Conference Paper

NREL/CP-550-46517

September 2009

How do Wind and Solar Power 

Affect Grid Operations: 

The Western Wind and Solar 

Integration Study

Preprint

D. Lew and M. Milligan

National Renewable Energy Laboratory

G. Jordan, L. Freeman, N. Miller, K. Clark, and 

R. Piwko

GE

To be presented at the 8th International Workshop on Large Scale 

Integration of Wind Power and on Transmission Networks for Offshore 

Wind Farms

Bremen, Germany

October 14–15, 2009
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Debra Lew1), Michael Milligan1), Gary Jordan2), Lavelle Freeman2), Nick Miller2), Kara Clark2), Richard Piwko2)

1) National Renewable Energy Laboratory, Golden, CO, USA. +1 303 384 7037, e-mail debra.lew@nrel.gov

2) GE, Schenectady, NY, USA 

Abstract — The Western Wind and Solar Integration Study is 

one of the largest regional wind and solar integration studies to 

date, examining the operational impact of up to 35% wind, 

photovoltaics, and concentrating solar power on the 

WestConnect grid in Arizona, Colorado, Nevada, New Mexico, 

and Wyoming. This paper reviews the scope of the study, the 

development of wind and solar datasets, and the results to date 

on three scenarios. 

Index Terms — grid integration, wind power, solar power, 

transmission, power system operation, reserve requirements.

I. INTRODUCTION

The Western Wind and Solar Integration Study (WWSIS) is 

one of the largest regional wind and solar integration studies 

to date [1]. It was initiated in 2007 to examine the 

operational impact of up to 35% energy penetration of wind, 

photovoltaics (PV), and concentrating solar power (CSP) on 

the power system operated by the WestConnect [2] group of 

utilities in Arizona, Colorado, Nevada, New Mexico and 

Wyoming. WestConnect also includes utilities in California, 

but these were not included because California [3

• Does geographic diversity of renewable energy 

resource help mitigate variability? 

] had 

already completed a renewable energy integration study for 

the state. [See Fig. 1] This study was set up to answer 

questions that utilities, PUCs, developers, and regional 

planning organizations had about renewable energy use in 

the west:

• How do local resources compare to out-of-state 

resources?

• Can balancing area cooperation help mitigate 

variability?

• What is the role and value of energy storage?

• Should reserve requirements be modified?

• What is the benefit of forecasting?

• How can hydro help with integration of renewables?

The WWSIS is sponsored by the U.S. Department of Energy 

(DOE) and run by the National Renewable Energy Laboratory 

(NREL) with WestConnect as a partner organization. The 

study was originally established to follow onto DOE’s 20% 

Wind Energy by 2030 report [4], which did not find any 

technical barriers to reaching 20% wind energy in the 

continental United States by 2030. This study and its partner 

study, the Eastern Wind Integration and Transmission Study, 

performed a more in-depth operating impact analysis to see if 

20% wind energy was feasible from an operational level. In 

DOE/NREL’s analysis, the 20% wind energy target required 

25% wind energy in the western interconnection; therefore, 

this study considered 20% and 30% wind energy to bracket 

the DOE analysis. Additionally, since solar is rapidly growing 

in the west, 5% solar was also considered in this study.

Fig. 1. WestConnect group of utilities. 

The goal of the WWSIS is to understand the costs and 

operating impacts due to the variability and uncertainty of 

wind, PV, and CSP on the grid. This is mainly an operations, 

not transmission study, although different scenarios model 

different transmission build-outs to deliver power. The study 

does not focus on the cost of generating wind or solar power, 

but rather the operational costs and savings due to fuel and 

emissions. 

II. MAJOR TASKS

In the WWSIS, the major tasks consisted of utility data 

collection, wind and solar dataset development, scenario 

development, statistical analysis, production simulation 

analysis, quasi-steady-state analysis, and analysis of 

mitigation options. The WWSIS was a large team effort, with 

Exeter Associates running data collection, 3TIER Group 

developing the wind dataset and the solar forecasts, 

SUNY/Albany modeling the solar resource, NREL modeling 

the PV and CSP power plants, Northern Arizona University

(NAU) validating the wind dataset, and GE developing 

scenarios and conducting the analysis.

