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

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Contract No. DE-AC36-08GO28308

Impact of High Solar Penetration 

in the Western Interconnection

Debra Lew

National Renewable Energy Laboratory

Nicholas Miller, Kara Clark, Gary Jordan,

and Zhi Gao

GE Energy

Technical Report

NREL/TP-5500-49667

December 2010
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.

National Renewable Energy Laboratory

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Contract No. DE-AC36-08GO28308

Impact of High Solar Penetration 

in the Western Interconnection

Debra Lew

National Renewable Energy Laboratory

Nicholas Miller, Kara Clark, Gary Jordan,

and Zhi Gao

GE Energy

Prepared under Task No. SM101610

Technical Report

NREL/TP-5500-49667

December 2010
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1 

Abstract — The western U.S. has tremendous solar 

potential. However, the variability of power generation from 

solar plants presents an operational challenge for grid 

system operators. Experience in power grids with 

significant penetration of variable renewable generation 

(both solar and wind power) has shown that the operating 

flexibility of the balance of the generation portfolio is a key 

element in secure and economic operation. 

This paper presents an overview of the variable 

characteristics of solar power, as well as the accompanying 

grid performance and operational economics for a system 

with significant solar generation. The paper will show 

results of economic operational simulations of a very high 

solar generation future for the western half of the United 

States. The evaluated system is subject to significant 

dynamic operating constraints; and the analytical methods 

used account for the critical interrelationships between 

system security, solar variability, and imperfect solar 

forecasts. 

I. INTRODUCTION

Today, the growth of solar generation is explosive in 

many countries around the world with a global average 

annual rate of over 30% per year. There are a wide 

range of environmental, social, technical and economic 

drivers that have resulted in solar as the fastest growing 

energy source in the US. With solar generation moving 

from a minor contributor to possibly substantial levels 

of penetration, a range of issues related to the operation 

and economic impacts on bulk power systems are rising 

in importance. 

The issues associated with integration of solar 

generation are a subset of broader industry concerns 

with integrating variable renewable generating 

resources. To an appreciable extent, solar growth is 

following the route forged by the dramatic global 

growth in wind power. 

Recently, the authors completed a large, multifaceted 

study called the Western Wind and Solar Integration 

Study (WWSIS) [1]. The focus of the WWSIS was to 

investigate the operational impact of up to 30% energy 

penetration of wind, plus an additional 5% energy 

production by solar generation in a large portion of the 

western part of the United States. The solar generation 

was composed of a mix of photovoltaic (PV) and 

concentrating solar power (CSP). 

This paper presents the results of an incremental 

exploration that is based on the exhaustive work of 

WWSIS. It is considerably less broad in scope, and the 

focus is solely on behavior and challenges associated 

with solar generation. High solar penetrations were not 

examined in WWSIS due to lack of data and models for 

large-scale solar plants, but as these models are being 

developed, the fast pace of solar growth is demanding 

investigation in this area. 

This paper provides a brief introduction to the scope of 

this incremental exploration, explains key aspects of 

the investigation and aspects of WWSIS that are 

necessary to understand the work reported here, inputs 

and scenario development, and the key findings. 

WWSIS provides more detail on all aspects of the two 

and a half year long study [1]. 

II. STUDY SYSTEM AND SCENARIOS

The entire interconnected grid of the western United 

States is modeled for the year 2017. This is the U.S. 

portion of the Western Electricity Coordinating Council 

(WECC), one of the major reliability regions in North 

America. The model includes details of load, all 

generation, and major transmission limitations. The 

focus of WWSIS and this exploration is on a subset of 

the grid that is operated by the WestConnect group of 

utilities in Arizona, Colorado, Nevada, New Mexico, 

and Wyoming1

. A map of WECC and WestConnect is 

shown in Figure 1. 

Figure 1 − Study Footprint Map; Western United States and 

WestConnect. 

