0.94 3.8% 0.89

5% - horizontal -0.10 0.95 3.3% 0.88

10% - horizontal -0.13 0.96 2.9% 0.84

15% - horizontal -0.14 0.95 2.9% 0.78

20% - horizontal -0.13 0.94 2.9% 0.70

2% - S30 -0.04 0.93 4.1% 0.86

5% - S30 -0.07 0.94 3.7% 0.86

10% - S30 -0.10 0.94 3.4% 0.81

15% - S30 -0.10 0.94 3.2% 0.73

20% - S30 -0.10 0.93 3.0% 0.64

2% - SW30 -0.14 0.97 3.2% 1.10

5% - SW30 -0.17 0.98 2.5% 1.10

10% - SW30 -0.20 0.98 2.4% 1.05

15% - SW30 -0.20 0.97 2.9% 0.95

20% - SW30 -0.18 0.95 3.2% 0.84

 3
0%

20%

40%

60%

80%

100%

0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9

SWPR

ELCC (2axis-2%)

elcc-2002/2003 data

elcc 1987/91 data

1987/91 best fit

2002/2003 best fit

Figure 3: Reference ELCC as a function of Summer-to-Winter Peak Load Ratio 

 Statewide averages in Table 2 should be considered in the 

context of sometimes large statewide min-max ranges. High 

ranges may be characteristic of sparse experimental points – 

i.e., high statistical uncertainty (e.g., North Dakota), but 

more often, they represent different trends within a given 

State. For instance, the 40-70% range in New York State 

represents the different demand patterns of upstate’s ~ rural 

utilities which are still nearly winter peaking vs. city utilities 

(New York City, Rochester) which are strongly summer 

peaking. 

The present study provides considerably more insight on the 

effects of geometry and penetration. With fixed optimized 

PV arrays, the national average for ELCC at low penetration 

is nearly 55%, reaching 65%+ in the best cases. ELCC 

erodes down to ~ 35% nationwide at 20% penetration 

reaching 45% in the best cases. 

Finally, it is important to remark that ELCCs can be 

increased to nearly 100% -- i.e., firm power equivalence -- 

with modest amounts of storage and/or load control, even at 

significant levels of penetration. Considering New York 

City’s ConEdison for instance, and considering a 15% load 

penetration with optimized fixed array, the ELCC of PV 

could be increased from 40% to 100% with 2.5 hours worth 

of storage and/or load control capability. The total amount 

of load control needed year-around to guaranty 100% ELCC 

would 23,000 MWh. Accomplishing the same load 

reduction without the benefit of PV would require nearly six 

times more load control. The use of load control and storage 

to provide firm capacity equivalence in the context of 

demand reduction programs is the focus of a follow-on 

phase of this work. 

4. ACKNOWLEDGEMENT

NREL subcontract # AEK-5-55057-01. Fred Calef III 

5. REFERENCE

Perez, R., R. Seals and R. Stewart, (1993): Assessing the 

Load Matching Capability of Photovoltaics for US Utilities 

Based Upon Satellite-Derived Insolation Data, IEEE 

Transactions, PV Specialists, pp. 1146-1149 

R. Perez, R. Seals and C. Herig, (1998): PV Can Add 

Capacity to the Grid. NREL Brochure DOE/GO-10096-262, 

NREL, Golden, CO 

http://www.nrel.gov/ncpv/documents/pv_util.html 

Justus , C. G., M.V. Paris and J. D. Tarpley, (1986): 

Satellite-Measured Insolation Data in the US and Mexico. 

Remote Sensing Env. 20. pp. 57-83 

 4
TABLE 2 

Statewide ELCCs 

Geometry 2 axis tracking Horizontal South 30o

 tilt Southwest 30o tilt

Penetration 2% 5% 10% 15% 20% 2% 5% 10% 15% 20% 2% 5% 10% 15% 20% 2% 5% 10% 15% 20%

Arkansas 71% 68% 61% 53% 45% 55% 52% 47% 42% 37% 57% 54% 47% 41% 36% 65% 61% 55% 48% 41%

Alabama 69% 66% 59% 51% 44% 54% 51% 46% 40% 36% 56% 52% 46% 40% 35% 63% 59% 53% 46% 40%

Arizona 71% 68% 61% 53% 45% 55% 52% 47% 42% 37% 57% 54% 47% 41% 36% 65% 61% 55% 48% 41%

California 75% 72% 65% 57% 48% 59% 56% 51% 45% 40% 61% 57% 51% 44% 38% 69% 66% 59% 52% 45%

Colorado 66% 63% 56% 48% 41% 51% 48% 43% 38% 33% 53% 50% 44% 38% 33% 59% 56% 50% 43% 37%

