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

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

M. Heaney, B.-M. Hodge, M. Hummon, 

and G. Stark

NREL

J. King

RePPAE

S.A. Lefton, N. Kumar, and D. Agan 

Intertek-APTECH 

G. Jordan and S. Venkataraman

GE Energy

Technical Report

NREL/TP-5500-55588

September 2013
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

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

The Western Wind and Solar 

Integration Study Phase 2

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

M. Heaney, B.-M. Hodge, M. Hummon, 

and G. Stark 

NREL

J. King 

RePPAE 

S.A. Lefton, N. Kumar, and D. Agan 

Intertek-APTECH 

G. Jordan and S. Venkataraman 

GE Energy

Prepared under Task Nos. OE10.3020, SS12.2720, 

SM12.2010, and WE11.0810

Technical Report

NREL/TP-5500-55588

September 2013
NOTICE

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iii 

Acknowledgments 

The National Renewable Energy Laboratory (NREL) thanks the U.S. Department of Energy’s 

(DOE’s) Office of Energy Efficiency and Renewable Energy and Office of Electricity Delivery 

and Energy Reliability for sponsoring Phase 2 of the Western Wind and Solar Integration Study. 

Contributors include the following: 

Project Manager 

NREL Debra Lew 

Technical Team Leader 

NREL Greg Brinkman 

Study Team 

NREL Eduardo Ibanez, Anthony Florita, Marissa 

Hummon, Bri-Mathias Hodge, and Greg 

Stark 

RePPAE Jack King 

Intertek-APTECH Steven A. Lefton, Nikhil Kumar, and 

Dwight Agan 

GE Energy Sundar Venkataraman and Gary Jordan 

Technical Review Committee 

Arizona Public Service Ron Flood 

Bonneville Power Administration Eric King 

California Independent System Operator Shucheng Liu 

DOE Venkat Banunarayanan, Charlton Clark, 

Richard Gilker, Sikander Khan, Kevin Lynn, 

Seungwook Ma, and Lawrence Mansueti 

Electric Power Research Institute Daniel Brooks and Aidan Tuohy 

Electric Power System Consulting Brendan Kirby 

Electricity Research Centre/ Mark O’Malley 

 University College Dublin 

Energy Exemplar Tao Guo 

GE Energy Richard Piwko 

Lawrence Berkeley National Laboratory Andrew Mills 

National Energy Technology Laboratory Joel Theis 

New England Independent System Operator Jonathan Black 

NextEra Energy Resources Mark Ahlstrom 

Northern Arizona University Tom Acker 

NREL Lori Bird, Kara Clark, Paul Denholm, and 

Brian Parsons 

NV Energy John Lescenski and Mike Smart 

PacifiCorp Jamie Austin 
iv 

Public Service Company of New Mexico Thomas Duane and Derek Stout 

Renewable Energy Consulting Services Edgar DeMeo 

Salt River Project Robert Hess and Brandon Quijada 

Sandia National Laboratories Abe Ellis and Clifford Hansen 

Tri-State Generation and Transmission Association Kevin Cox, Ray LaPanse, Art Mander, and 

Curtis Miller 

Tucson Electric Power Ron Belval and Gary Trent 

Utility Variable Generation Integration Charles Smith 

 Group 

WestConnect Charles Reinhold 

Western Area Power Administration Bob Easton and James Hirning 

Western Electricity Coordinating Council Matt Hunsaker, Bradley Nickell, and Heidi 

Pacini 

Western Governors’ Association Tom Carr 

Wind Wear Eugene Danneman 

Xcel Energy Stephen Beuning, Justin Briggs, Sean 

Connolly, and James Schetter 

Finally, we thank Aaron Bloom, Erik Ela, Neal Kruis, Noel Merket, Michael Milligan, and YihHuei Wan for their technical contributions and René Howard, Amy Vaughn, and Katie Wensuc 

for their editing. 
v 

List of Acronyms 

4HA 4-hour-ahead 

BA balancing authority 

C&M capital and maintenance 

CAISO California Independent System Operator 

CC combined-cycle 

CEM continuous emissions monitoring 

CF capacity factor 

CFE Comisión Federal de Electricidad 

CG Columbia Grid 

CO2 carbon dioxide 

CSP concentrating solar power 

CT combustion turbine 

CY calendar year 

DA day-ahead 

DC direct current 

DCOE delivered cost of energy 

DG distributed generation 

DOE U.S. Department of Energy 

EFOR equivalent forced outage rate 

EIA Energy Information Administration 

EPA Environmental Protection Agency 

EPRI Electric Power Research Institute 

ERCOT Electric Reliability Council of Texas 

FFT fast Fourier transform 

GHI global horizontal irradiance 

GT gas turbine 

GW gigawatt 

GWh gigawatt hour 

HRSG heat recovery steam generator 

IID Imperial Irrigation District 

ISO independent system operator 

LAWP Los Angeles Department of Water and Power 

LRS Loads and Resources Subcommittee 

MAE mean average error 

MISO Midwest Independent System Operator 

MMBtu million British thermal units 

MW megawatt 

MWh megawatt hour, energy 

MW-h megawatt hour, reserves 

NTTG Northern Tier Transmission Group 

NOX nitrogen oxides 

NREL National Renewable Energy Laboratory 

NWP numerical weather prediction 

O&M operations and maintenance 

PC1 Portfolio Case 1 
vi 

PPA power purchase agreement 

PV photovoltaic 

RBC reliability-based control 

ReEDS Regional Energy Deployment System 

RMSE root-mean-square error 

RPS renewable portfolio standard 

SMUD Sacramento Municipal Utility District 

SO2 sulfur dioxide 

SPP Southwest Power Pool 

SPI solar power index 

ST steam turbine 

TEPPC Transmission Expansion Planning Policy Committee 

TRC Technical Review Committee 

TW terawatt 

TWh terawatt hour, energy 

TW-h terawatt hour, reserves 

VG variable generation 

VOM variable operations and maintenance 

WC WestConnect 

WECC Western Electricity Coordinating Council 

WI Western Interconnection 

WITF Wind Integration Task Force 

WWSIS-1 Western Wind and Solar Integration Study Phase 1 

WWSIS-2 Western Wind and Solar Integration Study Phase 2 
vii

Executive Summary

The electric grid is a highly complex, interconnected machine, and changing one part of the grid can have consequences 

elsewhere. Adding wind and solar affects the operation of the other power plants and adding high penetrations can induce 

cycling of fossil-fueled generators. Cycling leads to wear-and-tear costs and changes in emissions. Phase 2 of the Western 

Wind and Solar Integration Study (WWSIS-2) evaluated these costs and emissions and simulated grid operations for a year to 

investigate the detailed impact of wind and solar on the fossil-fueled fleet. This built on Phase 1, one of the largest wind and 

solar integration studies ever conducted, which examined operational impacts of high wind and solar penetrations in the West 

(GE Energy 2010).

Frequent cycling of fossil-fueled generators can cause thermal and pressure stresses. Over time, these can result in premature 

component failure and increased maintenance and repair. Starting a generator or increasing its output can increase emissions 

compared to noncyclic operation. And operating a generator at part-load can affect emissions rates. Utilities are concerned 

that cycling impacts can significantly negate the benefits that wind and solar bring to the system. And to plan accordingly, 

power plant owners need to understand the magnitude of cycling impacts. 

In WWSIS-2, we calculated these wear-and-tear costs and emissions impacts. These data were incorporated into commercial 

software that simulates operations of the western grid (which includes the United States, Canada, and Mexico) on a subhourly 

basis, because wind and solar output can change within the hour. We designed five hypothetical scenarios to examine up to 

33% wind and solar energy penetration in the Western U.S. and to compare the impacts of wind and solar. We then examined 

how wind and solar affected operation, costs, and emissions from fossil-fueled generators. This work was overseen by a 

Technical Review Committee (TRC) to ensure that assumptions, methodologies, and analyses were realistic and credible. 

Our results are based on the specific characteristics of the western grid and key assumptions, including an average gas price 

of $4.60/MMBtu, significant balancing authority cooperation, and least-cost economic dispatch and transmission usage that 

does not model bilateral transactions. The goal of WWSIS-2 is to quantify the cycling impacts that are induced by wind and 

solar. It does not address whether wind and solar should be built, but rather what happens if they are built.

In this study, we found that up to 33% of wind and solar energy penetration increases annual cycling costs by $35–$157 

million in the West. From the perspective of the average fossil-fueled plant, 33% wind and solar penetration causes cycling 

costs to increase by $0.47–$1.28/MWh, compared to total fuel and variable operations and maintenance (VOM) costs of 

$27–$28/MWh. The impact of 33% wind and solar penetration on system operations is to increase cycling costs but also to 

displace annual fuel costs by approximately $7 billion. WWSIS-2 simulates production or operational costs, which do not 

include plant or transmission construction costs. From the perspective of wind and solar, these additional cycling costs are 

$0.14–0.67 per MWh of wind and solar generated compared to fuel cost reductions of $28–$29/MWh, based on the generator 

characteristics and modeling assumptions described in this report. 

This study finds that up to 33% wind and solar energy penetration in the United States’ portion of the Western grid (which is 

equivalent to 24%–26% throughout the western grid) avoids 29%–34% carbon dioxide (CO2) emissions, 16%–22% nitrogen 

oxides (NOx) emissions, and 14%–24% sulfur dioxide (SO2) emissions throughout the western grid. Cycling had very little 

(<5%) impact on the CO2, NOX, and SO2 emissions reductions from wind and solar. For the average fossil-fueled plant, we 

found that wind- and solar-induced cycling can have a positive or negative impact on CO2, NOx, and SO2 emissions rates, 

depending on the mix and penetrations of wind and solar. 

Motivation

Phase 1 of the Western Wind and Solar Integration Study (WWSIS-1) was a landmark analysis of the operational impacts of 

high penetrations of wind and solar power on the Western Interconnection (GE Energy 2010). The study found no technical 

barriers to accommodating the integration of 35% wind and solar energy on a subregional basis if adequate transmission 

was available and certain operational changes could be made. The two most important of the operational changes were 

increased balancing authority (BA) cooperation and increased use of subhourly scheduling between BAs for generation and 

interchanges.
viii

The variability and uncertainty of wind and solar can have profound impacts on grid operations. Figure ES-1 shows the most 

challenging week of the 3 years of data studied in WWSIS-1, when high penetrations of wind and solar caused fossil-fueled 

plants to cycle more frequently. In this report, cycling is a broad term that means shutting down and restarting, ramping up 

and down, and operating at part-load.

50,000

40,000

30,000

20,000

10,000

0

MW

10-Apr 11-Apr 12-Apr 13-Apr 14-Apr 15-Apr 16-Apr 

-1

Wind

PV

CSP

Combined cycle

Hydro

Coal

Nuclear

Pumped storage hydro

Gas combustion turbine

Figure ES-1. WWSIS-1 dispatch for the most challenging week of 3 years of data analyzed 

Notes: PV, photovoltaic; CSP, concentrating solar power

Utilities were concerned about this type of operation and its impacts on repair and maintenance costs and component 

lifetimes. In addition, some analysts asserted that the emissions imposed by cycling could be a significant fraction of—or 

even larger than—the emissions reduced by wind and solar (Bentek Energy 2010; Katzenstein and Apt 2009).

WWSIS-2 was initiated in 2011 to determine the wear-and-tear costs and emissions impacts of cycling and to simulate grid 

operations to investigate the detailed impact of wind and solar on the fossil-fueled fleet. WWSIS-1 focused on whether high 

penetrations were technically feasible. In WWSIS-2, we analyzed the cycling impacts in detail and with a higher degree of 

fidelity. WWSIS-2 simulates operation of the entire Western Interconnection but wind and solar is only added to the U.S. 

portion of the Western Interconnection because data from outside the United States are lacking.

In WWSIS-2, we dove deep into the impacts of cycling on the operation of fossil-fueled plants. We created new data sets and 

simulated subhourly grid operations to answer questions such as the following:

• What are the increased costs because of wear and tear on fossil-fueled plants? 

• Do these wear-and-tear costs significantly reduce the benefits of wind and solar?

• Will incorporating these costs into optimization of grid operation reduce cycling? 

• What are the emissions impacts of cycling? 

• How do wind impacts compare to solar impacts on cycling and grid operations?

This study focused on simulating grid operations on a subhourly basis. The results discussed here are specific to the Western 

Interconnection and the characteristics of the generation and transmission in the West. Adapting these results to other regions 

would require simulating the characteristics of those regions.
ix

WWSIS-2 was one piece in a larger puzzle of understanding the impacts of wind and solar on the electric power grid. 

Although WWSIS-2 needed hypothetical scenarios of renewable energy siting and transmission expansion, these were not

the main focus. System reliability and stability issues were not the focus of this study either, but are being examined in

Phase 3 of WWSIS.

Background

Impacts of cycling induced by wind and solar additions can be investigated in different ways. The first is from the perspective 

of a fossil-fueled plant. If that plant is required to cycle more frequently, this can affect wear-and-tear costs and emission 

rates, which in turn affect that plant’s marginal costs and emissions requirements. The second way to frame these impacts is 

from a system perspective. Wind and solar can impact grid operations by displacing fossil-fueled generation (and the costs 

and emissions associated with fossil fuels) but also increasing cycling (and the costs and emissions impacts associated with 

it). This study examines whether these cycling impacts significantly reduce the benefits of displacing fossil-fueled generation.

From the perspective of a power plant owner or a resource planner, the delivered cost of energy (DCOE) from a specific plant 

is important. From the perspective of the overall system or in terms of societal impacts, costs across the entire system are 

important. This report attempts to examine cycling impacts from all these perspectives.

The DCOE for a specific plant looks very different for a fossil-fueled plant than for a wind/solar plant, as shown in Figure 

ES-2. The DCOE for a fossil-fueled plant is a mix of fixed costs and production costs. The DCOE for a wind/solar plant 

is nearly all fixed capital costs. Fixed costs are those costs that do not change based on how much the plant is run, such as 

power plant and transmission construction costs and fixed operations and maintenance (O&M) costs. Production costs are 

the variable costs that increase as the plant produces more electricity and consist of fuel and VOM. VOM, in turn, comprises 

cycling O&M (which consists of start fuel plus wear and tear from starts and ramps) and noncyclic O&M (which are the 

routine overhauls and maintenance costs from the plant running at some steady-state output). The only capital costs included 

in production costs are capitalized maintenance (e.g., more frequent boiler tube replacements) because cycling and steadystate operation reduces the lifetimes of those components. Production simulation tools, such as the one used in this study, 

model operations of the power system. Production costs are key outputs of these tools.

Fossil-fueled generator Wind/solar

Production 

costs

O&M from cycling/ramping

O&M of steady state operation

Fuel

Fixed O&M

Capital cost of transmission

Capital cost of generation

Production costs Fixed costs

Fixed costs

Figure ES-2. Illustrative DCOE for a fossil-fueled plant and a wind/solar plant
x

Adding any new generation to the power system will change the way existing plants operate. Studies show that adding wind 

and solar can cause existing fossil-fueled plants to cycle more and have lower capacity factors (EnerNex 2011; GE Energy 

2010). Adding new, low-priced baseload generation can also cause the incumbent fossil-fueled plants to cycle more and have 

lower capacity factors (Milligan et al. 2011). An incumbent fossil-fueled plant that now has a lower capacity factor (and 

likely reduced revenue) and a higher O&M cost (because of cycling) might have a hard time remaining viable. This raises 

questions about who should pay for the cycling costs of incumbent plants or what happens in the marketplace to address the 

viability of a plant that might be needed for reliability but might no longer be profitable. These questions are not addressed in 

this technical report. 

When O&M from cycling increases, the cost of energy component circled in red in Figure ES-2 also increases. Before this 

study, little wear-and-tear data for different types of cycling operation were publicly available. WWSIS-2 investigates this 

cost in depth. It explores the magnitude of that cost, how that cost changes when wind and solar are added to the system, 

how that cost changes the fuel savings that wind and solar bring to the system, and how increased wind and solar penetration 

affects that cost. 

From a system perspective, utility planning decisions have resulted in a given portfolio of plants. Those fixed capital costs 

(or power purchase agreements [PPAs] if the utility is buying from an independent power producer) are now sunk costs. 

The system operator’s job is to manage operations of that portfolio to supply reliable power at low cost to consumers. The 

operators do not see the fixed costs, only the production costs. If we consider the what-if scenario of this same system with a 

new wind/solar plant (for simplicity, ignore the bilateral transactions and incentives such as production tax credits), we can 

see that the near-zero production cost of wind/solar will lead the operator to dispatch the wind/solar instead of fossil-fueled 

generation, as long as it is within all the constraints of transmission and operating limits. This displaces the fossil-fueled 

generators’ production costs (fuel and O&M). The change in production cost with and without wind/solar is shown in Figure 

ES-3. From the perspective of the system operators, this reduction in fuel cost is the benefit that wind and solar bring to the 

system. WWSIS-2 addresses how that benefit is affected when cycling costs are modeled in detail. The cost of wind/solar is 

the difference in fixed costs (capital costs of the wind/solar plants and transmission). WWSIS-2 did not conduct a cost-benefit 

analysis of wind and solar to determine profitability. Instead, it posited that if wind/solar is present, what benefit does it bring 

to the system and how much is that value reduced by cycling?

O&M from cycling/ramping

O&M of steady state operation

Fuel

Fixed O&M

Capital cost of transmission

Capital cost of generation

Production costs

Production

cost savings

of wind

& solar

Before renewables After renewables Production costs

Figure ES-3. Illustrative system-wide costs before and after wind/solar 

Notes: The wind/solar requires additional fixed costs but offsets production costs. 

The change in production cost from wind/solar is shown by the green arrow.
xi

Study Approach

WWSIS-2 examined the impact of up to 33% wind and solar energy penetration on the U.S. portion of the Western 

Interconnection. We explicitly calculated values for various types of wear-and-tear costs resulting from cycling. We used 

the wear-and-tear start costs to optimize detailed operations of the grid and included ramping costs in the total cost impact. 

We considered the impacts of both the variability and the uncertainty of wind and solar on starts, ramps, and operation of 

the power system. We modeled five scenarios that were designed to illuminate the impacts of increased wind and solar and 

compare the impacts of wind and solar on the power system.

To assess the cycling impacts on the fossil-fueled fleet induced by wind and solar, we needed the following information:

• Wear-and-tear costs and impacts for cycling

• Emissions impacts resulting from cycling

• Subhourly wind and solar plant output for future hypothetical plants

• A tool to model grid operations on a subhourly time frame.

This study was conducted by a team of researchers from the National Renewable Energy Laboratory (NREL), GE Energy, 

Intertek-APTECH (APTECH), and RePPAE. The TRC met every 2 months to discuss and review assumptions, data inputs, 

methodology, and results. TRC members included representatives from utilities, transmission planning groups, the Western 

Electricity Coordinating Council (WECC), and DOE and its laboratories, along with power system and fossil-fueled 

plant experts. As data sets or preliminary results were completed, they were vetted in public forums and peer-reviewed 

publications. This study has been thoroughly reviewed for technical rigor.

Wear-and-Tear Costs and Impacts Data

Cycling of thermal plants can create thermal and pressure stresses in power plant components. This leads to increased O&M 

costs, more frequent repairs, reduced component life, and more frequent forced outages. Power plants that were designed for 

baseloaded operation suffer much more wear-and-tear damage from cycling. In this report, a start is defined as “starting a unit 

that is offline.” Ramping is defined as “load-following operation in which a generating unit increases its production.” Cycling

includes both starts and ramping.

To address the lack of public data on the wear-and-tear costs and impacts from cycling of coal and gas generators, NREL and 

WECC jointly retained APTECH to create a data set. APTECH had previously investigated these costs for hundreds of plants 

around the world. For each plant, APTECH had determined a best fit and a lower-bound and an upper-bound fit for cycling 

costs, where the bounds reflected the uncertainty range for that plant. APTECH statistically analyzed those proprietary data 

to develop generic costs and impacts for seven categories of coal and gas generators (Kumar et al. 2012). Figure ES-4 shows 

the statistics for the lower-bound costs for cold starts for the seven plant types. The medians of these lower-bound costs were 

used in the operational optimization so that the wear and tear on fossil-fueled generators was considered in the decision to 

commit and dispatch units. Upper-bound start costs were then applied to this dispatch to estimate the range of start costs. 

This may yield a conservative estimate because using those upper-bound costs in the unit commitment process could reduce 

cycling. On the other hand, many plant operators do not consider wear-and-tear costs in their dispatch decisions, so this may 

reflect a realistic view of current operations. Unless otherwise specified, ranges of wear-and-tear costs in this report reflect the 

uncertainty range from the lower to the upper bound. High-impact, low-probability events such as a generator failure were 

not included in these wear-and-tear costs because there was not enough data to assess the impact of cycling on those events.
xii

400

300

200

100

0

Coal–small, subcritical

Coal–large, subcritical

Coal–supercritical

Gas–CC [GT+HRSG+ST]

Gas–large-frame CT

Gas–aero-derivative CT

Gas–steam

Cold start C&M low

estimate ($/MW)

Figure ES-4. Lower-bound costs for one cold start

Notes: C&M, capital and maintenance; CC, combined cycle; GT, gas turbine; HRSG, heat recovery steam generator; ST, steam turbine; CT, combustion turbine. The range shows the 

25th to 75th percentile, with the median shown within that range. Nonoutlier extrema are depicted by the whiskers in the plots. Outliers are represented as dots.

CO2, NOX, and SO2 Emissions Data

Starts, ramping, and part-loading also have impacts on emissions. To address the lack of emissions data from cycling, NREL 

analyzed unit-specific measured emissions from the U.S. Environmental Protection Agency (EPA) Continuous Emissions 

Monitoring (CEM) data set (EPA 2009) to develop refined emissions rates for most units in the U.S. portion of the Western 

Interconnection for CO2, NOx, and SO2. Figure ES-5 shows an example of how heat rates and emissions rates were calculated 

for part-load. In addition, unit-specific incremental emissions from starts and ramps were calculated using these measured 

emissions data. Part-loading generally results in a higher emission rate overall, except for NOx emission rates, which decrease 

for coal and gas steam units. Compliance with existing or proposed emissions regulations was not analyzed.

Wind and Solar Power Output Data

In WWSIS-2, we updated the wind and solar plant output and forecast data sets from WWSIS-1 to best represent current 

technologies and methodologies. For example, we capitalized on recent advances in modeling of utility-scale PV plants 

on a subhourly timescale. The following types of plants were modeled in WWSIS-2: utility-scale wind, rooftop distributed 

generation PV, utility-scale PV, and CSP with 6 hours of thermal storage.

ches Word le.

Generation (MW)

Heat Rate (MMBtu/MWh)

0 50 100 150 200 250

14

13

12

11

10

9

8

7

6

Figure ES-5. Heat-rate curve for a typical gas CC unit 

Notes: The black dots show measured emission rates for every hour of the year. The green line shows a local linear fit. 
xiii

Production Simulations and Scenarios

Production simulations were used as the primary tool to examine operations of the power system. These simulations produce 

extensive data outputs including generator commitment and dispatch, emissions, costs, and transmission path flows for each 

time step. Production costs are a key output. Fixed capital costs and PPAs are not included in these simulations.

We simulated scenarios in 2020 using the WECC Transmission Expansion Planning Policy Committee’s (TEPPC’s) 2020 

Portfolio Case 1 as the basis for the production simulation modeling (WECC 2011). Because that case had a relatively high ($7.28/

MMBtu) average gas price, we used the gas price projections from WECC TEPPC 2022, which averages $4.60/MMBtu, for the 

base runs. Load and weather data from 2006 were used. The following five scenarios were created, with penetrations by energy: 

• No Renewables—0% wind, 0% solar

• TEPPC—9.4% wind, 3.6% solar

• High Wind—25% wind, 8% solar

• High Solar—25% solar, 8% wind

• High Mix—16.5% wind, 16.5% solar. 

Table ES-1 shows installed capacities. NREL’s Regional Energy Deployment System (ReEDS) model was used to select which 

regions were optimal locations for siting the wind and solar based on resources, load, and transmission (Short et al. 2011). We 

used the commercial production simulation tool PLEXOS to model unit commitment, dispatch, and power flow for the system 

for a year. The power flow was an optimal direct current (DC) power flow, respecting transmission constraints and using power 

transfer distribution factors, not a simplified pipeline model. We added capacity to interfaces with high shadow prices and 

iterated until all shadow prices were within a consistent cutoff. The shadow price is the marginal value of relaxing the interface 

limit constraint. It defines the potential value of new transmission along each interface (but not the cost). The nearly 40 BAs in 

the Western Interconnection were modeled using the 20 WECC Load and Resource Subcommittee zones, which were the most 

granular we could obtain from WECC. The production simulation was run zonally so that collector systems would not need 

to be designed for each plant. This means that we assumed that sufficient intrazonal transmission was built for each plant and 

ignored local congestion that could result in curtailment.

Table ES-1. Installed Solar and Wind Capacity and Average Capacity Factor for Each State for Each Scenario 

TEPPC

Rooftop PV Utility Scale PV CSP Wind Total

State Capacity 

(MW) CF Capacity (MW) CF Capacity (MW) CF Capacity (MW) CF Capacity (MW) CF

Arizona 1,171 22% 472 43% 3,681 30% 5,324 30%

California 3,545 25% 3,221 44% 7,299 30% 14,065 32%

Colorado 1,342 20% 169 37% 3,256 29% 4,767 27%

Idaho 523 27% 523 27%

Montana 838 34% 838 34%

Nevada 304 22% 334 42% 150 25% 788 31%

New Mexico 140 27% 156 39% 494 28% 790 30%

Oregon 4,903 26% 4,903 26%

South Dakota

Texas

Utah 571 20% 323 31% 894 24%

Washington 4,652 27% 4,652 27%

Wyoming 1,784 42% 1,784 42%

Total 7,074 23% 4,352 43% 27,900 29% 39,326 30%
xiv

High Solar

Rooftop PV Utility Scale PV CSP Wind Total

State Capacity 

(MW) CF Capacity (MW) CF Capacity (MW) CF Capacity (MW) CF Capacity (MW) CF

Arizona 4,498 19% 9,570 23% 9,644 42% 270 33% 23,982 30%

California 9,006 18% 14,258 23% 9,197 43% 5,203 33% 37,663 28%

Colorado 1,127 18% 4,437 22% 1,440 35% 3,617 31% 10,620 27%

Idaho 3 15% 2 16% 583 28% 588 28%

Montana 25 15% 34 17% 988 35% 1,047 34%

Nevada 772 19% 6,503 24% 672 40% 150 25% 8,098 25%

New Mexico 943 20% 2,874 24% 574 38% 644 32% 5,034 26%

Oregon 101 14% 126 21% 4,665 26% 4,892 26%

South Dakota 4 17% 6 19% 330 37% 340 37%

Texas 233 20% 335 23% 568 22%

Utah 2,132 17% 3,759 21% 323 31% 6,214 20%

Washington 405 13% 759 19% 4,952 27% 6,116 25%

Wyoming 10 18% 18 21% 1,634 43% 1,662 42%

Total 19,261 18% 42,680 23% 21,526 42% 23,357 31% 106,824 27%

High Wind

Rooftop PV Utility Scale PV CSP Wind Total

State Capacity 

(MW) CF Capacity (MW) CF Capacity (MW) CF Capacity (MW) CF Capacity (MW) CF

Arizona 1,975 19% 2,330 25% 3,303 43% 4,941 30% 12,548 31%

California 4,875 18% 5,372 25% 2,469 45% 11,109 30% 23,824 28%

Colorado 1,059 18% 1,128 22% 169 37% 6,226 35% 8,581 31%

Idaho 3 15% 2 16% 1,333 29% 1,338 29%

Montana 22 15% 34 17% 6,658 36% 6,714 36%

Nevada 398 19% 344 22% 439 42% 3,270 31% 4,452 30%

New Mexico 172 20% 209 27% 156 39% 4,784 38% 5,321 37%

Oregon 91 14% 101 22% 5,473 26% 5,665 26%

South Dakota 4 17% 6 19% 2,640 36% 2,650 36%

Texas 76 20% 122 27% 198 24%

Utah 361 17% 489 21% 1,343 32% 2,193 27%

Washington 371 13% 492 20% 5,882 27% 6,745 26%

Wyoming 9 18% 18 21% 10,184 43% 10,211 43%

Total 9,417 18% 10,647 24% 6,536 43% 63,840 34% 90,439 32%
xv

High Mix

Rooftop PV Utility Scale PV CSP Wind Total

State Capacity 

(MW) CF Capacity (MW) CF Capacity (MW) CF Capacity (MW) CF Capacity (MW) CF

Arizona 3,655 19% 5,394 25% 9,374 42% 1,440 32% 19,863 33%

California 8,412 18% 9,592 23% 3,594 44% 6,157 31% 27,754 26%

Colorado 1,127 18% 1,653 22% 169 37% 4,396 33% 7,344 29%

Idaho 3 15% 2 16% 1,093 29% 1,098 29%

Montana 25 15% 34 17% 4,288 36% 4,347 36%

Nevada 772 19% 3,282 26% 562 40% 1,560 32% 6,177 28%

New Mexico 943 20% 1,280 27% 298 40% 3,134 38% 5,654 33%

Oregon 101 14% 126 21% 5,413 26% 5,640 26%

South Dakota 4 17% 6 19% 1,950 36% 1,960 36%

Texas 208 20% 193 25% 401 22%

Utah 1,204 17% 1,216 22% 683 33% 3,102 22%

Washington 405 13% 709 19% 5,762 27% 6,876 26%

Wyoming 10 18% 18 21% 7,244 44% 7,272 44%

Total 16,870 18% 23,504 24% 13,997 42% 43,118 34% 97,489 30%

Note: CF, capacity factor

Operations of the entire Western Interconnection were modeled in detail in PLEXOS. We ran a day-ahead (DA) unit 

commitment for all generation using DA wind and solar forecasts. Coal and nuclear units were committed during the DA 

market. We next ran a 4-hour-ahead (4HA) unit commitment to commit CC and gas steam units, using 4HA wind and solar 

forecasts. Finally, we ran a real-time economic dispatch on a 5-minute interval to dispatch all units (i.e., gas CT and internal 

combustion units were allowed to start during the real-time dispatch). 

Load forecasts were assumed to be perfect because we lacked a consistent set of load forecasts; as a result, all the uncertainty 

in operations came from wind and solar. This assumption may result in putting more of a burden on wind/solar than is 

realistic. Variability, on the other hand, came from both load and wind/solar.

Three types of operating reserves were held: contingency, regulating, and flexibility (or load-following). Contingency 

reserves were unchanged with wind and solar because no wind or solar plant was the single largest contingency. Regulating 

reserves covered 1% of load and 95% of the 10-minute forecast errors of wind and PV. Increases in regulation requirements 

were modest in the high-penetration scenarios: up to 10% greater than in the No Renewables Scenario. Finally, flexibility 

reserves, specifically to address load-following needs for wind and PV, were held to cover 70% of the 60-minute forecast 

errors of wind and PV. 

We conducted statistical analysis to examine the geographic diversity of wind, solar, and load. We investigated monthly, 

diurnal, hourly, and subhourly variability to determine increased ramping needs and correlations between load, wind, and PV. 

Extreme event analysis determined maximum ramping needs and tail events.

Production simulation models are not a perfectly accurate representation of operations. As much as possible, we used WECC 

TEPPC assumptions, data, and scenarios because they have been widely vetted. It is important to note the following:

• Most of the Western Interconnection (except California and Alberta) today operates on the basis of a combination of 

short-term and long-term bilateral contracts. This information is confidential and could not be used in this study. As a 

result, the grid was assumed to be operated on the basis of least-cost economic dispatch.
xvi

• Most of the Western Interconnection today primarily uses contractual obligations to schedule transmission. Transmission 

that is not accessible to other generation might be available. In this study, we did not model these contracts; instead, 

we assumed that existing available transmission capacity was used in a way that minimized production costs across the 

Western Interconnection. 

What are the impacts of these assumptions? If a bilateral contract results in operating a less economic plant, that increases 

production cost. It might also result in more wind/PV curtailment or less flexibility available to balance the system, which 

could increase cycling. If sufficient transmission capacity is not available, that might also result in more wind/PV curtailment.

Key Findings

Our analysis in WWSIS-2 yielded a tremendous amount of noteworthy results, which are detailed in the main report. All study 

results are in 2011 nominal dollars. Under the scenarios studied, we found the following for the Western Interconnection:

• High penetrations of wind and solar increase annual wear-and-tear costs from cycling by $35–$157 million1. This represents 

an additional $0.47–$1.28/MWh of cycling costs for the average fossil-fueled generator. Cycling diminishes the production 

cost reduction of wind and solar by $0.14–$0.67/MWh, based on the specific system and generator characteristics modeled. 

These costs are a small percentage of annual fuel displaced across the Western Interconnection (approximately $7 billion) 

and the reduction in fuel costs ($28–$29/MWh of wind and solar generated). The costs are, however, significant compared 

to the average steady-state VOM and cycling costs of fossil-fueled plants ($2.43–$4.68/MWh, depending on scenario). 

Production costs do not include the capital or PPA costs to construct power plants or transmission.

• CO2, NOX, and SO2 emissions impacts resulting from wind- and solar-induced cycling of fossil-fueled generators are a 

small percentage of emissions avoided by the wind and solar generation. Wind- and solar-induced cycling has a negligible 

impact on avoided CO2 emissions. Wind- and solar-induced cycling will cause SO2 emissions reductions from wind and 

solar to be 2%–5% less than expected and NOx emissions reductions to be 1%–2% larger than expected. From a fossilfueled generator perspective, this cycling can have a positive or negative impact on CO2, NOX, and SO2 emissions rates.

• Solar tends to dominate variability challenges for the grid; wind tends to dominate uncertainty challenges. Both of these 

challenges can be mitigated. Because we know the largest component of solar variability, the path of the sun through the 

sky, we can plan for this in the unit commitment. The DA wind forecast error can be mitigated with a 4HA commitment of 

gas units to take advantage of the improved forecasts.

• Although wind and solar affect the grid in very different ways, their impacts on system-wide production costs are 

remarkably similar.

Wind and Solar Displace Primarily Gas Generation and Increase

Coal Ramping

As the quantity of resources with zero or very low marginal cost (such as wind and solar, but also possibly hydropower 

[hydro] or nuclear) increases, the new resources displace higher-cost resources (such as gas). The new resources can, however, 

also start to displace more traditional low-cost resources (such as coal). Figure ES-6 shows the dispatch stacks in the summer, 

depicting the high loads that lead the increased wind/solar to displace mostly gas CC units. The significant solar output in the 

High Solar Scenario, though, resulted in some displacement of coal generation even in the summer.

The impacts on other resources were amplified in the spring, when loads are low and both wind and solar generation are 

high. Figure ES-7 shows the most challenging week, defined by the minimum net load condition (net load is load minus wind 

minus solar). In the High Wind Scenario, the significant wind on March 29 displaced nearly all the gas output and severely 

cut into the coal output. Some wind and PV was curtailed, as shown by the dashed line in the dispatch stack on March 29 and 

30. The curtailment occurred when the other types of generation hit their minimum generation levels. Coal was cycled, but 

without any periodicity and relatively slowly over days. The High Solar Scenario had a very different impact. Solar generation 

was high enough at midday to lead to significant curtailment of wind/PV and ramping of coal up and down on a daily basis. 

Impacts from wind- and solar-induced cycling are likely to be greater during the spring than during the summer.

1

 The low and high ends of this range give an uncertainty range for cycling costs and represent application of the lower-bound and 

upper-bound cycling costs, respectively. The high end of the uncertainty range is an overestimate because of the method used.
xvii

Curtailment

Wind

PV

CSP

Storage

Other

Gas CT

Gas CC

Hydro

Geothermal

Coal

Nuclear

0

50

100

150

Jul 24 Jul 25 Jul 26 Jul 27 Jul 28 Jul 29 Jul 30 Jul 31

Generation (GW)

Curtailment

Wind

PV

CSP

Storage

Other

Gas CT

Gas CC

Hydro

Geothermal

Coal

Nuclear

0

50

100

150

Jul 24 Jul 25 Jul 26 Jul 27 Jul 28 Jul 29 Jul 30 Jul 31

Generation (GW)

0

50

100

150

Jul 24 Jul 25 Jul 26 Jul 27 Jul 28 Jul 29 Jul 30 Jul 31

Generation (GW)

Curtailment

Wind

PV

CSP

Storage

Other

Gas CT

Gas CC

Hydro

Geothermal

Coal

Nuclear

Figure ES-6. Five-minute dispatch stacks for the (top) TEPPC, (middle) High Wind,

and (bottom) High Solar Scenarios for a week in July
xviii

Mar 25 Mar 26 Mar 27 Mar 28 Mar 29 Mar 30 Mar 31 Apr 01

0

25

50

75

100

Generation (GW)

Curtailment

Wind

PV

CSP

Storage

Other

Gas CT

Gas CC

Hydro

Geothermal

Coal

Nuclear

0

25

50

75

100

Mar 25 Mar 26 Mar 27 Mar 28 Mar 29 Mar 30 Mar 31 Apr 01

Generation (GW)

Curtailment

Wind

PV

CSP

Storage

Other

Gas CT

Gas CC

Hydro

Geothermal

Coal

Nuclear

es Word le.

0

25

50

75

100

Mar 25 Mar 26 Mar 27 Mar 28 Mar 29 Mar 30 Mar 31 Apr 01

Generation (GW)

Curtailment

Wind

PV

CSP

Storage

Other

Gas CT

Gas CC

Hydro

Geothermal

Coal

Nuclear

Figure ES-7. Five-minute dispatch stacks for the (top) TEPPC, (middle)

High Wind, and (bottom) High Solar Scenarios for a week in March 

Note: This week represented the minimum net load condition.
xix

Despite these challenges, the 5-minute production simulation results showed that the system can operate and balance load and 

generation. Operational results for contingency, regulating, and flexibility reserves were examined, and issues were minimal. 

There were no regulating reserve violations and very few contingency reserve violations. Figure ES-8 shows that wind and 

solar mostly displace gas CC generation. Displacement of coal increased with increasing penetrations of wind, because gas 

already tends to be decommitted or backed down at night when there are high levels of wind. 

The dispatch stacks showed that the system used the least expensive methods for flexibility from various types of generators 

to serve load and reserves. In the summer, capacity was required more than flexibility. In the spring, balancing the load with 

high instantaneous wind/solar penetrations required a lot of flexibility. Ramping hydro within its constraints was one source 

for flexibility; wind/PV curtailment was another. Cycling of fossil-fueled plants was a third, and we delve into that here. The 

High Solar Scenario ramped coal up and down on a daily basis. In the High Wind Scenario, coal was shut down and restarted 

on a weekly or longer frequency, especially during the low net load event on March 29. In all scenarios, CTs are shut down 

and restarted frequently, running for only several hours per start.

Over the course of 1 year, Figure ES-9 shows the cycling impact by plant type by scenario. Coal starts do not change 

appreciably but the High Wind Scenario decreased the average coal runtime per start by a third and the High Solar Scenario 

increased the number of ramps by an order of magnitude compared to the No Renewables Scenario. The High Wind Scenario 

required somewhat less ramping of coal units compared to the High Solar Scenario.

Wind and solar generation displaced primarily gas generation, based on an average gas price of $4.60/MMBtu.

HiSolar

HiMix

HiWind

TEPPC

0 25 50 75 100

Generation (%)

Scenario

Nuclear

Coal

Geothermal

Hydro

Gas CC

Gas CT

Other

Steam

Figure ES-8. Generation displaced by wind/solar, compared to the No Renewables Scenario

Increasing wind/solar first increased and then decreased the number of CC starts. Even moderate penetrations halved the

CC runtime per start, where it basically remained even at high penetrations. CC ramps actually decreased in the highpenetration scenarios.

Wind causes a significant reduction in CT cycling (and generation). The High Solar Scenario, however, shows more CT 

capacity started compared to the No Renewables Scenario, partly because of the correlation of evening peak load with 

decreased PV output at sunset.
xx

0

30,000

60,000

90,000

Number of ramps

0

100

200

300

Hours online per start

0

500

1,000

Coal Gas CC Gas CT

Coal Gas CC Gas CT

Coal

(a) (b)

(c)

Gas CC Gas CT

Capacity started (GW)

Scenario

NoRenew

TEPPC

HiWind

HiMix

HiSolar

Figure ES-9. (a) Capacity started, (b) average number of hours online per start (must-run CTs 

have been excluded), and (c) total number of ramps for each plant type by scenario for 1 year

To determine the importance of considering wear-and-tear start costs during optimization, we ran the High Wind Scenario 

without including wear-and-tear start costs (but including start fuel costs) to compare to the original High Wind Scenario. 

Although this had almost no impact on annual generation from different unit types, it had a very significant impact on the 

number of starts at CC and CTs, which have very low start fuel costs. This demonstrates that it is important to consider wearand-tear start costs during optimization.