The overall study footprint consists of five areas that 

closely correspond to the state borders of Wyoming, 

Colorado, New Mexico, Arizona, and Nevada, as 

shown in Figure 1. Outside of the footprint, several 

additional areas, again roughly corresponding to state 

or regional borders, were modeled. 

 1

 WestConnect also includes utilities in California, but these were 

not included in WWSIS because California had already completed a 

renewable energy integration study for the state. 

Impact of High Solar Penetration in the Western 

Interconnection 
2 

The scenarios used in this paper are based on the 

WWSIS solar plant distribution that provided 5% solar 

energy in each of the five areas of WestConnect. In 

that base case, solar plant sites were selected in each 

area to reach a total of 5% of that area’s annual load 

energy. Selection was made from about 260 candidate 

sites based on estimated energy production and market 

value of that energy. In the areas outside the study 

footprint, solar generation providing 3% of the load 

energy was selected from about 320 candidate sites. 

While the focus of the study is on the footprint, it is 

extremely important to include solar resources in 

adjacent systems as well, in order to properly anticipate 

the inevitable interaction driven by weather conditions 

and time of day between the various areas that exchange 

power and resources. 

The scenarios for this incremental exploration start with 

this nominal 5% solar case (3% outside of the 

footprint), removing all of the wind generation included 

in WWSIS. From that starting point, the power and 

energy of the selected sites are linearly scaled to meet 

10%, 15%, 20%, and 25% penetrations. This is an 

imprecise approximation to how solar penetrations of 

this level would be realized. Additional solar plants 

would necessarily be in different locations, and exhibit 

temporally more diverse behavior. This simple 

approach gives a reasonable, but pessimistic, 

approximation of solar power production because it 

ignores that additional diversity and its smoothing 

impact on total solar power output. 

Details of the scenarios are shown in Table 1. In this 

table, characteristics of the load, the 5% solar and 25% 

solar scenarios are summarized by area. The 10%, 15%, 

and 20% cases are linearly distributed between these two 

cases. In all scenarios, 30% of the solar generation was 

PV and 70% was CSP by energy. Since the PV and CSP 

have different capacity factors, the split in MW rating 

was 52% and 48%, respectively.

Table 1 − Power Ratings and Energy by Area, both Inside 

and Outside of the Study Footprint. 

III. STUDY OBJECTIVES AND CONSTRAINTS

This study, like the preceding WWSIS, undertakes 

detailed analysis and modeling of the power system, but 

it was meant to provide insight and be a complement to 

other in-depth studies. This is not a transmission 

planning study, a cost-benefit analysis, a full-blown 

reliability study, nor a dynamic stability study. 

In 2017, it is anticipated that WestConnect and WECC 

will operate differently from current practice. This study 

assumed the following changes from current operational 

practice: least-cost economic dispatch in which all 

generation resources are shared equally and not 

committed to specific loads; five regional balancing 

areas, instead of the 37 that exist today in WECC; and 

existing available transmission capacity, even though 

currently contractually bound, will be accessible to other 

generation on a short-term, non-firm basis. 

There are also reasons that the study results tend toward 

the conservative. It did not model a more flexible nonrenewable balance of generation than exists today or is 

presently planned for WECC. These levels of solar, if 

ever reached, are more likely to occur later than 2017 

when the load is greater, the balance of generation may 

be more flexible, and more transmission may be 

installed. The solar dataset was conservative in terms 

of overestimating the actual variability found in 

measured solar plant output. Finally, the base 

assumption of $9.50/MBTU for gas means that gas is 

displaced, and that leaves relatively inflexible coal to 

accommodate the variability of the wind and solar 

power. 

IV. CHARACTERISTICS OF SOLAR

A. Types of Solar Generation 

Production of electricity from solar insolation takes 

several fundamentally different forms. Technologies 

that directly transform energy from incoming photons 

to electricity are photovoltaic (PV). Technologies that 

collect and concentrate thermal energy to drive any of a 

variety of heat engines or turbines are generally 

grouped as concentrating solar plants (CSP); they are 

also known as solar thermal plants. 

