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.

Contract No. DE-AC36-08GO28308

The Western Wind and Solar 

Integration Study Phase 2

Preprint

D. Lew, G. Brinkman, E. Ibanez, 

and B.-M. Hodge

National Renewable Energy Laboratory

J. King

RePPAE

To be presented at the 11th Annual International Workshop on 

Large-Scale Integration of Wind Power into Power Systems as Well 

as on Transmission Networks for Offshore Wind Power Plants

Conference

Lisbon, Portugal

November 13–15, 2012

Conference Paper

NREL/CP-5500-56217

September 2012
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1 

The Western Wind and Solar Integration Study 

Phase 2

D. Lew, G. Brinkman, E. Ibanez, B.-M. Hodge

National Renewable Energy Laboratory

Golden, CO USA

debra.lew@nrel.gov

J. King

RePPAE

Wexford, PA USA

Abstract—The Western Wind and Solar Integration Study Phase 

1 (WWSIS1) investigated the impacts of high penetrations of 

wind and solar power on the Western Interconnection of the 

United States. The Western Wind and Solar Integration Study 

Phase 2 (WWSIS2) built on Phase 1 but with far greater 

refinement in the level of data inputs and production simulation. 

It considered the differences between wind and solar power on 

systems operations. It considered mitigation options to 

accommodate wind and solar when full costs of wear and tear 

and full impacts of emissions rates are taken into account. It 

determined wear-and-tear costs and emissions impacts. New 

data sets were created for WWSIS2, and WWSIS1 data sets 

were refined to improve realism of plant output and forecasts. 

Four scenarios were defined for WWSIS2 that examined the 

differences between wind and solar and penetration level. 

Transmission was built out to bring resources to load. Statistical 

analysis was conducted to investigate wind and solar impacts at 

timescales ranging from seasonal down to 5 min.

Keywords-wind; solar; integration; transmission; statistical 

analysis; production simulation; wear and tear; emissions

I. INTRODUCTION 

The Western Wind and Solar Integration Study Phase 1

(WWSIS1) was a landmark analysis of the operational impacts 

of high penetrations of wind and solar power on the Western 

Interconnection (WI) of the United States, shown in Fig. 1 [1].

It showed that up to 35% wind and solar energy penetration 

could be accommodated in the WestConnect subregion (and 

up to 27% across the entire WI) if certain operational changes 

could be made. The most important of the operational changes 

were increased balancing area (BA) cooperation and increased 

use of sub-hourly scheduling for generation and interchanges.

Phase 2 of the WWSIS (WWSIS2) was initiated in 2011 

because stakeholders noted the cycling and ramping of fossilfueled generators in the high-renewables scenarios and asked 

us to obtain higher fidelity on the wear-and-tear costs and 

emissions impacts of this type of operation. Additionally, 

advances in synthesizing sub-hourly utility-scale photovoltaic

(PV) plant output allowed us to include higher levels of solar 

penetration while maintaining technical rigor and credibility. 

Finally, Phase 2 took advantage of new production simulation

models that can dispatch sub-hourly so that sub-hourly 

impacts of variable generation (VG) can be investigated in 

detail.

This paper discusses the creation of scenarios, 

development of transmission build-outs for the scenarios, and 

the results of the statistical analysis. Other work [2–6] 

discussed the synthesis of solar and wind output and forecast 

data sets, reserves methodology, wear and tear, and emissions 

data. Production simulation analysis is currently underway.

Figure 1. Map of WI subregions. WestConnect encompasses the Sierra

Subregional Planning Group, Colorado Coordinated Planning Group, and 

Southwest Area Transmission subregions.

II. STUDY IMPROVEMENTS AND CONSTRAINTS

A. Differences Between Phase 1 and Phase 2 

Although the driver for WWSIS2 was largely the use of 

higher fidelity cycling and ramping costs and impacts, 

WWSIS2 was able to capitalize on improvements and 

refinements in many aspects of the data inputs and modeling. 

Stakeholder feedback on WWSIS1 was also addressed.

A number of new data sets were created for WWSIS2 that 

provided significant improvement over previous input data, 

including:

• Unit-specific emissions data as a function of ramping 

and cycling;

• Wear-and-tear costs and forced outage rate impacts 

data for seven plant types;
2 

• One-min resolution solar power output data for 

concentrating solar power (CSP) plants with thermal 

storage, rooftop PV, and utility-scale PV; and 

• Adjusted day-ahead, 4-h, and 1-h wind and solar 

forecasts. 

The WI was modeled using the commercial production 

simulation model PLEXOS. This model is able to dispatch 

down to a 5-min interval and optimize dispatch of CSP 

storage. It also can optimize security-constrained unit 

commitment and economic dispatch with a large number of 

constraints, including penalties for ramping of fossil-fueled 

generators to reflect wear-and-tear costs. 