In this study, we modeled the year 2017 three times, with the 

historical load and weather patterns from 2004, 2005, and 

2006. In this way, we examined interannual variability, which 

was not insignificant. It was important in this study to model 

not only the 35% renewable energy penetration within the 

study footprint, but also a significant (up to 23%) renewable 

energy penetration in the rest of the western interconnection, 

to address concerns of ‘exporting the variability’, which have 

How do Wind and Solar Power Affect Grid Operations: 

The Western Wind and Solar Integration Study

1
occurred in some other studies that have ignored significant 

renewable energy penetrations outside the area of interest.

III. WIND DATA DEVELOPMENT

3TIER Group developed the wind dataset for the study [5]. 

Over 75 GW of wind power sites needed to be modeled in the 

study. Lacking sufficient measurements to represent this wind 

build-out, it was decided to model the wind resource across 

the western United States to generate a consistent wind dataset 

in space and time. 3TIER Group used the Weather Research 

and Forecasting (WRF) mesoscale Numerical Weather 

Prediction Model (NWP) over the western United States at a 

2-km, 10- minute resolution for 2004-2006. In order to run the 

large region at this high a resolution, the region had to be split 

up into four domains which were run independently and then 

merged. The domains were run in three-day blocks which 

were merged together and the seams smoothed. While the 

seams were smoothed so that variability did not exceed 

realistic limits, the days with seams exhibited significantly 

more variability than the days with without seams. 

3TIER Group also developed day-ahead wind forecasts for 

each hour. To eliminate any systematic errors that would 

result in the forecasts being ‘too good’, a different input 

dataset was used for these model runs. The forecasts were run 

using a coarser resolution than the 2-km resolution of the 

‘actuals’ dataset. 

Over 960 GW of wind sites (32,000 sites of 30 MW each) 

were modeled, allowing for various scenarios as well as future 

studies to be run. Each 2-km x 2-km grid cell was assumed to 

contain 10 Vestas V90 3-MW wind turbines, yielding 30 MW 

per grid cell. Actual wind plants do not exhibit a deterministic 

‘wind plant power curve,’ so instead of using the sum of 10 

Vestas V90 wind turbine power curves, 3TIER Group’s 

stochastic SCORE methodology was used for power 

conversion [5]. The SCORE methodology uses measured 

probability density functions to produce the type of stochastic 

output that is observed in actual wind plants. While array and 

electrical losses were not explicitly taken into account in the 

power conversion, the wind plant output tended to correspond 

most closely to a net wind plant output and was used as such. 

The wind dataset is publicly available [6]. 

Fig. 2. Validation of the variability of the wind dataset, comparing the 10-

minute wind plant output deltas of the actual wind plant output and the 

mesoscale modeled dataset over a three year period. The dataset is 

considered to be conservative because it overestimates the variability of the 

wind. 

To assess the usefulness of this dataset for the WWSIS, 

extensive validation was undertaken on the dataset. Because 

this was the first time such a large, high resolution wind 

dataset had been created, it was critical to check the data in as 

many ways as possible. This included checks of the power 

curve, maximum and minimum output, largest ramps, average 

capacity factor, etc.

3TIER Group, NREL, and NAU validated the dataset against 

meteorological tower measurements of wind speed. In some 

cases, this was used to determine whether large wind ramps 

were real or artifacts of the model process. NREL also 

validated the dataset against wind plant output for nearly over 

1 GW of wind plants for which NREL could access data. The 

most critical check of a dataset for integration analysis is the 

accuracy of ramps, in this case, on a 10-minute and hourly 

timescale. A consistent over- or under-production bias is less 

important in assessing operational impacts. The wind dataset 

validation [See Fig. 2] showed that the dataset typically had 

higher variability than the actual wind plant output, indicating 

that the results are conservative, from a utility’s reliability 

perspective. 