The power production characteristics of each of these 

technologies have some important differences, which 

are examined below. However, all solar generation is 

dependent on the position of the sun which is perfectly 

predictable, but variable, and weather, which is not 

perfectly predictable. The simple reality that time of 

day and weather strongly govern system load means 

that analysis of grid performance and economics must 

include time-synchronized load and solar data. 

B. Photovoltaic Solar 

PV output varies with insolation. Output can be 

relatively steady during consistently clear or 

Minimum Maximum Energy Rating Energy Rating Energy

Area (MW) (MW) (GWh) (MW) (GWh) (MW) (GWh)

Arizona (AZ) 6,995 23,051 99,437 2,000 5,283 10,000 26,415

Colorado (CO) 5,204 13,115 70,080 1,700 4,005 8,500 20,025

New Mexico (NM) 2,571 5,320 31,260 700 1,894 3,500 9,470

Nevada (NV) 3,863 12,584 57,505 1,100 2,934 5,500 14,670

Wyoming (WY) 2,369 4,016 27,697 300 420 1,500 2,100

In Footprint 21,002 58,087 285,979 5,800 14,536 29,000 72,680

Minimum Maximum Energy Rating Energy Rating Energy

Area (MW) (MW) (GWh) (MW) (GWh) (MW) (GWh)

California (CA) 27,110 75,039 354,891 5,400 15,286 27,000 76,430

Idaho (ID) 1,647 4,957 24,869 200 274 1,000 1,370

Montana (MT) 1,149 2,337 14,143 100 127 500 635

Oregon (OR) &

Washington (WA) 14,278 30,953 178,359 1,300 1,645 6,500 8,225

Utah (UT) 2,263 7,274 38,022 500 1,240 2,500 6,200

Out of Footprint 46,448 120,561 610,284 7,500 18,572 37,500 92,860

Total 67,449 178,647 896,263 13,300 33,108 66,500 165,540

25% Solar

Load 3% Solar 15% Solar

Load 5% Solar
3 

consistently overcast weather. Conversely, power 

output at a single PV installation can be highly variable 

on partly cloudy days, with intermittent and fast 

moving clouds. While these variations can cause local 

problems, only the coincident variation of many PV 

installations is of concern to the bulk system. As an 

example, Figure 2 shows 1-minute resolution PV 

output profiles representing relatively large regions 

under relatively volatile conditions [3]. The fastest 

variations tend not to be temporally coincident, but 

significant net variation is still possible. 

Figure 2 − Example PV Solar Zip Code Profile for a July 

Morning. 

For the analysis reported here, a satellite cloud cover 

model was used to simulate the United States at a 10-km, 

hourly resolution [4]. PV was modeled in 100-MW 

blocks as distributed generation on rooftops because 

modeling information for large, central station PV plants 

was not available at the time of the study. Over 15 GW 

of PV plants were included in the dataset from which the 

6,900 MW were selected for the 5% scenario. 

C. Concentrating Solar Power 

CSP is much less variable, because there is substantial 

thermal mass in the energy conversion system. An 

illustrative day of production from two large 

commercial CSP projects [3] is shown in Figure 3. The 

output rises in the morning to the plant rating and then 

declines in the evening. The ramp rates during the 

morning and evening periods are substantial, but 

otherwise the plant output is quite steady. This is 

characteristic of CSP steam thermal plants. 

Figure 3 − Example Concentrating Solar Project Profile for a 

May Day. 

The CSP in this study was modeled as 100-MW blocks 

of parabolic trough plants with six hours of thermal 

storage, which was dispatched to a typical utility load 

pattern. The candidate CSP plants were included in the 

dataset totaled over 200 GW, from which 6,400 MW 

were selected for the 5% scenario. 

Other types of concentrating solar generation can have 

higher variability, because they have much less thermal 

mass. The power production from a representative day 

of operation of two Stirling solar plants [3] is shown in 

Figure 4. The variability is greater than the CSP plant, 

and is more like the output of PV. Stirling plants were 

not included in this study. 