Figure ES-10 gives a more detailed look into the starts and ramps. The solid line shows the committed coal capacity and the 

shaded area shows the dispatched capacity. The white area between the solid line and the shaded area illustrates how far the 

coal capacity has been backed down. In the No Renewables and TEPPC Scenarios, there is little change in coal commitments 

and the coal plants are typically running at or near full output, with an exception during the minimum net load day of March 

29. In the high-penetration scenarios in the spring, coal capacity is shut down approximately each week, and the coal is 

ramped up and down each day, especially with high penetrations of solar. In the summer, coal is ramped very little except for 

some ramping in the High Solar Scenario during the day.
xxi

0

10

20

30

0

10

20

30

0

10

20

30

0

10

20

30

0

10

20

30

NoRenew TEPPC HiWind HiMix HiSolar

03/25 03/26 03/27 03/28 03/29 03/30 03/31 04/01 07/24 07/25 07/26 07/27 07/28 07/29 07/30 07/31

Generation (GW)

Spring Summer

Figure ES-10. (Solid line) capacity committed and (shaded area)

dispatched for coal units during March and July sample weeks
xxii

Wind- and Solar-Induced Cycling Affects Fossil-Fueled Plant Operations 

and Maintenance Costs

Figure ES-11 shows the production cost (operational cost of meeting load in the Western Interconnection in 2020) of each 

scenario. Production costs do not include any capital costs, except capitalized maintenance caused by cycling or noncyclic 

operation. The production cost was dominated by fuel costs, assuming an average natural gas price of $4.60/MMBtu and 

a zero carbon price. Noncyclic VOM costs comprise about a tenth of the total production cost. Cycling VOM costs (starts, 

start fuel, and ramping costs) were all a small percentage of the total production cost. They range from 1.5% of the total 

production cost in the No Renewables Scenario using lower-bound cycling costs to 7% in the High Solar Scenario using 

upper-bound cycling costs.

NoRenew TEPPC HiWind HiMix HiSolar NoRenew TEPPC HiWind HiMix HiSolar

0

5

10

15

Production cost (billion $)

Component

Ramping VOM

Start-up fuel

Start-up VOM

Noncyclic VOM

Fuel

Lower Bound Upper Bound

Figure ES-11. Production cost for each scenario showing the (left) lower and (right) upper bound for the cycling costs

0

200

400

600

800

NoRenew TEPPC HiWind HiMix HiSolar NoRenew TEPPC HiWind HiMix HiSolar

Cycling cost (million $)

Component

Ramping VOM

Start-up fuel

Start-up VOM

Lower Bound Upper Bound

Figure ES-12. Production cost components resulting from cycling, showing

the (left) lower and (right) upper-bound wear-and-tear costs for each scenario 

Note: Cost components have been broken down into starts, start fuel, and ramping costs.
xxiii

Figure ES-12 shows only the cycling portion of these same costs. The cycling costs range from about $270 million in the 

No Renewables Scenario using the lower-bound cycling costs to about $800 million in the High Solar Scenario using the 

upper-bound cycling costs. When wind and solar are added to the system, cycling costs increase by $35–$157 million, or 

13%–24%. Interestingly, the High Mix Scenario has a higher wind/solar penetration but lower cycling costs than the TEPPC 

Scenario. There is not necessarily a monotonic increase in cycling costs with wind/solar penetration. In terms of cycling 

costs, there may be a big step in going from 0% to 13% wind/solar, but a much smaller step in going from 13% to 33%.

Wind- and solar-induced cycling increases average fossil-fueled plant O&M costs by $0.47–$1.28/MWh 

in the high-penetration scenarios. Gas CTs bear the highest wear-and-tear cycling costs.

We first examine these costs from the perspective of the fossil-fueled plants. Figure ES-13 divides the cycling costs shown 

in Figure ES-12 by each MWh of fossil-fueled generation. These cycling O&M costs increase from $0.45–$1.07/MWh in 

the No Renewables Scenario to $0.63–$1.51/MWh in the TEPPC Scenario, where the ranges reflect the uncertainty in the 

wear-and-tear costs. This cycling wear and tear increases to $0.92–$2.36/MWh in the high-penetration scenarios. Table ES-2 

shows the cycling cost impacts of wind and solar for each scenario.

Table ES-2. Increase in Cycling Cost (Compared to No Renewables Scenario)

Scenario Total (Million $) Per MWh Wind/Solar Generation Per MWh of Fossil-Fueled Generation

TEPPC 42–108 $0.41–$1.05/MWh $0.18–0.44/MWh

High Wind 50–127 $0.20–$0.50/MWh $0.52–1.24/MWh

High Mix 35–95 $0.14–$0.38/MWh $0.47–1.14/MWh

High Solar 52–157 $0.22–$0.67/MWh $0.50–1.28/MWh

NoRenew TEPPC HiWind HiMix HiSolar NoRenew TEPPC HiWind HiMix HiSolar

0.0

0.5

1.0

1.5

2.0

2.5

Cycling cost ($/MWh)

Lower Bound Upper Bound

Component

Ramping VOM

Start-up fuel

Start-up VOM

Figure ES-13. Cycling cost, showing the (left) lower- and (right) upper-bound wear-and-tear costs for each scenario

Note: These cycling costs are defined as the total system-wide cycling costs per MWh of fossil-fueled generation.
xxiv

Figure ES-14 further disaggregates the cycling cost by plant type. For confidentiality reasons, only the lower bounds can 

be shown. Note, however, that although the absolute magnitudes of costs are higher with the upper bounds, the relative 

comparisons discussed here also hold true for the upper bounds. CTs (must-run CTs were excluded to delve into these 

impacts) bear the brunt of the wear-and-tear costs (Figure ES-14, right). Notably, these cycling costs actually decrease at low 

wind/solar penetrations (TEPPC Scenario) and do not change in the High Wind Scenario from the No Renewables Scenario. 

For the coal plants (Figure ES-14, left), cycling costs are only slightly affected. For the CC plants (Figure ES-14, center), 

cycling costs increase with increasing wind/solar penetrations.

0

5

10

NoRenew

TEPPC

HiWind

HiMix

HiSolar

NoRenew

TEPPC

HiWind

HiMix

HiSolar

NoRenew

TEPPC

HiWind

HiMix

HiSolar

Lower-bound cycling cost ($/MWh)

Coal Gas CC Gas CT

Component

Ramping VOM

Start-up fuel

Start-up VOM

Figure ES-14. Lower-bound cycling cost for (left) coal,

(center) gas CC units, and (right) gas CTs (excluding the must-run CTs) 

Note: Total, system-wide, lower-bound cycling costs were disaggregated by plant type and divided by MWh of generation of that plant type.

Cycling Increases Production Costs Slightly

We next examine these costs from a system perspective. When we compared the production cost of each scenario to the No 

Renewables Scenario, we saw a decrease of $3.34–$3.43 billion at low penetrations (TEPPC Scenario) and $7.12–$7.65 

billion in the high-penetration scenarios (see Section 6). This change in production cost is dominated by displaced fuel costs. 

Dividing this production cost reduction by the amount of wind and solar energy delivered yielded a production cost reduction 

of $32.6–$33.2/MWh in the TEPPC Scenario and $29.4–$30.6/MWh in the high-penetration scenarios (see Table ES-3 for 

details). Figure ES-15 breaks down the production cost reduction into cost components. Cycling costs (shown by the positive 

values) offset $0.14–$0.67 of the fuel and VOM reduction per MWh of wind and solar generated in the high-penetration 

scenarios. This production cost reduction does not reflect fixed capital costs or PPA costs. Utility planners conducting a costbenefit analysis of wind and solar might want to weigh such fixed capital costs against production costs, but that analysis is 

not conducted here.

Cycling costs increase by $0.14–$0.67 per MWh of wind and solar generated in the high-penetration 

scenarios, based on the specific system characteristics of the Western Interconnection.
xxv

TEPPC HiWind HiMix HiSolar

−30

−20

−10

0

Production cost change ($/MWh)

Component

Ramping VOM

Start-up fuel

Start-up VOM

Noncyclic VOM

Fuel

Lower Bound Upper Bound

TEPPC HiWind HiMix HiSolar

Figure ES-15. The change in production cost for each scenario relative to the No Renewables Scenario,

per MWh of wind and solar generation, for the (left) lower-bound and (right) upper-bound wear-and-tear costs

Note: Production costs do not include any fixed capital or PPA costs.

Table ES-3. Change in Production Cost, Compared to No Renewables Scenario

Scenario Production Cost Reduction

(Billion $)

Production Cost Reduction

($ per MWh of Wind and Solar Generated)

TEPPC 3.34–3.43 32.6–33.2

High Wind 7.48–7.56 29.4–29.7

High Mix 7.59–7.65 30.2–30.4

High Solar 7.12–7.23 30.2–30.6

Note: Production costs do not include any fixed capital or PPA costs.

CO2, NOX, and SO2 Emissions Reductions Are Significantly Greater Than 

Cycling Emissions

Figure ES-16 (left) shows the total CO2 emissions for each scenario. Ramping had no significant impact on CO2 emissions, 

so those estimates are not shown. The start-up CO2 emissions (shown by the thin, dark green line at the top of each bar) 

were negligible in all cases. Figure ES-16 (right) shows the CO2 emissions saved by each MWh of wind/solar. Avoided 

CO2—considering part-load, ramping, and starts—was 1,100 lb/MWh to 1,190 lb/MWh of wind and solar produced in the 

high-penetration scenarios (see Table ES-4). CO2 emissions from starts were negligible. We also calculated the part-load 

penalty—which was the incremental CO2 emissions from part-loading—as negligible. This emissions analysis reflects 

aggregate emissions across the Western Interconnection. Any specific plant might have lower or higher emissions than shown 

here. Because wind tended to displace more coal compared to solar, and because coal emission rates of CO2, NOx, and SO2

are higher than those of gas, higher penetrations of wind resulted in higher levels of avoided emissions.

Starts, ramps, and part-loading had a negligible impact on CO2 emissions reductions of wind and solar. 
xxvi

0

200

400

600

800

TEPPC HiWind HiMix HiSolar

 CO2 emissions (billion lb)

0

500

1,000

1,500

HiWind HiMix HiSolar

CO2 emission savings (lb/MWh)

Full−load rate

Part−load adjustment

Start

Start

Operation

Figure ES-16. CO2 emissions by scenario: (left) absolute CO2 emissions for operation and starts

and (right) CO2 emission reductions compared to the No Renewables Scenario, separated into the

constant emissions rate assumption and adjustments for part-load and starts

Note: Ramping emissions are excluded because they have no significant impact on CO2 emissions.

Table ES-4. Emissions Avoided per MWh of Wind and Solar—Considering Part-Load, Ramping, and Start Impacts

Scenario Avoided CO2 (lb/MWh) Avoided NOx (lb/MWh) Avoided SO2 (lb/MWh)

High Wind 1,190 0.92 0.56

High Mix 1,150 0.80 0.44

High Solar 1,100 0.72 0.35

Note: Part-load impacts were not studied for SO2 because of inadequate data.

From the fossil-fueled plant perspective, average CO2 emission rates of coal, CCs, or CTs change only slightly with wind and 

solar as shown in Figure ES-17 (top). Figure ES-17 (bottom) shows that adding wind and solar can positively or negatively 

affect emissions rates, depending on plant type and scenario. Generally for coal and CCs, wind/solar improves emissions 

rates by up to 1%. The largest negative impact of wind- and solar-induced cycling is in the High Wind Scenario on the CTs 

where the emissions rate increases by 2%. This is on average; individual units might be more or less affected.

Wind- and solar-induced cycling can have a small positive or negative impact on 

NOx and SO2 emissions rates, and it depends on the pollutant and mix of wind and solar.

Figure ES-18 shows the analysis for NOx emissions. There was a negligible impact of starts on NOx. Ramping reduced the 

avoided NOx by 2% to 4%. This is shown in Figure ES-18 (right) as a small negative contribution. Part-loading impacts, on 

the other hand, increased avoided NOx by 4% to 6%. On average, coal units in the West emit less NOx per MWh of generation 

at part-load. The net impact of considering cycling improved avoided NOx emissions from wind/solar by 1% to 2%.
xxvii

−1

0

1

2

 CO2 emissions rate change (%)

Coal Gas CC Gas CT

0

500

1,000

1,500

2,000

 CO2 emission rate (lb/MWh)

Coal Gas CC Gas CT

NoRenew

TEPPC

HiWind

HiMix

HiSolar

Figure ES-17. (Top) average CO2 emission rates by plant type (defined as CO2 emissions divided by MWh of coal, CC, 

or CT generation) for each scenario and (bottom) change in emissions rate compared to the No Renewables Scenario

0

300

600

900

TEPPC HiWind HiMix HiSolar

NOx emissions (million lb)

0.00

0.25

0.50

0.75

1.00

HiWind HiMix HiSolar

 NOx emission savings (lb/MWh)

Full−load rate

Part−load adjustment

Ramp

Start

Start

Ramp

Operation

Figure ES-18. NOX emissions by scenario: (left) absolute NOX emissions for operation, ramps,

and starts, and (right) NOX emission reductions compared to the No Renewables Scenario, separated

into the constant emissions rate assumption and adjustments for part-load, ramps, and starts

Figure ES-19 shows that average NOx emission rates for different plants can also be positively or negatively affected by 

wind/solar. Wind- and solar-induced cycling impacts on NOx emissions rates are relatively small. Impacts on coal units are 

negligible, but high-penetration scenarios increase overall CC NOx emission rates by approximately 5%. CTs show the largest 

impacts. The scenarios with a high wind-to-solar ratio show reductions in CT emissions rates by approximately 10% and the 

scenario with a high solar-to-wind ratio shows increases in CT emissions rates by approximately 10%. This is on average; 

individual units might be more or less affected.
xxviii

Figure ES-20 shows the emissions analysis for SO2. Because there were inadequate data to create SO2 emission part-load 

curves, part-load impacts were not studied for SO2. Ramping impacts on avoided SO2 were modest for the high-penetration 

scenarios, reducing avoided SO2 by 2% to 5%. Start-up emissions affected the avoided emissions rates by significantly less 

than 1%. The net impact of considering starts and ramps lessened avoided SO2 from wind/solar by 2% to 5%.

Figure ES-21 shows the SO2 emissions rates for coal plants. The High Wind Scenario improves the SO2 emission rate by 1%; 

the High Solar Scenario increases the SO2 emission rate by 2%. NOx emission rate change (%) NOx emission rate (lb/MWh)

NoRenew

TEPPC

HiWind

HiMix

HiSolar

0

1

2

3

0.00

0.02

0.04

0.06

0.08

0.00

0.05

0.10

0.15

0.20

−10

−5

0

5

10

Coal Gas CC Gas CT

Coal Gas CC Gas CT

Figure ES-19. (Top) average NOX emissions rates by plant type for each scenario

and (bottom) change in NOX emissions rate from the No Renewables Scenario

Note: Observe the difference in y-axes.

0.0

0.2

0.4

HiWind HiMix HiSolar

 SO2 emission savings (lb/MWh)

0

200

400

600

TEPPC HiWind HiMix HiSolar

 SO2 emissions (million lb)

Start

Ramp

Operation Start

Ramp

Operation

Figure ES-20. SO2 emissions by scenario: (left) absolute SO2 emissions for operation, ramping,

and starts, and (right) SO2 emission reductions compared to the No Renewables Scenario,

separated into the constant emissions rate assumption and adjustments for ramps and starts.
xxix

 SO2 emission rate change (%)

SO2 emission rate (lb/MWh)

NoRenew

TEPPC

HiWind

HiMix

HiSolar

0.0

0.5

1.0

1.5

2.0

−1

0

1

2

Coal Coal

Figure ES-21. (Left) average SO2 emission rate for each scenario and

(right) change in SO2 emission rate from No Renewables Scenario

Sometimes, transmission congestion or minimum generation levels of the thermal plants result in a need for curtailment. We 

curtailed wind and solar in these situations. Wind/solar curtailment was highest in the High Wind and High Solar Scenarios, 

and much reduced (to below 2%) in the High Mix Scenario (see Figure ES-22). High solar penetrations resulted in the highest 

curtailment, but curtailment was still modest (below 5%). High solar penetrations resulted in curtailment midday; high wind 

penetrations more frequently resulted in curtailment at night. We did not model take-or-pay contracts or production tax 

credits, which would result in a cost for wind/solar curtailment, and possibly reduced wind/solar curtailment at the expense of 

increased fossil-fueled plant cycling. Because wind/solar curtailment was low, however, we do not think a cost for wind/solar 

curtailment would change our results significantly.

0

1

2

3

4

5

TEPPC HiWind HiMix HiSolar

Curtailment (%)

Scenario

TEPPC

HiWind

HiMix

HiSolar

Figure ES-22. Curtailment as a percentage of potential wind and solar generation

The high-penetration scenarios saw the least curtailment with a balanced mix of wind and solar.
xxx

Gas Price Has a Greater Impact on Cycling Costs than Wind and

Solar Penetration

To understand the impacts of gas prices on the results, we modeled the High Mix and No Renewables Scenarios with 

gas prices averaging $2.30/MMBtu, $4.60/MMBtu (the core assumption), and $9.20/MMBtu. In the $2.30 case, system 

operations changed significantly because gas CC units often became cheaper than coal units. As a result, the gas CC units 

were often operated as baseload and cycled less. Adding wind and solar in all cases, however, displaced approximately onequarter coal and three-quarters gas CC generation. Figure ES-23 shows the annual generation for all unit types. 

0

100

200

300

Nuclear Coal Geothermal Hydro Gas CC Gas CT Other Steam CSP PV Wind

Generation (TWh)

Scenario

NoRenew ($2.30 gas)

NoRenew ($4.60 gas)

NoRenew ($9.20 gas)

HiMix ($2.30 gas)

HiMix ($4.60 gas)

HiMix ($9.20 gas)

Figure ES-23. Annual generation by type in the gas price sensitivities

Figure ES-24 shows the capacity started in the gas price sensitivities. This plot also illustrates that gas CC units are operated 

as baseload units in the $2.30 No Renewables Scenario, and as “peakers” (meaning that they are run for a relatively short 

period each time they are turned on) in the $9.20 cases. Coal units are started less often (and generate less power) in the $2.30 

cases because gas CC units are cheaper. 

0

1,000

2,000

3,000

Coal Gas CC Gas CT

Capacity started (GW)

Scenario

NoRenew ($2.30 gas)

NoRenew ($4.60 gas)

NoRenew ($9.20 gas)

HiMix ($2.30 gas)

HiMix ($4.60 gas)

HiMix ($9.20 gas)

Figure ES-24. Capacity started in the gas price sensitivities
xxxi

Figure ES-25 shows that cycling costs are affected much more by gas price assumptions than by wind and solar penetration. 

In the $2.30 and $9.20 gas price sensitivities, adding wind and solar actually reduces the overall cycling cost slightly because 

some of the starts are displaced at various unit types. Because fossil-fueled generation is displaced, though, adding wind and 

solar increases the cycling cost per MWh of fossil-fueled generation by $0.30–$1.16, a range that is relatively consistent 

regardless of gas price. Cycling costs increase at fossil-fuel units despite the reduction in overall cycling costs because fossilfuel unit operation is significantly reduced in the High Mix Scenario. 

0

400

800

1,200

NoRenew ($2.30 gas)

NoRenew ($4.60 gas)

NoRenew ($9.20 gas)

HiMix ($2.30 gas)

HiMix ($4.60 gas)

HiMix ($9.20 gas)

NoRenew ($2.30 gas)

NoRenew ($4.60 gas)

NoRenew ($9.20 gas)

HiMix ($2.30 gas)

HiMix ($4.60 gas)

HiMix ($9.20 gas)

Cycling cost (million $)

Lower Bound Upper Bound

Component

Ramping VOM

Start-up fuel

Start-up VOM

Figure ES-25. Cycling cost in the gas price sensitivities showing (left) lower and (right) upper bounds

The price of gas has a much greater impact on system-wide cycling costs than 

the addition of wind and solar. Adding wind and solar affects the cycling cost 

per MWh at thermal units similarly under various gas price assumptions. 

Solar Dominates Variability and Wind Dominates Uncertainty

Many integration studies have investigated high wind penetrations (EnerNex 2011; Charles River Associates 2010; New York 

Independent System Operator 2010; Intelligent Energy 2009; GE Energy 2008; United Kingdom Department of Enterprise, 

Trade, and Investment 2008; EnerNex 2006). Fewer studies have examined high penetrations of solar—in part because high 

solar penetrations have only recently become a concern and in part because of lack of data to model solar well (Orwig et al. 

2012; Navigant Consulting et al. 2011). 

Utilities have concerns about whether fast-moving clouds over PV plants might result in high variability. PV has two 

characteristics that affect this variability: (1) the size of the plant and (2) the number of plants. A small plant, such as a 

rooftop PV system, might see high variability from clouds, but the impact of a small system’s variability on the bulk power 

system is minimal. Impacts could be seen on a distribution level, but WWSIS-2 focuses only on impacts at the transmission 

level. A large plant can have a higher impact on the bulk power system, but its larger area helps to smooth out the variability. 

With additional PV plants, the geographic diversity of the plants and the improbability of cloud fronts obscuring all PV plants 

at the same time result in further smoothing of this variability, as shown in Figure ES-26.
xxxii

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

6:00 8:00 10:00 12:00 14:00 16:00 18:00

Normalized output

One Plant

Two plants

Six Plants

25 plants

So. Calif.

Figure ES-26. Normalized power output for increasing aggregation of PV in Southern California for a partly cloudy day

Solar dominates variability extreme events. At a system level, however, most of this variability 

comes from the known path of the sun through the sky, instead of from fast-moving clouds.

The sunrise and sunset do, however, affect variability significantly with high penetrations of solar. High penetrations of solar 

dominate variability on a 5-minute and an hourly basis, and extreme events are because of sunrise and sunset (see Figure 

ES-27). Although extreme variability events increase, they can also be relatively easily mitigated because we know when the 

sun sets and rises every day. In fact, because we know the path of the sun through the sky for every hour of the year, system 

operators can accommodate much of this diurnal variability. We removed this known diurnal variability when we calculated 

reserves for solar (see Section 5).

5-minute dierence in power (MW)

0.16

0.14

0.12

0.1

0.08

0.06

0.04

0.02

0

Density

-3,000 -2,000 -1,000 0 1,000 2,000 3,000 -3,000 -2,000 -1,000 0 1,000 2,000 3,000

0.01

0.008

0.006

0.004

0.002

0

Density

5-minute dierence in power (MW)

Load only

HiWind

HiSolar

Load only

HiWind

HiSolar

Figure ES-27. (Left) distributions of 5-minute changes in power output for load only and the net load

for the High Wind and High Solar Scenarios and (right) an enlargement of the tails of the distribution
xxxiii

Wind, on the other hand, led to greater uncertainty. The high penetrations of wind led to greater extremes in the DA 

forecast error, as shown in Figure ES-28. Because the 4HA wind forecasts are much more accurate, shown by the tighter 

distribution in Figure ES-29, this uncertainty in the DA time frame can be mitigated with a 4HA unit commitment of CCs 

and CTs. Similarly, higher penetrations of wind led to higher reserve requirements (Ibanez et al. 2013) than those with high 

penetrations of solar because reserve requirements for wind/solar are driven by short-term uncertainty.

Wind dominated the uncertainty extreme events in the DA forecast. We can mitigate this 

by committing gas CCs and CTs in the 4HA time frame, in which forecasts are more accurate.

0

0.05

0.1

0.15

0.2

0.25

Density

TEPPC

HiWind

HiMix

HiSolar

Wind & PV day-ahead forecast error (MW)

-12,000 -9,000 -6,000 -3,000 0 3,000 6,000 9,000 12,000

Figure ES-28. DA wind and PV forecast error for the TEPPC, High Wind, High Mix, and High Solar Scenarios

-12,000 -9,000 -6,000 -3,000 0 3,000 6,000 9,000 12,000

Density

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0

Wind & PV 4 hour-ahead forecast error (MW)

TEPPC

HiWind

HiMix

HiSolar

Figure ES-29. 4HA wind and PV forecast error for the TEPPC, High Wind, High Mix, and High Solar Scenarios
xxxiv

Conclusions

We conducted a detailed operational analysis of the Western Interconnection, focusing on the wear-and-tear costs and 

emissions impacts from cycling of fossil-fueled plants. Detailed wear-and-tear costs and forced outage rate impacts were 

determined for seven categories of plants for starts, ramps, and noncyclic operation. Emissions impacts were obtained for 

every power plant for starts, ramps, and part-load operation. Subhourly impacts were examined using a unit commitment and 

an economic dispatch model with 5-minute dispatch resolution.

In this study, we found that wind and solar increase annual cycling costs by $35–$157 million, or 13%–24%, across the 

Western Interconnection. Cycling costs for the average fossil-fueled plant increase from $0.45–$1.07/MWh to $0.63–$2.36/

MWh, compared to total fuel and VOM costs of $27–$28/MWh. Any specific unit could see more or less cycling and 

associated costs. Starts, not ramps, drive total cycling costs. CTs bear the brunt of the cycling costs, although CT cycling 

costs do not increase in the High Wind Scenario and are actually decreased in the TEPPC Scenario. Wind and solar lead to 

markedly increased ramping for coal generators, and coal runs fewer hours per start with high wind penetrations. Coal units 

ramp daily instead of weekly as wind/solar, especially solar, penetrations increase. Wind and solar have a relatively small 

impact on the number of starts for coal units. Wind and solar mostly displace gas CC generation and cut CC unit runtime 

per start in half. Gas CTs start and ramp less often in scenarios with high ratios of wind to solar penetration. High solar 

penetrations, on the other hand, lead to more starts, shorter run times, more ramping, and more generation for CTs. 

From a system perspective, the $35–$157 million cycling cost increase is a small percentage of the annual fuel displaced by 

wind and solar of approximately $7 billion. Each MWh of wind and solar generation displaces $29.90–$33.60 of fuel and 

VOM costs. Wind- and solar-induced cycling offsets $0.14–$0.67/MWh of this reduction in the high-penetration scenarios 

and $0.41–$1.05/MWh in the low-penetration scenario, based on the specific generator and system characteristics modeled for 

the Western Interconnection.

We found that cycling impacts on CO2 emissions are negligible. Emissions reductions of NOx are 1%–2% more than 

expected when considering cycling and part-load in detail because, on average, coal plants in the West have lower NOx

emissions rates at part-load. Emissions reductions of SO2 are 2%–5% less than expected because of cycling.

We also compared the impacts of wind and solar, using new data sets that illuminated the subhourly variability of utility-scale 

PV. Wind and solar generation affect the system in different ways. They both mostly displace gas CC generation, but wind 

also tends to displace more coal. Solar tends to dominate variability extremes, but it can be mitigated because most of this 

variability is known and can be anticipated in the unit commitment. Wind tends to dominate uncertainty extremes because 

of tail events in the DA wind forecast error. This can be mitigated by committing gas CC units in the 4HA time frame and 

gas CTs in shorter time frames. High wind/solar penetrations result in modest curtailment—up to 5%. WWSIS-2 finds that a 

balanced mix of wind and solar reduces curtailment to less than 2%. 
xxxv

Future Work

Even though system-wide impacts of cycling are modest, an individual unit could suffer higher than average cycling. Plant 

owners in this situation will want to know whether they should retrofit their unit or change their operations to better manage 

cycling at a lower overall cost. Ongoing work includes research on potential retrofits or operational strategies to increase the 

flexibility of fossil-fueled generators. This includes analysis of the costs and benefits of retrofitting existing plants for options 

such as lower minimum generation levels or faster ramp rates.

Additional analysis work that would illuminate the impacts of cycling and further compare wind and solar includes

the following:

• Market impacts on fossil-fueled plants: How do increased O&M costs and reduced capacity factors affect cost recovery 

for fossil-fueled plants? What market structures might need revision in a high wind and solar paradigm? How do the 

economics look for those plants that were most affected?

• Fuel-price sensitivities: How are operations and results affected by different fuel prices for coal and gas?

• Different retirement scenarios: How are operations and results affected if significant coal capacity is retired or if the 

balance of plants is flexible versus inflexible?

• Storage: Does storage mitigate cycling and is it cost effective?

• Impacts of dispersed versus centralized PV: How does rooftop versus utility-scale PV affect the grid?

• Reserves requirement testing to fine tune flexibility reserves: What confidence levels of flexibility reserves are most cost 

effective and still retain reliable grid operation?

• Scenarios with constrained transmission build-outs: If transmission is constrained, what is grid performance and how is 

cycling affected?

• Reserve-sharing options: How do different reserve-sharing options affect grid operations?

• Increased hydro flexibility and modeling assumptions: How does flexibility in the hydro fleet affect grid operations and 

what is the impact on cycling?

• Hurdle rates to represent market friction: With higher hurdle rates to mimic less BA cooperation, how are grid operations 

and cycling affected?

• Comparison of the detailed 5-minute production simulation modeling with cycling costs to hourly production simulation 

modeling without cycling costs: How much more accurate is the detailed modeling?

• Gas supply: Is additional gas storage needed? How does increased wind/solar affect gas scheduling and supply issues? 

• Market sequence: How much does the system benefit from the 4HA market? 
xxxvi 
xxxvii 

Table of Contents 

1 Introduction ........................................................................................................................................... 1

1.1 Background and Objectives........................................................................................................... 1

1.1.1 Wear-and-Tear Cost and Impact Data for Cycling .......................................................... 3

1.1.2 Emissions Impacts From Cycling .................................................................................... 4

1.1.3 Operational Impact Analysis ............................................................................................ 4

1.2 Technical Review .......................................................................................................................... 4

1.3 Study Improvements and Constraints ............................................................................................ 5

1.4 Organization of This Report .......................................................................................................... 7 

2 Input Data .............................................................................................................................................. 8

2.1 Wear-and-Tear Costs and Impacts ................................................................................................ 8

2.1.1 Start Costs ...................................................................................................................... 10

2.1.2 Ramping (Load-Following) Costs .................................................................................. 12

2.1.3 Noncyclic Variable Operation and Maintenance Costs.................................................. 12

2.2 Emissions .................................................................................................................................... 13

2.2.1 Heat Rate ........................................................................................................................ 14

2.2.2 Part-Load Emissions ...................................................................................................... 15

2.2.3 Start-Up Emissions ......................................................................................................... 15

2.2.4 Ramping Emissions ........................................................................................................ 16

2.3 Load Data .................................................................................................................................... 17

2.4 Wind Data ................................................................................................................................... 17

2.4.1 Wind Plant Output Data ................................................................................................. 17

2.4.2 One-Minute Wind Plant Output Data ............................................................................. 17

2.4.3 Wind Power Forecasts .................................................................................................... 19

2.5 Solar Data .................................................................................................................................... 22

2.5.1 Solar Power Output Data ................................................................................................ 22

2.5.2 Solar Forecast Data Synthesis ........................................................................................ 25 

3 Scenarios and Operational Assumptions ........................................................................................ 26

3.1 Siting ........................................................................................................................................... 26

3.2 Production Simulation Methodology and Operational Assumptions .......................................... 30

3.3 Transmission ............................................................................................................................... 35 

4 Statistical Analysis ............................................................................................................................. 41

4.1 Geographic Diversity .................................................................................................................. 45

4.2 Monthly Penetration .................................................................................................................... 46

4.3 Diurnal Variability ...................................................................................................................... 47

4.4 Hourly Variability ....................................................................................................................... 49

4.4.1 Hourly Weather Variability of Solar .............................................................................. 52

4.5 Subhourly Variability .................................................................................................................. 54

4.5.1 Subhourly Weather Component of Solar Variability ..................................................... 57

4.6 Weekly Time Series .................................................................................................................... 58

4.7 Extreme Events ........................................................................................................................... 60

4.8 Day-Ahead Forecast Error Analysis ............................................................................................ 64

4.9 Four-Hour-Ahead Forecasts ........................................................................................................ 70 
xxxviii 

5 Reserves .............................................................................................................................................. 75

5.1 Operating Reserves ..................................................................................................................... 75

5.2 Wind Reserve Methodology ........................................................................................................ 75

5.3 Solar Reserve Methodology ........................................................................................................ 77

5.3.1 Solar Forecast Errors ...................................................................................................... 77

5.3.2 Explanatory Variables .................................................................................................... 79

5.3.3 Application of Reserve Requirements ............................................................................ 80

5.3.4 Total Reserve Requirements .......................................................................................... 81 

6 Production Simulation Results ......................................................................................................... 84

6.1 System Dispatch .......................................................................................................................... 84

6.2 Generation ................................................................................................................................... 92

6.3 Fossil-Fueled Unit Cycling ......................................................................................................... 96

6.4 System Production Costs ........................................................................................................... 103

6.5 Emissions .................................................................................................................................. 109

6.6 Reserves and Unserved Load .................................................................................................... 115

6.7 Curtailment ................................................................................................................................ 116

6.8 Transmission Flows ................................................................................................................... 119

6.9 Gas Price Sensitivities ............................................................................................................... 121

6.10 Production Simulation Conclusions .......................................................................................... 126 

7 Conclusions ...................................................................................................................................... 129

References ............................................................................................................................................... 132 

Appendix A: Correction of the WWSIS Wind Production Data Seams Issue ................................... 137

Appendix B: One-Minute Wind Data Development .............................................................................. 145

Appendix C: Transmission Graphs ....................................................................................................... 150

Appendix D: Statistical Graphs ............................................................................................................. 159
xxxix 

List of Figures 

Figure 1. Map of the Western Interconnection subregions...................................................................... 1 

Figure 2. WWSIS-1 system dispatch for the most challenging week of 3 years of data analyzed ......... 2 

Figure 3. (a) Hot-, (b) warm-, and (c) cold-start lower-bound costs per start per MW of capacity ...... 11 

Figure 4. Lower-bound ramping costs per ramp per MW of capacity .................................................. 12 

Figure 5. Lower-bound noncyclic VOM costs per MW of capacity ..................................................... 13 

Figure 6. Typical heat-rate curve for a gas CC unit from a measured EPA CEM data set ................... 14 

Figure 7. One-hour change in wind output for the (a) original and (b) corrected data .......................... 18 

Figure 8. Comparison of interpolated 1-minute wind power data to actual data for (a) August 

2011 (time between 3,400 and 4,200 minutes) and (b) December 2011 (time between 

3,600 and 4,400 minutes) ....................................................................................................... 19 

Figure 9. Depiction of the matching process between BA level errors in the WWSIS data set and 

the adjusted forecast errors..................................................................................................... 21 

Figure 10. Comparison between the observed 4HA forecasts from an (red) operational system and 

the (blue) persistence forecasts with a (left) 2-hour time lag and a (right) 3-hour time lag ... 22 

Figure 11. Examples of the five classes of temporal variability are shown in plots (a) through (e) ....... 23 

Figure 12. Snapshot satellite clearness index (left) and temporal GHI (right) from NREL on 

August 22, 2005 ..................................................................................................................... 24 

Figure 13. Siting of PV, CSP, and wind plants for the (a) TEPPC, (b) High Solar, (c) High Mix, 

and (d) High Wind Scenarios ................................................................................................. 30 

Figure 14. Iterative transmission build-out for the High Wind Scenario showing the different 

cutoff shadow prices .............................................................................................................. 37 

Figure 15. Comparison of transmission build-out metrics for the TEPPC, High Solar, High Wind, 

and High Mix Scenarios, showing (a) change in production cost versus MW built, 

(b) net benefit of transmission expansion versus MW built, and (c) curtailment versus 

MW built ................................................................................................................................ 38 

Figure 16. Final transmission build-out by scenario (using the $10 cutoff shadow price) ...................... 40 

Figure 17. Hourly (a) wind, (b) PV, and (c) CSP duration curves for the (blue) High Solar, 

(red) High Mix, (green) High Wind, and (red) TEPPC Scenarios ......................................... 42 

Figure 18. Net load duration curve including CSP for the (blue) High Solar, (red) High Mix, 

(green) High Wind, and (red) TEPPC Scenarios ................................................................... 42 

Figure 19. Contour plots of monthly average net load for each hour of the day for each scenario 

(clockwise from upper left): High Solar, High Mix, High Wind, and TEPPC ...................... 44 

Figure 20. Normalized PV output for increasing aggregation of PV plants in Southern California 

for a partly cloudy day ........................................................................................................... 45 

Figure 21. Normalized variability as a function of aggregate capacity for (a) rooftop PV, 

(b) utility-scale PV, and (c) wind ........................................................................................... 46 

Figure 22. Monthly penetration of wind, PV, and CSP for the (a) High Solar, (b) High Mix, and 

(c) High Wind Scenarios ........................................................................................................ 47 

Figure 23. (a) Diurnal variability of the load (green line) and the hourly change in load (white 

line—median; diamond—mean; bar—standard deviation; whiskers—minimum and 

maximum); diurnal variability of net load and hourly change in net load for the 

(b) High Solar, (c) High Mix, and (d) High Wind Scenarios ................................................. 48 

Figure 24. (a) Hourly wind production change versus hourly load change for the High Wind 

Scenario and (b) hourly PV production change versus hourly load change for the High 

Solar Scenario ........................................................................................................................ 50 

Figure 25. Hourly load change versus hourly wind and PV production change for the (a) High 

Solar, (b) High Mix, and (c) High Wind Scenarios for 2006 ................................................. 51 

Figure 26. Weather variability component of PV variability for the High Solar Scenario ..................... 53 
xl 

Figure 27. Total wind and PV variability excluding the diurnal solar variability for the High Solar 

Scenario .................................................................................................................................. 53 

Figure 28. Five-minute changes in load versus 5-minute changes in wind and PV output for the 

(a) High Solar and (b) High Wind Scenarios ......................................................................... 54 

Figure 29. (a) Histogram of 5-minute changes in net load for the (grey) load-only, (green) High 

Wind, and (orange) High Solar Scenarios and (b) an expanded view of the 

distribution tails ...................................................................................................................... 56 

Figure 30. Five-minute total wind and PV variability excluding diurnal solar variability for the 

High Solar Scenario ............................................................................................................... 57 

Figure 31. Distribution of 5-minute changes in load and net load for High Wind and High Solar 

Scenarios ................................................................................................................................ 58 

Figure 32. Profiles for the (a) High Solar and (b) High Wind Scenario for a week in July .................... 59 

Figure 33. Profiles for the (a) High Solar and (b) High Wind Scenario for a week in March ................ 60 

Figure 34. Highest hourly positive ramp (up-ramp) of 26,878 MW in the High Solar Scenario 

occurs on February 2 at 16:00 ................................................................................................ 61 

Figure 35. Highest hourly negative ramp (down-ramp) of 20,934 MW in the High Solar Scenario 

occurs on April 23 at 7:00 ...................................................................................................... 61 

Figure 36. The highest hourly up-ramp of 13,533 MW in the High Wind Scenario occurred on 

February 2 at 16:00 ................................................................................................................ 62 

Figure 37. The largest hourly down-ramp of 12,323 MW in the High Wind Scenario occurred on 

August 31 at 20:00 ................................................................................................................. 62 

Figure 38. Minimum net load is 17,944 MW at noon on March 29 in the High Solar Scenario ............ 63 

Figure 39. Minimum net load is 22,547 MW at 2:00 on February 24 in the High Wind Scenario ......... 63 

Figure 40. DA wind forecast error distribution for the U.S. Western Interconnection ........................... 64 

Figure 41. DA PV forecast error distribution for the U.S. Western Interconnection .............................. 65 

Figure 42. DA wind and PV forecast error distribution for the U.S. Western Interconnection .............. 65 

Figure 43. Total forecast error versus net load for the (a) High Solar and (b) High Wind Scenarios ..... 66 

Figure 44. Contour plot of DA VG forecast errors for the High Solar Scenario ..................................... 69 

Figure 45. Contour plot of DA VG forecast errors for the High Wind Scenario .................................... 69 

Figure 46. Four-hour-ahead wind forecast error distribution for the U.S. Western Interconnection ...... 70 

Figure 47. Four-hour-ahead PV forecast error distribution ..................................................................... 71 

Figure 48. Four-hour-ahead wind plus PV forecast error distribution .................................................... 71 

Figure 49. Four-hour-ahead wind plus PV forecast error scatter for the High Solar Scenario................ 72 

Figure 50. Detail of extreme 4HA solar forecast error caused by methodology used to generate 

4HA forecasts ......................................................................................................................... 72 

Figure 51. Four-hour-ahead forecast error scatter for the High Wind Scenario ...................................... 73 

Figure 52. Wind 10-minute forecast errors versus power output, with 95% confidence interval 

bands for the High Solar Scenario ......................................................................................... 76 

Figure 53. Wind power production and dynamic regulation and flexibility reserve requirement 

for the Western Interconnection in the High Wind Scenario ................................................. 77 

Figure 54. Power and clear-sky power output and ramps ....................................................................... 78 

Figure 55. Graphical representation of short-term forecast for solar ...................................................... 78

Figure 56. Ten-minute forecast error distribution with and without the clear-sky correction ................. 79 