IV. SOLAR DATA DEVELOPMENT

The State University of New York (SUNY)/Albany developed 

the solar resource dataset for the study. SUNY/Albany used a 

satellite cloud cover model run over the United States at a 10-

km, hourly resolution [7]. This dataset includes global 

horizontal, direct normal and diffuse radiation. 

PV was modeled in the WWSIS as distributed generation on 

rooftops because of the limited knowledge at that time of the 

variability of large, central station PV. Preliminary data, 

analysis, and operating experience from the 4.6-MW 

Springerville Generating Station Solar System in Arizona 

indicated that central station PV could have significant 

impacts on the grid, but there was little other data to determine 

whether the Springerville climate was an anomaly or typical. 

Weather stations in the western United States were modeled 

using PV Watts to create PV output in block sizes of 100 

MW. In order to model distributed generation, PV Watts was 

run using 11 different configurations of tilt, orientation, and 

tracking/flat-plate and outputs were aggregated. The hourly 

PV profiles are available on the web [8]. 

To downscale the PV output to a 10-minute resolution, NREL 

developed a model that compared the hourly average PV 

output to the clear sky (no clouds) PV output and added 

variability. The amount of variability added was based on 

measured PV output from many small PV plants in Arizona 

Public Service’s Solar Test and Research (STAR) program, 

the Springerville system, and several small PV plants in 

Colorado. 

CSP was modeled in the WWSIS as 100-MW blocks of 

parabolic trough plants with 6 hours of thermal storage. Over 

200 GW of CSP plants were modeled in the study and these 

profiles are available on the web [9]. The storage was initially 

dispatched to a typical utility load pattern (in this case, 

Southern California Edison). Six hours of storage requires that 

the solar field (solar collectors) be approximately twice as 

large as a system without storage. The Solar Advisor Model

[10] was used for the power conversion using NREL’s 

2
Excelergy model to represent the parabolic trough plants with 

thermal storage. Losses associated with the thermal storage 

are estimated to be minimal for storage of several hours. 

Because the CSP with thermal storage produces a very stable 

output, the 10-minute dataset was created simply by 

interpolating the hourly dataset.

V. SCENARIOS

A large number of transmission projects are being planned in 

the Western Electricity Coordinating Council (WECC). To 

better understand the trade-offs between using local and 

remote resources, two ‘bookend’ scenarios were established – 

an In-Area (IA) scenario using local resources and a MegaProjects (MP) scenario using higher quality, remote resources. 

Additionally, a ‘middle ground’ scenario – the Local Priority 

(LP) scenario – was created to look at a more realistic 

buildout of sites and transmission. The WestConnect footprint 

was modeled by physical transmission area, where 

transmission area was defined by a combination of respecting 

physical transmission areas and trying to match transmission 

areas as much as possible to state boundaries. [See Fig. 3] 

Fig. 3. The transmission areas modeled in the WestConnect study footprint 

are shown in white, and roughly match the boundaries of the corresponding 

state. 

The IA scenario was built by selecting the best sites according 

to a mix of energy value, geographic diversity, and capacity 

value in each transmission area to make up each penetration 

level. The MP scenario was built by trading out the lowest 

ranked sites of the IA scenario for sites that had a lower 

capital cost (taking into account capital equipment, 

transmission to the area being served, and losses). This 

included the following cost assumptions: $1600/MW-mile 

transmission capital cost; $2000/kW wind capital cost; 

$4000/kW solar capital cost; and 1% losses per 100 miles cost 

penalty. The transmission build-out was then ‘rationalized’ so 

that transmission lines used typical transmission ratings and 

smaller ties were consolidated or disregarded as appropriate. 

The LP scenario was built similarly to the MP scenario, but 

with a 10% cost benefit for in-area wind and solar sites. [See 

Fig. 4] It is understood that some intra-area transmission 

build-out is needed for each of the scenarios, but this was not 

included in the costing of the scenarios. 

Fig. 4. MP and LP scenarios at 30% wind (shown in blue and red dots) and 

5% solar energy penetration. 

TABLE I. Wind and solar penetration levels (by energy) used 

in the study.