Figure 4 − Example Stirling Solar Project Profile for a May Day. 

D. Daily, Monthly, Seasonal, and Annual 

Characteristics 

The average January daily output shapes of the PV and 

CSP in the study footprint with 25% solar energy 

penetration are shown in Figure 5. The average January 

load shape is also shown. 

On average, solar generation is increasing during a 

relatively flat load period after the morning load rise, 

and decreasing during the late afternoon/early evening 

load rise. 

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The PV production tends to peak about 6 hours before 

the load. CSP without additional thermal storage 

would show a similar average shape. However, the 

addition of thermal storage tends to shift some of the 

energy production closer to the peak load hours, 

resulting in a second peak in the CSP profile. This 

peak is still about 3 hours before the load peak. 

Managing the stored energy could provide additional 

operational flexibility, reduce system operating costs, 

and increase the value of the solar power. While 

redispatch of the stored energy was not examined at 

these penetration levels, it was part of the original 

WWSIS analysis. 

Figure 5 − Average January Solar Profiles. 

The average daily PV and CSP shapes for other 

seasonally representative months are shown in Figure 6 

to Figure 8. Again, these are for 25% solar energy 

penetration. 

The double peak in the January CSP profile is also 

observed, to a lesser extent, in April and October. In 

all months, CSP production peaks about 3 hours before 

the load. PV production peaks 4-8 hours before the 

load. 

On average, solar generation is decreasing during the late 

afternoon/early evening load rise. The morning increase 

in solar generation is more coincident with the morning 

load rise in April and July, and less so in October. Solar 

generation in April, with its generally clear skies, exceeds 

that of July, which is generally hazier. 

Figure 6 − Average April Solar Profiles. 

Figure 7 − Average July Solar Profiles. 

Figure 8 − Average October Solar Profiles. 

The above discussion focused on average daily solar 

power profiles for representative months. The 

following discussion focuses on solar energy across 

months and years. The annual energy production for 

2004, 2005, and 2006 in GWh are summarized in Table 

2, along with the average of the three years and 

deviations from the average of the individual years. 

Table 2 − Annual Energy Production. 

Figure 9 shows the study footprint monthly solar 

energy for the three years. Similarly, Figure 10 shows 

the monthly solar energy as a percentage of load energy 

(i.e., penetration) for the same three years. For 

reference, the 25% annual energy penetration level is 

shown. 

First, while there is relatively little total annual 

variation between years, there is significant year-toyear variation in the monthly energy. Second, 25% 

annual energy penetration is not 25% energy 

penetration at all times. In December, the energy 

penetration is about 12%. In April and May, the 

penetration is about 37%. 

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2004 33610 1.54%

2005 32927 -0.52%

2006 32759 -1.03%

Average 33099
5 

Figure 9 − Monthly Solar Energy for 3 Years. 

Figure 10 − Monthly Energy Penetration for 3 Years. 

V. NET SYSTEM CHARACTERISTICS

The results presented in this section are for the entire 

study footprint. 

The challenge to the power system is strongly 

correlated to the instantaneous penetration, i.e., the 

fraction of total system load that is being provided by 

solar generation at a given instant in time. Duration 

curves for hourly energy production and hourly 

instantaneous penetration are shown in Figure 11. The 

left vertical-axis scale applies to the energy production, 

and the right vertical-axis scale applies to the 

penetration. 

Figure 11− Solar Energy Production and Penetration Duration 

Curves. 

The production of solar power will displace other nonzero marginal cost generation whenever the system can 

accept it. Thus, it is useful to view the solar generation 

as negative load, which combined with the actual 

system load, yields a “net load”; the power that must be 

supplied by other resources on the system. The 

instantaneous penetration of solar will be high when 

this net load is relatively low. Figure 12 shows the 

study area load duration curve, and then five net loadduration curves for progressively greater levels of solar 

generation. By the 25% case, roughly one-third of 

operating hours have net load levels lower than the base 

system minimum load. The extreme hour (8760) 

corresponds to highest penetration in Figure 11. 