Figure 57. Regulation reserve requirements by scenario for the entire Western Interconnection ........... 80 

Figure 58. Dynamic regulation and flexibility reserve requirements for the Western 

Interconnection for the High Solar Scenario .......................................................................... 81 

Figure 59. Distribution of regulation reserves by scenario...................................................................... 82 

Figure 60. Distribution of flexibility reserve requirements by scenario .................................................. 82 

Figure 61. Power, regulation, and flexibility reserves for a few selected days in July, by scenario ....... 83 

Figure 62. Summer dispatch stacks during the last week of July for the No Renewables Scenario ....... 84 

Figure 63. Summer dispatch stacks during the last week of July for the TEPPC Scenario .................... 85 
xli 

Figure 64. Summer dispatch stacks during the last week of July for the High Wind Scenario ............... 85 

Figure 65. Summer dispatch stacks during the last week of July for the High Mix Scenario ................. 86 

Figure 66. Summer dispatch stacks during the last week of July for the High Solar Scenario ............... 86 

Figure 67. Spring dispatch stacks during the last week of March for the No Renewables Scenario ...... 87 

Figure 68. Spring dispatch stacks during the last week of March for the TEPPC Scenario .................... 87 

Figure 69. Spring dispatch stacks during the last week of March for the High Wind Scenario .............. 88 

Figure 70. Spring dispatch stacks during the last week of March for the High Mix Scenario ................ 88 

Figure 71. Spring dispatch stacks during the last week of March for the High Solar Scenario .............. 89 

Figure 72. Hydro, coal, gas CC, and gas CT dispatch in the (left) summer and (right) spring for 

all scenarios ............................................................................................................................ 90 

Figure 73. CSP dispatch in the spring for all scenarios ........................................................................... 92 

Figure 74. Renewable energy penetration of CSP, PV, and wind in the U.S. portion of the 

Western Interconnection for each scenario in 2006 ............................................................... 93 

Figure 75. Generation from each type of generation unit by scenario .................................................... 93 

Figure 76. Generation displaced by wind and solar compared to No Renewables Scenario................... 94 

Figure 77. Daily wind and solar energy penetration for each scenario ................................................... 95 

Figure 78. Duration curve of 5-minute wind/solar penetration for each scenario ................................... 96 

Figure 79. (a) Capacity started and (b) number of hours online per start by plant type for each 

scenario .................................................................................................................................. 97 

Figure 80. (a) Total number of ramps and (b) ramps per day by plant type for each scenario ................ 98 

Figure 81. Number of starts versus capacity for every unit by unit type and scenario ............................ 98 

Figure 82. Number of ramps versus capacity for every unit by type and scenario ................................. 99 

Figure 83. (Solid line) capacity committed and (shaded area) dispatched for coal units during 

March and July sample weeks for each scenario ................................................................. 100 

Figure 84. (Solid line) capacity committed and (shaded area) dispatched for gas CC units during 

March and July sample weeks for each scenario ................................................................. 101 

Figure 85. Generation by type for the High Wind Scenario with zero wear-and-tear start costs 

compared to lower-bound wear-and-tear start costs ............................................................ 102 

Figure 86. Starts by type for the High Wind Scenario with zero wear-and-tear start costs 

compared to lower-bound wear-and-tear start costs ............................................................ 103 

Figure 87. Total cost of each scenario broken down into fuel, noncyclic VOM, and lower- and 

upper-bound start wear-and-tear, start fuel, and ramping wear-and-tear costs .................... 104 

Figure 88. Cycling cost components, showing lower- and upper-bound costs from starts, start 

fuel, and ramping ................................................................................................................. 105 

Figure 89. Change in production cost (compared to No Renewables Scenario) per MWh of wind 

and solar generation ............................................................................................................. 107 

Figure 90. Lower-bound cycling costs per MWh of (left) coal, (middle) gas CC, and (right) gas 

CT generation ...................................................................................................................... 108 

Figure 91. Lower- and upper-bound cycling costs per MWh of fossil-fueled generation for all 

scenarios ............................................................................................................................... 108 

Figure 92. CO2 emissions by scenario: (a) absolute CO2 emissions for operation and starts and 

(b) CO2 emission savings per MWh of wind and solar generation compared to the No 

Renewables Scenario, separated into the constant emissions rate assumption and 

adjustments for part-load impacts and starts ........................................................................ 111 

Figure 93. NOX emissions by scenario: (a) absolute NOX emissions for operation, ramping, and 

starts and (b) NOX emission savings compared per MWh of wind and solar generation 

to the No Renewables Scenario, separated into the constant emissions rate assumption 

and adjustments for part-load impacts, ramping, and starts ................................................. 112 
xlii 

Figure 94. SO2 emissions by scenario: (a) absolute SO2 emissions for operation, ramping, and 

starts and (b) SO2 emission savings per MWh of wind and solar generation compared 

to the No Renewables Scenario, separated into the constant emissions rate assumption 

and adjustments for ramping and starts ................................................................................ 112 

Figure 95. CO2 emission rates from the average coal, gas CC, and gas CT unit .................................. 113 

Figure 96. Change in CO2 emission rates from the average coal, gas CC, and gas CT unit 

compared to the No Renewables Scenario ........................................................................... 114 

Figure 97. NOX emission rates from the average coal, gas CC, and gas CT unit .................................. 114 

Figure 98. Change in NOX emission rates from the average coal, gas CC, and gas CT unit 

compared to the No Renewables Scenario ........................................................................... 114 

Figure 99. SO2 emission rates from the (a) average coal unit and (b) change compared to the No 

Renewables Scenario ........................................................................................................... 115 

Figure 100. Unserved load and contingency and regulation reserve shortfalls, by scenario (as a 

fraction of total reserve requirement) ................................................................................... 116 

Figure 101. Curtailment as a percentage of available wind and PV generation ...................................... 117 

Figure 102. Curtailment diurnal profile for each scenario ...................................................................... 117 

Figure 103. Average diurnal profile of curtailment by season for each scenario .................................... 118 

Figure 104. Interface flow map with prices for all scenarios .................................................................. 120 

Figure 105. Flow duration curves across selected interfaces for each scenario ...................................... 121 

Figure 106. Annual generation by type in the gas price sensitivities ...................................................... 122 

Figure 107. Generation displaced by renewables in the High Mix compared to No Renewables 

Scenarios for each gas price ................................................................................................. 122 

Figure 108. Capacity started in the gas price sensitivities ....................................................................... 123 

Figure 109. Number of ramps in the gas price sensitivities .................................................................... 123 

Figure 110. Cycling cost in the gas price sensitivities showing (left) lower and (right) upper bounds . 124 

Figure 111. Curtailment in the gas price sensitivities ............................................................................. 125 

Figure 112. Production cost change per MWh of wind and solar generation in the gas price 

sensitivities ........................................................................................................................... 126 

Figure 113. Distribution of wind changes by 10-minute interval number .............................................. 138

Figure 114. Standard deviation of 10-minute wind deltas by 10-minute interval number ...................... 139

Figure 115. Corrected site data ................................................................................................................ 140

Figure 116. Standard deviation for 10-minute intervals for a single site ................................................ 141

Figure 117. Ten-minute wind change distribution by interval for all regions aggregated ...................... 142

Figure 118. Standard deviation by 10-minute interval for all regions aggregated .................................. 142

Figure 119. One-hour changes in wind for 25% Western Interconnection scenario by hour number .... 143

Figure 120. Sigma for 1-hour wind changes for 25% Western Interconnection scenario by hour 

number ................................................................................................................................. 143

Figure 121. Bar and whisker for hourly changes for original data .......................................................... 144

Figure 122. Bar and whisker for hourly changes for corrected data ....................................................... 144

Figure 123. Power spectrums of the Cedar Creek turbines for (a) August 2011 and (b) 

December 2011 .................................................................................................................... 146

Figure 124. Power spectrums using simulated (red) high-frequency noise overlaid on (black) 

power spectrums from actual data for (a) August 2011 and (b) December 2011 ............... 147

Figure 125. Comparison of interpolated 1-minute wind power data to actual data for August 2011 

(time between 3,400 and 4,200 minutes) ............................................................................. 148

Figure 126. Comparison of interpolated 1-minute wind power data to actual data for 

December 2011 (time between 3,600 and 4,400 minutes) ................................................... 148

Figure 127. Power spectrum from spliced Cedar Creek turbine data for 1 full year ............................... 149

Figure 128. Transmission expansion for the TEPPC Scenario showing the (gray) initial and (black) 

additional transmission for each shadow price cutoff value tested ...................................... 151
xliii 

Figure 129. Transmission expansion for the High Wind Scenario showing the (gray) initial and 

(black) additional transmission for each shadow price cutoff value tested .......................... 152

Figure 130. Transmission expansion for the High Mix Scenario showing the (gray) initial and 

(black) additional transmission for each shadow price cutoff value tested .......................... 153

Figure 131. Transmission expansion for the High Solar Scenario showing the (gray) initial and 

(black) additional transmission for each shadow price cutoff value tested .......................... 154

Figure 132. TEPPC Scenario—installed capacity by state ...................................................................... 159

Figure 133. High Solar Scenario—installed capacity by state ................................................................ 160

Figure 134. High Mix Scenario—installed capacity by state .................................................................. 161

Figure 135. High Wind Scenario—installed capacity by state ................................................................ 162

Figure 136. High Solar Scenario—monthly profiles ............................................................................... 163

Figure 137. High Mix Scenario—monthly profiles ................................................................................. 164

Figure 138. High Wind Scenario—monthly profiles .............................................................................. 165

Figure 139. TEPPC Scenario—monthly profiles .................................................................................... 166

Figure 140. TEPPC Scenario—average diurnal profile .......................................................................... 167

Figure 141. High Solar Scenario—average diurnal profile ..................................................................... 168

Figure 142. High Mix Scenario—average diurnal profile ....................................................................... 169

Figure 143. High Wind Scenario—average diurnal profile..................................................................... 170

Figure 144. High Solar Scenario—hourly variability by season ............................................................. 171

Figure 145. High Mix Scenario—hourly variability by season ............................................................... 172

Figure 146. High Wind Scenario—hourly variability by season ............................................................ 173

Figure 147. Load alone with monthly average values in MW ................................................................ 174

Figure 148. Net load with monthly average values in MW for each scenario ........................................ 175

Figure 149. Delta load only with monthly average changes in MW/h .................................................... 176

Figure 150. Delta net load with monthly average changes in MW/h for each scenario .......................... 177

Figure 151. Distribution of DA wind forecast errors .............................................................................. 178

Figure 152. Distribution of DA PV forecast errors ................................................................................. 178

Figure 153. Distribution of DA wind plus PV forecast errors ................................................................. 179

Figure 154. Wind and PV DA forecast error as a function of load level for the High Solar Scenario ... 183

Figure 155. Wind and PV DA forecast error as a function of wind and PV output level for the 

High Solar Scenario ............................................................................................................. 184

Figure 156. Wind plus PV DA forecast error as a function of net load for the High Solar Scenario ..... 185

Figure 157. Wind and PV DA forecast error as a function of load level for the High Wind Scenario .. 186

Figure 158. Wind and solar DA forecast error as a function of wind and solar output level for the 

High Wind Scenario ............................................................................................................. 187

Figure 159. Wind plus PV DA forecast error as a function of net load for the High Wind Scenario .... 188

Figure 160. Distribution of 4HA wind forecast errors ............................................................................ 188

Figure 161. Distribution of 4HA solar forecast errors ............................................................................. 189

Figure 162. Distribution of 4HA wind plus PV forecast errors ............................................................... 189

Figure 163. Wind and PV 4HA forecast errors as a function of load level for the High Solar 

Scenario ................................................................................................................................ 194

Figure 164. Wind and PV 4HA forecast error as a function of wind and PV output level for the 

High Solar Scenario ............................................................................................................. 195

Figure 165. Wind plus PV 4HA forecast error as a function of net load level for the High Solar 

Scenario ................................................................................................................................ 196

Figure 166. Wind and PV 4HA forecast error as a function of load level for the High Wind 

Scenario ................................................................................................................................ 197

Figure 167. Wind and PV 4HA forecast error as a function of wind and solar output level for the 

High Wind Scenario ............................................................................................................. 198

Figure 168. Wind plus PV 4HA forecast error as a function of net load level for the High Wind 

Scenario ................................................................................................................................ 199
xliv 

List of Tables 

Table 1. Emissions Penalty for Part-Load Operation ........................................................................... 15 

Table 2. Start-Up Emissions per MW Capacity ................................................................................... 16 

Table 3. Ramping Emissions per MW Capacity .................................................................................. 16 

Table 4. Renewable Energy Penetration Levels ................................................................................... 27 

Table 5. Installed Solar and Wind Capacity and Average Capacity Factor for Each State for 

Each Scenario ......................................................................................................................... 28 

Table 6. Penalties for Unserved Load and Reserves ............................................................................ 32 

Table 7. Average Characteristics Used for Thermal Units in PLEXOS Optimization ........................ 34 

Table 8. Transmission Build-Out for the High Wind Scenario ............................................................ 36 

Table 9. Transmission Build-Outs for Four Scenarios With $10/MWh Cutoff ................................... 39 

Table 10. Wind and Solar Penetration by Subregion ............................................................................. 43 

Table 11. Statistics for 1-Hour Changes in Net Load for the High Solar Scenario for the U.S. 

Portion of the Western Interconnection Footprint and Selected Subregions ......................... 52 

Table 12. Statistics for 5-Minute Changes in Net Load for the High Solar Scenario for the U.S. 

Portion of the Western Interconnection Footprint and Selected Subregions ......................... 55 

Table 13. DA Forecast Error Statistics for the High Solar Scenario ...................................................... 67 

Table 14. DA Forecast Error Statistics for the High Wind Scenario ..................................................... 68 

Table 15. Four-Hour-Ahead Forecast Error Statistics for the High Solar Scenario ............................... 73 

Table 16. Four-Hour-Ahead Forecast Error Statistics for the High Wind Scenario .............................. 74 

Table 17. Requirements for Spinning Reserves ..................................................................................... 81 

Table 18. Total Amounts of Reserves .................................................................................................... 82 

Table 19. Weekly Wind/Solar Energy Penetration During Sample Weeks ........................................... 84 

Table 20. Cycling Costs (Starts, Start Fuel, and Ramping Costs) for All Scenarios ........................... 105 

Table 21. Production Cost Reductions Due to Wind and Solar for Each Scenario .............................. 106 

Table 22. Cycling Cost Impacts of Wind and Solar ............................................................................. 107 

Table 23. Emissions Avoided per MWh of Wind and Solar Generation— Considering Combined 

Part-Load, Ramping, and Start Impacts ............................................................................... 110 

Table 24. Emissions Reductions Compared to Wind and Solar Penetration for All of the 

Western Interconnection (including Canada and Mexico) ................................................... 110 

Table 25. Impacts of Part-Load, Ramping, and Starts on CO2, NOX, and SO2 Avoided by Wind 

and Solar Generation ............................................................................................................ 113 

Table 26. Curtailment by Zone ............................................................................................................ 119 

Table 27. Cycling Cost Impacts of Wind and Solar in High Mix Scenario (Gas Price Sensitivity) .... 124 

Table 28. Emissions Avoided per MWh of Wind and Solar Generation— Considering Combined 

Part-Load, Ramping, and Start Impacts ............................................................................... 125 

Table 29. Comparison of Actual Noise Standard Deviation to Simulated Noise Standard 

Deviation for August 2011 and December 2011 .................................................................. 146 

Table 30. Transmission Expansion for Each Scenario Showing the Increased Path Capabilities 

for Different Shadow Price Cutoff Values ........................................................................... 155 

Table 31. Transmission Expansion for Each Shadow Price Cutoff Values Showing the Increased 

Path Capabilities for Different Scenarios ............................................................................. 157 

Table 32. DA Forecast Error Statistics for the High Solar Scenario .................................................... 179 

Table 33. DA Forecast Error Statistics for the High Mix Scenario ..................................................... 180 

Table 34. DA Forecast Error Statistics for the High Wind Scenario ................................................... 181 

Table 35. DA Forecast Error Statistics for the TEPPC Scenario ......................................................... 182 

Table 36. 4HA Forecast Error Statistics for the High Solar Scenario .................................................. 190 

Table 37. 4HA Forecast Error Statistics for the High Mix Scenario ................................................... 191 

Table 38. 4HA Forecast Error Statistics for the High Wind Scenario ................................................. 192 

Table 39. 4HA Forecast Error Statistics for the TEPPC Scenario ....................................................... 193 
1 

1 Introduction 

This section presents an overview of the project, including the study’s objectives and major 

tasks; covers the technical review process used to develop the study; describes how this study 

improves on previous work; and outlines the report’s organization. 

1.1 Background and Objectives 

The Western Wind and Solar Integration Study is a landmark analysis of the operational impacts 

of high penetrations of wind and solar power on the Western Interconnection power system of 

the United States. The goal is to understand the effects of and investigate mitigation options for 

the variability and uncertainty of wind, photovoltaic (PV), and concentrating solar power (CSP). 

In Phase 1 of the Western Wind and Solar Integration Study (WWSIS-1), we found no technical 

barriers to integrating 35% wind and solar energy in the subregions of Colorado Coordinated 

Planning Group, Sierra, and Southwest Area Transmission (shown in Figure 1), and up to 27% 

across the Western Interconnection, if operational changes could be made (GE Energy 2010). 

The two most significant operational changes suggested by the study were increased balancing 

authority (BA) cooperation and increased use of subhourly scheduling between BAs for 

generation and interchanges. 

Figure 1. Map of the Western Interconnection subregions 

High wind and solar output can cause coal and other generators to cycle more frequently (see 

Figure 2). In this report, cycling is a broad term meaning shutting down and restarting, ramping 

up and down, and operating at part-load. In this report, a start is defined as “restarting a unit that 

is offline.” Ramping is defined as “load-following operation in which a unit goes from full output 

to some minimum level and back to full output.” Cycling encompasses both starts and ramps and 

also refers to operation of a unit at less than full output. 
2 

Limited data have been available, however, to demonstrate the cost and emissions impacts of this 

type of operation. The increased cycling (see examples in Figure 2) prompted utility operators 

and policy makers to request quantification of the impacts of cycling on wear-and-tear costs and 

emissions impacts. To that end, we initiated Phase 2 of the WWSIS (WWSIS-2) in 2011, with a 

primary objective of quantifying wear-and-tear impacts and emissions effects created by cycling 

on the fossil-fuel–based generation fleet. 

Additionally, in WWSIS-1 solar penetrations were capped at 5%, and only CSP and rooftop PV 

were modeled because of limited capabilities to model utility-scale PV output on a subhourly 

timescale. Subsequent advances in synthesizing subhourly PV plant output allowed WWSIS-2 to 

model higher levels of solar penetration, including utility-scale PV, while maintaining technical 

rigor and credibility. 

Finally, because wind and solar generation varies on a subhourly basis, a new modeling method 

with subhourly capability was required. To meet the subhourly requirements, WWSIS-2 used 

Energy Exemplar’s commercial production simulation tool (PLEXOS) for the modeling work. 

PLEXOS can model a 5-minute dispatch, which allows subhourly impacts of wind and solar to 

be investigated in detail. 

Figure 2. WWSIS-1 system dispatch 

for the most challenging week of 3 years of data analyzed 

Note: This is the most challenging week due to the high wind 

penetration, low load, and significant variability in wind output 

throughout the week 
3 

1.1.1 Wear-and-Tear Cost and Impact Data for Cycling 

Cycling of thermal plants causes temperature swings that can lead to creep, fatigue, and fatiguecreep interaction in components and equipment. This in turn can lead to increased operations and 

maintenance (O&M) costs, more frequent repairs, reduced component life, and more frequent 

forced outages. Furthermore, some plants were designed for baseload operation and have limited 

ramping capability. We recognize that fossil-fueled units have varying capabilities, and in this 

study, industry-accepted ramp limits were used for each unit. 

In WWSIS-2, we divided the fossil-fuel–based fleet into seven categories, three for coal-fired 

plants, and four for gas-fired plants: 

 Coal-fired 

o Small, subcritical steam (35 MW–299 MW) 

o Large, subcritical steam (300 MW–900 MW) 

o Large, supercritical steam (500 MW–1,300 MW) 

 Gas-fired 

o Combined cycle (CC)—combustion turbine (CT)/steam turbine (ST), heat 

recovery steam generator (HRSG) 

o Simple-cycle, large-frame CT 

o Simple-cycle, aero-derivative CT 

o Steam (50 MW–700 MW). 

For each of these categories, the following input data were developed: 

 Start costs for hot, warm, and cold starts 

 Ramping costs for load-following from maximum output to minimum generation to 

maximum output 

 Noncyclic variable operations and maintenance (VOM) costs, defined as the variable 

O&M costs for when the plant is operating at steady state 

 Forced outage rate impacts of hot, warm, and cold starts 

 Long-term heat-rate degradation 

 Start fuel use 

 Start costs, excluding fuel costs but including auxiliary power, chemical, and water costs. 

Detailed discussions of the input data related to wear and tear, including definitions for each of 

the preceding terms (e.g., warm start) can be found in Section 2.1. 

It is important to note that high-impact, low-probability events such as a generator failure were 

not included in these wear-and-tear costs because there were not enough data to assess the 

impact of cycling on those events. This is an area that requires further research. 
4 

1.1.2 Emissions Impacts From Cycling 

In WWSIS-2, we developed a unit-specific database of emissions of carbon dioxide (CO2), 

nitrogen oxides (NOx), and sulfur dioxide (SO2) for fossil-fueled generators, including starts, 

ramping, and part-load operation. The development of these emissions rates and impacts is 

discussed in Section 2. 

1.1.3 Operational Impact Analysis 

In WWSIS-2, we used these newly developed input data to analyze operational impacts. This 

included statistical analysis of net load (net load is load minus wind minus solar) and extreme 

events, along with unit commitment and economic dispatch. 

WWSIS-2 modeled up to 33% wind and solar energy penetration in the U.S. portion of the 

Western Interconnection. Five primary scenarios were created to study the impacts of wind and 

solar penetration and compare wind and solar: 

 No Renewables (0% wind, 0% solar) 

 TEPPC1

 Scenario (9.4% wind, 3.6% solar) 

 High Wind Scenario (25% wind, 8% solar) 

 High Solar Scenario (8% wind, 25% solar) 

 High Mix Scenario (16.5% wind, 16.5% solar). 

Transmission was expanded to bring the new wind and solar resources to load. The Western 

Interconnection was simulated using a 5-minute production simulation model (PLEXOS) to 

study scenarios in 2020. TEPPC’s 2020 Portfolio Case 1 (PC1) was used as a basis for the 

modeling. Metrics such as production cost, generation displacement, emissions, starts, hours run, 

ramps, and reserve violations were used to assess performance of the scenarios. 

When we conducted WWSIS-1, utilities asked us to consider wear-and-tear costs from cycling. 

When we asked utilities for cycling costs for their generators, however, we found that most did 

not know their cycling costs, which may be because historically, many of their generators have 

not performed significant cycling. Those that did have cycling costs (Connolly et al. 2011) held 

that information as confidential. WWSIS-2 is one of the few studies (Connolly et al. 2011; GE 

Energy 2011) that explicitly considers wear-and-tear costs and is the only study we know of that 

incorporates wear-and-tear costs into the unit commitment and dispatch optimization process. 

1.2 Technical Review 

A Technical Review Committee (TRC) for WWSIS-2 was established to review input data, 

assumptions, methodologies, and results. The TRC included relevant utility and transmission 

planning staff in the Western Interconnection. Because the focus of Phase 2 was on emissions 

and wear and tear, the project team specifically incorporated coal and gas plant operation and 

wear-and-tear industry expertise in the TRC. The TRC met approximately every other month. 

Smaller working groups were established to review details, including solar data, wear-and-tear 

 

1

 TEPPC stands for Transmission Expansion Planning Policy Committee, a committee of the Western Electricity 

Coordinating Committee (WECC). WECC is the regional reliability organization for the Western Interconnection. 
5 

impacts, hydropower (hydro) assumptions, and reserves. These smaller groups met on an asneeded basis. In addition, other utility industry experts, such as WECC working group leaders or 

reserve sharing group administrators, were consulted as appropriate. 

1.3 Study Improvements and Constraints 

Although the driver for WWSIS-2 was industry’s concern about the impact of cycling on their 

plants, emissions, and cost recovery, we were also able to capitalize on improvements and 

refinements in many aspects of the data inputs and modeling that had been developed since 

WWSIS-1. In addition, we addressed stakeholder feedback from WWSIS-1. 

To help answer questions related to cycling, we created two new data sets for WWSIS-2 (see 

Section 2): 

 The National Renewable Energy Laboratory (NREL), jointly with WECC, contracted 

with Intertek-APTECH to develop wear-and-tear costs and forced outage rate impact data 

for seven plant types based on their cycling cost studies of 400 plants around the world 

(Kumar et al. 2012). This improves on WWSIS-1, where we were only able to use 

standard planning inputs for start costs with no specific consideration for wear and tear 

because data were lacking. 

 NREL developed unit-specific emissions data for emissions rates as a function of 

generator output and for starts and ramps. In WWSIS-1, we used block emission rates for 

various levels of output for each generator with no specific consideration for starts or 

ramping. The production simulation analysis was conducted throughout the entire 

interconnection to capture all emissions impacts from wind and solar. 

To help assess the contribution from wind and solar, the wind and solar data sets were refined 

as follows: 

 Solar data: When we conducted WWSIS-1, the capability to model utility-scale solar 

plant output on a fast time frame was not yet available. This limited the study to a 5% 

solar energy penetration of distributed generation (DG) rooftop PV and CSP. WWSIS-2 

helped to create a capability to model utility-scale PV, and all solar plant outputs were 

revised accordingly. This allowed us to credibly model 25% solar energy penetrations. 

We synthesized 1-minute PV and CSP plant output in WWSIS-2. 

 Wind data: In WWSIS-1, an artifact of the modeling process left increased variability in 

the wind plant output data on a 3-day interval. As a result, WWSIS-1 was forced to 

exclude every third day of data in the statistical analysis. This 3-day seam was corrected 

for WWSIS-2 (see Appendix A). 

 The original wind forecasts for WWSIS-1 did not have a statistical correction, resulting 

in a positive bias in forecasts compared to plant output. Actual wind forecast error 

distributions from three BAs were used to refine the wind forecast errors in WWSIS-2, 

resulting in a much smaller bias and forecast error distributions that better represent 

today’s forecasting capabilities. 

 Four-hour-ahead (4HA) wind and solar forecasts were developed for WWSIS-2 to allow 

for commitment of gas CC and gas steam units. 
6 

WWSIS-1 modeled Western Interconnection operations on an hourly time frame. In WWSIS-2, 

the Western Interconnection was modeled using PLEXOS, which can dispatch down to a 5-

minute interval and optimize dispatch of CSP storage. It can also 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. WWSIS-1 used a dayahead (DA) commitment and real-time dispatch every hour. WWSIS-2 used a DA and 4HA 

commitment and a real-time dispatch every 5 minutes. 

In WWSIS-1, no additional reserves were held in the core cases for wind and solar. Demand 

response was found to be a cost-effective alternative to increasing reserve requirements. In 

WWSIS-2, a new methodology was developed to determine regulation and flexibility reserve 

requirements for wind and PV. This methodology assesses the fast- and medium-term variability 

of wind and PV and holds reserves to meet a fixed confidence interval of that variability. We 

found that sun movement, not cloud movement, dominates PV variability and removed the 

known diurnal PV variability from our reserves calculation. 

Conceptual transmission build-outs were generated using expert judgment for WWSIS-1 to bring 

resources to load. In WWSIS-2, iterative PLEXOS runs were conducted for each scenario for a 

year to bring shadow prices across interfaces down to a consistent cutoff level. The shadow price 

is the marginal value of relaxing the interface limit constraint. It defines the potential value of 

new transmission along each interface (but not the cost). WWSIS-2 used an optimal DC power 

flow (with static power transfer distribution factors) respecting transmission constraints instead 

of the pipeline model used in WWSIS-1. 

WWSIS-1 scenarios compared the use of local versus remote resources and varying levels of 

wind and solar penetration. WWSIS-2 scenarios compared wind versus solar on the power 

system. The high-penetration case of WWSIS-1 modeled 35% wind/solar energy in some 

subregions with 27% across all of the Western Interconnection. WWSIS-2 modeled 33% 

wind/solar energy across the U.S. portion of the Western Interconnection. The 33% value was 

arbitrary but chosen because it matches the California Independent System Operator (CAISO) 

2020 renewable portfolio standard (RPS) requirement. Both studies, WWSIS-1 and WWSIS-2, 

modeled the entire Western Interconnection and excluded wind/solar resources in Mexico and 

Canada because of limited data from those countries. 

There were limitations to what modeling was feasible during the time frame of this project. 

Examples are given in the following list: 

 The Western Interconnection currently operates with nearly 40 BAs, and only two of 

these are independent system operators (ISOs). WWSIS-2 did not model confidential 

information about bilateral contracts that supply power to the BAs. Instead, the Western 

Interconnection was modeled as 20 zones with interface constraints between them but 

without hurdle rates. As a consequence, the economic interchange between BAs was 

optimized by the program without any penalties or costs for such transfers between BAs. 

A 5-minute economic dispatch was used between zones. 

 WWSIS-2 was not intended as a transmission planning study. To reduce impacts of 

transmission-related effects, transmission was expanded at a zonal level to bring 

resources to load in a consistent manner across scenarios. 
7 

 WWSIS-2 modeled wear-and-tear impacts with nominal values for each plant type. 

This is a simplification in that wear-and-tear impacts are specific to each plant and vary 

with age, vintage (plants from different eras have different capabilities), design, and 

operating history. 

 WWSIS-2 was conducted as a snapshot in time of a future wind and solar penetration. It 

did not study the interim steps required to reach that future snapshot. Although 33% wind 

and solar penetration across the U.S. portion of the Western Interconnection in 2020 is 

not likely, we wanted to use a stakeholder-vetted model, so we built our model on the 

WECC TEPPC 2020 PC1 case. 

 WWSIS-2 did not address dynamic response and reliability of the power system with 

high penetrations of wind and PV. Frequency response and transient stability were not 

considered here but are being addressed in Phase 3 of WWSIS. 

 The amount of conventional generation that is installed in the Western Interconnection is 

the same for all scenarios. It is not reduced with increasing wind and solar penetration. 

1.4 Organization of This Report 

The rest of this report is organized as follows: 

 Section 2 describes the input data used in the study. This includes the wear-and-tear costs 

and impacts as well as emissions impacts from cycling of fossil-fueled generators. The 

section also discusses refinements to the wind and solar output and forecast data sets. 

 Section 3 presents the scenarios and operational assumptions, including the siting of the 

five scenarios and the transmission expansion for each scenario. The section also 

discusses the production simulation methodology and assumptions. 

 Section 4 reviews the statistical analysis for the five scenarios. 

 Section 5 discusses regulation, flexibility, and contingency reserves for each scenario. 

WWSIS-2 developed new methodologies for holding reserves based on the 

characteristics of wind and solar variability and uncertainty. 

 Section 6 presents the operational impact results from the production simulation modeling. 

 Section 7 assesses findings and conclusions from the study and presents 

recommendations for future work. 
8 

2 Input Data 

Input data sets for WWSIS-2 included wear-and-tear costs, emissions impacts, wind plant 

forecasts and output, and solar plant forecasts and output. 

2.1 Wear-and-Tear Costs and Impacts 

Cycling, or varying the load level of a fossil unit—including starts, ramping or load-following, 

and operation at minimum load—can create thermal and pressure stresses in the boilers, steam 

lines, turbines, and auxiliary components, and these stresses can accelerate wear in various 

systems. This wear and tear may result in increased capital and maintenance costs and/or reduced 

life expectancies for components that may increase equivalent forced outage rates (EFOR; 

Connolly et al. 2011; Electric Power Research Institute [EPRI] 2001). Additionally, varying the 

load level over prolonged periods typically degrades a fossil unit’s fuel conversion efficiency 

(i.e., heat rate; see Lefton and Besuner 2001). 

Although the effects of cycling are understood in qualitative terms, most integration studies to 

date have not quantified these values, nor have they examined whether the effects are significant 

in comparison to other items (e.g., initial capital costs, fuel costs, and wear and tear associated 

with running a unit at design capacity, among others). Consequently, to more accurately assess 

impacts of wind and solar, WWSIS-2 needed the wear-and-tear costs and impacts for cycling of 

the fossil-fueled fleet. 

Cycling-related wear mechanisms are complex and often involve multiyear time lagging. 

Because these effects are difficult to assess, utilities often have not quantified the costs or 

impacts related to cycling. NREL, jointly with WECC, retained Intertek-APTECH (APTECH) to 

develop this data. This section describes the wear-and-tear cost data set but for more details, 

please see APTECH’s report (Kumar et al. 2012). 

APTECH has conducted 400 in-depth cycling studies for coal and gas plants around the world 

using a combined bottom-up and top-down approach (Kumar et al. 2012). Bottom-up, componentlevel studies used real-time monitoring data, previous engineering assessments of critical 

components, and a survey of plant personnel. The bottom-up accounting techniques broke down 

component-specific costs. Top-down studies used lightly screened annual maintenance, capital and 

forced outage costs, unit composite damage accumulation models, and statistical regression 

methods. The top-down analysis can capture major direct effects and even operator error and other 

indirect effects to estimate cycling costs. High-impact, low-probability events such as a generator 

failure were not included in these wear-and-tear costs because there were not enough data to assess 

the impact of cycling on those events. This is a topic for future study. 

For each plant, a best-fit estimate for cycling costs was created through linear regression between 

annual O&M costs and operational data (including the number of hot, warm, and cold starts; 

ramping; and total generation). Uncertainty in this regression resulted from the limited sample 

size, the noise inherent in variations of annual cost and cycling characteristics, and the standard 

and heuristic numerical procedures; the upper and lower bounds describe the uncertainty range. 

These were determined by re-running the regression analysis while forcing the cycling cost 

estimates to deviate from the best estimate. The range of solutions was assessed visually and by 

goodness of fit statistics. 
9 

APTECH provided both lower- and upper-bound data for WWSIS-2 but requested that the raw 

upper-bound data (or analyses using the upper-bound data that would allow readers to back out the 

raw upper-bound data) be kept confidential. Raw lower-bound data are presented here. Both the 

lower- and upper-bound data are used in the WWSIS-2 production simulation analyses and 

aggregate results are presented here. These wear-and-tear results, then, represent “bookend” costs. 

From these 400 studies, APTECH extracted data from 170 plants located in North America that 

used a consistent assessment methodology. The data from these units were statistically analyzed 

to derive cycling costs and impacts. Next, the data were used to develop operating data for seven 

categories of plants, with each category including between 11 and 39 plants. Here are the 

categories, followed by a description of the data developed for each category: 

 Large, coal-fired, subcritical steam (300 MW–900 MW) 

 Small, coal-fired, subcritical steam (35 MW–299 MW) 

 Large, coal-fired, supercritical steam (500 MW–1,300 MW) 

 Gas-fired CC, which consists of CT/ST and HRSG 

 Gas-fired, simple-cycle, large-frame CT 

 Gas-fired, simple-cycle, aero-derivative CT 

 Gas-fired steam (50 MW–700 MW). 

In this analysis, the following wear-and-tear parameters were estimated for each of the 

seven categories: 

 Cost of a hot start 

 Cost of a warm start 

 Cost of a cold start 

 Cost of ramping 

 VOM costs for noncyclic operation 

 Increased EFOR because of a cold start 

 Increased EFOR because of a warm start 

 Increased EFOR because of a hot start 

 Long-term heat-rate degradation. 

Each plant category consisted of a variety of plants with different operating histories, ages, and 

designs. It is likely that a newer plant that was designed for cycling would have lower cycling 

wear-and-tear costs and an older plant that was not designed for cycling but was heavily cycled 

would have higher costs. APTECH conducted statistical analysis of the costs for these various 

plants, thereby showing the costs of the average plant in its sample as well as the range of costs. 

We applied the median of the lower bounds and the median of the upper bounds to each plant 

category in WWSIS-2. Therefore, WWSIS-2 does not capture detailed cycling costs specific to 

any individual plant’s age, design, vintage, or operating history. The increased EFOR rates and 

long-term heat degradation were not used in the WWSIS-2 analysis. In the future, these data 

could help understand some of the long-term impacts of increased cycling. 
10 

2.1.1 Start Costs 

This section describes the various components that are included in the start costs and shows the 

lower-bound hot, warm, and cold start cost distributions for the seven unit types. 

In this study, the start costs were divided into four categories: 

 Maintenance and capital expenditures 

 Operational heat-rate impacts 

 Start fuel 

 Start auxiliary power, chemicals, and labor (costs not captured in the preceding 

categories). 

Although some of these costs are directly observable (e.g., the cost of fuel), others must be 

inferred (e.g., start-related maintenance and heat-rate degradation costs are not known until years 

after the event), with some uncertainty. To help characterize this uncertainty, upper and lower 

bounds were developed for each of the 170 plants in the APTECH study. This information was 

then used to develop the cost distributions shown in Figure 3. 

Figure 3 illustrates the distribution of estimated lower-bound hot-, warm-, and cold-start costs for 

the various unit types per start for each MW of capacity. The definition of hot, warm, and cold 

starts is based on the number of hours the unit was offline and varies with the type of unit 

(Kumar et al. 2012). The figure illustrates a statistically significant difference between the start 

costs of the various unit types and shows that cost within a category can vary significantly. 

The study found that most coal units cost more to start than gas units; however, the distributions 

overlap for most unit types, and some coal units cost less to cycle than some gas units. Small 

coal units have the highest cost per MW for starts. This is partly because of the historical 

operation at these units with extensive cycling. 
11 

(a) 

(b) 

(c) 

Figure 3. (a) Hot-, (b) warm-, and (c) cold-start lower-bound costs per start per MW of capacity 

 

Notes: The range shows the 25th to 75th percentile; the median is shown within that range. 

Nonoutlier extrema are depicted by the whiskers in the plots. Outliers are represented as dots. 
12 

For most unit types, cold starts at the lower-bound cost approximately double the lower-bound 

cost of hot starts, with warm starts somewhere in between. Starts for aero-derivative CT 

generators were found to be the same for hot, warm, and cold starts at the lower bound, because 

the units cool so quickly that all starts are classified as cold starts. 

Note that only lower-bound costs are shown because the upper-bound data are confidential. 

Although not shown, the upper-bound costs were used in the analysis and results are presented in 

Section 6. 

2.1.2 Ramping (Load-Following) Costs 

Figure 4 shows the cost (per MW of capacity) of ramping the unit by approximately 30% of its 

maximum capacity (e.g., from 90% of capacity to 60% and back to 90%). Cycling damage and 

therefore cycling cost is driven by thermal and pressure stresses. Because these stresses are not 

as large during ramping as they are during starts, the cost per ramping event is typically two 

orders of magnitude lower than cold starts. Consequently, ramping costs are very unlikely to 

affect the commitment or dispatch of generators. Ramping, however, is much more common than 

starts for many unit types, so the costs can still be important to consider when calculating the 

impacts of wind and solar penetration on the electric power system. To simplify modeling, then, 

we did not use the ramping costs in the unit commitment and dispatch modeling but instead 

applied them to the dispatch results to determine aggregate system ramping costs. 

Figure 4. Lower-bound ramping costs per ramp per MW of capacity 

Notes: The range shows the 25th to 75th percentile; the median is shown 

within that range. Nonoutlier extrema are depicted by the whiskers in the 

plots. Outliers are represented as dots. 

2.1.3 Noncyclic Variable Operation and Maintenance Costs 

Previous studies typically used a single VOM cost per MWh of generation in production cost 

modeling to represent VOM costs regardless of whether the unit was started, ramped, or in 

noncyclic operation. In this study, we explicitly disaggregated VOM into start costs, ramping 

costs, and noncyclic VOM. APTECH provided these three cost data sets so that the sum of these 
13 

yielded the entire VOM cost for a plant, thus covering all types of operation consistently. Figure 

5 shows the distribution of the lower-bound estimates for noncyclic VOM costs for the seven 

categories. Steam units (including coal-fired and gas-fired) tend to have higher noncyclic VOM 

costs than CCs and CTs. 

Low-load operation can also increase costs. For example, cycling can increase wear-and-tear 

damage because of process changes such as wet steam conditions in the low-pressure turbine and 

feedwater heaters. 

Figure 5. Lower-bound noncyclic VOM costs per MW of capacity 

Notes: The range shows the 25th to 75th percentile; the median is 

shown within that range. Nonoutlier extrema are depicted by the 

whiskers in the plots. Outliers are represented by dots. 

2.2 Emissions 

Typical integration studies use average emissions rates for ranges of output levels for each unit. 

Cycling a fossil-fueled unit, however, can affect emission rates. In WWSIS-2, we more 

accurately determined total CO2, SO2, and NOx emissions by calculating emissions for noncyclic 

operation at various output levels and also for starts and ramps. 