With the new developments in synthesizing sub-hourly, 

utility-scale PV plant output, WWSIS2 was able to address 

high solar penetrations in detail as well as compare solar to 

wind. As a result, WWSIS2 scenarios focused on the 

differences between wind and solar impacts on the power 

system. 

The WI contains nearly 40 balancing areas (BAs) that must 

balance their load and generation. The base scenarios in 

WWSIS1 were run with the WI operating as 5 BAs. WWSIS2 

modeled the WI as 20 zones with interface constraints 

between them. 

Conceptual transmission build-outs were generated using 

expert judgment for WWSIS1 to bring resources to load. In 

WWSIS2, iterative PLEXOS load flows were run to bring 

shadow prices across interfaces down to a consistent cutoff 

level. 

III. MODEL SETUP

Modeling a power system as large as the WI requires a 

balance of detail (to ensure important inputs are properly 

characterized) and simplifying assumptions (to create a 

manageable model that can be run within a reasonable amount 

of time). We based our inputs and assumptions as much as 

possible on the Western Electricity Coordinating Council 

(WECC) Transmission Expansion Policy Planning Committee 

(TEPPC) model, which has been thoroughly vetted through a 

public stakeholder process. WECC is the regional reliability 

organization for the WI. 

A Technical Review Committee (TRC) was established to 

provide expert and stakeholder oversight and review of data 

inputs, assumptions, methodologies, model configuration, and 

results. The TRC included WECC, experts in operations of 

fossil-fueled generators, and utilities in the WI. 

It is very difficult to model the WI as it is actually operated 

because most of the WI is comprised of vertically integrated 

utilities that balance their system with their own generation 

and bilateral transactions with their neighbors that are 

confidential. Not having access to that information, we 

modeled the WI assuming rational economic dispatch. In 

WWSIS2, the WI was modeled as a set of BAs with hurdle 

rates between them. 

WWSIS2 modeled the WI zonally, using the 20 WECC 

Load and Resource Subcommittee zones. This obviated the 

need to design transmission collector systems for each wind 

and solar plant. Each plant was assigned to a high-voltage bus. 

The year 2020 was modeled using historical weather 

patterns and loads from the years 2004, 2005, and 2006. The 

2020 reference scenario was based on the WECC TEPPC 

Portfolio Case #1 (PC1) case [7]. 

We modeled regulation, flexibility, and contingency 

reserves, basing regulation and flexibility reserves on the 

variability of the wind and solar output but tailored only to 

account for the unpredictable component of variability of solar 

because of cloud movement [2]. 

A. Wind Data 

The original wind output data (“actuals”) used in WWSIS1 

had increased variability every three days because of a restart 

of the Numerical Weather Prediction (NWP) model used to 

synthesize that data [1,8,9,10]. Although the wind output for 

each site respected realistic 10-min maximum changes in 

output, unrealistic 10-min variability resulted when sites were 

aggregated. Various fixes were evaluated for realism in 

variability when sites were aggregated and for realism in 

spatial correlation between sites. Fig. 2 shows the fix that 

worked best, which included random splicing of data from 

unaffected days to the affected seams. This 10-min data set 

was converted to 1-min output using statistical downsampling based on measured 1-min output from wind plants 

in the WI. 

(a) 

(b) 

Figure 2. One-h change in wind output for the (a) original and (b) corrected 

data. The years 2004 to 2006 have been parsed into 3-day intervals to 

illuminate the 3-day seam that is seen by increased variability at the end of 

the third day in the original data. The whiskers are the min and max values 

for each hour of the 3-day intervals. The bars show the mean value plus and 

minus one standard deviation. 
3 

The original wind forecasts in WWSIS1 were synthesized 

using the same NWP model as the actuals but with a different 

input data set. Because these forecasts did not receive a 

statistical correction, there were some bias issues that resulted 

in forecasts tending to be 10% to 15% higher than actuals on 

average. Additionally, forecasting techniques have improved 

over time. To best reflect realism in forecasts, we analyzed 

measured wind forecast errors from the Public Service 

Company of Colorado, California Independent System 

Operator, and the Electric Reliability Council of Texas. We 

then adjusted the forecast error distributions of our forecasts 

from WWSIS1 to match the measured wind forecast error 

distributions [3]. 

B. Solar Data

The original solar data used in WWSIS1 was based on 

limited knowledge of sub-hourly solar PV variability and 

excluded utility-scale PV plants. In WWSIS2, new techniques 

were developed to characterize sub-hourly temporal variability 

based on spatial variability. Hourly satellite images from the 

10 km x 10 km grid cell of interest plus the surrounding grid 

cells were characterized into five types of cloud patterns. This 

was then translated into sub-hourly temporal variability [4]. 