During these periods, some energy will be exported, 

fewer other generation units will be committed, and the 

dispatch of committed generation will tend to be lower. 

The variability of load and solar will drive 

maneuvering of the other generation resources on the 

system. When load and solar power are both 

increasing, the need for the other generation, i.e., that 

serving net load, to maneuver will tend to decrease. 

This is true for coincident decreases in load and solar as 

well. Figure 13 shows a year of hourly load and solar 

power changes for the 25% solar case. Hours of the 

year are segregated into four seasons with four colors. 

Concurrent positive changes in load and solar 

generation are shown in the 1st quadrant, and 

concurrent drops in the 3rd quadrant. Hours that have 

an increase in solar and drop in load are in the 2nd

quadrant. Hours that have an increase in load and drop 

in solar are in the 4th quadrant. The dotted lines show 

loci of 5,000 MW changes in net load. Ordinarily, this 

is the condition that can cause the most operational 

concern, as the resulting increase in net load has the 

potential to stress the grid and operating reserves. 

Hours below the 5,000-MW line have net load 

increases greater than 5,000 MW, roughly the largest 

load increase ever seen by this system. 

The points below the 5,000-MW line in the 3rd quadrant 

are interesting. For these hours, the system load is 

dropping, but the net load is increasing faster than the 

fastest morning load ramp of the base system. Failure 

to forecast such changes could be operationally 

difficult. 

The hours of extreme net load change in Figure 13 are 

due in part to the way the data for this exploration was 

created. The hourly energy production for the 5% 

scenarios was simply scaled up for the 25% case. This 

approach is overly conservative in that it loses the 

benefits of the increased spatial diversity that would 

accompany such a build-out and unrealistically increases 

the variability. Thus, the hourly variability would lower 

with a more geographically diverse build-out. This is a 

subject for further investigation. 

In addition to being variable, the production of solar 

cannot be perfectly predicted. Forecasting solar 

production is receiving a considerable amount of 

attention in the industry. The state of the art has not 

progressed as far as that for wind generation. 

Nevertheless, as has been shown for wind power, 

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6 

forecasting of solar generation for high penetration 

systems will be required for economic operation. 

Figure 14 is the duration curve of the day-ahead solar 

forecast, actual production and the forecast error used 

in this study. The forecast used here is relatively crude, 

and could be expected to improve with technology. 

The combination of variability and uncertainty impacts 

the operation of the grid. 

Figure 12 − Total Net Load Duration Curves. 

Figure 13 − Hourly Variability of Load and Solar Generation. 

Figure 14 − Solar Actual, Forecast, and Error Duration Curves 

for 25% Solar Case. 

VI. OPERATIONS WITH 25% SOLAR

The previous section examined the characteristics of 

solar generation on an annual, seasonal, monthly, and 

daily basis. These characteristics, including the 

variability and forecasts, were used in an examination 

of the operational impact of 25% solar penetration as 

presented in this section. 

Production simulation analysis with GE’s MAPS 

(Multi-Area Production Simulation) program was used 

to evaluate hour-by-hour grid operation of each 

scenario for 3 years with different solar and load 

profiles. WECC was represented as a set of 106 zones, 

which were grouped into 20 transmission areas. The 

following assumptions were made: 

- All study results are in 2017 nominal dollars with 

2% escalation per year; 

- $2/MBTU coal; 

- $9.50/MBTU natural gas; 

- Carbon dioxide costs of $30/metric ton of CO2; 

- Extensive balancing area cooperation; 

- All units are economically committed and 

dispatched while respecting existing and new 

transmission limits and generator cycling 

capabilities and minimum turndowns; 

- Existing available transmission capacity is 

accessible to renewable generation; and 

- Generation equivalent to 6% of load is held as 

contingency reserves – half is spinning and half 

is non-spinning. 