Scenario 

name

Study Footprint Rest of WECC

Wind Solar Wind Solar

Baseline Existing Wind & Solar sites

10 10% 1% 10% 1%

20 20 % 3% 10% 1%

30 30 % 5% 20% 3%

The scenarios were run on three levels of penetration as 

shown in Table I. The solar consisted of 70% CSP and 30% 

PV.

VI. GENERAL RESULTS

The study is not yet complete, but there has been a 

tremendous amount of data development and analysis 

conducted to date [11

•Balancing area coordination is imperative to integrate 

35% renewables in the study footprint.

]. Some major insights gleaned thus far 

in the study are:

•Aggregation of wind and solar sites mitigates the 

relative impacts of the large ramps.

3
•The operational impacts do not differ much between 

using local resources versus remote, higher quality 

resources. The overall cost savings, displaced 

generation, spot prices, etc. are very similar.

•What happens in the study footprint depends very much 

on what is happening in the rest of WECC. The study 

footprint typically exports power to WECC, but this 

decreases significantly when the renewables 

penetration in the rest of WECC increases from 11% 

to 23%.

•Pumped hydro storage usage increases but no need for 

increased pumped hydro storage was identified.

•Perfect forecasts have a modest benefit over state-of-theart forecasts.

VII. STATISTICAL ANALYSIS

GE conducted statistical analysis on the 10-minute and hourly 

datasets. As discussed in section III, the wind data was 

modeled in three-day blocks and the seams were smoothed, 

but increased variability was identified in the dataset every 

third day. Therefore, every third day was removed from the 

dataset and the statistics scaled up by 50% to simulate a full 

year. Key findings include:

•There is significant year-to-year and month-to-month 

variation in energy from the wind and solar. There is 

less scenario-to-scenario variation in the study 

footprint’s energy from the wind and solar.

•The size of the area (in terms of MW load) matters. For 

small areas, such as Wyoming, wind can easily 

provide over 100% of the load needs. This study 

finds that high penetrations of wind and solar cannot 

be met without some kind of cooperation between 

balancing areas.

•

Fig. 5. Actual wind output versus hourly wind output delta as percentage of 

installed wind capacity at a 30% wind penetration level. Top left: New 

Mexico; top right: Colorado West; bottom left: study footprint; bottom right: 

all of WECC.

•Geographic diversity helps mitigate variability. The 

relative variability in any particular state is much 

higher than the relative variability of the aggregated 

study footprint. Interestingly, the variability does not 

decrease much in going from the study footprint to 

the entire WECC region. [see Fig. 5] 

•Drops in wind and solar combined with evening load 

rise drive extreme net load up-ramps in late 

afternoons during the late fall and winter. Extreme 

down-ramps are driven by summer/early fall evening 

load roll-off.

Fig. 6 shows the study footprint’s net load in the month of 

April and July. In April, when the wind is strong, the operator 

faces significant challenges in meeting the net load and there 

is a time on April 15 when the net load is negative. In 

contrast, in July, when the wind is weak and the solar is strong 

and coincident with load, the net load is very similar to the 

load alone. 

Fig. 6. Net load during the month of April 2006 (top) and July 2006 (bottom) 

for the LP scenario with 30% wind, 5% solar penetration. The load (pink) is 

what the operator formerly had to meet. The wind (dashed green), PV (dashed 

purple), and CSP (dashed orange) generation is shown. The net load (blue) is 

what the operator now must meet in a high renewables penetration world.

Fig. 7 shows the hourly load ramps versus the hourly wind 

ramps for the LP scenario. In Q1 and Q3, the load and wind 

deltas offset each other – the wind helps the operator balance 

the system. In Q2 the wind is increasing while the load is 

decreasing – these are times when the wind could potentially 

be curtailed to help the operator balance the system. It is Q4 

that is of greatest concern to operations – these are events 

when the load is increasing but the wind is dropping off. 

Without wind, the maximum hourly load delta is 3674 MW. 

With wind, the net load delta exceeds that maximum 66 hours 

during the year of 2006. From this, a good case can be made 

for programs that incent interruptible load.