The impact of cycling on avoided emissions from wind and solar is an area of controversy. Some 

studies claim wind-induced cycling significantly impacts or even completely negates emissions 

reductions (Bentek Energy 2010; Katzenstein and Apt 2009). Other analyses suggest otherwise 

(Fripp 2011; Pehnt et al. 2008). Some studies use regression models to correlate wind output 

with emissions. It is difficult for those methods to capture the interchanges between BAs or the 

intertemporal impacts of hydro redispatch or changing unit commitments. Other studies balance 

the variability from a single wind plant with a single fossil-fueled unit. It is difficult for those 

methods to capture the smoothing impact from the geographic diversity of wind/solar plants in 

that BA, the fact that wind may be moving in the same direction as load and thus helping the 

system balance, or the depth of the dispatch stack of fossil-fueled and hydro plants that balance 
14 

the system. WWSIS-2 used unit-specific emission rates (based on starts, ramps, and noncyclic 

operation), modeled many wind/solar plants in detail including their geographic diversity, and 

ran a production simulation analysis of the entire interconnection to determine emission impacts. 

We analyzed the measured CO2, SO2, and NOx emissions and generation from nearly all fossil 

units in the United States using the 2008 U.S. Environmental Protection Agency (EPA) 

Continuous Emissions Monitoring (CEM) data set (EPA 2009). Emissions monitoring is required 

on all sulfur-emitting units and all other units above 25-MW capacity. The EPA reports hourly 

generation and heat input (fuel usage in MMBtu), along with CO2 and NOx emissions. Based on 

U.S. Energy Information Administration (EIA) Form 923 data (EIA 2009), 94% of all generation 

from fossil-fueled units came from units with CEM. We developed a methodology to determine 

the additional emissions because of starts and ramps and characterized emission rates for partload operation. Results were categorized for coal units and for gas CC, CT, and steam units. 

Heat rate, part-load emissions, and start-up emissions are discussed in the following sections. 

2.2.1 Heat Rate 

Emissions and heat-rate curves were fit for each unit using hourly data points for generation, heat 

input, and NOx. Heat input and NOx emissions were fit with generation as the independent 

variable. CO2 emissions were calculated directly based on heat input and carbon content of the 

fuel. A nonparametric local linear fit with tri-cube weighting was used so that no predefined 

functional form was set to the units (see Figure 6). Hours immediately following starts were not 

considered in the fit. The units with correlation coefficients below 0.7 between the actual 

emissions and predicted emissions (based on the local linear fit to generation) were not included 

in the analysis. Combined cycle units with average heat rates larger than 9.2 MMBtu/MWh were 

replaced with average CC properties. This results from the way the data are reported; CC units 

are not required to report generation from the steam generator. In the Western United States, a 

small number of CC units do not report generation from the steam generator (this is more 

common in the Eastern United States). 

Figure 6. Typical heat-rate curve for a gas CC unit from a measured EPA CEM data set 

Notes: The green line shows local linear fit. Residuals are defined as the difference between 

the measured emissions and the fit. 
15 

2.2.2 Part-Load Emissions 

Heat rates were compared for CCs, CTs, coal, and gas steam units. Table 1 shows the resulting 

penalties for operating at part-load. This penalty is defined as the percentage increase in 

emissions rate (lb/MWh) when the average unit is operating at 50% maximum capacity. CC units 

are the most efficient at full load and part-load. CCs and CTs, though, have the most significant 

penalties for operating at 50% compared to 100% maximum generation. Coal, CTs, and gas 

steam units have similar heat rates when operating at full load, but at 50% the coal and gas steam 

units have heat rates only 5%–6% higher than at full loading. In general, typical CTs are less 

efficient (e.g., heat rates are 18% higher) at part-load. 

The average NOx emissions as a function of load were compared for the different unit types. 

Steam units (coal and gas) emit approximately an order of magnitude more NOx per MWh than 

gas CC units and CTs. Although part-load operation leads to a NOx penalty for the CCs and CTs, 

this was found to benefit the coal-fired steam units. For example, coal units operating at 50% 

were found to emit 14% less NOx per MWh compared with full-loading operation; gas CC units 

were found to emit 22% more NOx per MWh at 50% load compared with full load. Most of the 

NOx from all units is created from nitrogen in the combustion air (“thermal” NOx), as opposed to 

in the fuel, so flame temperature is likely a primary driver of NOx emissions. Because of the 

significant part-time usage of pollution control equipment for SO2, it was impossible to create 

part-load emission curves. 

Table 1. Emissions Penalty for Part-Load Operation 

Type of Unit CO2 (%) NOX (%) 

Coal 5 –14 

Gas CC 9 22 

Gas CT 18 15 

Gas Steam 6 –14 

2.2.3 Start-Up Emissions 

Starting an offline fossil-fueled unit increases emissions for several reasons. It takes fuel to bring 

the unit online, and that fuel adds emissions without adding energy to the grid. Starts can 

increase emissions rates because most pollution-control equipment does not become fully 

effective until the exit flue gas temperatures are high enough to support the chemical reactions 

needed to remove the pollutants. Pre-combustion techniques tend to work when flame 

temperatures are in the normal operating range (after start-up). 

To estimate start-up emissions, each start was analyzed for deviation between actual and 

predicted emissions. The difference between actual and predicted emissions during the time 

period until the generator reached its typical minimum generation level was classified as start-up 

emissions. These emissions were then averaged for all the starts in 2008 for each unit. If 

emissions occurred before the unit started to generate power, these emissions were also counted. 

The production simulation modeling used the actual unit-specific emissions rates; however, for 

presentation purposes, generation-weighted averages are used here. As a result, units that produced 

large amounts of electricity had more influence than units that did not produce much electricity. 
16 

Table 2 shows the start penalties for different types of units and different emissions. They are 

expressed in MMBtu or lb/MW of unit capacity. For example, a coal unit emits 1.98 lb/MW 

capacity of excess NOx during start-up. This is equivalent to operating the unit at full load for 

approximately 30 minutes. Although coal units emit the most NOx during starts, CCs and CTs 

emit more as a fraction of full-load emissions. During starts, CCs emit the same NOx as 

approximately 7 hours of full-load operation. For the purposes of this study, starts were not 

segregated among cold, warm, and hot because many units did not have enough data to justify 

the split. Coal units emit the same amount of SO2 during start-up as approximately 30 minutes of 

full-load operation. 

Table 2. Start-Up Emissions per MW Capacity 

Type of Unit Heat Input (MMBtu/MW) NOX (lb/MW) SO2 (lb/MW) 

Coal 16.5 1.98 3.9 

Gas CC 2.0 0.53 n/a 

Gas CT 3.5 0.79 n/a 

Gas Steam 13.7 0.84 n/a 

Most coal units are started using oil or gas, so the heat input penalty and the CO2 emissions 

penalty will likely differ because the carbon content of the start fuel is different from that of the 

operating fuel. 

2.2.4 Ramping Emissions 

Ramping emissions were estimated in a similar way to the start-up emissions. Deviations from 

predicted emissions after ramping events were analyzed to understand how emissions change 

during and after ramping events. We analyzed several sensitivities for the definition of a ramp. The 

definition that included the most number of ramps but still had potentially significant emissions 

impacts was a ramp that covered 30% of the unit’s capacity within 4 hours. To account for any 

residual impacts of the ramp, any deviation from predicted emissions within 12 hours of the ramp 

is counted toward ramping emissions. The total ramping emissions are divided by the number of 

ramps for each unit to calculate the average ramping emissions for each unit. Table 3 presents 

generation-weighted averages for the units in the Western Interconnection. Ramping emissions are 

much lower than start-up emissions. The most significant ramping emission impact is the NOx

emissions from coal units (equivalent to 10–15 minutes of full-load operation). CO2 ramping 

emission impacts are equivalent to less than 3 minutes of full-load operation. 

Table 3. Ramping Emissions per MW Capacity 

Type of Unit Heat Input (MMBtu/MW) NOX (lb/MW) SO2 (lb/MW) 

Coal 0.57 0.73 0.82 

Gas CC 0.08 0.00 n/a 

Gas CT 0.28 0.02 n/a 

Gas Steam –0.09 0.05 n/a 
17 

2.3 Load Data 

It is important to use time-synchronized load and weather data because there are correlations 

between load, wind, and solar irradiance. The load and weather patterns from the historical year 

2006 were used in this study, projected out to the year 2020. One-minute load data were obtained 

from the WECC Variable Generation Subcommittee. 

2.4 Wind Data 

This section discusses the type of wind data used in the study as well as the methods used to 

develop the data. Data were developed for wind plant output and wind forecasts. 

2.4.1 Wind Plant Output Data 

Integration studies typically model hypothetical wind plants for which limited measured data 

exist. As a result, a consistent data set of wind outputs must be generated synthetically. In 

WWSIS-1, 3TIER used a numerical weather prediction (NWP) model to synthesize wind speeds 

on a 10-minute, 2-km interval in the U.S. portion of the Western Interconnection. 3TIER 

generated 960 GW of hypothetical wind plant output for 2004 to 2006 (3TIER 2010; Potter et al. 

2008; Potter et al. 2007). Those original wind output data (“actuals”) had increased variability 

every 3 days because the modeling process included 3-day restarts of the NWP model (Lew et al. 

2011; 3TIER 2010; GE Energy 2010). Although the wind output for each site respected realistic 

10-minute maximum changes in output, unrealistic 10-minute variability resulted when sites 

were aggregated. 

WWSIS-2 evaluated various approaches to yield realistic variability when sites were aggregated, 

along with realistic spatial correlation between sites. Figure 7 shows the method that worked 

best, which included random splicing of data from unaffected days to the affected seams. This 

method, which is detailed in Appendix A, was used to correct the wind data for WWSIS-2. 

2.4.2 One-Minute Wind Plant Output Data 

For the subhourly production simulation modeling, WWSIS-2 required higher time-resolution 

data, so the 10-minute data set was converted to 1-minute output. This task, which is detailed in 

Appendix B and summarized here, was accomplished using statistical down-sampling based on 

measured 1-minute output from wind plants in the Western Interconnection. The high-frequency 

range of actual data was used to simulate the high-frequency variability between the 10-minute 

intervals of wind power data provided by 3TIER. A fast Fourier transform (FFT), which has 

previously been used for power spectrums of wind data (Edwards 2009), was applied to 

measured wind plant output from the Cedar Creek Wind Farm in Colorado. 

First, a cubic spline interpolation was applied to the 10-minute data to obtain power estimates at 

1-minute intervals. Then high-frequency data from the measured Cedar Creek wind plant were 

applied to generate the noise component of the wind power data. 
18 

(a) 

(b) 

Figure 7. One-hour change in wind output for the (a) original and (b) corrected data 

Notes: The years from 2004 to 2006 were parsed into 3-day intervals to illuminate the 3-

day seam that is shown by increased variability at the end of the third day in the original 

data. The whiskers are the minimum and maximum values for each hour of the 3-day 

intervals. The bars show the mean value plus and minus one standard deviation. 

Figure 8 compares the simulated interpolated data to actual data for a short segment of time 

between August 2011 and December 2011. Also plotted is actual noise, which is the actual data 

minus the cubic spline fit and the simulated noise. Although the noise of the simulated data was 

more uniform and less in magnitude than that of the actual data, in general the simulated data did 

resemble the actual data. 
19 

(a) 

(b) 

Figure 8. Comparison of interpolated 1-minute wind power data to actual data for 

(a) August 2011 (time between 3,400 and 4,200 minutes) and 

(b) December 2011 (time between 3,600 and 4,400 minutes) 

2.4.3 Wind Power Forecasts 

Wind forecasts were developed for DA, 4HA, and 1-hour-ahead time horizons. The following 

sections describe the methods used to develop the forecasts as well as how the current forecasts 

differ from those used in WWSIS-1. 
20 

2.4.3.1 Day-Ahead Forecasts 

In addition to wind power actuals, wind power forecasts for hypothetical wind plants had to be 

synthesized. The original wind forecasts in WWSIS-1 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, some bias issues arose, resulting in forecasts that tended to be 10% to 15% 

higher than actuals on average. Additionally, forecasting techniques have improved since those 

original data sets were created. 

To best reflect realism in forecasts, we analyzed measured DA wind forecast errors from the 

Public Service Company of Colorado, CAISO, and the Electric Reliability Council of Texas 

(ERCOT). We then adjusted the forecast error distributions of our forecasts from WWSIS-1 to 

match the measured wind forecast error distributions. This process is detailed in Hodge et al. 

(2012) and described in the following paragraphs. Hodge et al. (2011a, 2011b) show more 

examples of statistical distributions of wind forecasts. 

We analyzed the first four statistical moments of the measured wind forecast error distributions: 

the mean, variance, skewness (which measures symmetry of the distribution), and kurtosis 

(which measures the relationship between the peak and the tails of the distribution). Inadequate 

data prevented us from adjusting the mean and skewness, and the variance and kurtosis were the 

main statistical features from an error frequency viewpoint, so the revisions focused on these 

measures. Although some of the bias was adjusted out through this process, some bias remained 

in the year-to-year error distribution as a remnant of the original forecasts. 

It is important to retain realistic temporal and spatial correlations in wind forecasts for these data 

sets (Lew et al. 2011) for accuracy in operational modeling. If, for example, one plant misses its 

forecast significantly, it is likely that a nearby plant will also miss its forecast significantly. 

Similarly, if a plant misses its forecast significantly in one hour, it is likely to do so again in the 

subsequent hour. Spatial correlations were inherent in the original forecast data set because of the 

NWP generation process. In this adjustment process, we focused on getting the BA-level forecast 

error distributions correct, because the forecasts were used in WWSIS-2 to commit units in each of 

20 zones where each zone was roughly equivalent to a BA. This means that on an individual plant 

or interconnection level, the forecast error distributions might not have been as realistic. 

Figure 9 gives a visual representation of the modification process. The first step was to 

hyperbolically fit the BA-level data. Using the operational data, the second step was to 

interpolate what the moments should be (empirically) as a function of a given BA’s capacity. 

Analysis of the data revealed that the variance and kurtosis values of both the operational and 

original forecast data were strongly correlated with the wind capacity considered, an indirect 

measure of the geographic diversity. 

The third step involved in determining the values of the hyperbolic parameters was ensuring that 

the moments were matched in an optimal fashion. The optimization process involved a particle 

swarm routine that evaluated the moment-matching subroutine until convergence on the optimal 

hyperbolic parameters. As a result of this process, the character of the original forecast errors 

was maintained, minus the statistical anomalies, and the empirical information about moments 

was imparted on the updated forecasts. 
21 

The fourth step involved determining the error at each hour of the year according to its quantile. 

This was achieved by stepping through the original forecasts, determining its quantile at that 

hour, and mapping that to the quantile of the theoretical distribution to allow the determination of 

the updated error at each hour. 

The fifth and final step was to take the 8,760 hourly errors and partition them to the site level to 

produce the updated forecasts. This was accomplished by uniformly distributing the error to each 

site, respecting physical boundary conditions. The sign of the error was always maintained from 

the original to the updated forecasts. 

Figure 9. Depiction of the matching process between BA level errors 

in the WWSIS data set and the adjusted forecast errors 

2.4.3.2 Four-Hour-Ahead Forecasts 

The persistence model, which is often used in short-term forecasting, follows the simple 

assumption that the power output in a future time frame will be the same as it is in the current 

time frame. Although the forecasts produced by operational forecasting systems generally 

outperform the persistence forecast at the 4HA time frame, these data were not available from 

the original WWSIS-1 forecasting model runs, and additional NWP runs were not possible. For 

these reasons, we tried to find the persistence time frame that best matched the forecast error 

distribution shape from operational 4HA forecasts. The reference data came from an 

approximately 300-MW wind plant in the Xcel Energy service territory. A collection of ten 30-

MW WWSIS-1 wind plants in a similar geographic location were aggregated to provide a 

comparable persistence forecast error data set. We examined 1-hour, 2-hour, 3-hour, and 4-hour 

persistence error distributions and found that the 2-hour-ahead persistence distribution matched 

the operational 4HA forecast the best, especially at the tails of the distribution, which are the 

critical operational impact events (see Figure 10). As a result, we used a 2-hour-ahead 

persistence to synthesize our 4HA forecasts. 
22 

Figure 10. Comparison between the observed 4HA forecasts from an (red) operational system 

and the (blue) persistence forecasts with a (left) 2-hour time lag and a (right) 3-hour time lag 

2.4.3.3 One-Hour-Ahead Forecasts 

One-hour-ahead forecasts were developed in WWSIS-1 using 1-hour-ahead persistence 

forecasts. The forecasts take an average output value of the previous 60 minutes and apply it as 

the forecast for the next 60 minutes. These forecasts were unchanged in WWSIS-2. 

2.5 Solar Data 

This section discusses the type of solar data used in the study as well as the methods used to 

develop the data. Data were developed for both solar plant output and solar forecasts. 

2.5.1 Solar Power Output Data 

The original solar data used in WWSIS-1 was based on limited knowledge of spatial-temporal 

correlations of PV output. For instance, as PV plant capacity increases, the relative variability 

should decrease because of spatial averaging of localized irradiance fluctuations. The 

characteristics of this effect were not well understood at that time and so only DG rooftop PV was 

modeled. Utility-scale PV was excluded. In WWSIS-2, new techniques were developed to 

characterize subhourly temporal variability based on spatial variability (Hummon et al. 2012). This 

allowed WWSIS-2 to include DG rooftop PV, utility-scale PV, and CSP with thermal storage. 

Hummon and colleagues (2012) developed an algorithm to generate synthetic global horizontal 

irradiance (GHI) values on a 1-minute interval using satellite-derived, 10 km × 10 km gridded, 

hourly irradiance data. The satellite-derived irradiance data, from Clean Power Research’s 

SolarAnywhere database, was based on a semi-empirical model developed by Perez (2002) and 

Perez and colleagues (2002). During each hour, the observed GHI value of the grid cell of 

interest and the surrounding grid cells was related, via probability distributions, to one of five 

temporal cloud-coverage classifications, shown in Figure 11. 
23 

Figure 11. Examples of the five classes of temporal variability are shown in plots (a) through (e) 

Notes: Classes I–III (a–c) are based on the width of the distribution of ramps. Classes IV–V (d and e) 

are characterized by a rapid change between two or more different cloud-cover densities (e.g., clear 

sky with small, dense clouds moving at a high altitude). Panel (f) shows how the temporal classes are 

defined in terms of the mean (σci) and standard deviation of the clearness index (σci) for 60 

consecutive minutes. 

Subhourly irradiance data was collected through NREL’s Measurement and Instrumentation 

Data Center (www.nrel.gov/midc) for seven sites. Synthesis algorithms were used to select 1-

minute interval GHI values based on the classification of the grid cell of interest in a 

particular hour. 

Sandia National Laboratories (Hansen 2012) conducted an independent validation of the 

subhourly algorithm described here and found that the modeled data could reasonably be used 

for WWSIS-2. Most of the validation sites showed that the modeled data agreed reasonably with 

the measured data. The modeled data showed significant disagreement with two sites that may 

have microclimate conditions. 
24 

Figure 12. Snapshot satellite clearness index (left) 

and temporal GHI (right) from NREL on August 22, 2005 

Notes: Satellite clearness index patches represent the 

snapshot at the end of the hour. Measured (black circle) 

and modeled (green triangle) time series GHI, located at 

39.911 N, –105.235 W, with a time step of 1 minute, are 

shown on the right. The temporal variability class of the 

measured data (black) and modeled data (gray) are located 

above each time series plot. 
25 

2.5.2 Solar Forecast Data Synthesis 

In addition to synthesizing hypothetical solar power plant outputs, we needed to synthesize solar 

uncertainty, or forecast error, for each plant. 

DA solar forecasts made use of the WWSIS-1 solar forecasts conducted by 3TIER based on 

NWP simulations (3TIER 2010). No regional operational solar forecast error data were available 

to use to adjust these forecasts for current forecast methodologies, so these forecasts were 

unchanged from WWSIS-1. 

Four-hour-ahead solar forecasts were modeled using 2-hour persistence of cloudiness because it 

was found that with wind forecast errors, 2-hour persistence matched 4-hour forecast errors 

reasonably well. One-hour-ahead forecasts were modeled using 1-hour persistence of cloudiness 

(based on average power the previous hour). A persistence forecast would have ignored the fact 

that the sun traces a known path through the sky. Because we wanted to incorporate this known 

diurnal solar variability into our synthesized forecasts, we used persistence of cloudiness. 
26 

3 Scenarios and Operational Assumptions 

WWSIS-2 modeled up to 33% wind and solar energy penetration in the U.S. portion of the 

Western Interconnection. Five scenarios were created to study the impacts of wind and solar 

penetration on cycling and compare the effects of wind and solar: 

 No Renewables (0% wind, 0% solar) 

 TEPPC Scenario (9.4% wind, 3.6% solar) 

 High Wind Scenario (25% wind, 8% solar) 

 High Solar Scenario (8% wind, 25% solar) 

 High Mix Scenario (16.5% wind, 16.5% solar). 

Because the Western Interconnection already has a small percentage of wind and solar, the No 

Renewables Scenario is somewhat artificial. However, the No Renewables Scenario typically was 

used as a reference, though, to capture the effect of all wind and solar in the system and not only 

the incremental changes between the TEPPC and the high-penetration scenarios. The No 

Renewables Scenario is the equivalent of the TEPPC Scenario with all wind and solar removed. 

All the high-penetration scenarios also include all of conventional generation from the TEPPC data 

set. In other words, no additional retirements resulted from adding wind and solar. That is a topic 

for future study. 

This section describes the methods used to site the variable generation in the scenarios, model 

the scenarios, and build out the transmission to accommodate the new wind and solar. 

3.1 Siting 

NREL’s Regional Energy Deployment System (ReEDS) model (Short et al. 2011) was used to site 

the wind and solar plants for each scenario. The TEPPC Scenario (9.4% wind, 3.6% solar) was based 

on the WECC TEPPC 2020 PC1 base-case scenario. The TEPPC Scenario was designed by WECC 

to include the amount of wind and solar that western states would need to meet their 2020 RPS 

targets. The high-penetration scenarios (High Wind, High Solar, and High Mix) were designed so 

that wind and solar provided a nominal 33% of electricity in the U.S. portion of the Western 

Interconnection in 2004, 2005, and 2006. Note that the 33% penetration is by energy, not capacity. 

The penetration levels would be higher if measured by capacity because CFs of wind and solar are 

less than those of conventional plants. The 33% penetration level was chosen to be consistent with 

CAISO’s 2020 RPS requirements, which are the strictest in the Western Interconnection. The No 

Renewables Scenario did not have any wind or solar, so no siting work was required. 

Table 4 shows the penetration levels for each scenario. Wind and solar are nominally built to 33% 

energy penetration considering the historical weather patterns of 2004, 2005, and 2006. The 

analysis in this report is for the year 2006, which had typical solar but better wind than the average 

of the years 2004–2006. After curtailment,2

 the 2006 penetration levels are 30%–33% of U.S. load 

in the Western Interconnection and 24%–26% of total load in the Western Interconnection. 

 

2

 In this case, curtailment includes CSP storage curtailment. Some of this curtailment is built into 

the design of the generator. 
27 

Within the Western Interconnection, the interties from the United States to Canada and Mexico 

are relatively small, compared with the U.S. system. Therefore, wind/solar variability primarily 

impacts the U.S. generators, where the nominal penetration levels are 33%. More than 85% of 

total cycling costs and more than 90% of the incremental cycling costs because of renewable 

penetration are incurred by U.S. generators in all scenarios. However, because the addition of 

wind/solar can change how these interties are operated and result in displacement of generation 

across the border, total cost and emissions results should be assessed across the entire Western 

Interconnection, in which nominal penetration levels are 26%. 

Table 4. Renewable Energy Penetration Levels 

 2006 Penetration 

Level Across U.S. 

WECC After All 

Curtailment* 

2006 Penetration 

Level Across All 

WECC After All 

Curtailment* 

TEPPC 13.2 10.5 

High Wind 32.6 26.0 

High Mix 32.2 25.6 

High Solar 30.2 24.1 

* “All Curtailment” includes CSP storage curtailment, some of 

which is built into the design of the CSP generators. 

For all the scenarios, solar was defined as 60% PV and 40% CSP. This CSP included 6 hours of 

thermal storage. The solar plants modeled for WWSIS-2 were as follows: 

 Rooftop PV—aggregated, DG PV on residential and commercial rooftops. This 

represents 40% of installed PV in all scenarios. 

 Population-weighted PV—utility-scale PV plants in urban and suburban areas (e.g., 

airport installations and other MW-scale installations). This represents 20% of the 

installed PV. 

 Best-resource-located PV—utility-scale PV plants in desert and remote locations where 

the resource is exceptional. This represents 40% of the installed PV. 

 CSP—utility-scale power plants with 6 hours of thermal storage. The storage was 

dispatched using the PLEXOS model. 

Additional information about each scenario is given in Table 5 and shown in Figure 13. 
28 

Table 5. Installed Solar and Wind Capacity 

and Average Capacity Factor for Each State for Each Scenario 
29 
30 

Figure 13. Siting of PV, CSP, and wind plants for the 

(a) TEPPC, (b) High Solar, (c) High Mix, and (d) High Wind Scenarios 

Notes: Wind, PV, and CSP plants are shown in green, yellow, and red, 

respectively; larger plants are depicted with larger circles. 

3.2 Production Simulation Methodology and Operational Assumptions 

Modeling a power system as large and complex as the Western Interconnection requires 

balancing detail (to ensure that important inputs are properly characterized) with 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 WECC TEPPC model, which 

has been thoroughly vetted through a public stakeholder process. 

With the exception of the large ISO in California and Alberta, the Western Interconnection 

consists of nearly 40 vertically integrated utilities that balance their systems with their own 

generation and confidential bilateral transactions with neighboring utilities—creating a system that 

is difficult to model because much of the information about the system’s operation is confidential. 

WECC TEPPC, the subregional transmission planning groups, and others who model the Western 

Interconnection face this same constraint. Consequently, simplifying assumptions were used to 

model the Western Interconnection. In a manner similar to that used for the WECC TEPPC 2020 

(a) (b) 

(c) (d) 
31 

PC1 model, we modeled the Western Interconnection assuming least-cost economic dispatch, 

without consideration of the confidential bilateral contracts. The Western Interconnection was 

modeled zonally, using the 20 WECC Loads and Resources Subcommittee (LRS) zones. By 

working at the zonal level, we obviated the need to design transmission collector systems for each 

wind and solar plant and instead assigned each plant to a high-voltage bus. 

We modeled the future year 2020 using historical weather patterns and loads from 2006. To 

account for the variance in start day between the two years (i.e., 2006 started on a Sunday, and 

2020 will start on a Wednesday), we time shifted the load and weather data. WWSIS-2 used the 

WECC TEPPC 2020 PC1 case as the basis for the Western Interconnection model. All data are 

given in Pacific Standard Time. 

The WECC TEPPC 2020 PC1 case was procured from Energy Exemplar, the creators of the 

PLEXOS tool. The TEPPC Scenario was based on the WECC TEPPC 2020 PC1 case (WECC 

2011). All wind and solar plants were removed from this scenario to create the No Renewables 

Scenario. The high-penetration scenarios used the wind and solar siting discussed in Section 3.1. 

The WECC LRS provided NREL with estimated transmission capacity between the zones for 

2010, and adjustments were made based on projects that have been added to the TEPPC 2020 

case. Transmission capacity was added between zones for each scenario based on a methodology 

described in Section 3.3. An initial error in the transmission database had occurred when the 

WECC TEPPC database was converted into the PLEXOS format. This was later corrected to 

include only foundational and other transmission projects that were expected to be in service by 

2020. This correction did not take place until after the transmission build-outs of Section 3.3 

were completed. After the correction, the transmission build-out work was reexamined and found 

to be satisfactory. 

We modeled regulation, flexibility, and contingency reserves. Regulation and flexibility reserves 

were modeled on the variability and short-term uncertainty of wind and solar output with the 

reserves for solar designed so that the known solar variability (e.g., sunrise and sunset) was 

removed from the reserve calculations. Section 5 discusses the reserve requirement calculation 

and methodology in detail. 

It is unclear what reserve sharing will be in place in the near future. The study team developed 

reserve sharing assumptions through discussions with the subregional reserve sharing groups. 

These assumptions were then vetted through the TRC. Regulation reserves were held assuming 

sharing across the entire U.S. portion of the Western Interconnection. The choice was an attempt 

to model the reliability-based control (RBC) metric that is currently in field trials across the 

United States. RBC requires little regulating reserve control action when the interconnection 

frequency is near its target. Because frequency is the same across the interconnection, RBC 

essentially looks like a single regulating-reserve sharing group. Flexibility and contingency 

reserves were held by zone. Although contingency reserve sharing groups exist in the Western 

Interconnection, their rules differ. As a result, we used a conservative approach and required 

each zone to meet its own flexibility and contingency reserve requirements. 

Although hurdle rates can be assigned between zones to reflect “friction” of interchanges and the 

differing levels of BA cooperation between zones (Jordan and Piwko 2013), no hurdle rates were 
32 

assigned in our scenarios. Essentially, this choice modeled the Western Interconnection as a 

single BA with flexibility and contingency reserves held on a zonal level. Future work could 

include the use of hurdle rates to model multiple BAs. 

The unit commitment and economic dispatch modeling consisted of three separate market runs: 

DA, 4HA, and real-time markets. The three markets were used so that the unit commitment 

simulation could incorporate improving forecasts over the three time horizons. The DA run used 

DA forecasts for wind and solar generation. The optimization horizon for the unit commitment in 

the DA market was 48 hours. The unit commitment from the first 24 hours of each step was 

saved and input to the 4HA market for coal, nuclear, and biomass generators. These generators 

typically have long start times and need to be committed more than 4 hours ahead of time. The 

extra 24 hours in the unit commitment horizon (for a full 48-hour window) were necessary to 

properly commit the generators with high start costs and the dispatch of CSP with thermal 

storage. For hydro, the nominal hourly production was allowed to vary in the DA market; 

however, the flows calculated in the DA market were then used to fix energy production in the 

4HA and real-time markets. 

The 4HA market was used to commit the CC, oil, and gas steam units. The commitments for the 

coal, nuclear, and biomass generators were input from the DA unit commitment for these 

generators. The 4HA market was modeled in 24-hour windows. Results from only the first 4 

hours from each run were saved for the real-time model, and the extra 20 hours in the unit 

commitment window helped optimally dispatch the CSP thermal storage. It was sometimes 

optimal for CSP units to store their energy and deliver it when prices were highest, which 

occasionally occurred during the load rise the following morning before PV generation began. 

This demonstrates the value of a 24-hour optimization window. 

The unit commitment for all thermal units except CT and internal combustion units was passed 

from the 4HA market to the real-time market. It was assumed that all CT and internal 

combustion units could be started in the real-time market; they would not require any additional 

lead time for starts. The real-time market was a dispatch with 5-minute time steps and without 

any foresight of future 5-minute intervals. 

Regulating and contingency reserves were held in the DA, 4HA, and real-time markets. Flexibility 

reserves were held in the DA and 4HA markets and released in the real-time market. Penalties for 

violating load and regulating, contingency, and flexibility reserve requirements are shown in Table 

6. These penalties were chosen to be high enough that starting a new unit to provide reserves 

would typically lower system costs compared to allowing the reserves to go unserved. 

Table 6. Penalties for Unserved Load and Reserves 

Load and Reserves Penalty for Violating 

Constraint ($/MWh) 

Load 6,000 

Regulating Reserves 4,100 

Contingency Reserves 4,000 

Flexibility Reserves 3,900 
33 

The hydro flexibility assumptions were similar to those used in the WECC Energy Imbalance 

Market study (Milligan et al. 2012) and based on discussions with Bonneville Power 

Administration and Western Area Power Administration. Approximately one-third of all hydro 

energy follows its profile from 2006 because of the extensive constraints (environmental, 

institutional, and—in some cases—physical) on operation. Two-thirds of all hydro energy was 

allowed some flexibility. These generators have monthly energy limits, along with maximum 

capacity and minimum generation levels that change monthly. The PLEXOS model optimized 

the usage of the hydro between these limits. 

Start costs were based on the analysis described in Section 2.1. Because of a lack of wear-andtear start cost data on nuclear, biomass, and internal combustion units, we applied the costs of 

small, subcritical coal units (the most expensive units to cycle) to nuclear units, the costs of 

large, subcritical coal units to biomass units, and the costs of aero-derivative CTs (the least 

expensive units to cycle) to internal combustion units. Start fuel requirements were also included 

in the database based on the analysis in Section 2.1. It was assumed that all coal units start with 

natural gas (or a fuel with equivalent cost); all other units started with their primary fuel. 

Although it is understood that start fuels do vary markedly in cost, the start fuel costs were small 

so this simplification was found to be adequate for modeling purposes. 

We first ran the production simulation analysis with the median lower bounds for start costs so 

that commitment and dispatch was optimized with these wear-and-tear costs taken into account. 

Ramping costs were difficult to incorporate into the model and small, which made them unlikely 

to affect commitment and dispatch. Consequently, ramping costs were added to the model 

dispatch results after the optimization was performed. Next we ran the production simulation 

analysis with the median upper-bound start cost and fuel usage data, but found that properly 

committing large coal units and other units with large start costs using upper-bound start costs 

required a unit commitment horizon of significantly more than the 48-hour window used in this 

study. For the upper bound of each range, the upper-bound start costs were calculated after the 

optimization was done using lower-bound start costs. Although upper-bound start costs could 

lead to a slight change in unit commitment, we did not consider this in this study, so these results 

are a ceiling on the impact of upper-bound start costs. Future work could include an iteration on 

WWSIS-2 results, applying upper-bound costs to units that cycle frequently and lower-bound 

costs to units that cycle rarely. 

Several important industry changes have occurred since the original TEPPC 2020 data set was 

created. The two most significant of these changes are the number of expected retirements and 

repowering of existing units, and fuel price assumptions. 

Because of recent changes in air and water pollution legislation in the United States, system 

planners have projected that more units will retire in the near future (before 2020) than originally 

expected. Some of these units have retired and/or been replaced already. As part of updating 

their data set, TEPPC members solicited advice from WECC members on which units were 

planned to retire or be replaced or repowered. This new information (now part of WECC’s 

updated TEPPC 2022 data set) was believed to more accurately represent the conditions that will 

exist in 2020 than the information in the WECC TEPPC 2020 data set, so the updated retirement 

schedule was integrated into the WWSIS-2 scenarios. 
34 

Similarly, recent gas price projections differ significantly from the higher projections in the 

WECC TEPPC 2020 data set. Consequently, gas price assumptions from the WECC TEPPC 

2022 data set were incorporated into the study’s TEPPC 2020 scenario. The average gas price for 

the Western Interconnection used in this study was $4.60/MMBtu. 

Because the U.S. system in the Western Interconnection has connections with Canada and Mexico 

that are relatively small compared to the size of the full system, the analysis in this project focused 

on the U.S. portion of the Western Interconnection. New transmission to Canada and Mexico was 

not considered in the transmission build-out. Most results presented are focused on the generators 

and loads within the U.S. portion of the Western Interconnection; however, all of the Western 

Interconnection, including Canada and Mexico, was modeled to maintain consistency in 

assumptions about imports and exports with the United States. To avoid unserved load in Canada 

and Mexico, natural gas CC generation was added to these regions until they had sufficient 

capacity to provide electricity locally. This was necessary because of extensive load growth and 

lack of information on Canada and Mexico generation capacity growth for the TEPPC 2020 cases. 

An additional change was that unit heat-rate curves from the WECC TEPPC 2020 PC1 data set 

were replaced with observations from the EPA CEM data analysis (see Section 2.2). To speed up 

model runtime, the new heat-rate curves were limited to two-part piece-wise linear heat-rate 

curves. This produced similar results compared to using more divisions in the piece-wise linear 

curve. Fuel usage from starts, based on findings and assumptions from Kumar and colleagues 

(2012), was also included within the optimization.CO2 emissions were calculated using the 

modeled fuel usage. NOx and SO2 emissions were calculated after the optimization based on sixpart piece-wise linear NOx and SO2 curves for each unit. If no matching units were found in the 

CEM data set—or if a good fit could not be performed—the average heat-rate and emission 

curves were used for each unit type. 

Table 7 shows the key assumptions for the thermal (fossil-fueled and nuclear) units in the 

PLEXOS optimization. Heat rates and start costs were created specifically for this study. Ramp 

rates and minimum generation levels were based on standard TEPPC assumptions (except for 

nuclear minimum generation levels). Note that nonfuel start costs are much higher for the 

WWSIS-2 study compared to TEPPC assumptions, except for the gas CT units. These units are 

typically smaller than the coal or gas CC units, and the TEPPC assumptions were based on perunit start costs. This led to artificially high start costs per MW for the typical gas CT units. 

Table 7. Average Characteristics Used for Thermal Units in PLEXOS Optimization 

Type of Unit 

Minimum Generation 

(as a % of maximum 

capacity) 

Ramp 

Rate 

(%/min) 

Heat Rate 

(at full load)

WWSIS-2 

Nonfuel Start 

Cost ($/MW) 

TEPPC 

Nonfuel Start 

Cost ($/MW) 

Coal 40a 1.1a 10.5 124 11a

CC 52a 0.9a 7.6 81 47a

CT 38a 4.5a 10.7 67 93a

Steam 12a 1.7a 10.7 86 12a

Nuclear 95 0.3a 11.0 155 - a

a

 Denotes an original assumption from the TEPPC database (aggregated for all units of each type). Other information 

in this table was created for this study as described in Section 2. The TEPPC start costs were not used for this study. 
35 

3.3 Transmission 

The transmission expansion methodology and results are summarized in this section. Appendix 

C contains additional graphs and tables of the details. We ran iterative PLEXOS runs for the 

system for a year to bring shadow prices across interfaces down to a consistent cutoff level. The 

timing of the transmission builds was not modeled. WWSIS-2 used an optimal DC power flow 

respecting transmission constraints instead of a simplified pipeline model. We used static power 

transfer distribution factors based on the WECC TEPPC 2020 PC1 case. To simplify the 

transmission expansion, these power transfer distribution factors were assumed to be unchanged 

with the addition of new transmission. 

Forty-four transmission paths were considered at a zonal level. Nodal transmission build-outs 

might need to be considered in future analyses to examine details of congestion and flows, but that 

will require design of collector systems for the new wind and solar resources. Because WWSIS-2 

was not meant to be a transmission planning study, transmission expansion was limited to 

increasing capabilities on existing paths instead of adding new source and sink pathways. 

It is important to note that in much of the Western Interconnection, utilities have physical, rather 

than financial, rights to transmission (i.e., 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 Western Interconnection, we did not build 

additional transmission to those zones; instead, we 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, which is based on the fact that actual flows between Canada’s Alberta 

Electric System Operator and the United States, for example, are very limited. 

We developed a methodology to expand capabilities on existing transmission paths by running 

the scenarios in PLEXOS for a full year and examining shadow prices across interfaces. We built 

500 MW of additional transmission across interfaces where shadow price exceeded a fixed cutoff 

value. 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. 

We tested cutoff values 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 an 11% fixedcharge rate. Figure 14 shows successive transmission build-outs for decreasing cutoff values for 

the High Wind Scenario, compared to the initial capacity (in gray). As described subsequently, 

$10/MWh was selected as the best cutoff value. 

Transmission build-outs were evaluated considering transmission costs, production cost, 

and curtailment. For a fixed cutoff value, as the wind and solar penetration—especially 

wind—increased, the transmission built also increased. Curtailment decreased with 

expanded transmission. 
36 

Table 8 gives the transmission build-out metrics for the different cutoff values shown in 

Figure 14. 

Table 8. Transmission Build-Out for the High Wind Scenario 

Metric Initial $20 Cutoff $15 Cutoff $10 Cutoff $5 Cutoff 

Cumulative 

Additional 

Transmission 

Capacity (MW) 

0 5,500 7,500 10,500 18,000 

Cumulative 

Transmission 

Annualized Cost 

(million $/yr) 

0 242 330 462 792 

Production Cost 

(billion $/yr) 11.8 11.3 11.1 10.9 10.6 

Cumulative 

Production Cost 

Change from 

Initial Build-Out 

(million $/yr) 

 546 721 923 1,220 

Average 

Benefit/Cost Ratio 2.26 2.18 2.00 1.54 

Curtailment (TWh) 24.8 16.7 13.4 9.2 3/5 

Curtailment as 

Fraction of 

Potential Wind 

and Solar 

Production 

0.096 0.065 0.052 0.035 0.014 

Transmission Cost 

per MWh 

Curtailment 

Savings ($/MWh) 

 30.1 29.0 29.7 37.3 

Figure 15 depicts some of the metrics used to evaluate the transmission build-outs. 