Figure 15 shows the aggregate generation dispatch for 

an example week in July without any solar. The top of 

the curve represents the total load plus exports for the 

study area. Each type of generation is represented by a 

different color. The nuclear units (black) have a 

constant output. The coal units (gray) have a nearly 

constant output, with some reductions during light load 

periods. The combined cycle units (purple) vary more 

than the coal, but still operate at consistently high 

output levels. Hydro (dark blue), pumped storage 

hydro (light blue), and peaking gas turbines (red) are 

largely operating during peak load periods. 

Figure 15 − Study Area Dispatch for a Week in July without Solar. 

Figure 16 shows the aggregate generation dispatch for 

the same July week with 25% solar penetration. The PV 

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7 

(yellow) and CSP (orange) generation displaces about 

10% of the combined cycle plants and all of the peaking 

gas turbine generation. The exports from the study area 

also increase substantially (54%, 767 GWh). Hydro 

generation is shifted to the latter half of the load peaks 

and into the subsequent load troughs. Coal generation 

sees more reductions, which are shifted into the morning 

load rise period. 

Figure 16 − Study Area Dispatch for a Week in July with 25% Solar. 

Figure 17 and Figure 18 show similar information for 

an example week in April. The total load plus exports 

is about 75% of that in July. 

In Figure 17, the nuclear units have a constant output, 

except for the start of a planned maintenance outage on 

Saturday. The coal units are also constant. The 

combined cycle units follow the load, but still operate 

at consistently high output levels. Hydro operates at all 

but the lowest load levels. No gas turbines operate. 

Figure 18 shows that operating the system with 

significant solar generation is more challenging. Solar 

generation has displaced significant amounts of 

combined cycle generation. In fact, the combined cycle 

units are almost reduced to zero output during peak 

load periods. Many of the combined cycle plants see 

daily start/stop cycling. The coal units are also reduced 

during this time. That the coal is backed down mid-day 

is a marked contrast to minimum load challenges 

associated with high wind generation scenarios [1] that 

result in coal cycling during the night. The hydro 

generation has largely shifted to the load peak and into 

the subsequent load trough. And finally, gas turbines 

are necessary on days with actual solar generation 

below forecast or with peak loads occurring after the 

solar generation declines. While the impact of 25% 

solar on the thermal generators is not as challenging as 

the impact of 35% wind and solar investigated in 

WWSIS, there is still a significant impact on when and 

how other generators must operate to accommodate the 

solar power. 

Nonetheless, the system can operate with balancing area 

cooperation. Without balancing area cooperation, 

operations during this week would be extremely difficult, 

if not impossible, for individual balancing areas. 

The power system is designed to handle variability in 

load. With solar, the power system is called on to handle 

variability in the net load (load minus solar), which can 

be considerable during certain periods of the year. 

Figure 17 − Study Area Dispatch for a Week in April without Solar.

Figure 18 − Study Area Dispatch for a Week in April with 25% Solar. 

The behavior of the combined cycle plants has 

potentially interesting economic implications. There is 

still a substantial amount of energy coming from them 

over the week; 984 GWh, or about 35% of the 

2,822 GWh for the no-solar base case. However, they 

need to not only maneuver, but start and stop. Plants 

that have the capability to do so will be needed and will 

probably receive preferential commitment and dispatch. 

There is also need for peaking units during the poor 

solar day of Friday, April 14. This would be provided 

by gas turbines. Since significant energy is required 

during the week from combined cycle plants, the 

economics will likely favor flexible combined cycle 

plants. Ones that have the ability to start (and stop) by 

allowing their gas turbines to run in simple cycle 

quickly and on short notice, and ones with the ability to 

transition to more efficient combined cycle mode 

should the need for power persist, will be the ones that 

are dispatched. 

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8 

From an operational perspective, these exploratory cases did 

not show any substantial adverse impacts in WestConnect up 

to the 25% case, given balancing area cooperation. 