4
Fig. 7. Study footprint wind deltas versus load deltas by season for 2006, 

based on the 35% renewable energy LP scenario. In Q2, there are 3 hours 

where the net load down-ramps are more than the largest load-alone downramp. In Q4, there are 66 hours where the net load up-ramps are more than 

the largest load-alone up-ramps.

VIII. OPERATIONAL ANALYSIS

GE conducted hourly production simulation analysis of the 3 

scenarios at 3 renewable energy penetration levels over the 

three years using MAPS. Here the full wind dataset was used, 

but results were checked to ensure the increased 3rd day 

variability did not lead to unrealistic results. The following 

assumptions were used: $2/MBTU coal price, $9.5/MBTU 

natural gas price, and $30/ton of carbon dioxide tax. An 

economically rational, WECC-wide commitment and dispatch 

was undertaken recognizing transmission limitations. 

Additional capacity of 24 GW was added to the existing 

system to maintain reserve margins. Operating savings were 

calculated, reflecting the avoided cost of fuel and emissions, 

and losses in efficiency due to plants not operating at optimal 

output. ‘Wear and tear’ costs due to increased or harder 

cycling of units were not taken into account because these 

have not been adequately quantified. 

The wind ‘forecasts’, which were developed using a separate

input dataset from the wind ‘actuals’, had a positive bias of 

approximately 10% in the study footprint and 20% outside the 

study footprint. As a result, a sensitivity analysis was done on 

the forecasts using the raw forecasts, and the forecasts were 

discounted by increasing amounts.

The production simulation analysis across the three (IA, LP, 

MP) scenarios showed the following results: 

•Interestingly, no significant variation in operational 

results were found between the various scenarios. 

There was significant difference, however, in the 

different renewable energy penetration levels.

•Forecasts are critical. There are significant variations in 

impact for the same wind variability with different 

forecasts.

•No significant operational issues were identified at 

penetrations up to 23% in the study footprint and 

11% outside the study footprint. The impact is more 

severe at 35% inside the study footprint and 23% in 

the rest of WECC.

•The operational impact is dependent on what your 

neighbor is doing. As the penetration increases from 

23% inside the footprint and 11% in the rest of 

WECC to 35% inside the footprint and 23% in the 

rest of WECC, exports from the study footprint 

decrease by about 10%. 

•For a perfect forecast, increased renewable energy 

penetration drives the spot prices lower. Because 

there are some forecast errors, these errors drive the 

spot prices in the 35% renewables case back up.

•Displaced generation is mostly combined cycle and gas 

turbine units. At 35% renewables, coal units are also 

starting to be displaced.

•The operational cost savings due to fuel savings and 

emissions displacements are slightly over $20B, or 

$82/MWh, for all of WECC in the IA scenario. The 

value of the renewable energy forecast for the 35% 

renewables case for all of WECC is about 

$2.20/MWh.

•The incremental value of renewables based on spot price 

revenue in the IA scenario in the study footprint is 

around $60/MWh for the CSP and PV energy and 

about $38/MWh for wind energy. For all of WECC, 

this decreases to around $50/MWh for the CSP and 

PV and slightly more than $30/MWh for wind.

•With higher penetrations of renewables, unserved 

energy increased significantly (from negligible to 46 

GWh as the renewables penetration increased from 

23% to 35% in the IA scenario) due to occasional 

over-forecasting of wind generation. By discounting 

the wind forecast from 0 to 25% in the IA 35% 

renewables scenario, unserved energy drops 

dramatically from 46 to 4 GWh, which is tiny 

compared to the total study footprint load being met 

of 286,000 GWh. Spilled energy increases only 

slightly from about 780 to 960 GWh, which is small 

compared to the total study footprint renewable 

energy generation of 100,000 GWh. 

•At higher penetrations, it is essential that the load be an 

active participant such as through interruptible load 

or demand response programs. Interruptible loads are 

easily cost justified, with average costs of reducing 

unserved energy of around $6000/MWh when the 

forecast is discounted by 10%.

•Year to year comparisons did not show significant 

changes in results.