Figure 15 (a) shows the change in production cost for each scenario as a function of cutoff 

shadow price and transmission MW built. The transmission built in the High Solar Scenario at 

the $5/MWh cutoff shadow price was 10,000 MW, which was comparable to the transmission 

built in the High Wind Scenario at the $10/MWh cutoff shadow price. That 10,000 MW of 

transmission in the High Wind Scenario saves $923 million/year, however, which is about 50% 

higher than the $638 million/year saved in the High Solar Scenario. 
37 

Figure 14. Iterative transmission build-out for the High Wind Scenario 

showing the different cutoff shadow prices 

Notes: SMUD, Sacramento Municipal Utility District; LAWP, Los Angeles 

Department of Water and Power; IID, Imperial Irrigation District. The initial

transmission capacity for major interfaces is shown by the width of the gray 

lines. New transmission capacity is shown by the width of the black lines.
38 

Figure 15 (b) shows the net benefit of the transmission expansion, defined as the change in 

production cost minus the approximate transmission cost. As the cutoff value decreased and 

transmission was expanded, the net benefit increased, topped out, and then decreased. The cutoff 

value where this net benefit topped out varied but was generally $10/MWh. As a result, we 

selected the $10/MWh cutoff value to define the transmission build-out for each scenario. 

Curtailment decreased with expanded transmission. 

Figure 15 (c) shows the curtailment as a function of cutoff shadow price and transmission MW 

built. Because solar peaks midday and wind is often stronger at night, wind curtailment is much 

greater than solar curtailment, even as transmission was built out for the High Wind Scenario. 

These results reflect only the DA model, so forecast error is not considered and curtailment is 

lower compared with the final runs presented in Section 6. 

(a)

(b) (c)

Figure 15. Comparison of transmission build-out metrics for the TEPPC, High Solar, High Wind, 

and High Mix Scenarios, showing (a) change in production cost versus MW built, 

(b) net benefit of transmission expansion versus MW built, and (c) curtailment versus MW built 
39 

Table 9 and Figure 16 summarize the transmission build-outs at the $10/MWh cutoff value for 

the scenarios. Note that transmission capacity was added to all scenarios, including the No 

Renewables and TEPPC case. 

Table 9. Transmission Build-Outs for Four Scenarios With $10/MWh Cutoff 

Metric TEPPC High Wind High Mix High Solar 

Cumulative 

Additional 

Transmission 

Capacity (MW) 

4,000 10,500 9,000 6,500 

Cumulative 

Transmission 

Annualized Cost 

(million $/yr) 

176 462 396 586 

Production Cost 

(billion $/yr) 

15.2 10.9 10.6 10.9 

Cumulative Change 

in Production Cost 

(M$/yr) 

255 923 733 561 

Average Benefit/Cost 

Ratio 

1.45 2.00 1.85 1.96 

Incremental 

Benefit/Cost Ratio 

0.27 1.54 1.11 1.65 

Curtailment (TWh)

 

0.2 9.2 2.3 1.3 

Curtailment as 

Fraction of Potential 

Wind and Solar 

Production 

0.002 0.035 0.009 0.005 

Transmission Cost 

per MWh Curtailment 

Savings ($/MWh) 

122 29.7 55.7 129.5 
40 

Figure 16. Final transmission build-out by scenario (using the $10 cutoff shadow price) 

Notes: Initial transmission capacity for major interfaces is shown by the width of the gray 

lines. New transmission capacity is shown by the width of the black lines.
41 

4 Statistical Analysis 

We conducted statistical analysis on the 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 (Lew et al. 

2012). We also investigated solar variability in detail, finding that most of it is known because of 

the path of the sun through the sky. Additional plots can be found in Appendix D. 

Statistical and extreme event analysis was undertaken to examine the variability of solar, wind, 

and net load (defined as load minus solar minus wind) on a subregional and regional basis. All 

results in this section focus on and within the U.S. portion of the Western Interconnection. The 

subregional entities considered for detailed analysis were Columbia Grid (CG), Northern Tier 

Transmission Group (NTTG), WestConnect (WC), and CAISO. 

Figure 17 (a)–(b) shows the hourly duration curves for wind and PV. The figure shows available 

wind and PV output, prior to the production simulation runs when curtailment is determined. The 

High Wind Scenario and the High Solar Scenario 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. 

CSP with 6 hours thermal storage can be considered dispatchable. The dispatch of the storage 

was optimized in the production simulation runs (see Section 5) and the resulting CSP duration 

curve is shown in Figure 17 (c). Because the CSP in this study is dispatchable, we include CSP 

in the statistical analysis only when it is appropriate. Much of the variability and uncertainty 

analysis focuses on wind and PV. 

Figure 18 shows the net load duration curve, including CSP (wind, PV, and CSP output have all 

been subtracted from the load). Net loads are depressed consistently with high penetrations. The 

minimum net load is 30,000 MW lower in the High Solar than in the TEPPC Scenario. 
42 

(a) 

(b) (c)

Figure 17. Hourly (a) wind, (b) PV, and (c) CSP duration curves 

for the (blue) High Solar, (red) High Mix, (green) High Wind, and (red) TEPPC Scenarios 

Figure 18. Net load duration curve including CSP 

for the (blue) High Solar, (red) High Mix, (green) High Wind, and (red) TEPPC Scenarios 
43 

Table 10 shows the wind and solar penetration for selected subregions for each scenario. Note 

that wind and solar penetrations could be higher than the target penetration level because the 

penetration targets were designed for 2004–2006 and these data show 2006 only. 

Table 10. Wind and Solar Penetration by Subregion 

Penetrations for 

Selected 

Subregions 

High Solar High Mix High Wind TEPPC 

Energy 

(TWh) 

Penetration 

(%) 

Energy 

(TWh) 

Penetration 

(%) 

Energy 

(TWh) 

Penetration 

(%) 

Energy 

(TWh) 

Penetration 

(%) 

Wind 

CAISO 12.7 5 13.6 5 21.7 8 15.2 6 

CG 16.9 15 22.4 19 25.7 22 15.6 13 

NTTG 16.7 14 39.2 32 53.4 44 17.2 14 

WC 19.1 7 59.1 21 102.0 36 26.2 9 

U.S. Western 

Interconnection 65.5 8 134.3 17 202.8 26 74.2 9 

Solar 

CAISO 60.2 23 37.1 14 22.7 9 14.2 5 

CG 1.9 2 1.8 2 1.5 1 0.6 1 

NTTG 9.5 8 3.8 3 1.3 1 7.8 6 

WC 124.7 44 86.6 31 37.0 13 8.1 3 

U.S. Western 

Interconnection 196.3 25 129.4 17 62.5 8 30.7 4 

Total 

CAISO 72.9 28 50.7 19 44.4 17 29.4 11 

CG 18.9 16 24.2 21 27.2 23 16.2 14 

NTTG 26.3 22 43.0 35 54.7 45 25.0 21 

WC 143.8 51 145.7 51 139.0 49 34.3 12 

U.S. Western 

Interconnection 261.8 33 263.7 34 265.3 34 104.9 13 

Figure 19 shows the contour plots for the net load, excluding CSP (wind and PV output have 

been subtracted from the load) for each scenario. The TEPPC Scenario, with 13% wind/solar, 

showed high summer peaks in the afternoon and early evening in the U.S. portion of the 

Western Interconnection. The High Wind Scenario depressed much of this peak and also 

exacerbated the net load minimums during the night in the winter. Decreasing minimum 

generation levels of fossil-fueled plants through retrofits and design of new generation will be 

helpful for high wind penetrations. 
44 

Figure 19. Contour plots of monthly average net load for each hour of the day for each scenario 

(clockwise from upper left): High Solar, High Mix, High Wind, and TEPPC 

Note: CSP is not included in these plots, so net load here is load minus wind minus PV. 

The High Solar Scenario clearly showed the diurnal double peak caused by the depression of 

net load at midday when solar output is highest. Contour lines that are close together, such as 

those in the nonsummer months after the morning net load peak and again before the evening 

net load peak, indicated steep net load ramps. Increasing ramping capabilities or reducing start 

times of fossil-fueled generation might be helpful in this scenario. Decreasing minimum 

generation levels through retrofits and new design of future plants could help manage winter 

midday net load minimums. 

High Solar Scenario Net Load (MW)

46000.00

54000.00

54000.00

62000.00

62000.00

62000 0. 0

62000.00

70000.00

70000 0. 0

70000.00

70000.00

70000.00

70000.00

70000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00

86000.00

86000.00

86000.00

86000.00

94000.00

94000.00

102000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

110000

102000

94000

86000

78000

70000

62000

54000

46000

38000

30000

High Mix Scenario Net Load (MW)

46000.00

54000.00

54000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

70000.00

70000.00

70000.00

70000.00

70000.00

70000 0. 0

70000.00

70000.00

78000.00

78000.00

78000.00

78000.00

78000.00

86000.00

86000.00

94000.00

102000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

110000

102000

94000

86000

78000

70000

62000

54000

46000

38000

30000

TEPPC Scenario Net Load (MW)

62000.00

62000.00

70000.00

70000.00

70000.00

70000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00

86000.00

86000.00

86000.00

86000.00

86000.00

86000.00

94000.00

94000.00

94000.00

94000.00

94000.00

102000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

110000

102000

94000

86000

78000

70000

62000

54000

46000

38000

30000

High Wind Scenario Net Load (MW)

46000.00

46000.00

54000.00

54000 0. 0

54000.00

54000.00

54000.0

5

0

4000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

70000 0. 0

70000.00

70000.00

70000.00

70000.00

78000.00

78000.00

78000.00

78000.00

78000.00

86000.00

86000.00

94000.00

102000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

110000

102000

94000

86000

78000

70000

62000

54000

46000

38000

30000
45 

4.1 Geographic Diversity 

Utilities have concerns about whether fast-moving clouds over PV plants could result in high 

variability. Two characteristics of PV plants affect this variability: (1) the size of the plant and 

(2) the number of plants. Clouds over a small plant, such as a rooftop PV system, can cause high 

variability, but the impact of a small system’s variability on the bulk power system is minimal. 

There can be impacts on a distribution level, but WWSIS-2 focuses only on impacts at the 

transmission level. A large plant can have a higher impact on the bulk power system, but its 

larger area helps to smooth out the variability. With additional PV plants, the geographic 

diversity of the plants and the unlikelihood of cloud fronts obscuring all PV plants at the same 

time result in further smoothing of this variability. 

Figure 20 shows the smoothing of solar output with increasing aggregation of solar sites 

throughout Southern California. At the individual plant level, cloud events were seen with the 

fast ramps in the PV plant output. These events smoothed out as output from 6 and then 25 plants 

were aggregated. 

At the subregional level, individual cloud events could not be discerned. 

Figure 20. Normalized PV output for increasing aggregation 

of PV plants in Southern California for a partly cloudy day 

We used this same approach to compare rooftop PV, utility-scale PV, and wind in Figure 21. 

Total aggregate capacity was defined by increasing concentric circles that included larger areas 

and higher capacity. The analysis was performed on solar sites in Southern California. 

Variability was defined here as one standard deviation of the hourly change in output, or Δσ

(delta-sigma). The utility-scale PV and wind profiles started with relatively high variability when 

a few plants were examined. Wind variability dropped off rapidly as plants were aggregated, 

with the normalized variability leveling off at about 1%. Utility-scale PV variability leveled off 

at slightly less than 4%. Rooftop PV, already an aggregation of many small plants, and therefore 

starting with low variability, showed relatively little benefit as increasing amounts were 

aggregated throughout larger areas. The residual variability results mainly from the highly 

correlated change in position of the sun. 
46 

Wind shows the greatest benefit from geographic diversity. Rooftop PV has modest variability 

even at small aggregate levels. 

(a) 

 (b) (c) 

Figure 21. Normalized variability as a function of aggregate capacity 

for (a) rooftop PV, (b) utility-scale PV, and (c) wind 

Notes: Observe the difference in y-axis scales. ∆σ is one standard deviation 

of the hourly change in output. 

4.2 Monthly Penetration 

Figure 22 shows the monthly energy penetration for three of the scenarios. The High Solar 

Scenario is shown in Figure 22 (a), where the total penetration peaks in May. Solar dropped 

some in the summer months compared to May, with a slight drop in PV and a more pronounced 

drop in CSP. This appeared to be a characteristic of the 2006 solar data chosen for the study. In 

addition, higher temperatures lead to lower PV panel efficiency. Wind peaked in January, with 

the minimum appearing in the summer months. 

Figure 22 (b) shows the penetration for the High Mix Scenario, with peak energy in April and 

minimum energy in the summer months, when wind was lowest. The monthly energy penetration 

for the High Wind Scenario is shown in Figure 22 (c). The overall profile closely followed the 

wind component, with peak energy in the winter and minimum energy in the summer. 
47 

(a) 

 (b) (c)

Figure 22. Monthly penetration of wind, PV, and CSP 

for the (a) High Solar, (b) High Mix, and (c) High Wind Scenarios 

4.3 Diurnal Variability 

Figure 23 (a) shows the diurnal variability of the U.S. portion of the Western Interconnection 

load. On average, the morning load pickup was followed by a gradual rise to an evening peak, 

with a nighttime minimum. The hourly change in load, which experienced significant variability 

in the late afternoon, is also depicted. 

Figure 23 (b)–(d) shows the net load (including CSP) variability of the high-penetration 

scenarios. In the High Solar Scenario, the high solar output at midday led to a double peak in net 

load, once in the morning and again in the evening. The hourly net load delta shows that the 

variability increased considerably at sunrise and sunset. For the High Mix and High Wind 

Scenarios, as the solar penetration decreased and the wind increased, the double peak in net load 

disappeared, with the net load shape being similar to the load shape, depressed by about 10,000 

MW to 15,000 MW.
48 

Figure 23. (a) Diurnal variability of the load (green line) and the hourly change in load (white line—median; d

bar—standard deviation; whiskers—minimum and maximum); diurnal variability of net load and hourly cha

for the (b) High Solar, (c) High Mix, and (d) High Wind Scenarios 

Note: CSP dispatch is included in these plots, so net load is load minus wind minus solar.

(a) (b) 

(c) (d) 
49 

4.4 Hourly Variability 

The hourly variability was also examined. 

Figure 24 (a) shows the hourly wind change versus the hourly load change for the High Wind 

Scenario (25% wind). Figure 24 (b) shows the hourly PV change versus the hourly load change 

for the High Solar Scenario (15% PV; CSP is not shown) for 8,760 hours of 2006. For these 

hourly variability plots, it is important to note that the top right and bottom left quadrants show 

the hours when solar and wind moved in the same direction as the load, helping the power 

system meet load. 

Variability is not always undesirable. Wind and solar often help meet load. 

The top left quadrant shows solar and wind increasing as load decreases. Operators may be able 

to curtail solar and wind production to balance load. The bottom right quadrant depicts the 

difficult hours for an operator, when solar and wind decreased while load increased. The bottom 

right quadrant extrema are particularly challenging. Ensuring enough up-reserves during these 

hours is critical. 

The variability in solar resources needs to be interpreted a bit differently from the wind 

variability. The solar variability has two components: one that is caused by atmospheric 

conditions like clouds and one that is known because of the motion of the sun. Much of the solar 

variability results from the latter, which causes it to be much higher than wind variability. 

Figure 24 (a) shows that wind variability was generally uncorrelated with both load variability 

and season. In contrast, Figure 24 (b) shows that PV helped the load during a significant number 

of hours, during sunrise and morning load rise. In the winter, however, load was increasing while 

PV output was decreasing during many hours. This is because the evening load peak occurs 

during the sunset hours (partly because of lighting). Figure 24 (b) also shows a large number of 

nighttime hours where the PV change was zero. 
50 

(a) 

(b) 

Figure 24. (a) Hourly wind production change versus hourly load change 

for the High Wind Scenario and (b) hourly PV production change 

versus hourly load change for the High Solar Scenario 

Note: The colors depict different seasons. 

Figure 25 (a)–(c) shows the hourly load change versus the hourly variability of the wind and PV 

output for the three high-penetration scenarios for 8,760 hours of 2006. They are plotted on the 

same y-axis scale, showing that the wind and PV variability in the High Solar Scenario was 

much greater than that of the High Mix Scenario and far greater than that of the High Wind 

Scenario. This disparity is entirely the result of the perfectly known solar variability from the 

motion of the sun, as the next section shows. 
51 

(a) 

 (b) (c) 

Figure 25. Hourly load change versus hourly wind and PV production change 

 for the (a) High Solar, (b) High Mix, and (c) High Wind Scenarios for 2006 

Note: The colors depict different seasons. 

The maximum hourly wind and PV change in the High Solar Scenario was +22,000 MW/h and –

17,000 MW/h, which was significantly higher than the +8,000 MW/h and –7,900 MW/h of the 

High Wind Scenario. This illustrates the significant variability that the system must 

accommodate for high solar penetrations. Section 6 will show how CTs start and ramp more 

frequently and how coal ramps more frequently to manage this variability. 

Table 11 gives statistics for the 1-hour changes in net load for selected subregions. Subregions in 

the north, such as CG and the NTTG, had relatively less solar capacity and showed relatively 

modest changes in the standard deviation of 1-hour changes in net load and the minimum and 

maximum changes for both the High Solar and High Wind Scenarios. Subregions in the south, 

such as CAISO and WC, have much higher solar capacities. WC has a particularly high solar 

penetration level. As a result the 1-hour changes in net load were significantly affected in the 

High Solar Scenario. 
52 

Table 11. Statistics for 1-Hour Changes in Net Load for the High Solar Scenario 

for the U.S. Portion of the Western Interconnection Footprint and Selected Subregions 

 CG NTTG WC CAISO Footprint 

Sigma (MW) 

Load Alone 672 593 1,492 1,527 3,929

Reference Scenario 720 649 1,513 1,514 3,983

High Solar Scenario 735 777 3,505 1,985 6,279

High Mix Scenario 744 699 2,412 1,767 4,954

High Wind Scenario 747 728 1,773 1,547 4,189

Maximum Negative Delta 

(MW) 

Load Alone –2,545 –1,842 –4,613 –5,182 –11,534

Reference Scenario –3,422 –2,418 –4,802 –5,135 –12,827

High Solar Scenario –3,437 –2,580 –13,197 –6,663 –22,257

High Mix Scenario –3,590 –2,615 –8,695 –5,183 –15,661

High Wind Scenario –3,544 –2,834 –6,395 –5,107 –13,478

Maximum Positive Delta 

(MW) 

Load Alone 2,971 2,096 3,774 4,721 10,451

Reference Scenario 3,180 2,112 4,327 5,553 11,194

High Solar Scenario 3,175 3,145 14,037 9,874 26,671

High Mix Scenario 3,256 2,491 10,220 8,736 20,391

High Wind Scenario 3,254 2,838 6,923 7,163 14,166

No. of Drops Less than 3 * Load Sigma 

 

Load Alone 1 3 2 13 0

Reference Scenario 14 24 6 9 1

High Solar Scenario 14 104 519 86 222

High Mix Scenario 20 38 249 17 31

High Wind Scenario 22 71 45 9 6

No. of Drops Greater than 3 * Load Sigma 

 

Load Alone 74 14 0 3 0

Reference Scenario 103 24 0 22 0

High Solar Scenario 110 127 707 197 371

High Mix Scenario 117 63 400 131 217

High Wind Scenario 120 69 109 75 39

4.4.1 Hourly Weather Variability of Solar 

Solar variability consists of two components: diurnal variability (the perfectly known variability 

from sun movement) and weather variability (the variability from cloud movement and other 

atmospheric conditions). Because system operators can plan for the diurnal variability, we 

removed this component to assess the magnitude of the weather variability. 
53 

Figure 26 shows the weather variability component of solar variability for the High Solar 

Scenario. This figure can be directly compared to Figure 24 (b) to show that solar variability is 

dominated by diurnal variability. The weather variability component is similar to the wind 

variability in the High Wind Scenario as shown in Figure 24 (a). 

Figure 26. Weather variability component of PV variability for the High Solar Scenario 

The total wind and PV variability, excluding the diurnal solar variability, is depicted in 

Figure 27. This can be compared directly to Figure 25 (a), which shows more than double 

the variability. 

Figure 27. Total wind and PV variability 

excluding the diurnal solar variability for the High Solar Scenario 
54 

4.5 Subhourly Variability 

At the 5-minute level, correlations between changes in load and changes in wind and PV output 

were similar to those at the hourly timescale, as shown in Figure 28. 

Again, significantly more 5-minute variability occurred in the High Solar Scenario. 

(a) 

(b) 

Figure 28. Five-minute changes in load versus 5-minute changes 

in wind and PV output for the (a) High Solar and (b) High Wind Scenarios 

Table 12 gives statistics for the 5-minute changes in net load for selected subregions. As with the 

hourly changes in Table 11, the subregions with lower solar capacities, such as CG and NTTG, 

showed relatively modest impacts in the subhourly time frame across scenarios. Subregions with 

higher solar penetrations, such as CAISO and WC, have much greater impacts, especially in the 

High Solar Scenario. 
55 

Table 12. Statistics for 5-Minute Changes in Net Load for the High Solar Scenario 

for the U.S. Portion of the Western Interconnection Footprint and Selected Subregions 

 CG NTTG WC CAISO Footprint

Sigma (MW) 

Load Alone 65 59 153 145 358

Reference Scenario 74 68 158 146 366

High Solar Scenario 76 80 312 183 548

High Mix Scenario 77 77 231 166 447

High Wind Scenario 78 82 187 150 390

Maximum Negative Delta 

(MW) 

Load Alone –712 –885 –1,084 –916 –1,541

Reference Scenario –741 –872 –1,128 –911 –1,551

High Solar Scenario –739 –882 –1,771 –961 –2,966

High Mix Scenario –730 –861 –1,164 –913 –2,015

High Wind Scenario –739 –858 –1,289 –901 –1,879

Maximum Positive Delta 

(MW) 

Load Alone 538 722 1,401 957 1,991

Reference Scenario 577 722 1,396 941 2,001

High Solar Scenario 576 726 2,090 1,290 3,611

High Mix Scenario 575 750 1,358 1,113 2,355

High Wind Scenario 633 760 1,388 902 2,020

No. Drops < 3 * Load Sigma 

Load Alone 103 146 471 177 97

Reference Scenario 541 655 500 190 137

High Solar Scenario 590 1,238 5,081 575 1,596

High Mix Scenario 669 1,296 2,040 327 431

High Wind Scenario 736 1,859 875 204 219

No. Rises > 3* Load Sigma 

Load Alone 1,011 394 260 244 174

Reference Scenario 1,402 768 293 450 209

High Solar Scenario 1,499 2,066 7,296 2,015 3,973

High Mix Scenario 1,602 1,463 4,129 1,586 2,340

High Wind Scenario 1,632 1,816 1,352 915 791

The tail events of the distribution are shown more clearly in Figure 29. The High Wind Scenario 

had little impact on the maximum and minimum 5-minute changes in net load but did increase 

the number of extreme events from load alone. The High Solar Scenario further increased the 

number of extreme events and caused them to be more extreme. 
56 

(a) 

(b) 

Figure 29. (a) Histogram of 5-minute changes in net load for the (grey) load-only, (green) 

High Wind, and (orange) High Solar Scenarios and (b) an expanded view of the distribution tails 
57 

4.5.1 Subhourly Weather Component of Solar Variability 

We removed the diurnal component of the PV variability as we did for the hourly variability and 

compared the result to Figure 28 (a). Again, the weather component of subhourly solar 

variability is much smaller than the diurnal component of solar variability. 

Figure 30. Five-minute total wind and PV variability 

excluding diurnal solar variability for the High Solar Scenario 

An interesting effect of removing the diurnal solar variability is that events are more prominent. 

For instance, the cluster of events in the fall from 2,000 to 2,500 MW/5-minute stands out as 

unique. These points are caused by a single event where there is cloud cover until mid-morning 

with a very fast up-ramp occurring at around 9 a.m. This ramp goes from near 0 output to near 

clear sky value in 45 minutes. 

Figure 31 shows the distribution of the 5-minute changes shown in Figure 29 (a) with the diurnal 

component of solar variability removed. Comparing Figure 31 to Figure 29 shows a substantial 

reduction in the 5-minute changes associated with the weather portion of the PV resources seen 

in the scenarios. 
58 

Figure 31. Distribution of 5-minute changes 

in load and net load for High Wind and High Solar Scenarios 

System operators can hold additional reserves to handle the increased variability from wind and 

PV. Wind flexibility reserve requirements are typically based on analysis of wind variability 

(EnerNex 2011; Milligan et al. 2011). Applying the same analytical techniques to solar would 

result in holding too much reserves because solar variability is dominated by the known diurnal 

variability. Therefore, we calculate flexibility reserve requirements based on the weather 

component of solar variability (Ibanez et al. 2013) as discussed in Section 5. 

4.6 Weekly Time Series 

Wind and solar tend to have a beneficial impact on the system in the summer months, when solar 

contributes to high summer peaks and winds tend to be moderate and complement the solar 

production. This can be seen in Figure 32, which shows the load, wind, PV, CSP, and resulting 

net load during a week in July. Additionally, because of the large number of generators online in 

the summer, significant resources are available to accommodate the variability and uncertainty of 

wind and solar. 
59 

(a) 

(b) 

Figure 32. Profiles for the (a) High Solar and (b) High Wind Scenario for a week in July 

In the spring, however, wind and solar tend to have a significant impact on the rest of the system. 

Both wind and solar output is high and loads are low in the spring in the Western Interconnection. 

Figure 33 shows the most challenging week of the year of data studied. Here most challenging is 

defined by the minimum net load condition (curtailment is not taken into account, so this is defined 

as load minus available wind and solar) that occurs on March 29. Note that the high solar 

production midday exacerbates the diurnal net load cycle, whereas wind production ramps more 

slowly over the course of several days. Because loads are low, fewer generators are online to help 

manage this variability, making this a challenging period for operators. 
60 

(a) 

(b) 

Figure 33. Profiles for the (a) High Solar and (b) High Wind Scenario for a week in March 

4.7 Extreme Events 

Most of the time, the power system can accommodate the variability and uncertainty of wind and 

solar, but extreme events can seriously challenge the system. Utility operators need to understand 

the nature of these extremes so they can plan accordingly. Extreme events can include the 

steepest ramps, the minimum net load, or the biggest forecast errors. 

Figure 34 shows the hour with the highest net load up-ramp in the High Solar Scenario. A 

combination of the evening load ramp with sunset decreasing the solar output results in an upramp of 26,878 MW on February 2 at 16:00. System operators will need to have sufficient upramping capability to meet this up-ramp. Interestingly, the CSP thermal storage is not dispatched 
61 

by the production cost model to help meet that large up-ramp. Instead it is held until the next 

morning load rise when prices are higher. There was adequate up-ramp capability available 

during the net load up-ramp to keep prices reasonable so that the CSP storage was saved for the 

next morning, when prices were higher. 

Figure 34. Highest hourly positive ramp (up-ramp) of 26,878 MW in the High Solar Scenario occurs 

on February 2 at 16:00 

Figure 35 shows the largest down-ramp in the High Solar Scenario. Here sunrise causes a steep 

PV and CSP up-ramp, so that even though the load is increasing, the net load decreases sharply. 

If this ramp represents a potential problem to the system, a possible approach to mitigating 

sunrise ramps is to use solar as a resource for down reserves. This would be done by curtailing 

solar during the steep sunrise ramp. 

Figure 35. Highest hourly negative ramp (down-ramp) of 20,934 MW 

in the High Solar Scenario occurs on April 23 at 7:00 
62 

Figure 36 shows that the highest up-ramp occurs during the same hour for both the High Wind 

and High Solar Scenarios. Because the High Wind Scenario has less solar capacity, the up-ramp 

in this case is reduced to 13,533 MW. 

Figure 36. The highest hourly up-ramp of 13,533 MW 

in the High Wind Scenario occurred on February 2 at 16:00 

Figure 37 shows the biggest down-ramp in the High Wind Scenario. This occurred at night when 

the load was dropping off and wind output was increasing, resulting in a 1-hour 12,323-MW 

down-ramp. Interestingly, CSP storage was dispatched during this time, indicating that the ramp 

was manageable and did not lead to excessively low prices or curtailment. 

Figure 37. The largest hourly down-ramp of 12,323 MW 

in the High Wind Scenario occurred on August 31 at 20:00 
63 

Minimum net loads are of concern, especially in this study, where we consider the impact of 

running fossil-fueled generators down to their minimums. System operators are currently 

accustomed to low net loads at night, especially if they have wind on their system. High solar 

penetrations change that. Figure 38 shows that the minimum net load condition in the High Solar 

Scenario occurs on March 29 at noon, caused primarily by high PV output and low springtime 

loads. CSP output is backed down and saved for the evening peak but some CSP output plus 

some wind output contributes to this condition. 

Figure 38. Minimum net load is 17,944 MW at noon on March 29 in the High Solar Scenario 

Figure 39 shows that in the High Wind Scenario, the minimum net load condition occurs in the 

middle of the night, when loads are low and winter winds are high. 

Figure 39. Minimum net load is 22,547 MW at 2:00 on February 24 in the High Wind Scenario 
64 

4.8 Day-Ahead Forecast Error Analysis 

The accuracy of the DA forecasts for wind and solar strongly influence system operation, and 

especially system economics. Studies show that using DA forecasts in operations saves 

significant money in fuel costs (GE Energy 2010; GE Energy 2008; GE Energy 2007) by 

allowing conventional units to be decommitted if sufficient wind and solar production is forecast. 

This section addresses the DA forecast error statistics for this study. 

The forecast error was calculated by subtracting the actual value realized from the DA forecasted 

value, so that positive forecast errors are over-forecasts and negative errors are under-forecasts. 

Figure 40 shows the forecast error distribution for the DA wind forecast. Note that with higher 

wind penetration, the distribution of errors spread out with a higher standard deviation. 

Figure 40. DA wind forecast error distribution for the U.S. Western Interconnection 

Figure 41 shows the distribution of DA forecast error for PV. Nighttime hours were eliminated 

from this histogram calculation, so this represents about half as many hours as the wind 

distribution. This distribution is much tighter in general, with smaller tails showing that the solar 

forecasts seemed to be more accurate and had fewer extreme points. Note the difference in both 

x and y scales between the wind and PV error distribution plots. 
65 

Figure 41. DA PV forecast error distribution for the U.S. Western Interconnection 

Figure 42 shows the resulting distribution when the wind and PV data were combined and the 

total wind and PV forecast error was analyzed. The shape more closely resembles the wind 

distributions seen in Figure 40, but with even larger shoulders and tails. 

Figure 42. DA wind and PV forecast error distribution for the U.S. Western Interconnection 

Figure 43 shows scatter plots of the total wind and PV forecast error plotted against net load for 

the entire U.S. Western Interconnection for the (a) High Solar and (b) High Wind Scenarios. In 

the High Solar Scenario, the highest forecast errors tended to occur with net load above the 

minimum of its range. This corresponded mainly to late morning to early afternoon hours, with 

which some of the highest uncertainty is associated. The most extreme errors seemed to occur in 

the winter and spring months. 
66 

(a) 

(b) 

Figure 43. Total forecast error versus net load for the (a) High Solar and (b) High Wind Scenarios 

Note: WI, Western Interconnection 

The High Wind Scenario shows that the largest under-forecasts (negative values in Figure 43) 

seemed to occur at the low net load levels that would be associated with nighttime hours. Overforecasts occurred most at mid-range net load levels, with the most extreme showing up in the 

late evening. 

High Solar Scenario – Total Forecast Error Versus Net Load

High Wind Scenario – Total Forecast Error Versus Net Load
67 

Table 13 gives the DA forecast error statistics for the High Solar Scenario. We measured the 

overall error characteristic using two values: the mean absolute error (MAE) and the root-meansquare error (RMSE). The table shows how these values changed for the various regions in the 

study as well as for the entire U.S. Western Interconnection. 

Table 13. DA Forecast Error Statistics for the High Solar Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CAISO 4,406 451 10 649 15 –2,958 –67 2,384 54 

CG 6,581 631 10 946 14 –5,101 –78 4,973 76 

NTTG 5,787 495 9 711 12 –3,052 –53 3,807 66 

WC 6,584 403 6 562 9 –2,726 –41 2,848 43 

U.S. Western 

Interconnection 23,357 1,387 6 1,849 8 –8,458 –36 8,169 35 

Solar (PV) 

CAISO 17,134 713 4 1,055 6 –5,310 –31 6,589 38 

CG 1,211 105 9 156 13 –667 –55 619 51 

NTTG 5,270 285 5 440 8 –2,103 –40 2,070 39 

WC 36,526 1,248 3 1,771 5 –6,018 –16 9,189 25 

U.S. Western 

Interconnection 60,141 1,882 3 2,628 4 –9,867 –16 16,974 28 

Total 

CAISO 21,539 692 3 989 5 –5,243 –35 7,592 24 

CG 7,792 639 8 947 12 –5,101 –64 4,973 65 

NTTG 11,057 560 5 771 7 –3,169 –34 3,807 29 

WC 43,110 936 2 1,435 3 –6,168 –22 9,576 14 

U.S. Western 

Interconnection 83,498 2,004 2 2,676 3 –10,806 –21 17,714 13 

Note that the error statistics (% MAE and % RMSE) were much higher for wind than for PV, but 

that the total errors tended to be closer for solar. The error statistics show that as the size of the 

regions increased, the error measures tended to decrease. This is because of the effects of 

aggregation of the error across large geographic areas. Table 14 shows these statistics for the 

High Wind Scenario. 
68 

Table 14. DA Forecast Error Statistics for the High Wind Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CAISO 7,970 693 9 977 12 –4,832 –61 3,756 47 

CG 9,101 822 9 1,188 13 –6,587 –72 5,895 65 

NTTG 16,854 1,179 7 1,582 9 –6,501 –39 7,584 45 

WC 32,224 1,838 6 2,438 8 –9,558 –30 10,078 31 

U.S. Western 

Interconnection 66,150 3,183 5 4,115 6 –16,574 –25 17,381 26 

Solar (PV) 

CAISO 9,772 376 4 543 6 –2,776 –28 2,885 30 

CG 969 82 8 121 12 –507 –52 491 51 

NTTG 2,312 44 2 67 3 –377 –16 305 13 

WC 14,979 369 2 520 3 –1,992 –13 2,356 16 

U.S. Western 

Interconnection 28,032 660 2 909 3 –4,550 –16 5,340 19 

Total 

CAISO 17,743 766 4 1,042 6 –4,879 –28 4,963 28 

CG 10,070 825 8 1,188 12 –6,587 –59 5,895 65 

NTTG 19,167 1,181 6 1,583 8 –6,501 –40 7,584 34 

WC 47,203 1,875 4 2,477 5 –9,545 –21 10,078 20 

U.S. Western 

Interconnection 94,183 3,229 3 4,161 4 –16,574 –18 17,381 18 

Another view of the forecast errors shows when the errors are occurring. By averaging the total 

forecast error at each hour during each month and producing a contour plot of those data, we can 

see, on average, when the most severe errors occurred. Figure 44 shows this plot for the High 

Solar Scenario. The largest under-forecasts (blue colors) were most frequent on late summer 

afternoons and mid-evening in fall and early winter. The most severe over-forecasts (red color) 

occurred on winter afternoons. Generally there was a bias toward over-forecasting. 

The High Wind Scenario presented a slightly different picture, as shown in Figure 45. The 

largest under-forecasts (blue color) occurred on late summer afternoons and evenings, with 

another peak in May around noon. The largest over-forecasts (red color) occurred at night in 

summer, with some occurring on the early spring mornings. 
69 

Figure 44. Contour plot of DA VG forecast errors for the High Solar Scenario 

Figure 45. Contour plot of DA VG forecast errors for the High Wind Scenario 

Average Forecast Error for High Solar Scenario

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4.9 Four-Hour-Ahead Forecasts 

Four-hour-ahead forecasts were used for a second unit commitment run performed with a 4-hour 

horizon. This run was able to commit units that could be started up within 4 hours, such as gas 

combined-cycle units. The 4HA forecasts that were developed for this purpose are described in 

Section 2.4.3. 

As expected, the accuracy of the 4HA forecasts is considerably better than that of the DA 

forecasts. Comparing Figure 46 with Figure 40 shows a much narrower distribution of the wind 

forecast errors. Note the different scales on the two distributions. The standard deviation for the 

DA error for the High Wind Scenario is 3,972 MW; the standard deviation for the 4HA forecast 

error is 2,100 MW. 

Figure 46. Four-hour-ahead wind forecast error distribution for the U.S. Western Interconnection 

Figure 47 shows the distribution of forecast errors for the 4HA PV forecasts. Figure 47 can be 

compared to Figure 41, the equivalent plot for the DA forecasts. The standard deviation for the 

High Solar case is 1,038 MW; the same quantity for the DA forecast is 1,820 MW. 
71 

Figure 47. Four-hour-ahead PV forecast error distribution 

Figure 48 shows the distribution for the 4HA wind plus PV forecast errors. Comparing this to 

Figure 42 again shows a large decrease in variability as measured by the width of the error 

distribution. The standard deviation of the error for the High Wind Scenario was decreased from 

3,937 MW for the DA forecast to 2,115 MW for the 4HA forecast. This illuminates the 

importance of the 4HA unit commitment. Recommitting plants to take advantage of significantly 

improved 4HA forecasts helps accommodate the uncertainty of wind and solar. 

Figure 48. Four-hour-ahead wind plus PV forecast error distribution 

Figure 49 shows a scatter plot of the 4HA total forecast errors compared to the system net load 

for the High Solar Scenario. Although the overall spread of the points was much narrower than 

the DA forecast error, several more extreme points were seen. These points are associated with 1 

day in the data series. 
72 

Figure 49. Four-hour-ahead wind plus PV forecast error scatter for the High Solar Scenario 

The source of these extreme points is shown in Figure 50, which plots the two forecasts and the 

actual solar data for 1 day in October from the data set. The nearly 20,000-MW events appear to 

be legitimate misforecasts in hour 4 of the 4HA forecast that did not appear in the DA forecast. 

The 10,000 MW missed at hour 2 resulted from an unforecasted event that occurred in both 

forecasts. These misforecasts are caused by the methodology that we used to synthesize the 4HA 

forecasts (see Section 2). We used 2-hour persistence to synthesize the 4HA forecast but because 

there is no persistence to use in the first 2 hours as the sun rises, we had to use the DA forecast as 

a proxy. The very low actual output in solar hour 2, then, is picked up by the 2-hour persistence 

method and results in a significant misforecast. Commercial 4HA forecasts would not use such 

simplified methodologies and should result in more accurate forecasts. 

Figure 50. Detail of extreme 4HA solar forecast error caused 

by methodology used to generate 4HA forecasts 

Figure 51 shows the scatter of 4HA total forecast errors for the High Wind Scenario. There is a 

range of roughly –8,000 MW to 8,000 MW in total forecast error compared to the ±17,000-MW 

range for the DA forecast shown in Figure 43 (b). 

High Solar Scenario – Total Forecast Error Versus Net Load
73 

Figure 51. Four-hour-ahead forecast error scatter for the High Wind Scenario 

Table 15 and Table 16 show the detailed statistics for the 4HA forecast errors for the High Solar 

and High Wind Scenarios. Comparing these tables to Table 13 and Table 14 further shows the 

reduction in error with the 4HA forecast. 

Table 15. Four-Hour-Ahead Forecast Error Statistics for the High Solar Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 4,406 299 7 438 10 –2,306 –52 2,200 50 

CG 6,581 312 5 454 7 –3,352 –51 2,485 38 

NTTG 5,787 248 4 353 6 –2,102 –36 2,034 35 

WC 6,584 269 4 356 5 –1,772 –27 1,688 26 

U.S. Western 

Interconnection 23,357 785 3 1,038 4 –5,153 –22 4,280 18 

Solar (PV) 

CA 17,134 418 2 597 3 –3,788 –22 2,658 16 

CG 1,211 46 4 69 6 –339 –28 368 30 

NTTG 5,270 157 3 249 5 –1,906 –36 1,573 30 

WC 36,526 667 2 966 3 –14,380 -39 7,711 21 

U.S. Western 

Interconnection 60,141 991 2 1,423 2 –19,982 –33 10,323 17 

Total 

CA 21,539 424 2 596 3 –3,728 –17 3,247 15 

CG 7,792 316 4 456 6 –3,352 –43 2,485 32 

NTTG 11,057 282 3 392 4 –2,102 –19 2,040 18 

WC 43,110 516 1 776 2 –13,643 –32 7,631 18 

U.S. Western 

Interconnection 83,498 1,020 1 1,382 2 –19,156 –23 10,544 13 

High Wind Scenario – Total Forecast Error Versus Net Load
74 

Table 16. Four-Hour-Ahead Forecast Error Statistics for the High Wind Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 7,970 441 6 619 8 –2,956 –37 3,052 38 

CG 9,101 377 4 532 6 –3,464 –38 2,608 29 

NTTG 16,854 550 3 725 4 –3,273 –19 3,878 23 

WC 32,224 988 3 1,275 4 –5,691 –18 5,412 17 

U.S. Western 

Interconnection 66,150 1,637 2 2,100 3 –7,864 –12 7,685 12 

Solar (PV) 

CA 9,772 217 2 307 3 –1,596 –16 1,388 14 

CG 969 37 4 55 6 –261 –27 300 31 

NTTG 2,312 26 1 40 2 –232 –10 193 8 

WC 14,979 201 1 289 2 –3,132 –21 1,880 13 

U.S. Western 

Interconnection 28,032 352 1 496 2 –4,179 –15 2,617 9 

Total 

CA 17,743 477 6 651 4 –2,853 –16 3,089 17 

CG 10,070 378 4 533 5 –3,464 –34 2,608 26 

NTTG 19,167 550 3 725 4 –3,273 –17 3,851 20 

WC 47,203 990 3 1,281 3 –5,691 –12 5,412 11 

U.S. Western 

Interconnection 94,183 1,650 2 2,115 2 –8,021 –9 8,499 9 
75 

5 Reserves 

5.1 Operating Reserves 

Operations of power systems occur at a range of timescales that can be summarized, from longer to 

shorter, as unit commitment, DA scheduling, load-following, and regulation (Ela et al. 2011). Unit 

commitment and scheduling are performed throughout days to economically commit the units in 

the system to meet forecasted load and other system requirements. During shorter periods of time 

(minutes to hours), the system redispatches its units to counteract deviations from the schedule 

through load-following. Similarly, traditional units are redispatched to perform regulation, which 

is the fast response of generators to changes that range from seconds to minutes. 