Connecticut 62% 58% 51% 44% 37% 47% 44% 39% 34% 30% 49% 46% 40% 34% 30% 54% 50% 44% 38% 33%

Delaware 62% 58% 51% 44% 38% 47% 44% 40% 35% 30% 50% 46% 40% 35% 30% 55% 51% 45% 39% 34%

Florida 57% 53% 46% 40% 34% 43% 40% 35% 31% 27% 46% 42% 36% 31% 27% 49% 46% 40% 35% 30%

Georgia 69% 65% 58% 50% 43% 53% 50% 45% 40% 35% 55% 52% 46% 39% 34% 62% 59% 52% 45% 39%

Hawaii 51% 47% 40% 34% 28% 42% 39% 34% 30% 26% 41% 38% 32% 27% 23% 43% 40% 34% 29% 25%

Idaho 67% 62% 55% 47% 40% 50% 47% 42% 37% 32% 53% 49% 43% 37% 32% 58% 54% 47% 41% 35%

Illinois 70% 66% 59% 51% 44% 54% 51% 46% 41% 36% 56% 53% 46% 40% 35% 63% 59% 53% 46% 40%

Indiana 64% 60% 53% 46% 39% 49% 46% 41% 36% 32% 51% 48% 42% 36% 31% 57% 53% 47% 41% 35%

Iowa 73% 69% 62% 54% 46% 57% 54% 48% 43% 38% 59% 55% 49% 42% 37% 66% 63% 56% 49% 42%

Kansas 75% 72% 65% 57% 48% 59% 56% 50% 45% 40% 61% 57% 51% 44% 38% 69% 66% 59% 51% 44%

Kentucky 53% 49% 42% 36% 30% 39% 37% 32% 28% 24% 42% 39% 33% 28% 24% 45% 42% 36% 31% 26%

Louisianna 71% 68% 61% 53% 45% 55% 53% 47% 42% 37% 58% 54% 48% 41% 36% 65% 61% 55% 48% 41%

Massachussetts 56% 52% 45% 39% 33% 42% 39% 34% 30% 26% 45% 41% 35% 30% 26% 48% 45% 39% 33% 29%

Maryland 60% 56% 49% 42% 36% 46% 43% 38% 33% 29% 48% 45% 39% 33% 29% 52% 49% 43% 37% 32%

Maine 28% 23% 16% 12% 10% 17% 15% 11% 8% 7% 21% 17% 13% 10% 8% 17% 14% 10% 7% 6%

Michigan 65% 61% 54% 47% 40% 49% 47% 42% 37% 32% 52% 48% 42% 37% 32% 57% 54% 48% 41% 36%

Minnesota 46% 42% 35% 29% 24% 33% 30% 26% 22% 19% 36% 32% 27% 23% 20% 37% 34% 28% 24% 20%

Missouri 72% 69% 62% 54% 46% 56% 54% 48% 43% 38% 59% 55% 49% 42% 37% 66% 63% 56% 49% 42%

Mississipi 71% 68% 61% 53% 45% 55% 52% 47% 42% 37% 57% 54% 47% 41% 36% 64% 61% 54% 48% 41%

Montana 73% 71% 65% 57% 49% 58% 56% 51% 45% 40% 60% 57% 51% 44% 39% 69% 66% 60% 53% 46%

North carolina 56% 52% 45% 39% 33% 42% 39% 34% 30% 26% 45% 41% 35% 30% 26% 48% 45% 39% 33% 29%

North Dakota 49% 45% 38% 32% 27% 36% 33% 29% 25% 22% 39% 35% 30% 26% 22% 41% 37% 32% 27% 23%

Nebraska 74% 71% 64% 56% 48% 58% 55% 50% 44% 39% 60% 57% 50% 44% 38% 68% 65% 58% 51% 44%

New Hampshire 43% 38% 31% 26% 22% 30% 27% 23% 20% 17% 33% 30% 25% 21% 18% 33% 30% 25% 21% 17%

New Jersey 64% 60% 53% 46% 39% 49% 46% 41% 36% 31% 51% 48% 41% 36% 31% 56% 53% 47% 40% 35%

New Mexico 62% 58% 51% 44% 37% 47% 44% 39% 35% 30% 50% 46% 40% 35% 30% 55% 51% 45% 39% 33%

Nevada 59% 55% 48% 41% 35% 45% 42% 37% 32% 28% 47% 44% 38% 33% 28% 51% 48% 42% 36% 31%

New York 53% 48% 40% 34% 28% 38% 35% 30% 26% 22% 41% 37% 32% 27% 23% 43% 39% 33% 28% 24%

Ohio 63% 59% 52% 45% 38% 48% 45% 40% 35% 31% 50% 47% 41% 35% 30% 55% 52% 46% 40% 34%