Fig. 8 shows the operation of the system going from no new 

renewables to 11% to 23% to 35% renewables using the IA 

scenario during a week in April 2006. At 35% renewables, 

the combined cycle units are almost completely off, gas 

turbine output has increased, and the coal plants are cycling 

significantly. Even the nuclear units are trying to cycle some, 

which more likely would indicate a need to spill some of the 

wind generation. Total generation inside the study area drops 

because the rest of the WECC, with 23% renewables 

penetration, is now reducing their need for imports from the 

study area. This particular week in April was a severe wind 

week. Other months did not have as much impact. The 5th

plot shows operation for a week in mid-July for the 35% 

penetration scenario, which did not seem to present any 

operational strain on the system. 

5
Hydro operation was examined in detail. The scenarios were 

run with the hydro scheduled after the forecasted wind and 

solar was taken into account. The hydro schedule varied with 

increasing renewable energy penetration. There was an 

economic impact of not rescheduling the hydro based on the 

renewable power forecasts.

Fig. 8. Dispatch of units in study footprint for April 2006: Top: baseline case 

with no new renewables; 2nd: 11% renewables; 3rd: 23% renewables; 4th: 35% 

renewables; and the 5th shows the 35% renewables case but in July 2006.

Additionally, the pumped storage hydro (PSH) operation was 

investigated. While PSH usage increased with the higher 

penetrations of renewables, it did not top-out. This indicated 

that no need for additional PSH was called for in the study. 

This seemed counterintuitive, so the PSH was encouraged to 

run more, by discounting the pumping costs. However, that 

led to an increase in total variable costs, justifying the initial 

conclusion that additional PSH or additional usage of the 

existing PSH was not needed.

A sensitivity analysis was run on the gas price, due to the 

extreme volatility of gas prices in recent years. The base cases 

assumed $30/ton of carbon dioxide, which translates to an 

adder of $0.60/MWh for combined cycle and $1/MWh for 

coal units. Decreasing the gas price from $9.5/MBTU to 

$3.5/MBTU shifts the displaced units from the combined 

cycle and gas turbine units to the steam coal units, and 

decreases operational cost savings by about 40%.

A sensitivity analysis was run on the transmission build-outs, 

by running the LP and MP scenarios without the requisite 

transmission. That is, the better quality resource sites were 

built out but additional transmission to bring those resources 

to load was not built out. This essentially replicates the 

transmission buildout of the IA scenario but shifts the 

renewable energy sites to different locations. The unserved 

energy at 35% renewables remains pretty constant between 

the IA scenario and the LP and MP scenarios with zero interarea transmission. The spilled energy roughly doubles from 

the IA scenario to the LP with zero inter-area transmission, 

6
and increases tremendously to approximately 20% of the wind 

output in the MP scenario with zero inter-area transmission.

IX. RELIABILITY ANALYSIS

Reliability analysis was conducted on the study footprint 

without transmission constraints to determine the capacity 

value of the renewable resources compared to the generation 

resources and load profiles. Capacity values for the three 

penetration levels for the IA scenario in 2006 are shown in 

Table II. It should be noted that the PV capacity rating was 

done on the DC output of the panels, resulting in an AC rating 

that is 23% lower. Thus the PV capacity value is lower than 

one would expect if the rating was done on the AC output.

TABLE II. Capacity Value analysis

Penetration 

level

Wind+ 

CSP+ PV

Wind 

only

CSP only PV only

10% wind, 

1% solar

15.8% 11.4% 92.6% 28.6%

20% wind, 

3% solar

17.7% 10.8% 93.3% 26.9%

30% wind, 

5% solar

18.5% 10.7% 92.2% 26.9%

The significantly higher capacity value of CSP serves to 

highlight the benefits of the CSP with thermal storage in 

meeting peak demand. Fig. 9 shows how much better the CSP 

with storage matches up to the late afternoon peak demand 

than the PV.

Fig. 9. Diurnal profiles of the study footprint load, PV, and CSP (with 6 hours 

of thermal storage) for the LP scenario of 5% solar energy penetration. 

The capacity values were also run for the LP and MP 

scenarios and found to be fairly consistent across scenarios.