The operator requires operating reserves so the system can positively respond to forecast errors 

and events that cannot be accounted for in the scheduling process. In the United States, the most 

common are regulation reserves, although load-following, or flexible, reserves are becoming 

more popular. Both are designed to account for the system’s variability (expected changes in the 

system) and uncertainty (unexpected changes). Both load and conventional generators affect the 

variability and uncertainty through forecast errors and unexpected outages, respectively. 

Wind and solar are variable and uncertain in nature because the unit output depends on everchanging wind speeds and solar irradiance that cannot be perfectly predicted ahead of time. 

Consequently, high penetrations of these resources lead to an increase in reserves necessary in 

the system. These requirements are especially critical in long-term integration studies, such as 

the Eastern Wind Integration and Transmission Study (EnerNex 2011) or Phases 1 and 2 of the 

WWSIS, which simulate higher renewable energy penetrations than are seen in today’s systems. 

When treating reserves in this study, we determined the reserve requirements necessary to cover 

a significant level of uncertainty in wind and solar variability (Ibanez et al. 2013). The next part 

of our approach was adding these reserves appropriately to the requirements specified by 

TEPPC. The rest of this section follows this philosophy. Because the CSP plants have 6 hours of 

thermal storage that makes those plants dispatchable, albeit with constraints, we only increase 

reserves for the uncertainty and variability in the wind and PV output. 

5.2 Wind Reserve Methodology 

Previous work (Ela et al. 2011; EnerNex 2011) quantified the uncertainty of wind power. 

Because short-term variations in wind power output are small, persistence forecasts are a good 

predictor with which to calculate uncertainty. For instance, for an economic dispatch model run 

in 5-minute intervals (and assuming that 5 additional minutes are required to perform calculation 

and dispatch communication), 10-minute persistence forecasts would be used to estimate the 

uncertainty that the power system must be handle between dispatch points. The forecast errors 

can be calculated by comparing the forecasted and the actual power output. 

Figure 52 shows that wind forecast errors are highest at moderate total wind production levels. 

Changes are expected to go up or down, and the wind turbine power curve is expected to be 

steepest at moderate wind speeds. In this study we only consider reserves caused by down-ramps 

from wind/solar because we assumed that excessive energy from wind and solar could be 

curtailed if the system was unable to accommodate it. 
76 

Confidence intervals (represented as red and blue lines) were used to determine up- and downreserve requirements so that a certain percentage of occurrences were covered by the reserves. In 

Figure 52, the range of power (horizontal axis) was divided in 10 groups with the same number 

of points. For each group, the average power was calculated as well as the confidence intervals 

that covered 95% of forecast errors. The confidence intervals bands were then interpolated from 

the group averages. Beyond the first and last group mean point, the requirements were kept 

constant as a simplified conservative approach. A higher number of groups or a more 

sophisticated method (e.g., a moving window across the cloud of points) would yield a better fit 

around the lower and upper power regions. 

Figure 52. Wind 10-minute forecast errors versus power output, 

with 95% confidence interval bands for the High Solar Scenario 

The result was a dynamic determination of reserve requirements, as represented in Figure 53. 

The flexibility reserve requirements were based on 1-hour persistence forecasts. The confidence 

intervals covered 70% of events, which approximate levels of coverage used in past integration 

studies (EnerNex 2011).3

 Any remaining forecast error would be covered by online units not 

providing reserves or by offline quick-start units. 

 

3

 The Eastern Wind Integration and Transmission Study (EnerNex 2011) assumed that spinning reserves covered 1 

standard deviation of the 1-hour ramps. That coverage approximates to 70% under a normal distribution. 
77 

Figure 53. Wind power production and dynamic regulation and flexibility reserve requirement 

for the Western Interconnection in the High Wind Scenario 

Note: Observe that the y-axis scales differ. 

5.3 Solar Reserve Methodology 

The proposed solar reserve methodology builds on the wind methods previously presented. Some 

adjustments were necessary to take into account solar daily patterns, but the process followed 

three distinct steps: (1) defining forecast error; (2) using explanatory variables to group similar 

patterns; and (3) applying the reserve requirements based on the explanatory variables. 

As a reminder, the wind forecast errors were calculated based on persistence forecasts. Power 

output was used as an explanatory variable to find reserve requirements (Figure 52) and to create 

the dynamic reserve requirements (Figure 53). The following sections develop similar concepts 

for PV power. 

5.3.1 Solar Forecast Errors 

Solar-based generation presents clear patterns because of its dependency on the sun’s path across 

the sky. These patterns are best captured with clear-sky simulations, which calculate the power 

output in the absence of clouds. The left panel in Figure 54 represents the actual and clear-sky 

power outputs in the Western Interconnection during three summer days in the High Solar 

Scenario. The right panel represents the 10-minute ramps in the same timescale. 
78 

Figure 54. Power and clear-sky power output and ramps 

If the same power-persistence forecast used for wind had been applied in this case, the largest 

demand for reserves would have occurred consistently around sunrise and sunset. However, 

reserves are not needed to cover known changes in power output because this is considered in the 

unit commitment. Figure 56 shows, however, that the power deltas could be decomposed into the 

contribution from the clear-sky power and a smaller, high-frequency variation because of 

weather. In other words, if the clear-sky trends (which are known) were removed from the power 

deltas, the reserve requirements would be smaller. 

The first step in the creation of the short-term solar forecast was the definition of the solar power 

index (SPI), which represents the ratio between actual power, P, and clear-sky power, PCS. 

The forecast was based on the persistence of SPI. To obtain the forecast, we added the clear-sky 

ramp scaled by the SPI to the current power output. The forecast error could then be calculated as: 

Error(t) = P(t+1) – {P(t) + SPI(t) × [PCS(t+1) – PCS(t)]} 

Figure 55. Graphical representation of short-term forecast for solar 
79 

Figure 56 shows the effect of using the clear-sky information to correct the short-term forecasts. 

The SPI persistence forecast has a much narrower distribution than the simpler powerpersistence forecast and the standard deviation for both distributions are 274 and 1,273 MW, 

respectively. A more accurate short-term forecast ultimately leads to a significant reduction in 

reserve requirements. Clear-sky ramps will be met through the commitment of units with the 

remaining variability met through increased reserves, similar to the way that dispatch meets the 

forecasted load ramps and regulation is used only to meet load variations outside the forecasted 

ramps from one interval to the next. 

Figure 56. Ten-minute forecast error distribution with and without the clear-sky correction 

5.3.2 Explanatory Variables 

The challenge of determining suitable explanatory variables was finding the balance between the 

overall minimization of reserve requirements and simplicity. We found that the following two 

variables were especially effective at fulfilling this goal: 

 SPI, as defined in the previous subsection, which effectively separated “cloudy” and 

“sunny” days 

 Clear-sky ramps, which separated the different times of the day (positive in the morning, 

close to 0 at midday, and negative toward sunset). 

For WWSIS-2, we calculated the reserves based on 5-minute time series. We first created 10 

divisions; subsequently, we formed 100 groups by combining both variables. For each group, we 

calculated reserves by taking the appropriate percentiles (e.g., 2.5% and 97.5% to create 95% 

confidence intervals). To prevent outliers from dominating the reserves, we did not calculate 

reserves for a group if it presented less than 20 members. In that case, we used the reserves for 

the closest group instead. 
80 

We applied this method to all scenarios in WWSIS-2. We calculated regulation reserves using 

10-minute time steps and 95% confidence intervals for the entire footprint. The results are 

represented in Figure 57, which suggests that reserve requirements depend on the combination of 

both explanatory variables, SPI and clear-sky ramps. The highest down-reserve requirements 

were usually located on the top right corner, which corresponded to sunrises where SPI was close 

to 1. On such occasions, the calculation of SPI was highly unstable given that the denominator 

(clear-sky power, PCS) was very small. At times, the forecast called for a “sunny” sunrise, and 

the clear-sky correction was heavily weighted in the error calculation. The inability to produce a 

good forecast for these particular instances created the high reserve requirements. Figure 58 also 

shows that reserves were higher in the middle of days (clear-sky ramps close to 0) that were 

partly cloudy (SPI around 50%). For particularly sunny days (SPI close to 1), requirements were 

much smaller. Flexibility reserves were calculated for the different subregions using hourly time 

steps and 70% confidence intervals. 

Figure 57. Regulation reserve requirements by scenario 

for the entire Western Interconnection 

5.3.3 Application of Reserve Requirements 

After the reserve requirements were determined, they could be applied to the time series by 

finding the combination of the explanatory variables that best fit each point in time. For example, 

Figure 58 represents the resulting requirements for regulation and flexibility reserves in the 

Western Interconnection for selected days in July. 
81 

Figure 58. Dynamic regulation and flexibility reserve requirements 

for the Western Interconnection for the High Solar Scenario 

5.3.4 Total Reserve Requirements 

The reserve methodologies for wind/solar were used, along with the base requirements for 

contingency and regulation reserves (as determined by WECC’s TEPPC). Three types of spinning 

reserves were considered in this study and the total requirements were calculated as follows: 

 Contingency reserves: 3% of the load with no consideration of wind/solar. The 

requirement was held for each zone independently. 

 Regulation reserves: Geometric sum of base requirement (1% of load) and contribution of 

wind and PV (which cover 95% of 10-minute forecast errors). The requirement was held 

for the entire interconnection. 

 Flexibility reserves: Geometric sum of wind and PV forecast errors (covering 70% of 1-

hour errors). No load forecast was available, so load did not contribute to this 

requirement. The requirement was held for each zone independently. 

The different requirements were added geometrically given that for short time steps, the forecast 

errors were considered to be uncorrelated. Table 17 summarizes the requirements for spinning 

reserve. Nonspinning reserves were not modeled in the study. 

Table 17. Requirements for Spinning Reserves 

Reserve Calculation Applied to 

Contingency 3% ݈݋ܽ݀ Zones 

Regulation ඥሺ1%	݈݋ܽ݀ሻଶ ൅ ሺܹ݅݊݀ ݐݍݎሻଶ ൅ ሺܸܲ ݐݍݎሻଶ Interconnection 

Flexibility ඥሺܹ݅݊݀ ݐݍݎሻଶ ൅ ሺܸܲ ݐݍݎሻଶ Zones 

Table 18 presents the total amounts of reserves required for each scenario. Contingency reserves 

remain unchanged. Total regulation reserves increase slightly and were higher with increasing 

penetration of wind. The distribution of reserves (Figure 59) shows that the High Solar Scenario 

presented the largest absolute requirement but, on average, requirements are lower. 
82 

Table 18. Total Amounts of Reserves 

Scenario Contingency (TW-h) Regulation (TW-h) Flexibility (TW-h)

No Renewables 29.44 9.81 0 

TEPPC 29.44 10.14 10.45 

High Wind 29.44 10.83 22.77 

High Mix 29.44 10.61 17.83 

High Solar 29.44 10.57 13.53 

Figure 59. Distribution of regulation reserves by scenario 

Note: The thick horizontal line is the median. The box shows the 

25th to 75th percentile. The lines show the extent of the maximum and minimum. 

Flexibility reserves only depend on wind/solar because of the absence of load forecasts. Table 18 

and Figure 60 show that the larger uncertainty of wind drove the flexibility reserves. 

Figure 60. Distribution of flexibility reserve requirements by scenario 

Note: The thick horizontal line is the median. The box shows the 

25th to 75th percentile. The lines show the extent of the maximum and minimum. 
83 

Finally, Figure 61 combines the wind and PV power time series with regulation and reserve 

requirements for a few selected days in July. The total reserves are represented, along with the 

load, wind, and PV components. The geometric addition causes regulation requirements to be 

dominated by the load component. Wind components contribute more evenly, explaining the 

overall higher requirements. PV tends to fluctuate throughout the day. 

Figure 61. Power, regulation, and flexibility reserves for a few selected days in July, by scenario 
84 

6 Production Simulation Results 

The results of running the unit commitment and economic dispatch modeling (using the 

PLEXOS model) are given in this section. All results are in 2011 nominal dollars. 

6.1 System Dispatch 

Dispatch stacks can be used to help understand how generators operate in various scenarios and 

how flexibility can be provided to handle variability and uncertainty in load and generation. 

System conditions vary greatly throughout the year, and Figure 62 through Figure 71 show 5-

minute time-step dispatch stacks for the U.S. portion of the Western Interconnection for 2 

significant weeks: the highest net load period of the year (during July) and the lowest net load 

period of the year (during March). Net load here was calculated after the system was dispatched, 

in contrast to in Section 4 where net load was calculated on the basis of total available wind/solar 

(before curtailment occurred). Table 19 shows weekly wind and solar penetration as a fraction of 

U.S. generation. 

Table 19. Weekly Wind/Solar Energy Penetration During Sample Weeks 

Scenario Week of July 24 

Penetration (%) 

Week of March 25 

Penetration (%) 

No Renewables 0.0 0.0 

TEPPC 7.9 16.2 

High Wind 17.9 39.7 

High Mix 21.8 38.1 

High Solar 26.4 34.2 

Figure 62 depicts dispatch for a week in July for the No Renewables Scenario. This shows 

essentially baseloaded operation of nuclear and coal units with mild load-following of the hydro 

units. The gas CC units pick up the bulk of the load-following duty with the gas CT units 

meeting the daily peak. 

Figure 62. Summer dispatch stacks during the last week of July for the No Renewables Scenario 
85 

The TEPPC Scenario (shown in Figure 63) shows essentially the same operation of the existing 

units with renewable energy generation contributing a small amount to overall generation during 

this week with high load. Coal and hydro generation is unaffected by the additional wind/solar, 

which affect primarily the gas CC and CT units. Curtailment is negligible across scenarios for 

the week. 

Figure 63. Summer dispatch stacks during the last week of July for the TEPPC Scenario 

Figure 64 shows the same week in the High Wind Scenario. Impacts are modest, and the wind 

and solar mostly displace the CC and CT generation. Wind production picks up around the daily 

peak and drops off during the night and early morning. 

Figure 64. Summer dispatch stacks during the last week of July for the High Wind Scenario 
86 

Figure 65 shows the dispatch for the same week for the High Mix Scenario. Again, the CCs and 

CTs are mostly affected and displaced. The higher solar penetration in the summer helps to 

reduce the magnitude of the CC cycling. 

Figure 65. Summer dispatch stacks during the last week of July for the High Mix Scenario 

Figure 66 shows the summer dispatch for the High Solar Scenario. In addition to the CCs and 

CTs, coal is also displaced by the solar generation. This occurs primarily in the late morning 

hours, when PV is generating near maximum capacity but load is still low compared to the peak. 

The coal units are not shut down in most cases; they are operated at lower generation levels. 

Figure 66. Summer dispatch stacks during the last week of July for the High Solar Scenario 
87 

As in WWSIS-1, spring is much more challenging for high-penetration scenarios than summer 

because the wind/solar output is high and the load is low. Figure 67 through Figure 71 show the 

dispatch stacks for a week in March. This week was selected for display because it represents the 

minimum net load condition, which occurs on March 29. The significant amount of wind on this 

day in the High Wind Scenario (Figure 69) displaces nearly all of the gas output and markedly 

cuts into the coal output, even starting to affect the nuclear units. The nuclear units were 

constrained to not run below 95%, so the impact on nuclear generation is small, although there 

are changes in the commitment of nuclear plants primarily because of the optimization of 

maintenance schedules for each scenario. On March 30, a significant DA wind forecast error 

occurs. Even though plenty of coal capacity is available, the coal units are not committed 

because of the DA forecast error (an over-forecast). Instead, the 4HA wind forecast foresees the 

reduction in wind and commits the CCs, which pick up the reduced wind output. The coal units 

do not get committed until later in the evening when load peaks. 

Figure 67. Spring dispatch stacks during the last week of March for the No Renewables Scenario 

Figure 68. Spring dispatch stacks during the last week of March for the TEPPC Scenario 
88 

Figure 69. Spring dispatch stacks during the last week of March for the High Wind Scenario 

Even the High Mix (Figure 70) and High Solar (Figure 71) Scenarios see low net loads during 

this time. The wind generation is high even at midday as the solar output is peaking, and this 

combination has a significant impact on the coal and gas units. In the High Solar Scenario, nearly 

all online coal generation is generating at minimum stable levels around noon each day, when 

PV output is the greatest but before load has peaked. Even with the coal at minimum stable 

levels, significant solar energy is still curtailed during the day as shown by the dotted line. This 

energy is curtailed partly because the start costs of coal generators do not justify turning them off 

in the morning and back on for the evening load peak. 

Figure 70. Spring dispatch stacks during the last week of March for the High Mix Scenario 
89 

Figure 71. Spring dispatch stacks during the last week of March for the High Solar Scenario 

The dispatch stacks show how the system uses the least expensive sources of supply-side 

flexibility to serve load and reserves in the presence of variability and uncertainty. System 

requirements in the spring differ greatly from those in the summer. In the summer, capacity is 

required more than flexibility, but in the spring, flexibility becomes more important. Flexibility 

can be provided to the system in a variety of ways: 

 Cycling CTs—all scenarios 

 Ramping and cycling CCs—TEPPC scenario 

 Ramping coal on a daily basis—the High Solar Scenario 

 Cycling coal on a weekly (or longer) basis4 

 Curtailing renewable generators to avoid cycling coal generation—all high renewable 

scenarios 

 Ramping hydro generation within its minimum and maximum generation constraints 

 Utilizing storage (CSP thermal storage and pumped hydro storage) during high-load 

periods. 

Figure 72 shows the dispatch of hydro, coal, CC, and CT generators during a spring and summer 

week. These plots help make direct comparisons between scenarios for different unit types. 

 

4

 Many coal units were offline during the low net load event on March 29 in the High Wind Scenario. 
90 

Figure 72. Hydro, coal, gas CC, and gas CT dispatch 

in the (left) summer and (right) spring for all scenarios 
91 

The hydro units are used similarly in all scenarios. As explained in Section 4, the hydro dispatch 

is highly constrained. Compared to the week in March, the week in July sees higher maximum 

generation and similar minimum generation. The summer week shows very little variability 

between scenarios as net load peaks at similar times in all scenarios. The spring week has similar 

hydro dispatch in most scenarios, with the sharpest peaks and valleys occurring at slightly 

different times because the wind/solar varies between the scenarios. 

Coal generation is dispatched similarly in the summer in most scenarios, although the High Solar 

Scenario does see some reduction just before noon most days that is not seen in the other 

scenarios. This reflects the net load reaching its minimum during those summer periods. In the 

spring, most coal units ramp on a diurnal basis down to minimum generation levels and back up 

to maximum capacity, especially in times of high wind/solar penetration. This cycling is most 

pronounced in the High Solar Scenario. Additionally, in all high renewable scenarios, there is 

some cycling of coal generators on an approximately weekly timescale. Although the coal units 

do not start much more in the high renewable scenarios, the units do ramp considerably more 

often. This ramping, described in more detail in this section, is the most significant change in 

how any generator type is operated with high renewable penetration. 

Gas CC generators behave predictably in the summer. The dispatch of CCs in the No 

Renewables and TEPPC Scenarios is similar, and more solar penetration leads to increased 

displacement of CCs. The diurnal cycle of the generators ramping is similar in all scenarios, with 

the peak CC generation occurring later in the day with increasing solar penetration because the 

net load peaks after sunset in the scenarios with higher solar penetrations. In the spring, all 

scenarios show daily ramping of CC units. The high renewable scenarios show much less usage 

of the CC generators with less consistent patterns in the spring, however. 

Gas CT generators are dispatched as peaker units in all scenarios, meaning that they are run for a 

relatively short period each time they are turned on, particularly in the spring. The CTs are used 

at a consistently later time of day in the High Solar Scenario because the PV reduces the net load 

until after sunset. About 1 GW of must-run CT generation is always online. 

Figure 73 shows the CSP dispatch in the spring. This figure shows how the CSP thermal storage 

is used to minimize overall production costs. CSP often peaks once in the early morning hours, 

when prices are high before PV generation comes online. During the midday hours, CSP 

generates at a lower level because PV generation is high and net load and prices are low. The 

largest peak comes during evening hours when the PV generation is zero, load is still high, and 

prices are typically the highest. 
92 

Figure 73. CSP dispatch in the spring for all scenarios 

The electric power system provides supply-side flexibility in a number of ways. Cycling is one of 

those methods, along with hydro flexibility, storage dispatch, and curtailment of renewable 

resources. 

6.2 Generation 

This section examines the results of simulating the operation of the Western Interconnection for 

2006. Figure 74 shows the wind/solar penetration for each scenario. Penetration targets of 33% 

were set based on the average wind and solar profiles over the 3 years: 2004, 2005, and 2006. 

Penetration does not total 33% because 2006 was a better wind year and a worse solar year than 

the 2004–2006 average. Also, CSP curtailment is not included on this plot because some of the 

curtailment is built into the design of the unit based on the ratio of storage size to turbine size. 

This explains the discrepancy between wind/solar penetrations in these high-penetration 

scenarios. Curtailment, which is modest, is described in detail in Section 6.7. All results in 

WWSIS-2 use hourly and 5-minute profiles for load, wind, PV, and CSP generation based on 

2006 meteorological profiles. WWSIS-1 studied 2004 and 2005 as well and found no difference 

in the major results between the different years, but future work could examine the 

meteorological profiles of 2004 and 2005 using WWSIS-2 models and methodologies. 
93 

Figure 74. Renewable energy penetration of CSP, PV, and wind 

in the U.S. portion of the Western Interconnection for each scenario in 2006 

Figure 75 shows the generation for the scenarios by unit type. For reference, total generation for 

the U.S. portion of the Western Interconnection in most of the scenarios is approximately 800 

TWh. Significant gas CC generation is displaced by renewable generation, as is some coal 

generation, especially in the High Wind Scenario. Gas CT use increases slightly with higher 

renewable penetrations in the High Solar Scenario. 

Figure 75. Generation from each type of generation unit by scenario 
94 

Figure 76 shows the amount of energy displaced for each unit type. In the TEPPC case, the 13% 

wind and solar mostly displaces gas units (primarily CCs) because these units have higher 

marginal costs than coal units. Coal makes up less than 7% of the displaced generation from the 

TEPPC wind and solar. In the high-penetration scenarios, coal makes up a larger fraction of the 

displaced generation because the online gas resources are almost fully displaced by wind and 

solar during some time periods. Among the high-penetration scenarios, the cases with more solar 

displace more gas and less coal because the solar generation is correlated with time periods 

where load is higher and more gas units are online. Section 6.9 discusses the impact of varying 

gas prices on displaced generation. 

Figure 76. Generation displaced by wind and solar compared to No Renewables Scenario 

Figure 77 shows the daily penetration of wind and solar on the power system, measured as total 

daily wind, PV, and CSP generation divided by total daily U.S. portion of the Western 

Interconnection load. The daily peak penetration is approximately 60% in the High Wind 

Scenario and is slightly lower in the High Mix and High Solar Scenarios. The High Wind 

Scenario has the most variability between days and between seasons. In all scenarios, the months 

of July through September have the lowest average penetration levels, which is partly the result 

of the higher load at that time of year and partly because of lower wind generation levels. 
95 

Figure 77. Daily wind and solar energy penetration for each scenario 

Figure 78 shows a duration curve of the renewable penetration levels during every 5-minute 

interval. This is the actual penetration, after considering curtailment. Penetrations stay below 

60%, partly because of the hydro, nuclear, and coal units that stay online and their downward 

constraints, which can result in wind/solar curtailment. The High Solar Scenario has more hours 

of very high penetration compared to the other high-penetration scenarios (these occur mostly at 

midday) and also more hours of very low penetration (these occur at night). In comparison, 

Xcel/Public Service Company of Colorado, SPP, ERCOT, and the Midwest Independent System 

Operator (MISO) have reached hourly instantaneous wind penetrations of 56%, 30%, 26%, and 

25%, respectively (ERCOT 2012; Goggin 2012; MISO 2012; Jaffe 2011). Portugal, Spain, and 

Ireland have reached very high instantaneous penetrations of 85%, 60%, and 50% wind 

penetration, respectively (Estanqueiro et al. 2012). Remember that WWSIS-2 did not examine or 

account for frequency response and potential reliability issues that could arise if a fault were to 

occur simultaneously with high instantaneous wind and PV penetrations. That is being examined 

in Phase 3 of WWSIS. 
96 

Figure 78. Duration curve of 5-minute wind/solar penetration for each scenario 

Wind and solar in the U.S. portion of the Western Interconnection primarily displace generation 

from natural gas CC units. Low wind/solar penetrations displace very little coal, but higher 

penetrations displace up to 30% coal and 70% natural gas generation based on an assumed gas 

price that averages $4.60/MMBtu. Peak instantaneous renewable penetration rates stay below 

60% in all scenarios. 

6.3 Fossil-Fueled Unit Cycling 

WWSIS-2 investigated the cycling of fossil-fueled generators, a major issue that includes starts, 

ramps, and part-load generation. Figure 79 (a) shows the generation capacity started (the sum of 

the number of starts times the capacity of each unit) for coal, gas CC, and gas CT units. Coal unit 

starts increase modestly in the High Mix and High Wind Scenarios and remain unchanged in the 

High Solar Scenario. Gas CC units start more in the TEPPC Scenario, but as more CC generation 

is displaced in the high-penetration scenarios, starts are more similar to the No Renewables 

Scenario. Gas CT units start less in the High Wind and TEPPC Scenarios, but more in the High 

Mix and the most in the High Solar Scenario. This is partly because of the sharper peak that 

occurs in net load in the hours after sunset in the scenario with more PV generation. 
97 

(a) (b) 

Figure 79. (a) Capacity started and (b) number of hours online 

per start by plant type for each scenario 

Figure 79 (b) shows the average number of hours online per start for each plant type. Must-run 

gas CT generators are not included in this chart because they have a major impact on the average 

number of hours CT generators are operated per start. More starts by a generator category are 

associated with fewer hours online per start. High wind penetration increases the coal starts, and 

coal units are run for shorter periods of time. The TEPPC Scenario has modest impacts on how 

the coal units are used; the 13% penetration affects mostly the gas units. High solar penetration 

does not have a significant impact on starts at coal units. The CCs are consistently generating 

less when they are started in all wind/solar scenarios. Wind penetration (in both the TEPPC 

Scenario and the High Wind Scenario) reduces the number of CT starts because it reduces the 

usage of CT generation. The scenarios with higher solar penetrations lead to similar or higher 

numbers of CT starts and similar numbers of hours online per start. In all scenarios, gas CT 

generators are peakers that average just 4–6 hours online per start. 

Figure 80 shows (a) the total number of ramps and (b) the number of ramps per day of operation 

at coal and gas CC and CT units. Ramps are defined as a change in power output larger than 30% 

of maximum capacity. It is evident that wind and solar change coal operation. Coal units are 

ramped much more regularly in the wind/solar scenarios compared with the No Renewables 

Scenario, when coal units ramp an average of approximately once per week. In the highpenetration scenarios, ramping occurs approximately daily and the frequency is slightly higher in 

the scenarios with more solar. CC units ramp less in the wind/solar scenarios, but still ramp more 

per day of operation—more than two times per day in the high-penetration scenarios. CT 

operation is not very different across the scenarios—they are ramped regularly in all scenarios. 

Of all the cycling changes at fossil-fueled units, the most significant change in the way units are 

operated is that coal units are ramped much more in the wind/solar scenarios, especially the highpenetration scenarios. 
98 

 (a) (b) 

Figure 80. (a) Total number of ramps and (b) ramps per day by plant type for each scenario 

Figure 81 shows the number of starts versus capacity for every unit, sorted by unit type and 

scenario. This plot shows (a) the number of starts and the distribution of starts for each unit type 

and (b) the relationship between size of the unit and the number of starts. Most coal units are 

started 20–30 times per year in all scenarios; most gas CC units are started 0–60 times in all 

scenarios. In the TEPPC and High Wind Scenarios, almost all CT units are started less than 50 

times, but in the other scenarios some CTs are started up to 150 times per year. There is little 

relationship between the number of starts and unit size for the coal and gas CC units; however, 

for gas CT units, smaller units are started more often than larger units. 

Figure 81. Number of starts versus capacity for every unit by unit type and scenario 
99 

Figure 82 shows the number of ramps versus unit capacity and illustrates the significant change 

in ramping by coal generators in the high-penetration scenarios. Very little ramping of coal 

generators occurs in the No Renewables Scenario, but most coal units ramp several hundred 

times per year in the high-penetration scenarios. A few small coal units ramp more than once per 

day on average. Gas CC units are operated similarly in all cases and it is evident that CTs are 

ramped less in the High Wind and TEPPC Scenarios; this is because they are online less and 

generating less in these scenarios. 

Figure 82. Number of ramps versus capacity for every unit by type and scenario 

Figure 83 shows the committed capacity as a solid line and the actual dispatch as a shaded area 

for the coal generating units in all scenarios. This helps distinguish between starts, shutdowns, 

and part-loading of online generators when studying the dispatch. The online coal units are 

almost all operating at maximum capacity in the summer with the exception of some midday 

hours in the High Solar Scenario. In the spring in the high-penetration scenarios, the coal units 

show daily ramping (particularly the scenarios with more solar) and a weekly change in 

committed capacity because of a low load and high wind and solar output. Units are shut down 

between March 26 and 29 and started back up on March 30 and 31. More coal units are shut 

down in the High Wind Scenario compared to the High Solar Scenario, but more ramping occurs 

in the High Solar Scenario. 
100 

Figure 83. (Solid line) capacity committed and (shaded area) dispatched 

for coal units during March and July sample weeks for each scenario 
101 

Figure 84. (Solid line) capacity committed and (shaded area) dispatched 

for gas CC units during March and July sample weeks for each scenario 
102 

Figure 84 shows the commitment and dispatch of gas CC units. In the summer, there is not much 

difference between scenarios in how these units are operated. Commitment changes slightly 

through the week and the units are ramped daily to near minimum generation levels. In the spring, 

these units are mostly displaced by the wind and solar generation in the high-penetration scenarios. 

One of the questions that WWSIS-2 sought to answer was whether it was necessary to include 

wear-and-tear start costs (which are often unknown) within the system optimization or whether 

adding these costs after the optimization is sufficient. We ran a sensitivity analysis with zero 

wear-and-tear start costs. Figure 85 shows the generation by type for the case that included the 

lower-bound start costs (in blue) and the case that replaced the wear-and-tear start costs with 

zero (in orange). In both cases, start fuel costs (which are typically known) were included in the 

optimization. There is no significant change in annual generation for most generator types when 

wear-and-tear start costs are not included in the optimization. There is some trade-off between 

gas CC and CT generation. Interestingly, the CC generation is lower in the zero wear-and-tear 

start cost case. This is because the 4HA market sees the ability to shut down the CC generators 

for relatively short periods of time, yet if the actual wind and solar generation are lower than 

expected in real time, CTs need to be run to serve load that would have been served by a CC 

unit. Coal commitment and generation change very little in the zero wear-and-tear start cost case. 

This is partly because of the higher fuel cost for starting coal units, which means that the impact 

of the start VOM is not as significant. 

Figure 86 shows the capacity started for the two start cost sensitivities. This shows a significant 

difference between the two cases. Eliminating wear-and-tear start costs leads to very different 

operation of the CC and CT units. They are both started and shut down much more often in 

response to variations in net load. Although examining the annual generation by type shows little 

advantage to using wear-and-tear start costs in the optimization, analysis of starts shows that 

including wear-and-tear start costs within the optimization is important for detailed analysis of 

high wind/solar penetration impacts. 

Figure 85. Generation by type for the High Wind Scenario with zero wear-and-tear start costs 

compared to lower-bound wear-and-tear start costs 

Note: Start fuel costs were included in the optimization for both cases. 
103 

Figure 86. Starts by type for the High Wind Scenario with zero wear-and-tear start costs 

compared to lower-bound wear-and-tear start costs 

Note: Start fuel costs were included in the optimization for both scenarios. 

Coal units start a similar number of times in all of the scenarios. The largest difference in the 

way the coal units are operated in the high renewable scenarios is that they are ramped each 

day instead of each week. Gas CC units start more often and ramp more often per hour of 

operation in the high renewable scenarios. Gas CT units are used as peakers in all scenarios. 

6.4 System Production Costs 

Production cost changes from wind and solar generation are discussed in this section. Production 

cost changes comprise the change in fuel, noncyclic VOM, and start and ramping costs accruing 

from the presence of wind and solar. This section also shows the overall O&M cost impacts on 

the fossil-fueled units. Note that these are production simulation results and do not reflect capital 

costs of wind, solar, or transmission or the reduced capacity requirements throughout the system 

that result from the capacity value of renewable generation. The results also apply only to the 

Western Interconnection and are highly dependent on the existing generation mix in the region. 

Regions with significantly different generation mixes could see different production costs and 

emissions impacts. One of the main goals of this study was to understand how the costs of 

increased cycling by fossil-fueled generators might offset the production cost reduction from 

wind/solar generation. Although the system dispatch and generation starts showed only the U.S. 

portion of the Western Interconnection, the plots in this section include all of the Western 

Interconnection because the interchange between countries changes and the analysis must 

account for these cost (and emissions) impacts. 

For cycling cost results in this section, we supply a range. The ranges are based on the lowerand upper-bound costs, which give an uncertainty range for cycling costs as described in Section 

2. We ran the PLEXOS model using median lower-bound start costs to optimize unit 

commitment and dispatch to directly determine the system lower-bound cycling costs. We then 

applied median lower-bound ramp costs to the number of ramps. 
104 

We indirectly estimated the upper-bound cycling costs by applying the median upper-bound start 

and ramp costs to the number of starts and ramps. We did try to run PLEXOS using the upperbound start costs to calculate the upper-bound cycling costs directly; however, after examining 

the results from several of these runs, we determined that properly committing large coal units 

and other units with large start costs required a unit commitment horizon of significantly more 

than the 2-day window used in this study. This would have led to unacceptable runtimes. The 

impact of this approach is that this method may underestimate curtailment and overestimate 

cycling costs (higher start costs would reduce the number of starts). As a result, we regard these 

system upper-bound cycling costs as a ceiling. Future work could include a sensitivity run of 

PLEXOS with the upper-bound start costs. 

Figure 87 shows the total production cost (production costs do not reflect fixed capital or PPA 

costs) of each scenario. Cost components are broken down into fuel, noncyclic VOM, start costs 

(nonfuel and wear-and-tear start costs), start fuel costs, and ramping costs. Fuel costs dominate 

the production cost, and noncyclic VOM costs are also significant. Starts, start fuel, and ramping 

costs total between $271–$643 million (in the No Renewables Scenario) and $324–$800 million 

(in the High Solar Scenario; see Table 20). This represents a relatively small fraction (1.5%–

7.0%, depending on the scenario) of total production costs. Although the three high-penetration 

scenarios have similar production costs, the High Solar Scenario is slightly higher because the 

renewable penetration level is slightly lower (See Figure 74). Figure 88 focuses on the wear-andtear components of the production cost. 

(a) Lower bound (b) Upper bound 

Figure 87. Total cost of each scenario broken down into fuel, noncyclic VOM, 

and lower- and upper-bound start wear-and-tear, start fuel, and ramping wear-and-tear costs 
105 

 

Figure 88. Cycling cost components, showing lower- and upper-bound costs 

from starts, start fuel, and ramping 

Note: Production costs do not include any fixed capital or PPA costs. 

Table 20. Cycling Costs (Starts, Start Fuel, and Ramping Costs) for All Scenarios 

Scenario Cycling Costs (million $) Cycling Costs as a Fraction of 

Total Production Cost (%)

No Renewables 271–643 1.5–3.5 

TEPPC 313–751 2.1–4.9 

High Wind 321–769 3.0–6.9 

High Mix 306–738 2.8–6.7 

High Solar 324–800 2.9–7.0 

The change in production cost from each wind/solar scenario to the No Renewables Scenario 

was examined. Production cost does not include any capital costs or PPAs for thermal units, 

renewable generators, or transmission. Figure 89 shows the total change in production cost per 

MWh of wind and solar produced. The reduction in production cost from wind and solar in the 

TEPPC case is $32.6–$33.2/MWh, considering upper- and lower-bound start costs, respectively. 

The reduction in production cost from wind and solar on a per MWh basis is lower in the highpenetration scenarios because more coal is displaced. The changes in production cost for the 

high-renewables scenarios are shown in Table 21. These values are highly dependent on gas 

price assumptions (see section 6.9), which average $4.60/MMBtu. These values are significantly 

less than the approximately $80/MWh value found in WWSIS-1, because the core scenarios in 

WWSIS-1 assumed a $9.50/MMBtu gas price and a $30/ton carbon tax, which led to displacing 

less coal and using more gas. We did not model a carbon tax in WWSIS-2. 
106 

Table 21. Production Cost Reductions Due to Wind and Solar for Each Scenario 

Scenario Change in Production Cost 

(billion $) 

Change in Production Cost 

($/MWh) 

TEPPC 3.34–3.43 32.6–33.2 

High Wind 7.48–7.56 29.4–29.7 

High Mix 7.59–7.65 30.2–30.4 

High Solar 7.12–7.23 30.2–30.6 

Note: Production costs do not include any fixed capital or PPA costs. Change in production cost is shown 

as total change in production cost per MWh of wind and solar generated.

Start, start fuel, and ramping costs have a very small impact on the overall change in production 

cost from wind and solar. Fuel cost reductions account for the large majority of the production 

cost reductions. Noncyclic VOM also accounts for $340–$390 million of cost reductions 

between the high-penetration scenarios and the No Renewables Scenario. Wind and solar 

penetration does lead to increased starts for some plant types in some scenarios, but the increase 

in starts, particularly of coal (the most expensive units to start), is not large enough to 

significantly affect production cost. In the TEPPC Scenario, cycling costs offset the reduction in 

production cost of wind and solar by 1.2%–3.2%. In the high-penetration scenarios, the impact 

ranges from 0.5%–2.2% (see Table 22 for specific scenario results). These are system-wide 

changes in production cost; impacts on individual generators could be significantly different. 

Figure 89 shows the fuel, noncyclic VOM, and cycling changes in production cost per MWh of 

wind and solar generation. Negative values (e.g., for fuel) indicate that the wind/solar scenarios 

save fuel over the No Renewables Scenario. Positive values (e.g., for ramping) indicate that 

ramping-related costs increase in the wind/solar scenarios. 

Fuel cost reductions greatly outweigh any increase in ramping or start cost in the wind/solar 

scenarios, as shown by the size of the orange bars. Table 22 gives more detail on the impact of 

cycling on the change in production cost from wind and solar. Every MWh of wind and solar 

generation leads to $0.14–$1.05 in cycling costs, depending on scenario. The TEPPC Scenario 

shows the largest increase in cycling costs per MWh of wind and solar generation. The highpenetration scenarios show a $0.14–$0.67 increase in cycling cost per MWh of wind and solar 

generation. 
107 

 (a) Lower bound (b) Upper bound 

Figure 89. Change in production cost (compared to No Renewables Scenario) 

per MWh of wind and solar generation 

Note: Production costs do not include any fixed capital or PPA costs. 