This was also validated using measurements of irradiance and 

PV plant output [11]. For WWSIS2, rooftop PV, utility-scale 

PV, and CSP with 6 h of storage were modeled.

C. Wear-and-Tear Cost and Impacts Data

To determine the impacts of cycling and ramping on fossilfueled plants, in-depth studies have been conducted for 

specific power plants [12]. These types of studies were 

conducted over several decades for approximately 400 power 

plants. This proprietary data was distilled into generic data for 

wear-and-tear costs and impacts for 7 types of generators: 

small subcritical coal, large subcritical coal, supercritical coal, 

gas combined cycle, gas large frame combustion turbine, gas 

aeroderivative combustion turbine, and gas steam. Data 

included costs of cold, warm, and hot starts; costs of ramping; 

equivalent forced outage rate impacts from cold, warm, and 

hot starts; and long-term heat-rate degradation. [6,12,13,14] 

D. Emissions Impacts Data

To determine the emissions impacts of cycling and 

ramping, measured emissions of NOX, SO2, and CO2 from 

nearly every fossil-fueled plant in the United States were 

analyzed using the U.S. Environmental Protection Agency’s 

continuous emissions monitor data set. Emissions impacts 

from start-ups, ramps, and partial loading were determined for 

each generator as inputs in our model [6,14]. 

IV. SCENARIOS 

Four scenarios were defined and sited for the study using 

the National Renewable Energy Laboratory’s Regional Energy 

Deployment System model [15]. The Reference Scenario (8% 

wind and 3% solar) was based on the WECC TEPPC 2020 

PC0 base case scenario, which included enough renewables so 

that western states met their 2020 renewable portfolio 

standards targets. The High-Wind Scenario included 25% 

wind and 8% solar. The High-Solar Scenario included 25% 

solar and 8% wind. The High-Mix Scenario included 16.5% 

wind and 16.5% solar. Solar was defined as 60% PV and 40% 

CSP with 6 h of thermal storage. The scenarios are shown in 

Table 1.

TABLE I. SOLAR AND WIND BUILD-OUTS FOR EACH SCENARIO 

Reference

High-Solar

High-Wind

High-Mix
4 

V. TRANSMISSION

To bring resources to load, we expanded transmission 

using iterative load flows in PLEXOS. Forty-four transmission 

paths were considered at a zonal level so that collector systems 

did not need to be designed for this study. Nodal transmission 

build-outs may need to be considered in future analyses to 

examine details of congestion and flows. We did not add new 

source and sink pathways in this transmission expansion but 

rather increased capabilities on existing paths.

It is important to note that in much of the WI, utilities have 

physical rather than financial rights to transmission. That is, a 

transmission path may be fully contracted during some period 

of time yet not fully utilized during that period. Because those 

transmission contracts are confidential, we were unable to 

model them. Instead, we assumed that all transmission was 

used optimally.

Although parts of Canada and Mexico are in the WI, we 

did not build additional transmission to those zones, but rather 

built in enough conventional generation in those zones to meet 

load so that paths to those zones were not congested. This is 

consistent with WECC TEPPC practice (e.g., actual flows 

between Canada’s Alberta Electric System Operator and the 

United States are very limited).

(a)

(b)

(c)

Figure 3. Iterative transmission build-out for the High-Wind Scenario 

showing (a) initial and final interface capabilities for (b) the $20 cutoff 

shadow price and (c) the $5 cutoff shadow price. Locational marginal price is 

shown by the colors of each zone. The transmission capacity for major 

interfaces is shown by the width of the grey lines. New transmission capacity 

is shown by the width of the black lines.

We developed a methodology to expand capabilities on 

existing transmission paths by running the four scenarios in 

PLEXOS for a full year and examining shadow prices across 

interfaces. We “built” 500 MW of additional transmission 

across interfaces whose shadow price exceeded some cutoff. 

We then iterated and re-ran the revised scenario with the 

additional transmission in PLEXOS and added more 

transmission as appropriate until shadow prices no longer 

exceeded the cutoff.
5 

We tested cutoffs from $5/MWh to $20/MWh. These were 

consistent with the approximate transmission costs of 

$1,600/MW-mile for 250 miles of new transmission with a 

$0.11 fixed-charge rate. Fig. 3 (a) shows the starting 

transmission build-out and Figs. 3 (b) and 3 (c) show 

transmission build-outs for a high and low cutoff.

Transmission build-outs were evaluated considering 

transmission costs, production cost savings, and curtailment. 