X. NEXT STEPS

The study is anticipated to be complete in early 2010, with a 

draft report and stakeholder meeting to be held at the end of 

2009. The next steps in this study are to complete the IA, LP, 

and MP analyses, including the quasi-steady-state simulation 

of specific, difficult events at 1-minute time resolution. Intrahour variability is also underway and the implications of high 

penetrations of renewables on reserve requirements are being 

assessed. 

The study originally anticipated undertaking additional 

scenarios, but because the operational impacts of the three 

scenarios did not show great differences, the study will 

instead focus on other analysis before it is completed in early 

2010. The LP scenario will be re-run with a 23% renewable 

energy penetration in both the study footprint and the rest of 

WECC so that the results from stepping up from the 23% 

renewables (11% in rest of WECC) to the 35% renewables 

(23% in rest of WECC) can be understood. A second 

analysis will examine the role of storage on different 

timescales and look at the value of storage for various 

penetration levels. Plug-in hybrid electric vehicles and 

demand response will be considered in this analysis. 

Additional analysis may include an examination of the nonrenewables balance of the generation portfolio. The analysis 

conducted to date includes 24 GW of capacity additions to 

the existing system. It would be useful to examine variations 

on these additions in terms of flexible generation, or perhaps 

retirements scenarios of existing non-flexible units, or 

replacing some capacity additions with PSH.

After this study is complete, the next logical step would be to 

expand this study to include all of WECC, including Canada. 

Inclusion of large utility-scale (100 MW and up) PV plants 

would be important, since plants of this size are currently in 

interconnection queues in WECC. However, there is not 

much known about the variability of large, utility-scale PV 

plants on a fast time scale. Data collection, analysis, and 

better modeling are needed to be able to include large PV 

plants in such an integration study.

XI. REFERENCES

 

1 Corbus, D., Lew, D., Jordan, G., Winters, W., Van Hulle, F., 

Manobianco, J., and Zavadil, B.; Grid Interation and Transmission: 

Getting to Higher Levels of Wind”, to be published in IEEE Power 

and Energy , Nov/Dec 2009.

2 http://westconnect.com 

3 Bai, X., Clark, K, Jordan, G., Miller, N, Piwko, R.; “Intermittency 

Analysis project:Appendix B Impact of Intermittent Generation on 

Operation of California Power Grid” CEC-500-2007-081-APB, July 

2007. 

4 Lindenberg, S., Smith, B., O’Dell, K., DeMeo, E.; “20% Wind 

Energy by 2030: Increasing Wind Energy’s Contribution to U.S. 

Electricity Supply”, DOE/GO-102008-2567, July 2008.

5 Potter, C.W.; Lew, D.; McCaa, J.; Cheng, S.; Eichelberger, S.; 

Grimit, E. “Creating the Dataset for the Western Wind and Solar 

Integration Study (U.S.A.)” Wind Engineering, Vol 32(4), pp. 325-

338, 2008.

6 http://www.nrel.gov/wind/westernwind or 

http://mercator.nrel.gov/wwsi 

7 Wilcox, S.; Anderberg, M.; George, R.; Marion, W.; Myers, D.; 

Renne, D.; Lott, N.; Whitehurst, T.; Beckman, W.; Gueymard, C.; 

Perez, R.; Stackhouse, P.; Vignola, F. “Completing Production of 

the Updated National Solar Radiation Database for the United 

States”, NREL Report No. CP-581-41511, July 2007.

8 http://mercator.nrel.gov/wwsi 

9 http://mercator.nrel.gov/wwsi 

10 http://www.nrel.gov/analysis/sam 

11 Recent analysis results can be found at 

http://wind.nrel.gov/public/WWIS/stakeholder%20meetings/7-30-

09/ 

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How do Wind and Solar Power Affect Grid Operations: The Western 

Wind and Solar Integration Study; Preprint

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DE-AC36-08-GO28308

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Debra Lew, Michael Milligan, Gary Jordan, Lavelle Freeman, Nick 

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WER9.6020

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This paper reviews the scope of the Western Wind and Solar Integration Study, the development of wind and solar 

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Wind; solar; resource data; integration; grid studies; WWSIS; transmission; power system operation; reserve 

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