Table 22. Cycling Cost Impacts of Wind and Solar 

 Increase in Cycling Costs (compared to No Renewables Scenario) 

Scenario Total 

(million $) 

Per MWh Wind and Solar Generation 

($/MWh) 

Per MWh of FossilFueled Generation 

($/MWh) 

TEPPC 42–108 0.41–1.05 0.18–0.44 

High Wind 50-127 0.20–0.50 0.52–1.24 

High Mix 35-95 0.14–0.38 0.47–1.14 

High Solar 52-157 0.22–0.67 0.50–1.28 

We next consider the same cycling costs, but from the perspective of increased O&M of the 

fossil-fueled plants. Instead of dividing the cycling costs by each MWh of wind and solar 

generation, we now divide those same costs by each MWh of fossil-fueled generation. Figure 90 

and Figure 91 show the cycling cost impacts to fossil-fueled generators. Figure 90 shows the 

lower-bound impacts of cycling on the nonfuel production costs from coal, gas CC, and gas CT 

generators. The upper-bound analysis for generator types is not shown because it is confidential. 

Note, however, that although the absolute magnitudes of costs are higher with the upper bounds, 

the relative comparisons discussed here also hold true for the upper bounds. Although wind- and 

solar-induced cycling has very little impact on O&M costs at the average coal unit, it has more 

impact on the average gas CC and gas CT generator. Figure 91 shows the lower- and upperbound aggregate cycling costs per MWh of fossil-fueled generation. High-penetration levels of 
108 

wind and solar could add to the O&M of the average fossil-fueled generator by up to 

$1.28/MWh of fossil-fueled generation (see Table 22 for a range for each scenario). At 13% 

penetration, we find that the wind- and solar-induced cycling at the average fossil-fueled unit 

increases O&M by $0.18–$0.44/MWh. Note that Figure 88 shows that total cycling cost is more 

consistent between the scenarios compared to the cycling cost normalized by generation. 

Although cycling costs go up slightly in the renewable scenarios, the cycling costs per MWh of 

fossil-fueled generation go up more noticeably because the cycling costs are higher and the 

generation is lower in the high-renewable scenarios. 

Figure 90. Lower-bound cycling costs per MWh of (left) coal, 

(middle) gas CC, and (right) gas CT generation 

Figure 91. Lower- and upper-bound cycling costs 

per MWh of fossil-fueled generation for all scenarios 
109 

Annual cycling costs across the West increase from $271–$643 million in the No Renewables 

Scenario to $306–$800 million when wind and solar are added. Wind and solar increase annual 

cycling costs by $35–$157 million, or 13%–24%. Cycling costs do not necessarily increase with 

wind and solar penetration: the High Mix Scenario has lower cycling costs than the TEPPC 

Scenario. The average fossil-fueled plant sees an increase in cycling costs of $0.18–$0.44/MWh 

generated in the TEPPC Scenario and $0.47–$1.28/MWh in the high-penetration scenarios. 

Regardless of wind and solar, gas CTs have the highest cycling O&M cost per MWh of generation 

because gas CTs perform the most cycling. Although the TEPPC Scenario resulted in decreased 

cycling costs for gas CTs and the High Wind Scenario did not affect cycling costs for CTs, the two 

scenarios with higher solar penetrations resulted in significantly increased CT cycling costs 

because CTs were often started to handle the evening peak. From a system perspective, wind and 

solar displace $3 billion in fuel and VOM annually in the TEPPC Scenario and $7–$8 billion in 

the high-penetration scenarios. In the TEPPC Scenario, wind- and solar-induced cycling costs 

offset the production cost reduction of wind and solar by $0.41–$1.05/MWh to $32.6–$33.2/MWh 

of wind and solar generated. In the high-penetration scenarios, wind- and solar-induced cycling 

costs offset the production cost reduction of wind and solar by $0.14–$0.67/MWh to $29.4–

$30.6/MWh, based on the specific system characteristics modeled for the Western Interconnection. 

Production costs do not consider fixed capital or PPA costs. 

6.5 Emissions 

Wind and solar can displace energy and emissions that would otherwise be generated by fossilfueled units. One of the main goals of this study was to understand how wind and solar affect 

CO2, SO2, and NOx emissions. Emissions related to part-load operation, ramping, and starts were 

studied in detail, as described in Section 2. This section analyzes the emissions avoided by wind 

and solar penetration and also the changes in average emission rates from fossil-fueled units that 

result from the penetration of wind and solar generation. 

Table 23 shows the avoided emissions per MWh of wind and solar in the high-penetration 

scenarios. This number is calculated as the change in total emissions (considering part-load, 

ramping, and start impacts) compared to the No Renewables Scenario divided by the wind and 

solar generation for each scenario. 

Table 24 shows the reduction in emissions compared to the wind and solar penetration for each 

high-penetration scenario. These numbers include all generation and load in the Western 

Interconnection (including Canada and Mexico), which is why the penetration numbers are 

significantly lower than 33%. Emissions from all countries must be counted because the 

interchange between countries changes between the scenarios. CO2 is reduced by 29%–34% 

throughout the Western Interconnection from the scenarios with 24%–26% wind and solar 

penetration. This shows that CO2 is displaced at a higher fraction than the energy displacement 

from wind and solar. This occurs because the gas generation that is primarily displaced has a 

higher CO2 emissions rate compared to the Western Interconnection average, which includes 

sources like nuclear and hydro generation that have no emissions. NOx and SO2 are reduced less 

than the penetration rates in the high renewable scenarios. This is because NOx and SO2 emission 

rates are orders of magnitude lower from gas units compared to coal, so these numbers are very 

sensitive to how much coal is displaced. Because the High Wind Scenario displaces the most coal 

generation, the avoided emissions rates and overall reductions are the highest in this scenario. 
110 

The emissions changes from the TEPPC Scenario compared to the No Renewables Scenario are 

not given in this section because the coal and gas portfolio in the No Renewables Scenario is 

partially based on the existing wind and solar capacity; this likely leads to the TEPPC Scenario 

displacing an unrealistically small amount of coal, resulting in smaller emission reductions than 

would be expected. The high-penetration scenarios include a mixture of coal and gas 

displacement, so the comparison with the No Renewables Scenario is appropriate. 

Table 23. Emissions Avoided per MWh of Wind and Solar Generation— 

Considering Combined Part-Load, Ramping, and Start Impacts 

Scenario Avoided CO2 (lb/MWh) Avoided NOX

(lb/MWh) 

Avoided SO2

(lb/MWh) 

High Wind 1,190 0.92 0.56 

High Mix 1,150 0.80 0.44 

High Solar 1,100 0.72 0.35 

Table 24. Emissions Reductions Compared to Wind and Solar Penetration 

for All of the Western Interconnection (including Canada and Mexico) 

Scenario Interconnection-Wide 

Wind and Solar Penetration 

(%) 

CO2 Reduction 

(%) 

NOX

Reduction 

(%) 

SO2 Reduction 

(%) 

High Wind 26.0 33.5 22.3 24.1 

High Mix 25.6 31.9 19.2 18.7 

High Solar 24.1 28.8 16.2 14.1 

Figure 92 through Figure 94 show the impacts of wind and solar penetration on CO2, NOx, and 

SO2 emissions. Table 25 shows the impact of part-load, starts, and ramping on the overall 

avoided emissions rates. In general, impacts were relatively small and ranged from –4.5% 

(reducing the avoided emissions of wind and solar by 4.5%) to +6.3%. Start impacts were small 

in all cases (much less than 1%) because of the relatively modest change in the number of starts 

and the relatively small excess emissions caused by starting units for most pollutants. 

Figure 92 shows the CO2 emissions and changes for the wind and solar scenarios. The analysis 

described in Section 2 shows that ramping has no significant impact on CO2 emissions, so those 

estimates are not calculated. The start-up CO2 emissions are negligible (less than 0.1%) in all 

cases. The part-load CO2 impact is also negligible (less than 1%) because the CO2 penalty for 

part-load operation is relatively small. 
111 

 (a) (b) 

Figure 92. CO2 emissions by scenario: (a) absolute CO2 emissions for operation and starts 

and (b) CO2 emission reductions per MWh of wind and solar generation compared 

to the No Renewables Scenario, separated into the constant emissions rate assumption 

and adjustments for part-load impacts and starts 

The effects of cycling actually cause NOx emissions reductions from wind and solar to be larger 

than expected, as shown in Figure 93. The effect of considering part-load emissions rates 

increases estimates of avoided emissions by 4%–6%. Part-loading increases the estimate of 

avoided emissions because coal units in the western United States typically emit less NOx per 

MWh of generation at part-load compared to operating at maximum capacity (see Section 2). 

Ramping reduces avoided NOx estimates by 2%–4%. Although the emissions penalty per ramp is 

very small, ramping increases dramatically at coal units in the high renewable cases (see Figure 

80). Start-up emissions reduce the avoided NOx estimates by less than 0.1% in all cases. In total, 

ramping, part-loading, and starts improve avoided NOx emissions estimates by 1%–2%. 
112 

 (a) (b) 

Figure 93. NOX emissions by scenario: (a) absolute NOX emissions for operation, ramping, 

and starts and (b) NOX emission reductions compared per MWh of wind and solar generation 

to the No Renewables Scenario, separated into the constant emissions rate assumption 

and adjustments for part-load impacts, ramping, and starts 

Figure 94 shows the SO2 emissions by scenario. Because there was not enough information to 

create SO2 emission part-load curves, part-load impacts were not studied for SO2. Ramping 

impacts on avoided SO2 were modest for the high renewable scenarios, reducing avoided SO2 by 

2%–5%. This is also caused by extensive ramping at coal units. Start-up emissions impacts 

affected the avoided SO2 emissions rates by significantly less than 1%. 

 (a) (b) 

Figure 94. SO2 emissions by scenario: (a) absolute SO2 emissions for operation, ramping, 

and starts and (b) SO2 emission reductions per MWh of wind and solar generation compared 

to the No Renewables Scenario, separated into the constant emissions rate assumption 

and adjustments for ramping and starts 
113 

Table 25. Impacts of Part-Load, Ramping, and Starts on CO2, NOX, and SO2 

Avoided by Wind and Solar Generation 

Pollutant Scenario Part-Load Impact 

(%) 

Ramping Impact 

(%) 

Start Impact 

(%) 

CO2 High Wind 0.0 — 0.0 

CO2 High Mix +0.2 — 0.0 

CO2 High Solar +0.2 — 0.0 

 

NOX High Wind +3.6 –2.2 0.0 

NOX High Mix +4.6 –3.1 0.0 

NOX High Solar +6.3 –3.9 –0.1 

 

SO2 High Wind — –1.9 –0.2 

SO2 High Mix — –3.0 –0.2 

SO2 High Solar — –4.5 0.0 

Notes: Negative numbers denote that avoided emissions are reduced; positive numbers mean that more 

emissions are avoided. Impacts are measured in the percent change in avoided emissions resulting from 

the category of impact. For example, if avoided emissions were 10 lb/MWh and start impacts reduced that 

by 1 lb/MWh, that would be a –10% impact. 

We next view these same emissions from the perspective of a fossil-fueled generator that 

experiences emissions rate changes caused by cycling, instead of from the perspective of a 

wind/solar plant in which cycling changes avoided emissions. Figure 95 and Figure 96 show the 

CO2 emissions rates and changes in CO2 emissions rates for fossil-fueled generators for all 

scenarios. Although significant differences exist between the different technologies, very little 

difference in total emissions (including cycling-related emissions) per MWh of generation is 

seen between the scenarios for each technology. The largest difference between scenarios was a 

2% increase in the emissions rate for the average gas CT in the High Wind Scenario. Solar 

actually reduces average gas CC unit emission rates slightly. These results are on average; 

individual units could be higher or lower. 

Figure 95. CO2 emission rates from the average coal, gas CC, and gas CT unit 
114 

Figure 96. Change in CO2 emission rates from the average coal, gas CC, 

and gas CT unit compared to the No Renewables Scenario 

Figure 97 and Figure 98 show the NOx emission rates and changes between scenarios. Although 

NOx emissions differ between technologies by several orders of magnitude, wind/solar 

penetration has a modest impact on the emissions per MWh of generation from fossil-fueled 

units. For coal-fired units, the impact of wind and solar on emission rates is negligible. For CCs, 

high-penetration scenarios increase emission rates by approximately 5%. For CTs, wind tends to 

decrease emission rates and solar increases emission rates. 

Figure 97. NOX emission rates from the average coal, gas CC, and gas CT unit 

Figure 98. Change in NOX emission rates from the average coal, gas CC, 

and gas CT unit compared to the No Renewables Scenario 
115 

Figure 99 shows SO2 emission rates from coal units by scenario and the change compared to the 

No Renewables Scenario. Gas units emit negligible amounts of SO2. SO2 emissions per MWh of 

generation lessen slightly (1%) in the High Wind Scenario and increase slightly (2%) in the High 

Solar Scenario. 

Figure 99. SO2 emission rates from the (a) average coal unit 

and (b) change compared to the No Renewables Scenario 

A 24%–26% penetration of wind and solar throughout all of the Western Interconnection 

(including Canada and Mexico) leads to a system-wide 29%–34% reduction in CO2, a 16%–22% 

reduction in NOx, and a 14%–24% reduction in SO2. Wind penetration is more effective at 

reducing CO2, SO2, and NOx emissions than solar because it displaces a greater fraction of coal 

generation. Wind- and solar-induced cycling has a very small impact (less than 5%) on the 

avoided CO2, SO2, and NOx emissions from wind and solar, and wind/solar penetration has a 

small impact (always less than 10%, usually less than 2%) on emission rates (lb/MWh) of fossilfueled generators. These are average results; cycling impacts on emissions can be smaller or 

greater for an individual generating unit. 

6.6 Reserves and Unserved Load 

There was no unserved load in any scenario. Contingency, regulating, and flexibility reserves 

were held using the methodology in Section 5. Figure 100 shows the reserve violations for 

contingency and regulating reserves. No regulating reserve violations were seen. Contingency 

reserve violations were very close to zero. They ranged from 2–4 MW-h of shortfall per 1 

million MW-h required, and total shortfalls were less than 100 MW-h for all scenarios. This is in 

contrast to WWSIS-1 where contingency reserve violations totaled approximately 50 GWh. The 

difference is probably because WWSIS-2 held flexibility reserves in the unit commitment 

markets and also operated a 4HA market, neither of which was done in WWSIS-1. 

Contingency reserve violations were much higher in the No Renewables Scenario because the 

power system was slightly underbuilt in this scenario. The wind and solar capacity removed from 

the TEPPC case was not replaced with equivalent thermal generator capacity because the 

scenario was used only for comparison purposes. Figure 100 does not show the No Renewables 

Scenario reserve violations. 
116 

Figure 100. Unserved load and contingency and regulation reserve shortfalls, 

by scenario (as a fraction of total reserve requirement) 

There was no unserved load or regulation reserve shortfalls in any scenario and contingency 

reserve shortfalls are negligible. For every 1 million MW-h of contingency reserves required by 

the statistical methodology, between 2 and 4 MW-h goes unserved in the scenarios. Holding 

flexibility reserves in the unit commitment markets and running a 4HA market likely contribute 

to minimizing the unserved contingency reserves. 

6.7 Curtailment 

This section investigates curtailment of wind and PV. We exclude CSP in these graphs because 

its thermal storage alleviates the need to curtail CSP, although some curtailment of CSP is 

expected based on the ratio of storage to turbine size. Figure 101 shows the percentage of total 

available PV and wind generation that is curtailed. Curtailment is highest with high solar 

penetrations but is still less than 5%. The High Wind Scenario has slightly more than 3% 

curtailment. Notably, the High Mix Scenario has the least curtailment (1.6%). This demonstrates 

that a more balanced mix of wind and solar may be helpful to reduce curtailment. 

Figure 102 shows the annual average diurnal profile of curtailment. As expected, high solar 

penetrations result in midday curtailment, although interestingly, curtailment tends to occur 

before the solar peak. This is because the solar output is rising faster than the load, which peaks 

much later in the day. The High Wind Scenario illustrates wind curtailment in the late night/early 

morning when wind is high and load is low. Some midday curtailment also occurs because there 

is a modest amount of solar in this scenario as well. Curtailment assigned to wind or solar is not 

shown here because the marginal cost for both is zero; therefore, the selection made by the 

model is not meaningful. 

Figure 103 depicts the average hourly curtailment by season. The highest curtailment for the 

High Solar and High Mix Scenarios occurs in the spring when solar irradiance is high and 

summer thunderstorms have not yet begun. The High Wind Scenario sees most of its curtailment 

in the winter and spring when winds are high. 
117 

Figure 101. Curtailment as a percentage of available wind and PV generation 

Figure 102. Curtailment diurnal profile for each scenario 
118 

Figure 103. Average diurnal profile of curtailment by season for each scenario 

Table 26 shows the curtailment by zone for the wind/solar scenarios. Most zones have very 

modest curtailment, but a few zones have curtailment of >10% in some of the scenarios. For 

example, Montana sees 13% curtailment in the High Wind Scenario and northern Nevada sees 

16% curtailment in the High Solar Scenario. 
119 

Table 26. Curtailment by Zone 

Region TEPPC (%) High Wind (%) High Mix (%) High Solar (%) 

Arizona 0 1 2 9 

California, North 0 0 0 0 

California, South 0 0 0 1 

Colorado 0 5 1 0 

Idaho 0 0 0 0 

IID 4 1 2 2 

LAWP 0 0 0 0 

Montana 2 13 5 2 

Nevada, North 0 6 2 16 

Nevada, South 0 1 1 2 

New Mexico 0 1 3 12 

Northwest 0 2 1 0 

San Diego 0 0 1 4 

San Francisco 0 3 3 3 

Sacramento 

Municipal Utility 

District (SMUD) 0 0 0 0 

Utah 0 0 0 0 

Wyoming 0 5 1 0 

Curtailment in the high-penetration scenarios is modest (2%–5%). We find the least curtailment 

with a balanced mixture of wind and solar. Solar curtailment occurs primarily during spring 

daylight hours. Wind curtailment is distributed more evenly throughout the days in the winter 

and spring. 

6.8 Transmission Flows 

The wind/solar scenarios show different patterns of congestion and flows. Figure 104 shows the 

interface flows (in annual TWh) in each direction between the transmission zones (from the 

WECC LRS zones). As more solar is added, average flows go down in most areas and some 

corridors experience more power flowing in both directions. 
120 

 Figure 104. Interface flow map with prices for all scenarios 

Notes: The width of the lines connecting zones denotes annual interface flows. 

Bidirectional flows are shown with the smaller flow in gray and the larger flow in black. 
121 

Figure 105 shows flow duration curves across selected interfaces. Negative values represent 

flows in the opposite direction. In some scenarios, the flow limits are different because 

transmission build-outs differ. Flow limits can be seen on the plots when the curves flatten at the 

right or left side of the frame. For example, the MT to Pacific NW interface is congested 

approximately one-third of the time in all scenarios. Across some of the interfaces (e.g., AZ to 

Southern NV), there is very little change in flows between scenarios. Across others (e.g., Pacific 

NW to LAWP), the flows change significantly with scenario. Higher solar penetrations generally 

lead to lower flows and less congestion. 

Figure 105. Flow duration curves across selected interfaces for each scenario 

Higher wind penetrations generally lead to higher interface flows. Higher solar penetrations 

generally lead to slightly lower interface flows. 

6.9 Gas Price Sensitivities 

WWSIS-2 analysis is based on the WECC TEPPC assumption of an average natural gas price of 

$4.60/MMBtu across the Western Interconnection. Because operation is highly influenced by the 

ratio of coal to gas prices, we conducted a gas price sensitivity analysis using half and double the 

core gas price. We re-ran the production simulations for the No Renewables and High Mix 

Scenarios assuming an average natural gas price of $2.30/MMBtu (titled “$2.30 gas”) and 

$9.20/MMBtu (titled “$9.20 gas”). 
122 

Figure 106 shows the annual generation for all unit types. Very little difference is seen between 

the $4.60 and $9.20 gas cases because the natural gas units are already the most expensive and 

dispatch does not change dramatically if they become more expensive. With $2.30 gas, gas CC 

units are often cheaper than coal units, so generation from gas CC units replaces some generation 

from coal units in both the No Renewables and High Mix Scenarios. The differences in dispatch 

for other unit types are relatively minor. 

Figure 106. Annual generation by type in the gas price sensitivities 

Figure 107 shows the generation that is displaced in the High Mix compared to the No 

Renewables Scenarios for each gas price assumption. Despite major differences in coal dispatch 

in the $2.30 and $4.60 scenarios, the amount of coal displaced by wind and solar is quite similar 

between the cases. As gas becomes cheaper, more coal is displaced because coal is more likely to 

be the marginal fuel type. 

Figure 107. Generation displaced by renewables in the High Mix 

compared to No Renewables Scenarios for each gas price 
123 

Figure 108 and Figure 109 show the capacity started and the number of ramps, respectively, in 

the gas price sensitivities. In the $2.30 gas cases, coal units are started less (and produce less 

energy). In the $9.20 gas cases, gas CC units are started more frequently because they are 

operated more as peaking units. The most noticeable change in the ramping patterns is that coal 

ramps more and gas CC units ramp less in the low gas price sensitivity. This is because gas CC 

units replace some of the coal units as baseload generation. 

Figure 108. Capacity started in the gas price sensitivities 

Figure 109. Number of ramps in the gas price sensitivities 

Table 27 shows the increase in cycling costs resulting from renewable penetration compared to 

the corresponding No Renewables Scenario in each of the High Mix gas price sensitivities. In the 

original $4.60 case, adding wind and solar increased total cycling costs. In the $2.30 and $9.20 

cases, adding renewables actually reduced total cycling costs. In the $2.30 case, the reduction 

was very small, and was partially caused by the coal units starting (and operating) less. In the 

$9.20 case, the reduction was more significant because the gas CC units started much less. In the 
124 

No Renewables $9.20 case, the gas CC units were used as peaking units when possible (started 

regularly), but this usage was displaced in the High Mix $9.20 case. As seen in Figure 110, the 

price of gas had a much larger impact on the total cycling cost than the addition of wind and 

solar generation to the system. This primarily resulted from the increased cycling of gas CC units 

in the different scenarios (as the pattern of operation changed from baseload to peaking). 

Despite the significant difference in overall cycling costs between the cases, the impact on fossilfueled generation was similar in all of the cases. The cycling costs per MWh of fossil-fueled 

generation increased by $0.30 to $1.16, depending on the gas price and cycling cost assumptions 

(lower versus upper bounds). The increase in cycling costs at fossil-fuel units occurs despite the 

reduction in overall cycling costs because generation from fossil-fuel units is significantly 

reduced in the High Mix Scenario. 

Table 27. Cycling Cost Impacts of Wind and Solar in the High Mix Scenario (Gas Price Sensitivity) 

Scenario Increase in Cycling Costs (compared to corresponding No Renewables 

Scenario) 

Total (million $) Per MWh Wind and Solar 

Generation ($/MWh) 

Per MWh Fossil-Fueled 

Generation ($/MWh) 

High Mix, $2.30 –1 to –6 –0.02 to 0.00 0.30 to 0.81 

High Mix, $4.60 34 to 95 0.14 to 0.38 0.47 to 1.16 

High Mix, $9.20 –76 to –200 –0.30 to –0.80 0.47 to 0.92 

* The PLEXOS model was updated before these gas price sensitivity runs, and very minor (<1%) 

differences exist between the numbers here for the $4.60 case and the original High Mix and No 

Renewables Scenarios. 

Figure 110. Cycling cost in the gas price sensitivities 

showing (left) lower and (right) upper bounds 
125 

Table 28 shows the emissions impacts of wind and solar for the different gas price assumptions. 

The avoided emissions depend primarily on whether gas CC or coal generation was displaced. 

Because the displaced energy was relatively similar in all cases, the avoided emissions are 

somewhat similar as well. The differences are mostly due to the emission rates of the displaced 

units, not because the impact of cycling is different in any of the gas price sensitivities. 

Table 28. Emissions Avoided per MWh of Wind and Solar Generation— 

Considering Combined Part-Load, Ramping, and Start Impacts 

Scenario Avoided CO2 (lb/MWh) Avoided NOX

(lb/MWh) 

Avoided SO2

(lb/MWh) 

High Mix, $2.30 1,300 1.18 0.76 

High Mix, $4.60 1,150 0.81 0.44 

High Mix, $9.20 1,110 0.75 0.40 

Figure 111 shows curtailment in the gas price sensitivities. Although energy becomes more 

expensive with higher gas prices, curtailment also increases. This correlates with the amount of 

coal online in the given cases. More coal generation is associated with more curtailment because 

the cost of shutting down coal units for a few hours is higher than the cost to keep them online, 

even when zero-cost energy is available. 

Figure 111. Curtailment in the gas price sensitivities 

Figure 112 shows the production cost change per MWh of wind and solar generation for the 

High Mix case in the gas price sensitivities. Production cost reductions due to wind and solar 

become larger as gas price increases because of the increased value of the displaced fuel. 

Noncycling VOM and cycling costs have very little impact on the overall cost impacts. The total 

production cost reduction of wind and solar is approximately $18/MWh, $30/MWh, and 

$56/MWh for assumed natural gas prices of $2.30/MMBtu, $4.60/MMBtu, and $9.20/MMBtu, 

respectively. 
126 

Figure 112. Production cost change per MWh 

of wind and solar generation in the gas price sensitivities 

The price of gas had a much larger impact on the system cycling cost than the addition of wind 

and solar. With lower or higher gas prices, adding wind and solar actually lowers system 

cycling costs (up to $0.80/MWh of wind and solar). The effect that wind and solar have on the 

cycling cost per MWh of fossil-fuel generation is similar in all cases. 

6.10 Production Simulation Conclusions 

We ran PLEXOS for the five scenarios to determine operational impacts of wind and solar 

generation. The Western Interconnection can operate with 33% wind and solar energy with no 

unserved load, no regulating reserve shortfalls, and only minimal contingency reserve shortfalls. 

Spring is the most challenging time for the high-penetration scenarios because of the high wind 

and solar output combined with the low load. The electric power system provides supply-side 

flexibility in a number of ways. Cycling is one of those methods, along with hydro flexibility, 

storage dispatch, and wind/solar curtailment. 

We investigated the impacts of wind and solar on starts and ramping of fossil-fueled generators. 

The biggest impact of wind and solar on cycling is the increased ramping of coal units. Coal 

starts do not change appreciably. Coal units ramp daily in the spring down to minimum 

generation levels and back up to maximum capacity with high wind/solar penetrations, especially 

with high solar penetrations. Increasing wind penetrations are actually beneficial for gas CT 

cycling because they result in CTs being started and ramped less frequently. Gas CTs start more 

often with high solar penetration, helping to meet evening peaks as the sun sets. Gas CC units 

start more often and ramp more often per hour of operation with high wind/solar penetration. 

Wind and solar mostly displace gas CC generation. Instantaneous wind and solar penetration 

(averaged over a 5-minute interval) is below 60% in all scenarios. 
127 

Including wear-and-tear start costs within the production simulation optimization is important for 

conducting detailed analysis of high wind and solar penetration impacts. It is less important if the 

user is trying to understand high-level, system-wide impacts. 

In this study, cycling (start, start fuel, and ramping) costs total between $271–$643 million (in 

the No Renewables Scenario) and $324–$800 million (in the High Solar Scenario). The cycling 

costs were dominated by start costs. These ranges represent the uncertainty in cycling costs. The 

cycling costs represent a relatively small fraction of total fuel costs ($9–$17 billion). 

We can frame these costs from a system perspective, examining the impact of cycling on the 

change in production cost from wind and solar. Based on the specific system characteristics 

modeled for the Western Interconnection, these same cycling costs at low penetrations offset 

production cost reductions from wind and solar by $0.41–$1.05/MWh to $32.6–$33.2/MWh, and 

at high penetrations offset production cost reductions by $0.14–$0.67/MWh to $29.4–

$30.6/MWh. Production cost does not include any capital costs or PPAs for thermal units, 

renewable generators, or transmission. 

We can next frame these same cycling costs from the perspective of increased O&M of the 

fossil-fueled plants. High penetration levels of wind and solar could add to the O&M of the 

average fossil-fueled generator by up to $1.28/MWh. At low penetrations, we find that the windand solar-induced cycling at the average fossil-fueled unit increases O&M by $0.18–

$0.44/MWh. For high penetrations, this O&M increase is $0.47–$1.28/MWh. 

With or without wind and solar, gas CTs have the highest cycling O&M because CTs undergo 

the most cycling. Compared to the system without wind or solar, cycling O&M for CTs actually 

decreases with low wind/solar penetrations and does not change for the High Wind Scenario. As 

solar penetration increases, the cycling O&M of CTs significantly increases because CTs were 

often started to handle the evening peak. 

To determine emissions impacts, we had to aggregate emissions across the three countries in the 

Western Interconnection to capture the interchanges. The high penetration scenarios (24%–26% 

penetration of wind and solar throughout all of the Western Interconnection, including Canada 

and Mexico) leads to a 29%–34% reduction in CO2, a 16%–22% reduction in NOx, and a 14%–

24% reduction in SO2 system-wide. Because the High Wind Scenario displaces the most coal 

generation, the avoided emissions rates and overall reductions are the highest in this scenario. 

Cycling impacts on CO2, SO2, and NOx emissions were relatively small and ranged from –4.5% 

(reducing the avoided emissions of wind and solar by 4.5%) to +6.3% (increasing the avoided 

emissions of wind and solar). Start impacts were small in all cases (much less than 1%) because 

of the relatively modest change in the number of starts and the relatively small excess emissions 

caused by starting units for most pollutants. Cycling has a negligible impact on CO2 emissions. 

NOx reductions from wind and solar are actually 1%–2% higher than what would be expected if 

cycling is not considered because coal units in the West typically have lower NOx emissions 

rates at part-load. Impacts of starts and ramping (part-load could not be assessed) on SO2 were 

modest at high penetrations, reducing avoided SO2 by 2%–5%. These are averaged results—

cycling impacts on emissions could be smaller or greater for an individual generating unit. 
128 

We next view these same emissions from the perspective of a fossil-fueled generator that 

experiences emissions rate changes because of cycling. CO2, SO2, and NOx emissions rate 

changes were very small. Interestingly, wind and solar sometimes increased and sometimes 

decreased average emissions rates, depending on pollutant, plant type, and scenario. 

For CO2 emissions, the largest wind and solar impacts were a 2% increase in the emissions rates 

for the average gas CT in the High Wind Scenario and a 1% decrease in the average gas CC unit 

emissions rates in the High Solar Scenario. For NOx emissions, the largest wind and solar 

impacts were a 10% increase in the emissions rates for the average gas CT in the High Solar 

Scenario and a 10% decrease in the average gas CT emissions rates in the High Wind Scenario, 

although most types of units were affected by much less than 10%. For SO2 emissions, the 

largest wind and solar impacts were a 2.1% increase in the emissions rates for the average coal 

unit in the High Solar Scenario and a 1.4% decrease in the average coal emissions rates in the 

High Wind Scenario. These are averaged results—individual units could be higher or lower. 

We find modest curtailment up to 5% with high wind/solar penetrations. We find the least 

curtailment with a balanced mixture of wind and solar. Solar curtailment occurs primarily during 

spring daylight hours; wind curtailment is distributed more evenly throughout the days in the 

winter and spring. 

Finally, we examined transmission flows and congestion. We found that higher wind 

penetrations generally lead to higher interface flows and higher solar penetrations generally lead 

to lower interface flows. 
129 

7 Conclusions 

In this study, a detailed operational analysis of the Western Interconnection was conducted. 

The study focused on the wear-and-tear costs and emissions impacts from cycling of fossilfueled plants. Increasing penetrations of wind and solar can induce increased cycling. Because 

some plants—especially many baseloaded coal plants in the West—were not designed for this 

type of cyclic operation, they can suffer increased repair and maintenance costs. Some analysts 

have asserted that the additional emissions from this type of operation can be significant 

compared with the emissions reductions from wind and solar. To the contrary, we found that 

the emissions reductions from wind and solar far outweigh emissions imposed by wind- and 

solar-induced cycling. 

A detailed, consistent wear-and-tear cost and forced outage rate impact database was created for 

seven categories of plants for starts, ramps, and noncyclic operation. To our knowledge, this is 

the first public data set to calculate wear-and-tear costs and impacts for cycling, and one of the 

few integration studies that uses these explicit start costs to make unit commitment and economic 

dispatch decisions. 

A detailed, plant-specific emissions database was created for nearly every plant in the West. The 

database describes the incremental emissions from starts and ramps as well as their emissions 

rates at part-loading. 

Existing wind and solar output and forecast data sets were revised to reflect improvements in 

modeling utility-scale wind and solar plants and state-of-knowledge forecast errors. We 

developed a new reserves methodology for regulating and flexibility reserves that incorporates 

the short-term forecast error and the weather component of variability for wind and solar. 

Next, these new data sets were used in a 5-minute unit commitment and economic dispatch 

model to examine subhourly impacts of wind and solar up to 33% energy penetration levels. 

We investigated the impacts on cycling and the resulting costs and emissions. We compared 

impacts of wind and solar on the rest of the power system. Under the scenarios studied, we 

found the following: 

 Wind and solar increase cycling costs by $35–$157 million/year, or 13%–24%, across the 

Western Interconnection. Those costs are a small percentage of the displaced fuel costs of 

$7 billion/year that result from wind and solar. For the average fossil-fueled plant, cycling 

costs increase by $0.47–$1.28/MWh of fossil-fueled generation, which is a significant 

increase. Starts, not ramps, drive total cycling costs. CTs incur the most cycling costs per 

MWh of operation, although CT cycling costs do not increase in the High Wind Scenario 

and actually decrease in the TEPPC Scenario. From a system perspective, the production 

cost reduction from wind and solar is $29.4–$33.2/MWh of wind and solar generated. 

Without considering cycling costs, the reduction would have been $29.9–$33.6/MWh. This 

does not account for fixed capital or PPA costs. 
130 

 Wind and solar lead to markedly increased ramping for coal generators, and coal runs 

fewer hours per start with high wind penetrations. Typical coal units ramp daily instead 

of weekly as wind and solar penetrations, especially solar, increase. Coal units do not 

start considerably more as renewable penetrations increase. With wind and solar, runtime 

per start for gas CC plants is halved from the No Renewables Scenario. At low wind and 

solar penetrations, gas CC units are started much more, but this impact decreases with 

high penetrations. Gas CTs start and ramp less often in scenarios with high ratios of wind 

to solar penetration. High solar penetrations, on the other hand, lead to more starts, 

shorter runtimes, and more ramping of CTs. 

 This study finds that up to 33% wind and solar energy penetration in the United States’ 

portion of the Western grid (which is equivalent to 24%–26% throughout the Western 

grid) avoids 29%–34% CO2 emissions, 16%–22% NOx emissions, and 14%–24% SO2

emissions throughout the Western grid. Cycling impacts on CO2 emissions were 

negligible. Considering cycling and part-load in detail results in 1%–2% more NOx

reduction from wind and solar than expected because, on average, coal plants in the West 

have lower NOx emissions rates at part-load. Cycling results in 2%–5% less SO2

reduction from wind and solar than expected. 

 The impacts of wind and solar were compared, using new data sets that illuminated the 

subhourly variability of utility-scale PV. Wind and solar generation affect the system in 

different ways. They both mostly displace gas CC generation, but wind tends to displace 

more coal compared to solar. Solar tends to dominate variability extremes, but it can be 

mitigated because most of this variability is known and can be anticipated in the unit 

commitment. Wind tends to dominate uncertainty extremes, because of tail events in DA 

wind forecast errors. This can be mitigated by committing gas CC units in the 4HA time 

frame and gas CTs on shorter time frames. High wind and solar penetrations result in 

modest curtailment—up to 5%—and WWSIS-2 finds that a balanced mix of wind and 

solar reduces curtailment to 1.6%. 

Future Work 

Although system-wide impacts of cycling are modest, an individual unit might be cycled much 

more. In this case, plant owners will want to know whether they should retrofit their units or 

change their operations to better manage cycling at lower overall cost. Ongoing work includes 

research on potential retrofits or operational strategies to increase the flexibility of fossil-fueled 

generators. This includes analysis of the costs and benefits of retrofitting existing plants for 

options such as lower minimum generation levels or faster ramp rates. 

Additional work that would illuminate the impacts of cycling and further compare wind and solar 

includes analysis of the following: 

 Market impacts on fossil-fueled plants: How do increased O&M costs and reduced 

capacity factors affect cost recovery for fossil-fueled plants? What market structures 

might need revision in a high wind and solar paradigm? How do the economics look for 

those plants that were most affected? 
131 

 Different retirement scenarios: How are operations and results affected if significant coal 

capacity is retired or if the balance of plants is flexible versus inflexible? 

 Storage: Does storage mitigate cycling and is it cost effective? 

 Impacts of dispersed versus centralized PV: How does rooftop versus utility-scale PV 

affect the grid? 

 Reserves requirement testing to fine tune flexibility reserves: What confidence levels of 

flexibility reserves are most cost effective and still retain reliable grid operation? 

 Scenarios with constrained transmission build-outs: If transmission is constrained, how 

does the grid perform and how is cycling affected? 

 Reserve-sharing options: How do different reserve-sharing options affect grid operations? 

 Increased hydro flexibility and modeling assumptions: How does flexibility in the hydro 

fleet affect grid operations and what is the impact on cycling? 

 Higher resolution representation of BA coordination and transmission: With higher 

hurdle rates to mimic less BA cooperation, how are grid operations and cycling affected? 

With nodal instead of zonal production simulations, how do operations change? 

 Comparison of the detailed 5-minute production simulation modeling with cycling costs 

to hourly production simulation modeling without cycling costs: How much more 

accurate is the detailed modeling? 

 Gas supply: Is additional gas storage needed? How does increased wind and solar affect 

gas scheduling and supply issues? 

 Market sequence: How much does the system benefit from the 4HA market? 
132 

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137 

Appendix A: Correction of the WWSIS Wind 

Production Data Seams Issue 

3TIER (2010) developed a large wind speed and wind power database using an NWP model 

applied to the West. The model simulates a re-creation of the weather at any time or space, and 

wind speed data were sampled every 10 minutes for a 3-year period on a 2-km spatial resolution 

at representative hub heights for modern wind turbines. The resulting high-resolution data set 

captured the chronological behavior of the wind seen at locations around the West. The data set 

was then used to construct the various wind scenarios described throughout the report. 

The NWP model of the Western Interconnection contained temporal seams that were not 

possible to resolve. This resulted in unrealistic wind energy ramps near the temporal boundaries, 

which occurred every 3 days. A continuous annual record was needed to obtain a complete 

reserves and ramping analysis, so a method to smooth those ramps below statistical significance 

was required. Statistical comparisons to surround “correct” data can be used to improve on the 

correction that the data vendor applied. The goal is to reduce the variability of the output around 

the anomalies while leaving data outside the window of the anomalies unaffected. 

The temporal seams occurred every third day at the same time of day. We counted the days into 

3-day sections, starting on January 1, 2004, and continuing through the end of 2006. We ended 

up with 363 days labeled “1,” 363 days labeled “2,” and 363 days labeled “3.” The seam was 

found to be at midnight Coordinated Universal Time going from Day 3 to Day 1.5

 It was 

empirically determined that the 3TIER smoothing can cover a period of up to 6 hours (36 10-

minute intervals), 2 hours before and 4 hours after the seam. 

To illustrate the seam issue, consider Figure 113, which shows the 10-minute changes of output 

for a particular site. This plot shows the change in output verses the 10-minute interval of the 3-

day period, 1 to 432 (there are 144 10-minute intervals in a day). The first 10-minute intervals of 

all Type 1s were interval 1, and so on. Note the greater variability around the Day 3 to Day 1 

transition, highlighted by the circles. 

 

5

 When counting from the beginning of each year individually, the seam was at midnight on Day 3 to Day 1 for 2004 

and 2005. If counting began on January 1, 2006, the seam would have appeared at midnight on Day 1 to Day 2. This 

is because 2004 had 366 (divisible by three) days and 2005 had 365 (divisible by three, with a remainder of 2) days. 
138 

Figure 113. Distribution of wind changes by 10-minute interval number 

Figure 114 shows the standard deviation for the data at each interval. The first point is the 

standard deviation for all interval 1 data points, and so on. This figure clearly shows the higher 

variability around the seam, as indicated by the higher standard deviation values just before and 

after the seam. 

A number of approaches to mitigate the higher variability at the seam were evaluated. These 

included filtering the 3TIER supplied data, synthesizing new data based on the average sigma of 

the 10-minute changes, and randomly splicing data from “good” days to the affected ranges. 

The filtering was successful at the individual site level; however, as sites were aggregated to 

larger areas, it was found that the correlation between sites in the seam area was much higher 

than in the rest of the data. This resulted in increasingly higher variability as more sites were 

aggregated. This is an artifact of the method 3TIER used to smooth the seams, which led to 

unacceptable results as sites were aggregated to a BA level. 
139 

Figure 114. Standard deviation of 10-minute wind deltas by 10-minute interval number 

To limit the correlation, an approach of synthesizing new data was evaluated. This was done by 

calculating the average sigma for the 10-minute changes and then generating normally 

distributed values to add to a trend that matched the beginning and end of the seam-smoothing 

region. This approach showed no correlation between the sites and resulted in too little 

variability as aggregation level increased. Several distributions were tried, and none yielded 

satisfactory results. 