For a fixed cutoff value, as the penetration of renewables, 

especially wind, increased, the amount of transmission built 

also increased. Curtailment decreased with expanded 

transmission. Curtailment was much higher in the scenarios 

with higher penetrations of wind because wind tends to be 

higher at night, when base-load generators run down against 

their minimum generation limits.

Fig. 4 shows the net benefit of the transmission expansion, 

defined as the production cost savings minus the approximate 

transmission cost. As the cutoff value decreases and 

transmission is expanded, the net benefit increases and then 

tops out and decreases. The cutoff value where this net benefit 

tops out varies but is approximately $10/MWh. As a result, we 

selected the $10/MWh cutoff value to define the transmission 

build-out for each scenario. Table II shows the transmission 

build-outs at that $10/MWh cutoff value for the three highrenewables scenarios.

Figure 4. Comparison of transmission build-out metrics for the Reference, 

High-Solar, High-Wind, and High-Mix Scenarios, showing (a) production 

cost savings versus MW built, (b) curtailment versus MW built, and (c) net 

benefit versus MW built.

TABLE II. TRANSMISSION BUILD-OUTS WITH $10/MWH CUTOFF 

HighWind High-Mix HighSolar

Cumulative additional 

transmission capacity 

(MW)

10,500 9,000 6,500

Cumulative transmission 

annualized cost (M$/yr) 462 396 286

Production cost (B$/yr) 10.9 10.6 10.9

Cumulative production 

cost savings (M$/yr) 923 733 561

Average benefit/cost ratio 2.00 1.85 1.96

Incremental benefit/cost 

ratio 1.54 1.11 1.65

Curtailment (TWh) 9.2 2.3 1.3

HighWind High-Mix HighSolar

Curtailment as fraction of 

potential wind and solar 

production

0.035 0.0009 0.005

Transmission cost per 

MWh curtailment savings 

($/MWh)

29.7 55.7 129.5

VI. STATISTICAL ANALYSIS

We conducted statistical analysis on the four scenarios,

examining variability and uncertainty on various timescales, 

investigating penetration levels on various timescales, 

determining impacts of aggregation and geographic diversity, 

and comparing the impacts of wind and solar. Additional 

statistical analysis, including analysis of 5-min variability and 

solar aggregation, was also conducted [5].

Fig. 5 shows the hourly duration curves for wind and PV. 

The High-Wind Scenario had a 25% wind energy penetration 

and the High-Solar Scenario had a 15% PV (and 10% CSP, 

not shown here) energy penetration, but both produced similar 

peak output during the top wind and PV output hours. PV 

output was zero for half of the year during nighttime hours.

Fig. 6 shows the contour plots for the net load for each 

scenario. The Reference Scenario, with 11% VG, shows the 

high summer peaks in the afternoon and early evening in the 

WI. The High-Wind Scenario depresses much of this peak and 

also exacerbates the net load minimums during the night in the 

winter. Finding ways to decrease the minimum generation 

level of fossil-fueled units will be important in this scenario.

(a)

(b)

Figure 5. Hourly (a) wind and (b) PV duration curves for the High-Solar 

(blue), High-Mix (red), High-Wind (green), and Reference Scenarios.
6 

(a)

(b)

(c)

(d)

Figure 6. Net load as a function of month and hour of the day for the (a) Reference, (b) High-Wind, (c) High-Mix, and (d) High-Solar Scenarios. These are all 

plotted using the same color scale.

The High-Solar Scenario clearly shows the diurnal double 

peak caused by the depression of net load during midday when 

solar output is highest. Contour lines that are close together, 

such as those in the non-summer months after the morning net 

load peak and again before the evening net load peak, indicate 

steep net load ramps. Increasing ramping capabilities or 

reducing start-up times of fossil-fueled generation may be 

helpful in this scenario. Decreasing minimum generation 

levels will be important to manage winter midday net load 

minimums.

VII. CONCLUSIONS

WWSIS2 illuminates the challenges of integrating high 

penetrations of wind and solar power into the grid. It shows 

how important the need is for flexibility in the fossil-fleet, 

with an emphasis on lower minimum generation levels for 

both wind and solar and an emphasis on additional ramping or 

fast-start capability for high penetrations of solar power. 

Future and ongoing work is focused on operational 

simulations of these scenarios and retrofit options for the fossil 

fleet to determine how best to manage wind and solar 

variability and uncertainty. Ultimately, a cost-benefit analysis 

of retrofit and operational strategies for the fossil fleet will be 

completed.

VIII. ACKNOWLEDGMENTS

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

Energy under Contract No. DE-AC36-08-GO28308 with the 

National Renewable Energy Laboratory.

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