The impact on spot price is illustrated in Figure 19, 

which shows the spot price duration curve for Arizona, 

where a large portion of the solar generation and load 

in the study area resides. The figure shows that the 

addition of new, zero marginal cost resources will 

generally depress spot prices. However, a diminishing 

benefit is observed at high prices. This occurs at higher 

penetrations when high forecast errors (see Figure 14) 

cause expensive generation to be brought on line to 

make up for these large misses. This behavior is 

similar to that observed in high wind systems.

Figure 19 − Arizona Spot Price Duration Curves. 

VII. BENEFITS OF 25% SOLAR

Given the fuel prices and carbon tax assumed for this 

study, solar generation primarily displaces gas 

generation nearly all hours of the year. Across WECC, 

operating costs drop by $13 billion/yr ($11 billion/yr in 

2009$) from approximately $50 billion/yr ($43 billion/yr 

in 2009$). This represents about a 25% savings due to 

offset fuel and emissions (a $30/ton carbon tax is 

assumed). Figure 20 shows the overall impact on the 

operating costs of WECC for the various penetration 

levels with a state-of-the-art forecast. Figure 21 divides 

these values by the corresponding amount of renewable 

energy provided. In the 25% case, this equates to 

$83/MWh ($71/MWh in 2009$) of solar energy 

produced. 

As one might expect, there is diminishing returns on 

incremental solar build-out when the incumbent 

generation portfolio remains fixed. 

These operating cost savings would be applied toward 

the costs of the solar energy (which is primarily capital 

cost, not variable costs) and other necessary system 

infrastructure (e.g., transmission enhancements). 

Depending on the magnitude of the operating savings, 

they may or may not be sufficient to cover the costs. 

Figure 20 − Total WECC Variable Operating Cost Reductions. 

Figure 21− Operating Cost Reductions per MWh of Solar Energy. 

Since the solar power is a marginally zero cost resource, 

the system will accept it whenever the spot price is 

positive and the system is capable, subject to reliability 

constraints. As noted above, this analysis assumes that 

all generation is responsive to price signals. 

Figure 22 shows how other generation resources are 

displaced by the solar production. It is clear that there 

is nearly a 1:1 correlation between increased solar 

production and reduced combined cycle production. In 

the western U.S., for the fuel price and regulatory (i.e., 

carbon tax) assumptions of this study, gas generation 

was nearly always the marginal resource – that is, it is 

the first generation to be displaced by zero marginal 

cost resources. 

Figure 22 − Generation Displacement by Type. 

Total WECC reductions in NOx, SOx and CO2

emissions are shown in Figure 23. Total WECC 

reductions for the 25% case of CO2 emissions were 

nearly 75 million tons per year, or approximately 15%. 

SOx emissions would be reduced by approximately 

15,000 tons (~2%) and NOx would be reduced nearly 

60,000 tons per year (~10%). 

0

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25% Solar

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9 

Again, the cost of fuel and carbon policy can strongly 

affect these results as well. If coal were on the margin 

more often, it would be displaced more frequently. The 

result would be further reductions in carbon and SOx. 

Figure 23 – Reduction in Total System Emissions. 

VIII. CAPACITY VALUE OF SOLAR

Resource adequacy analysis involves loss-of-loadexpectation (LOLE) calculations using the Multi-Area 

Reliability Simulation program, MARS. The capacity 

value of variable resources, including solar, tends to 

decrease with increased penetration. This is because 

the contribution to system adequacy has an important 

temporal component that yields diminishing returns for 

additional resources of similar time dependence. 

An LOLE analysis was not conducted in this analysis. 

However, LOLE was performed in WWSIS [1] to 

determine the capacity value of both the wind and solar 

generation of that study. The capacity impacts of different 

types of intermittent renewable generation are different. PV 

tends to have peak output somewhat before system load peaks, 

whereas CSP with storage can be shifted to reduce the peak 

loads. The capacity values for 5% solar from WWSIS were 

about 30% for PV and about 92% for CSP. These values will 

decrease with increasing penetration, and the value of CSP will 

be significantly affected by the amount and use of thermal 

storage. 

IX. COMPARISON TO WWSIS RESULTS 

While this exploration is much less comprehensive than 

that of WWSIS, a few notable points of comparison arise. 