Oklahoma 68% 64% 57% 49% 42% 52% 49% 44% 39% 34% 54% 51% 45% 39% 34% 61% 57% 51% 44% 38%

oregon 42% 38% 31% 25% 21% 30% 27% 23% 19% 16% 33% 29% 24% 20% 17% 33% 29% 24% 20% 17%

Pennsylvania 53% 48% 41% 34% 29% 38% 35% 31% 27% 23% 41% 38% 32% 27% 23% 43% 40% 34% 29% 25%

Rhodes Island 64% 61% 54% 46% 39% 49% 46% 41% 36% 32% 52% 48% 42% 36% 31% 57% 54% 48% 41% 35%

South Carolian 57% 53% 47% 40% 34% 43% 40% 35% 31% 27% 46% 42% 36% 31% 27% 49% 46% 40% 35% 30%

Southe dakota 59% 55% 48% 41% 35% 44% 41% 37% 32% 28% 47% 43% 38% 32% 28% 51% 48% 42% 36% 31%

Tennesseee 51% 47% 40% 34% 28% 37% 35% 30% 26% 23% 40% 37% 31% 27% 23% 42% 39% 34% 29% 24%

Texas 64% 60% 53% 46% 39% 49% 46% 41% 36% 32% 51% 48% 42% 36% 31% 56% 53% 47% 41% 35%

Utah 42% 37% 30% 25% 21% 29% 27% 22% 19% 16% 32% 29% 24% 20% 17% 32% 29% 24% 20% 17%

Virginia 57% 53% 46% 39% 33% 43% 40% 35% 31% 27% 45% 42% 36% 31% 27% 49% 46% 40% 34% 29%

Vermont 46% 42% 35% 29% 24% 33% 30% 26% 22% 19% 36% 33% 27% 23% 20% 37% 33% 28% 23% 20%

Washington 17% 12% 7% 5% 4% 8% 6% 5% 4% 3% 11% 8% 6% 4% 4% 7% 6% 4% 4% 3%

Wisconsin 59% 55% 48% 41% 35% 44% 41% 37% 32% 28% 47% 43% 37% 32% 28% 51% 47% 42% 36% 31%

West Virginia 51% 47% 40% 33% 28% 37% 34% 30% 26% 23% 40% 36% 31% 26% 23% 42% 39% 33% 28% 24%

Wyomming 44% 39% 32% 27% 22% 31% 28% 24% 20% 18% 34% 30% 25% 21% 18% 34% 31% 26% 21% 18%

Garver, L. L., (1966): Effective Load carrying Capability of 

Generating Units. IEEE Transactions, Power Apparatus and 

Systems. Vol. Pas-85, no. 8 

Perez R., B. Collins, R. Margolis, T. Hoff, C. Herig J. 

Williams and S. Letendre, (2005) Solution to the Summer 

Blackouts – How dispersed solar power generating systems 

can help prevent the next major outage. Solar Today, 

July/August 2005 Issue. 

Federal Energy Regulatory Commission, (2005): Form 714 

- Annual Electric Control and Planning Area Report 

Data. http://www.ferc.gov/docs-filing/eforms/form714/data.asp#skipnavsub 

Perez R., P. Ineichen, K. Moore, M. Kmiecik, C. Chain, R. 

George and F. Vignola, (2002): A New Operational 

Satellite-to-Irradiance Model. Solar Energy 73, 5, pp. 307-

317. 

Perez R., P. Ineichen, M. Kmiecik, K. Moore, R. George 

and D. Renné, (2004): Producing satellite-derived 

irradiances in complex arid terrain. Solar Energy 77, 4, 363-

370 

National Climatic Data Center, (2005): Unedited 

Climatological Data http://cdo.ncdc.noaa.gov/ulcd/ULCD

 5
Figure 4: State-wide reference 2-axis-tracking 2% penetration ELCC 

Figure 5: Low-penetration ELCCs per state including statewide range

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14. ABSTRACT (Maximum 200 Words)

This paper provides an update on the U.S. distribution of effective load-carrying capability (ELLC) for photovoltaics by 

analyzing recent load data from 39 U.S. utilities and time-coincident output of PV installations simulated from highresolution, time/site-specific satellite data. Results show that overall regional trends identified in the early 1990s 

remain pertinent today, while noting a significant increase in PV ELCC in the western and northern United States, 

and a modest decrease in the central and eastern United States. 

15. SUBJECT TERMS

Solar photovoltaics; PV; large-scale PV; deployment; grid-connected PV; solar PV; electricity; carbon emissions; PV 

system; PV generation; American Solar Energy Society; ASES; Solar 2006 

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