Since solar is only producing power about 1⁄2 the time, 

instantaneous penetrations during the day at 25% 

energy can be very high. 

Compared to wind, minimum generation problems can 

still occur with solar, but penetration levels need to be 

pretty high. Minimum generation issues occur in the 

middle of the day, not at night. 

Hourly variability with solar can be substantial. This 

warrants closer investigation since the scenarios with 

penetration greater than 5% lack the benefits of spatial 

diversity that would come with these large build-outs. 

Use of peaking units to meet substantial forecast misses 

occurs with both solar and wind, but for the solar cases, 

the misses tend to be mid-day. 

This exploration did not examine sub-hourly 

performance and reserves issues. Similarly, it did not 

specifically identify impacts or shortfalls of 

contingency reserves. Results of WWSIS suggest that 

more sub-hourly reserves will be required, and that subhourly scheduling is likely to be needed. WWSIS 

results also suggest that inclusion of demand response 

as a resource for contingency reserves is likely to be 

effective and economic. 

X. KEY FINDINGS AND CONCLUSIONS 

The technical analysis performed in this exploration, in 

conjunction with the results of WWSIS, shows that it is 

operationally feasible for WestConnect to 

accommodate 25% solar energy penetration, assuming 

the following changes to current operational practice 

and infrastructure could be made over time: 

Operational practice should: 

- Substantially increase balancing area cooperation 

or consolidation, real or virtual; 

- Increase utilization of transmission; 

- Enable coordinated commitment and economic 

dispatch of generation over wider regions; and 

- Incorporate state-of-the-art wind and solar 

forecasts in unit commitment and grid operations. 

Additionally, from the more detailed examination 

undertaken in WWSIS, it is expected that the following 

operational and infrastructure changes will be 

necessary: 

- Commit additional operating reserves as 

appropriate; 

- Use operational flexibility of solar plants; 

- Increase the flexibility of dispatchable generation 

where appropriate (e.g., reduce minimum 

generation levels, increase ramp rates, reduce 

start/stop costs or minimum down time); 

- Build transmission as appropriate to 

accommodate renewable energy expansion; and 

- Target new or existing demand response 

programs (load participation) to accommodate 

increased variability and uncertainty. 

XI. ACKNOWLEDGEMENTS

This work was made possible by foundational support 

from the United States Department of Energy through 

the National Renewable Energy Laboratory. Readers 

should visit the NREL website: 

http://www.nrel.gov/wind/systemsintegration/wwsis.html

Contributions from WestConnect members, 3Tier 

Group, Northern Arizona State University, Exeter 

Associates, State University of New York at 

0

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NOx (tons) SOx (Tons) CO2 (kTons)

Emission Reductions (tons or ktons)

5% Solar

10% Solar

15% Solar

20% Solar

25% Solar
10 

Albany/Clean Power Research, members of the 

WWSIS technical review committee and GE associates 

are gratefully acknowledged. 

XII. REFERENCES

[1] GE Energy. May 2010. “Western Wind and Solar 

Integration Study: February 2008 – February 2010,” 

NREL Report No. SR-550-47434, 

http://www.nrel.gov/wind/systemsintegration/pdfs/201

0/wwsis_final_report.pdf. 

[2] NREL. Wind Integration Datasets, 

www.nrel.gov/wind/integrationdatasets. 

[3] California Energy Commission’s Intermittency 

Analysis Project Study “Appendix B - Impact of 

Intermittent Generation on Operation of California 

Power Grid,” 

http://www.energy.ca.gov/2007publications/CEC-500-

2007-081/CEC-500-2007-081-APB.PDF. 

[4] Wilcox, S., April 2007. “National Solar Radiation 

Database, 1999-2005: User’s Manual,” NREL Report 

TP-581-41364, 

http://www.nrel.gov/docs/fy07osti/41364.pdf. 
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This paper presents an overview of the variable characteristics of solar power, as well as the accompanying grid 

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