To match the correlation between sites, it was determined that splicing segments of the good 

days into the seams was a practical approach that should have the desired characteristics. Code 

was built that randomly selected a good day from the two-thirds of days that did not have seams. 

Care was taken to use splice data from only the same time of day of the good days. The 

variability of the splice data was isolated by subtracting the trend, then the splice was blended 

into the “bad” day by adding an appropriate trend to match the beginning and ending values 

surrounding the seam. 

Figure 115 shows results of the correction method applied to the sample site. The red dots are the 

synthesized intervals plotted on top of the original data (blue dots). As shown, only data 

surrounding the anomaly were affected. 
140 

Figure 115. Corrected site data 

To validate the results, we calculated the standard deviation for all the points in each interval of 

the 3-day window across the entire year of data. Figure 116 shows the interval standard 

deviations for both the uncorrected and spliced data. As expected, the values were identical 

except when the spliced data were substituted. Observing the spliced area shows that the 

variability in the anomaly has been reduced to approximately the same range as for the Day 1 to 

2 and the Day 2 to 3 transitions. 
141 

Figure 116. Standard deviation for 10-minute intervals for a single site 

This detailed analysis was done for 10 randomly selected sites for each year (2004–2006) with 

equally good performance for each site. 

The preceding analysis shows that the method produced reasonable results for a single site; 

however, it is very important that the method provides similar results for all levels of 

aggregation. To verify this performance, the detailed analysis was run for a number of regional 

combinations and for all of the wind assigned to the Western Interconnection for this study. 

Figure 117 shows the results for the aggregation of all BAs into approximately 25% penetration 

into the Western Interconnection. Note that the red dots are the corrected data plotted over the 

original data in blue. 

The performance of the method seemed quite good. There is a small “notch” in the negative side 

of the corrected data at the point where the correction ends (approximately Interval 24). This is 

probably because some sites had a smoothing period (3TIER correction) that was longer than the 

empirically determined 6 hours. This effect is shown in Figure 118, when the standard deviation 

for the 10-minute intervals of the original data slowly decayed from the high level shown in the 

anomaly region. 
142 

Figure 117. Ten-minute wind change distribution by interval for all regions aggregated 

Figure 118. Standard deviation by 10-minute interval for all regions aggregated 
143 

It is also important to ensure that the method yields acceptable results with 1-hour averaged data 

as well as the 10-minute data. Figure 119 shows the distribution of hourly changes for each hour 

of the 3-day period. Figure 120 shows the standard deviation of the data at each hour of the 72 

hours of the 3-day period. 

Figure 119. One-hour changes in wind for 25% Western Interconnection scenario by hour number 

Figure 120. Sigma for 1-hour wind changes 

for 25% Western Interconnection scenario by hour number 
144 

In WWSIS-1, GE Energy (2010) provided a different visualization of the hourly data to 

demonstrate the problem with the 3TIER smoothing. The bar-and-whisker plots in Figure 121 

and Figure 122 show the original and corrected average 1-hour change in wind output for the 

hour. The whiskers are the minimum and maximum values for each hour of the 3-day intervals. 

The bars show the mean value plus and minus one standard deviation. 

Figure 121. Bar and whisker for hourly changes for original data 

Figure 122. Bar and whisker for hourly changes for corrected data 
145 

Appendix B: One-Minute Wind Data Development 

This section describes the method used for simulating 1-minute wind power data. The WWSIS 

wind power data provided by 3TIER contained data at 10-minute intervals. A method was 

needed to interpolate data between each 10-minute interval and to simulate power variability 

from a set of real wind turbines with a total capacity of 30 MW. 

One-minute interval power data from several GE 1.5-MW wind turbines located at the Cedar 

Creek Wind Farm were analyzed. To achieve a capacity of 30 MW, data from 20 GE turbines 

were summed. Data from a second set of 20 GE turbines were summed to evaluate repeatability 

of profiles. 

Wind turbine power spectrums can be generated from turbine power data using a number of 

different types of Fourier transform methods (Orwig 2010). An FFT proved to be suitable for 

power spectrums of wind data (Edwards 2009), so an FFT method was applied to the Cedar 

Creek data. To check for differences throughout time, data from August 2011 and December 

2011 were analyzed. The FFT generated a magnitude and phase from the time series wind power 

data. The frequency range was limited by the time interval of the data and number of 

observations. Plots of magnitude versus frequency are referred to as power spectrums (MurAmada and Bayod-Rújula 2007). 

Wind data have a characteristic spectrum, and the high-frequency range of actual data can be 

used to simulate the high-frequency variability between the 10-minute intervals of wind power 

data supplied by 3TIER. Figure 123 shows power spectrums from Cedar Creek turbines for the 

two different time periods. In general, the spectrums were similar. There appeared to be a slight 

upturn in magnitude at the highest frequency range for the December 2011 data. Because the 

low-frequency profile (10 minutes and greater) existed in the supplied wind power data, only the 

high-frequency portion needed to be used for noise simulation. 

The general approach was to first apply a cubic spline interpolation to the 10-minute data to 

obtain power estimates at 1-minute intervals. The second step used high-frequency magnitude 

data combined with randomized phase to generate the noise component of the wind power data. 

Magnitude and phase are needed to calculate real and imaginary components that result in the 

high-frequency noise after applying the inverse Fourier transform. This noise was then added to 

the cubic spline interpolation to give the simulated wind power data. 
146 

Figure 123. Power spectrums of the Cedar Creek turbines 

for (a) August 2011 and (b) December 2011 

Note: Frequency is in Hz. 

An experimental approach was used to determine a reasonable cutoff frequency for the highfrequency portion of the spectrum to be used for simulating noise. The Cedar Creek wind turbine 

power data were used to test the cutoff frequency by extracting observations at 10-minute 

intervals and then simulating the high-frequency noise using different values for cutoff 

frequency. Actual noise was determined by subtracting the cubic spline fit away from the actual 

1-minute interval data. Table 29 compares the standard deviation of the actual noise to the 

standard deviation for simulated noise using different cutoff frequencies. 

Table 29. Comparison of Actual Noise Standard Deviation 

 to Simulated Noise Standard Deviation for August 2011 and December 2011 

Noise Cutoff Frequency (Hz) August 2011 Standard 

Deviation (MW) 

December 2011 

Standard Deviation 

(MW) 

Actual 0.943 0.554 

Simulated 0.10 0.308 0.185 

Simulated 0.05 0.504 0.296 

Simulated 0.04 0.588 0.357 

Simulated 0.02 0.946 0.604 

Based strictly on the standard deviation results, it appears that a cutoff frequency gives standard 

deviations comparable to actual noise. Nevertheless, comparisons of actual data to simulated data 

indicated that (1) the variability is not always uniform throughout time and (2) a cutoff frequency 

of 0.02 was resulting in some additional undesirable oscillations. Therefore, a cutoff frequency 

of 0.04 (less than 25 minutes) was selected, which gave reasonable noise simulations. 

(a) (b) 
147 

Figure 124 shows the power spectrums from August 2011 and December 2011, with simulated 

high-frequency noise overlaid on the original power spectrums from Figure 123. As shown, the 

power spectrums from 1-minute interpolated data agreed reasonably well with the power 

spectrums of actual data. The magnitude for the interpolated August 2011 data shows a broader 

range than actual but it is not excessive. 

(a) (b) 

Figure 124. Power spectrums using simulated (red) high-frequency noise overlaid 

on (black) power spectrums from actual data for (a) August 2011 and (b) December 2011 

Note: Frequency is in Hz. 

Figure 125 compares the simulated interpolated data to actual data for a short segment of time 

during August 2011. Also plotted were actual noise—which was the actual data minus the cubic 

spline fit—and the simulated noise. Although the noise of the simulated data was more uniform 

and less in magnitude than that from the actual data, in general, the simulated data resembled the 

actual data. Figure 126 compares simulated data to actual data for a short segment of time in 

December 2011. Again, there was reasonable resemblance between the simulated data and the 

actual data. 
148 

Figure 125. Comparison of interpolated 1-minute wind power data to actual data 

for August 2011 (time between 3,400 and 4,200 minutes) 

Figure 126. Comparison of interpolated 1-minute wind power data to actual data 

for December 2011 (time between 3,600 and 4,400 minutes) 
149 

Because the 10-minute data from 3TIER is stored in files containing 1 full year of data, it was 

desirable to process these files in one step. This required a file containing 1 full year of 

magnitudes to generate 1 full year of simulated noise. Only about one-half of a year of 1-minute 

data were collected at the time the interpolation was done. Available wind power data followed 

an annual cycle with more power being available in the winter, then declining in the spring, 

reaching a minimum in the summer, and increasing again in the fall. To approximate this trend, 

Cedar Creek data from July 2011 through December 2011 were combined. To approximate the 

time period from January 2011 through June 2011, the combined data were then reversed and 

reassigned to the time period of January 2011 through June 2011. This gave 1 full year of data 

from which simulated noise could be generated. Figure 127 shows the power spectrum from this 

spliced together data set. It resembles reasonably well the power spectrums of monthly data 

shown in Figure 123. 

Figure 127. Power spectrum from spliced Cedar Creek turbine data for 1 full year 
150 

Appendix C: Transmission Graphs 

This appendix details the transmission expansion for each scenario, showing the path capability 

added at each iteration of the PLEXOS runs. Figure 128 through Figure 131 show the iterative 

transmission expansion for each shadow price cutoff value for the TEPPC, High Wind, High 

Mix, and High Solar Scenarios. 
151 

Figure 128. Transmission expansion for the TEPPC Scenario showing the (gray) initial 

and (black) additional transmission for each shadow price cutoff value tested 
152 

Figure 129. Transmission expansion for the High Wind Scenario showing the (gray) initial 

and (black) additional transmission for each shadow price cutoff value tested 
153 

Figure 130. Transmission expansion for the High Mix Scenario showing the (gray) initial 

and (black) additional transmission for each shadow price cutoff value tested 
154 

Figure 131. Transmission expansion for the High Solar Scenario showing the (gray) initial 

and (black) additional transmission for each shadow price cutoff value tested 
155 

Table 30 and Table 31 show the tabular values for each path from Figure 128 through Figure 131. Table 30 show

expansion by Scenario and Table 31 shows the expansion by shadow price cutoff value. 

Table 30. Transmission Expansion for Each Scenario Showing the Increased Path Capabilities for Different Shado

Initial High Wind High Solar High Mix 

$20 

Cutoff 

$15 

Cutoff 

$10 

Cutoff 

$5 

Cutoff 

$20 

Cutoff 

$15 

Cutoff 

$10 

Cutoff 

$5 

Cutoff 

$20 

Cutoff 

$15 

Cutoff 

$10 

Cutoff 

C

Alberta to British Columbia 800 0 0 0 0 0 0 0 0 0 0 0 Alberta to Montana 325 0 0 0 0 0 0 0 0 0 0 0 Arizona to 

California_South 1,600 1,000 1,000 1,000 1,000 1,000 1,000 1,000 1,000 1,000 1,000 1,000 

Arizona to Colorado 200 0 500 500 1,500 0 0 0 500 0 0 500 Arizona to IID 195 500 500 500 500 500 500 500 500 500 500 500 Arizona to LAWP 468 500 500 500 500 500 500 500 500 500 500 500 Arizona to Nevada_South 4,785 0 0 0 0 0 0 0 0 0 0 0 Arizona to New Mexico 5,582 0 0 0 0 0 0 0 0 0 0 0 Arizona to San Diego 1,168 0 0 0 0 0 0 0 0 0 0 0 Arizona to Utah 250 0 500 500 500 0 0 0 500 0 0 500 British Columbia to 

Northwest 2,200 0 0 0 0 0 0 0 0 0 0 0 

California_North to 

California_South 3,000 0 0 0 0 0 0 0 0 0 0 0 

California_North to 

Nevada_North 100 1,000 1,500 1,500 2,500 1,000 1,000 1,500 2,500 1,000 1,000 1,500 

California_North to 

Northwest 4,200 0 0 0 1,000 0 0 0 0 0 0 0 

California_North to San 

Francisco 1,272 1,000 1,000 1,500 1,500 1,000 1,000 1,000 1,500 1,000 1,000 1,500 

California_North to SMUD 2,750 0 0 0 0 0 0 0 0 0 0 0 California_South to IID 600 0 0 500 500 0 0 500 500 0 0 0 California_South to LAWP 3,750 0 0 0 0 0 0 0 0 0 0 0 California_South to 

Nevada_North 200 0 0 0 0 0 0 0 0 0 0 0 

California_South to 

Nevada_South 2,814 0 0 0 0 0 0 0 0 0 0 0 

California_South to San 

Diego 2,440 0 0 0 0 0 0 0 0 0 0 0 
156 

Colorado to Montana 200 0 0 0 500 0 0 0 500 0 0 0 Colorado to New Mexico 664 0 0 0 0 0 0 0 0 0 0 0 Colorado to Utah 650 0 0 0 0 0 0 0 0 0 0 0 Colorado to Wyoming 1,595 0 0 0 0 0 0 0 0 0 0 0 Idaho to Montana 325 0 0 0 0 0 0 0 0 0 0 0 Idaho to Nevada_North 350 500 500 500 1,000 500 500 500 500 500 500 500 Idaho to Northwest 3,400 0 0 0 0 0 0 0 0 0 0 0 Idaho to Utah 2,250 0 0 0 0 0 0 0 0 0 0 0 Idaho to Wyoming 4,100 0 0 0 0 0 0 0 0 0 0 0 IID to San Diego 1,150 0 0 0 0 0 0 0 0 0 0 0 LAWP to Nevada_North 200 0 0 0 0 0 0 0 0 0 0 0 LAWP to Nevada_South 3,883 0 0 0 0 0 0 0 0 0 0 0 LAWP to Northwest 2,858 0 0 0 0 0 0 0 0 0 0 0 LAWP to Utah 1,920 0 0 0 0 0 0 0 0 0 0 0 Mexico (CFE) to New 

Mexico 200 0 0 0 0 0 0 0 0 0 0 0 

Mexico (CFE) to San 

Diego 408 0 0 0 0 0 0 0 0 0 0 0 

Montana to Northwest 2,000 0 500 1,500 3,000 0 0 0 500 0 500 1,000 Montana to Wyoming 400 0 0 0 0 0 0 0 0 0 0 0 Nevada_North to 

Nevada_South 2,000 0 0 0 0 0 0 0 0 0 0 0 

Nevada_North to 

Northwest 300 1,000 1,000 1,000 1,000 500 1,000 1,000 1,000 1,000 1,000 1,000 

Nevada_North to Utah 360 0 0 0 0 0 0 0 0 0 0 0 Nevada_South to Utah 600 0 0 0 500 0 0 0 0 0 0 0 Utah to Wyoming 2,100 0 0 1,000 2500 0 0 0 0 0 0 500 

Note: CFE, Comisión Federal de Electricidad 
157 

Table 31. Transmission Expansion for Each Shadow Price Cutoff Values Showing the Increased Path Capabilities 

Initial $20 Cutoff $15 Cutoff $10 Cutoff 

High Wind 

High Solar 

High

Mix Ref 

High

Wind 

High

Solar 

High

Mix Ref 

High Wind 

High

Solar 

High

Mix Ref 

H

W

Alberta to British Columbia 800 0 0 0 0 0 0 0 0 0 0 0 0 Alberta to Montana 325 0 0 0 0 0 0 0 0 0 0 0 0 Arizona to California_South 1,600 1,000 1,000 1,000 

1,00

0 1,000 1,000 1,000 

1,00

0 1,000 1,000 1,000 

1,00

0 

Arizona to Colorado 200 0 0 0 0 500 0 0 0 500 0 500 0 Arizona to IID 195 500 500 500 500 500 500 500 500 500 500 500 500 Arizona to LAWP 468 500 500 500 0 500 500 500 0 500 500 500 0 Arizona to Nevada_South 4,785 0 0 0 0 0 0 0 0 0 0 0 0 Arizona to New Mexico 5,582 0 0 0 0 0 0 0 0 0 0 0 0 Arizona to San Diego 1,168 0 0 0 0 0 0 0 0 0 0 0 0 Arizona to Utah 250 0 0 0 0 500 0 0 0 500 0 500 0 British Columbia to Northwest 2,200 0 0 0 0 0 0 0 0 0 0 0 0 California_North to California_South 3,000 0 0 0 0 0 0 0 0 0 0 0 0 California_North to Nevada_North 100 1,000 1,000 1,000 500 1,500 1,000 1,000 500 1,500 1,500 1,500 500 California_North to Northwest 4,200 0 0 0 0 0 0 0 0 0 0 0 0 California_North to San Francisco 1,272 1,000 1,000 1,000 500 1,000 1,000 1,000 500 1,500 1,000 1,500 500 

California_North to SMUD 2,750 0 0 0 0 0 0 0 0 0 0 0 0 California_South to IID 600 0 0 0 0 0 0 0 0 500 500 0 0 California_South to LAWP 3,750 0 0 0 0 0 0 0 0 0 0 0 0 California_South to Nevada_North 200 0 0 0 0 0 0 0 0 0 0 0 0 California_South to Nevada_South 2,814 0 0 0 0 0 0 0 0 0 0 0 0 California_South to San Diego 2,440 0 0 0 0 0 0 0 0 0 0 0 0 Colorado to Montana 200 0 0 0 0 0 0 0 0 0 0 0 0 Colorado to New Mexico 664 0 0 0 0 0 0 0 0 0 0 0 0 Colorado to Utah 650 0 0 0 0 0 0 0 0 0 0 0 0 Colorado to Wyoming 1,595 0 0 0 0 0 0 0 0 0 0 0 0 Idaho to Montana 325 0 0 0 0 0 0 0 0 0 0 0 0 
158 

Idaho to Nevada_North 350 500 500 500 500 500 500 500 500 500 500 500 500 Idaho to Northwest 3,400 0 0 0 0 0 0 0 0 0 0 0 0 Idaho to Utah 2,250 0 0 0 0 0 0 0 0 0 0 0 0 Idaho to Wyoming 4,100 0 0 0 0 0 0 0 0 0 0 0 0 IID to San Diego 1,150 0 0 0 0 0 0 0 0 0 0 0 0 LAWP to Nevada_North 200 0 0 0 0 0 0 0 0 0 0 0 0 LAWP to Nevada_South 3,883 0 0 0 0 0 0 0 0 0 0 0 0 LAWP to Northwest 2,858 0 0 0 0 0 0 0 0 0 0 0 0 LAWP to Utah 1,920 0 0 0 0 0 0 0 0 0 0 0 0 Mexico (CFE) to New Mexico 200 0 0 0 0 0 0 0 0 0 0 0 0 Mexico (CFE) to San Diego 408 0 0 0 0 0 0 0 0 0 0 0 0 Montana to Northwest 2,000 0 0 0 0 500 0 500 0 1,500 0 1,000 0 Montana to Wyoming 400 0 0 0 0 0 0 0 0 0 0 0 0 Nevada_North to Nevada_South 2,000 0 0 0 0 0 0 0 0 0 0 0 0 Nevada_North to Northwest 300 1,000 500 1,000 500 1,000 1,000 1,000 500 1,000 1,000 1,000 

1,00

0 

Nevada_North to Utah 360 0 0 0 0 0 0 0 0 0 0 0 0 Nevada_South to Utah 600 0 0 0 0 0 0 0 0 0 0 0 0 Utah to Wyoming 2,100 0 0 0 0 0 0 0 0 1,000 0 500 0 

Note: The Ref scenario refers to the TEPPC scenario. 
159 

Appendix D: Statistical Graphs 

This section contains a more complete set of statistical graphs than the body of the report. Each 

group of graphs is accompanied by a short introduction giving any interpretation information and 

special comments. Note that some information is labeled as Reference Scenario or Intermediate 

Scenario. For this study, the Reference Scenario was renamed as the TEPPC Scenario and the 

Intermediate Scenario was renamed as the High Mix Scenario. 

Figure 132 through Figure 135 show the siting of wind and solar plants by state for each 

scenario. The darker colors show higher installed capacity. The number of states at each installed 

capacity range is shown in parentheses in the legend. 

Figure 132. TEPPC Scenario—installed capacity by state 
160 

Figure 133. High Solar Scenario—installed capacity by state 
161 

Figure 134. High Mix Scenario—installed capacity by state 
162 

Figure 135. High Wind Scenario—installed capacity by state 

Figure 136 through Figure 139 show the monthly wind and solar energy production for several 

subregions: WC, CG, NTTG, and CAISO. The percentage of energy penetration for each month 

is labeled on the graphs. The aggregated wind and solar energy production across the Western 

Interconnection is also shown, in GWh and as a percentage of load. VG in these plots includes 

wind, PV and CSP. 
163 

Figure 136. High Solar Scenario—monthly profiles 
164 

Figure 137. High Mix Scenario—monthly profiles 
165 

Figure 138. High Wind Scenario—monthly profiles 
166 

Figure 139. TEPPC Scenario—monthly profiles 
167 

Figure 140 through Figure 143 show the average diurnal profiles of the load, wind, PV, and CSP 

for each scenario. The CSP shown in these profiles was dispatched by NREL’s System Advisor 

Model and does not reflect the final dispatch as modeled by PLEXOS. 

Figure 140. TEPPC Scenario—average diurnal profile 
168 

Figure 141. High Solar Scenario—average diurnal profile 
169 

Figure 142. High Mix Scenario—average diurnal profile 
170 

Figure 143. High Wind Scenario—average diurnal profile 
171 

Figure 144 through Figure 146 show the seasonal variability of wind, PV, and wind plus PV for 

the high-penetration scenarios. Each point on the plots represents a single ordered pair of load 

deltas with the time-synchronized resource deltas. 

Figure 144. High Solar Scenario—hourly variability by season 

 
172 

Figure 145. High Mix Scenario—hourly variability by season 
173 

Figure 146. High Wind Scenario—hourly variability by season 
174 

Figure 147 and Figure 148 show a surface indicating when load and net load values occur over 

the year of data used in the study. The x-axis is the hour of the day and the y-axis is the month of 

the year. The color scale shows the average monthly value of load or net load in the hour. 

Figure 147. Load alone with monthly average values in MW 

Load (MW)

74000.00

74000.00

81000.00

81000.00

81000.00

81000.00

88000.00

88000.00

88000.00

88000.00

88000.00

88000.00

95000.00

95000.00

95000.00

95000.00

95000.00

95000.00

95000.00

102000.00

102000.00

102000.00

102000.00

102000.00

102000.00

109000.00

116000.00

123000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

130000

123000

116000

109000

102000

95000

88000

81000

74000

67000

60000
175 

Figure 148. Net load with monthly average values in MW for each scenario 

High Solar Scenario Net Load (MW)

46000.00

54000.00

54000.00

62000.00

62000.00 62000.00

62000.00

70000.00

70000.00

70000.00

70000.00

70000.00

70000.00

70000.00

70000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00

78000.00 86000.00

86000.00

86000.00

86000.00

94000.00

94000.00

102000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

110000

102000

94000

86000

78000

70000

62000

54000

46000

38000

30000

High Mix Scenario Net Load (MW)

46000.00

54000.00 54000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

70000.00

70000.00

70000.00

70000.00

70000.00

70000.00

70000.00

70000.00

78000.00

78000.00

78000.00

78000.00

78000.00

86000.00

86000.00

94000.00

102000 00 .

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

110000

102000

94000

86000

78000

70000

62000

54000

46000

38000

30000

High Wind Scenario Net Load (MW)

46000.00

46000.00

54000.00

54000.00

54000.00

54000.00

54000.00

54000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

62000.00

70000 0. 0

70000.00

70000.00

70000.00

70000.00

78000.00

78000.00

78000.00

78000.00

78000.00

86000.00

86000.00

94000.00

102000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

110000

102000

94000

86000

78000

70000

62000

54000

46000

38000

30000

Reference Scenario Net Load (MW)

62000.00

62000.00

70000.00

70000.00

70000.00

70000.00

78000.00

78000.00

78000.00

78000.00 78000.00

78000.00

78000.00

86000.00

86000.00

86000.00

86000.00

86000.00

86000.00

94000.00

94000.00

94000.00

94000.00

94000.00

102000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

110000

102000

94000

86000

78000

70000

62000

54000

46000

38000

30000
176 

Figure 149 and Figure 150 show a surface indicating when load and net load changes occur over 

the year of data used in the study. The x-axis is the hour of the day and the y-axis is the month of 

the year. The color scale shows the monthly average change in load or net load in the hour. 

Figure 149. Delta load only with monthly average changes in MW/h 

WI Footprint Delta Load (MW / hr)

-8000.00

-6000.00

-6000.00 -6000.00

-6000.00

-6000.00

-6000.00

-4000.00

-4000.00

-4000.00

-4000.00

-4000.00

-2000.00

-2000.00

-2000.00

-2000.00

-2000.00

-2000.00

0.00 0.00 0.00

0.00

0.00

0.00

0.00

0.00

20000. 0

2000.00

2000.00

20000. 0

2000.00

2000.00

2000.00

2000.00

40000. 0

4000.00

4000.00

4000.00

4000.00

4000.00

4000.00

4000.00

6000.00

6000.00

6000.00

6000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

10000

8000

6000

4000

2000

0

-2000

-4000

-6000

-8000

-10000
177 

Figure 150. Delta net load with monthly average changes in MW/h for each scenario 

Note: Observe the difference in scales between the scenarios. 

High Solar Scenario WI Footprint Delta Net Load (MW/hr)

-14400.00

-14400.00

-9600.00

-9600.00

-4800.00

-4800.00

-4800.00

-4800.00

-4800.00

-4800.00

-4800.00

-4800.00

-4800.00

-4800.00

0.00

0.00 0.00

0.00

0.00 0.00

0.00

0.00

0.00

0.00

0.00

0.00

4800.00

4800.00

4800.00

4800.00

4800.00

4800.00

4800.00

4800 0. 0

9600.00

9600.00 14400.00

14400.00 19200.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

24000

19200

14400

9600

4800

0

-4800

-9600

-14400

-19200

-24000

High Mix Scenario Delta Net Load (MW/hr)

-7200.00

-7200.00

-7200.00

-7200.00

-7200.00

-3600.00

-3600.00

-3600.00 -3600.00

-3600.00

-3600.00

-3600.00

0.00

0.00

0.00

0.00 0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

3600.00

3600.00

3600 0. 0

3600.00 3600.00

3600.00

3600.00

3600.00

3600.00

3600.00

7200.00

7200.00

10800.00

10800.00 14400.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

18000

14400

10800

7200

3600

0

-3600

-7200

-10800

-14400

-18000

High Wind Scenario WI Footprint Delta Net Load (MW/hr)

-7200.00 -7200.00

-7200.00

-4800.00

-4800.00

-4800.00

-4800.00

-4800.00

-4800.00

-2400.00

-2400.00

-2400.00

-2400.00

-2400.00

-2400.00

-2400.00

0.00 0.00

0.00

0.00

0.00

0.00

0.00

0.00

2400.00

2400 0. 0

2400.00

2400.00

2400.00

2400 0. 0

2400.00

2400 0. 0

4800 0. 0

4800.00

4800 0. 0

4800.00

4800.00

4800.00

0.00

7200.00

7200.00 9600 0. 0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

12000

9600

7200

4800

2400

0

-2400

-4800

-7200

-9600

-12000

Reference Scenario Delta Net Load (MW/hr)

-8000.00

-6000.00

-6000.00

-6000.00

-6000.00 -6000.00

-6000.00

-4000.00

-4000.00

-4000.00

-4000.00

-4000.00

-4000.00

-2000.00

-2000.00

-2000.00

-2000.00

-2000.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

2000 0. 0 2000.00

2000.00

2000.00

2000.00

2000.00

2000.00

2000.00

4000 0. 0

4000.00

4000.00

4000.00

4000.00

4000.00

4000.00

4000.00

6000.00

6000.00

6000.00

6000.00

6000.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Hour

1

2

3

4

5

6

7

8

9

10

11

12

Month

10000

8000

6000

4000

2000

0

-2000

-4000

-6000

-8000

-10000
178 

The next section focuses on the DA forecast error analysis. Figure 151 through Figure 153 show 

DA forecast error analysis for wind, PV, and wind plus PV forecast errors. CSP is not included 

in this forecast error analysis because CSP has storage that allows it to be dispatched. Statistics 

by subregion are shown in Table 32 through Table 35. 

Figure 151. Distribution of DA wind forecast errors 

Figure 152. Distribution of DA PV forecast errors 
179 

Figure 153. Distribution of DA wind plus PV forecast errors 

Table 32. DA Forecast Error Statistics for the High Solar Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 4,406 451 10 649 15 –2,384 –54 2,958 67 

CG 6,581 631 10 946 14 –4,973 –76 5,101 78 

NTTG 5,787 495 9 711 12 –3,807 –66 3,052 53 

WC 6,584 403 6 562 9 –2,848 –43 2,726 41 

U.S. Western 

Interconnection 

23,357 1,387 6 1,849 8 –8,169 –35 8,458 36 

Solar (PV) 

CA 17,134 713 4 1,055 6 –6,589 –38 5,310 31 

CG 1,211 105 9 156 13 –619 –51 667 55 

NTTG 5,270 285 5 440 8 –2,070 –39 2,103 40 

WC 36,526 1,248 3 1,771 5 –9,189 –25 6,018 16 

U.S. Western 

Interconnection 

60,141 1,882 3 2,628 4 –16,974 –28 9,867 16 

Total 

CA 21,539 692 3 989 5 –7,592 –35 5,243 24 

CG 7,792 639 8 947 12 –4,973 –64 5,101 65 

NTTG 11,057 560 5 771 7 –3,807 –34 3,169 29 

WC 43,110 936 2 1,435 3 –9,576 –22 6,168 14 

U.S. Western 

Interconnection 

83,498 2,004 2 2,676 3 –17,714 –21 10,806 13 
180 

Table 33. DA Forecast Error Statistics for the High Mix Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 4,788 478 10 680 14 –2,462 –51 2,983 62 

CG 8,261 757 9 1,106 13 –5,550 –67 5,908 72 

NTTG 12,414 905 7 1,218 10 –6,123 –49 4,474 36 

WC 18,314 1,073 6 1,442 8 –6,763 –37 5,471 30 

U.S. Western 

Interconnection 

43,778 2,207 5 2,874 7 12,232 –28 11,098 25 

Solar (PV) 

CA 13,588 306 2 641 5 –5,172 –38 4,731 35 

CG 1,161 51 4 106 9 –588 –51 637 55 

NTTG 2,976 61 2 130 4 –814 –27 903 30 

WC 23,165 427 2 821 4 –6,173 –27 3,687 16 

U.S. Western 

Interconnection 

40,890 704 2 1,323 3 –11,373 –28 8,441 21 

Total 

CA 18,376 668 4 931 5 –6,228 –34 4,760 26 

CG 9,422 761 8 1,105 12 –5,550 –59 5,908 63 

NTTG 15,390 915 6 1,225 8 –6,123 –40 4,519 29 

WC 41,479 1,271 3 1,670 4 –7,047 –17 6,113 15 

U.S. Western 

Interconnection 

84,668 2,429 3 3,133 4 –12,525 –15 11,098 13 
181 

Table 34. DA Forecast Error Statistics for the High Wind Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 7,970 693 9 977 12 –3,756 –47 4,832 61 

CG 9,101 822 9 1,188 13 –5,895 –65 6,587 72 

NTTG 16,854 1,179 7 1,582 9 –7,584 –45 6,501 39 

WC 32,224 1,838 6 2,438 8 –10,078 –31 9,558 30 

U.S. Western 

Interconnection 

66,150 3,183 5 4,115 6 –17,381 –26 16,574 25 

Solar (PV) 

CA 9,772 188 2 384 4 –2,885 –30 2,776 28 

CG 969 42 4 86 9 –491 –51 507 52 

NTTG 2,312 23 1 49 2 –305 –13 377 16 

WC 14,979 199 1 382 3 –2,356 –16 1,992 13 

U.S. Western 

Interconnection 

28,032 362 1 673 2 –5,340 –19 4,550 16 

Total 

CA 17,743 766 4 1,042 6 –4,963 –28 4,879 28 

CG 10,070 825 8 1,188 12 –5,895 –59 6,587 65 

NTTG 19,167 1,181 6 1,583 8 –7,584 –40 6,501 34 

WC 47,203 1,875 4 2,477 5 –10,078 –21 9,545 20 

U.S. Western 

Interconnection 

94,183 3,229 3 4,161 4 –17,381 –18 16,574 18 
182 

Table 35. DA Forecast Error Statistics for the TEPPC Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 5,630 524 9 744 13 –2,725 –48 3,341 59 

CG 6,161 591 10 896 15 –4,762 –77 5,057 82 

NTTG 6,054 513 8 739 12 –3,912 –65 3,260 54 

WC 10,054 692 7 955 9 –4,150 –41 4,804 48 

U.S. Western 

Interconnection 

27,900 1,612 6 2,118 8 –8,929 –32 10,182 36 

Solar (PV) 

CA 2,798 63 2 144 5 –1,309 –47 1,078 39 

CG 0 0 - 0 - 0 - 0 - 

NTTG 364 12 3 28 8 –215 –59 187 51 

WC 3,912 84 2 169 4 –1,418 –36 827 21 

U.S. Western 

Interconnection 

7,074 135 2 272 4 –2,265 –32 1,170 17 

Total 

CA 8,428 545 6 762 9 –2,873 –34 3,194 38 

CG 6,161 591 10 896 15 –4,762 –77 5,057 82 

NTTG 6,418 514 8 738 12 –3,912 –61 3,260 51 

WC 13,967 719 5 980 7 –4,150 –30 4,804 34 

U.S. Western 

Interconnection 

34,974 1,640 5 2,144 6 –8,929 –26 10,182 29 
183 

Figure 154 through Figure 159 show the wind and solar DA forecast errors as a function of load, 

net load, wind, and solar levels. 

Figure 154. Wind and PV DA forecast error 

as a function of load level for the High Solar Scenario 
184 

Figure 155. Wind and PV DA forecast error as a function 

of wind and PV output level for the High Solar Scenario 
185 

Figure 156. Wind plus PV DA forecast error 

as a function of net load for the High Solar Scenario 
186 

Figure 157. Wind and PV DA forecast error 

as a function of load level for the High Wind Scenario 
187 

Figure 158. Wind and solar DA forecast error as a function 

of wind and solar output level for the High Wind Scenario 
188 

Figure 159. Wind plus PV DA forecast error 

as a function of net load for the High Wind Scenario 

The next section focuses on the 4HA forecast error analysis. Figure 160 through Figure 162 

show the 4HA forecast error analysis for wind, PV, and wind plus PV forecast errors. CSP is not 

included in this forecast error analysis. Statistics by subregion are shown in Table 36 through 

Table 39. 

Figure 160. Distribution of 4HA wind forecast errors 
189 

 

Figure 161. Distribution of 4HA solar forecast errors 

Figure 162. Distribution of 4HA wind plus PV forecast errors 
190 

Table 36. 4HA Forecast Error Statistics for the High Solar Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 4,406 299 7 438 10 –2,200 –50 2,306 52 

CG 6,581 312 5 454 7 –2,485 –38 3,352 51 

NTTG 5,787 248 4 353 6 –2,034 –35 2,102 36 

WC 6,584 269 4 356 5 –1,688 –26 1,772 27 

U.S. Western 

Interconnection 

23,357 785 3 1,038 4 –4,280 –18 5,153 22 

Solar (PV) 

CA 17,134 418 2 597 3 –2,658 –16 3,788 22 

CG 1,211 46 4 69 6 –368 –30 339 28 

NTTG 5,270 157 3 249 5 –1,573 –30 1,906 36 

WC 36,526 667 2 966 3 –7,711 –21 14,380 39 

U.S. Western 

Interconnection 

60,141 991 2 1,423 2 –10,323 –17 19,982 33 

Total 

CA 21,539 424 2 596 3 –3,247 –15 3,728 17 

CG 7,792 316 4 456 6 –2,485 –32 3,352 43 

NTTG 11,057 282 3 392 4 –2,040 –18 2,102 19 

WC 43,110 516 1 776 2 –7,631 –18 13,643 32 

U.S. Western 

Interconnection 

83,498 1,020 1 1,382 2 –10,544 –13 19,156 23 
191 

Table 37. 4HA Forecast Error Statistics for the High Mix Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 4,788 308 6 450 9 –2,244 –47 2,356 49 

CG 8,261 355 4 506 6 –2,523 –31 3,439 42 

NTTG 12,414 423 3 561 5 –3,241 –26 2,563 21 

WC 18,314 637 3 829 5 –3,590 –20 3,597 20 

U.S. Western 

Interconnection 

43,778 1,173 3 1,516 3 –5,951 –14 6,637 15 

Solar (PV) 

CA 13,588 356 3 510 4 –2,290 –17 2,507 18 

CG 1,161 45 4 67 6 –352 –30 321 28 

NTTG 2,976 66 2 104 3 –548 –18 545 18 

WC 23,165 438 2 634 3 –5,376 –23 8,820 38 

U.S. Western 

Interconnection 

40,890 696 2 989 2 –6,900 –17 11,781 29 

Total 

CA 18,376 407 2 567 3 –2,959 –16 2,825 15 

CG 9,422 357 4 508 5 –2,518 –27 3,439 37 

NTTG 15,390 425 3 564 4 –3,148 –20 2,563 17 

WC 41,479 708 2 929 2 –5,367 –13 7,991 19 

U.S. Western 

Interconnection 

84,668 1,261 1 1,631 2 –6,600 –8 10,996 13 
192 

Table 38. 4HA Forecast Error Statistics for the High Wind Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 7,970 441 6 619 8 –3,052 –38 2,956 37 

CG 9,101 377 4 532 6 –2,608 –29 3,464 38 

NTTG 16,854 550 3 725 4 –3,878 –23 3,273 19 

WC 32,224 988 3 1,275 4 –5,412 –17 5,691 18 

U.S. Western 

Interconnection 

66,150 1,637 2 2,100 3 –7,685 –12 7,864 12 

Solar (PV) 

CA 9,772 217 2 307 3 –1,388 –14 1,596 16 

CG 969 37 4 55 6 –300 –31 261 27 

NTTG 2,312 26 1 40 2 –193 –8 232 10 

WC 14,979 201 1 289 2 –1,880 –13 3,132 21 

U.S. Western 

Interconnection 

28,032 352 1 496 2 –2,617 –9 4,179 15 

Total 

CA 17,743 477 6 651 4 –3,089 –17 2,853 16 

CG 10,070 378 4 533 5 –2,608 –26 3,464 34 

NTTG 19,167 550 3 725 4 –3,851 –20 3,273 17 

WC 47,203 990 3 1,281 3 –5,412 –11 5,691 12 

U.S. Western 

Interconnection 

94,183 1,650 2 2,115 2 –8,499 –9 8,021 9 
193 

Table 39. 4HA Forecast Error Statistics for the TEPPC Scenario 

Region Capacity 

(MW) 

MAE 

(MW) 

MAE 

(%) 

RMSE 

(MW) 

RMSE 

(%) 

Maximum 

Negative 

Error 

(MW) 

Negative 

Error 

(%) 

Maximum 

Positive 

Error 

(MW) 

Maximum 

Positive 

Error (%) 

Wind 

CA 5,630 337 6 487 9 –2,470 –44 2,526 45 

CG 6,161 298 5 436 7 –2,431 –39 3,335 54 

NTTG 6,054 255 4 363 6 –2,110 –35 2,152 36 

WC 10,054 371 4 493 5 –2,336 –23 2,267 23 

U.S. Western 

Interconnection 

27,900 888 3 1,171 4 –4,636 –17 6,192 22 

Solar (PV) 

CA 2,798 82 2 132 5 –767 –27 1,063 38 

CG 0 0 - 0 - 0 - 0 - 

NTTG 364 16 3 27 8 –148 –41 201 55 

WC 3,912 90 2 130 4 –893 –23 1,561 40 

U.S. Western 

Interconnection 

7,074 145 2 218 4 –1,482 –21 2,824 40 

Total 

CA 8,428 347 4 494 6 –2,409 –29 2,445 29 

CG 6,161 298 5 436 7 –2,431 –39 3,335 54 

NTTG 6,418 255 4 363 6 –2,094 –33 2,152 34 

WC 13,967 376 3 499 4 –2,336 –17 2,184 16 

U.S. Western 

Interconnection 

34,974 889 3 1,172 3 –4,490 –13 6,192 18 
194 

Figure 163 through Figure 168 show how the wind and solar 4HA forecast errors vary with load, 

net load, wind, and PV output. 

Figure 163. Wind and PV 4HA forecast errors as a function 

of load level for the High Solar Scenario 
195 

Figure 164. Wind and PV 4HA forecast error as a function 

of wind and PV output level for the High Solar Scenario 

 
196 

Figure 165. Wind plus PV 4HA forecast error as a function 

of net load level for the High Solar Scenario 

 
197 

Figure 166. Wind and PV 4HA forecast error 

as a function of load level for the High Wind Scenario 
198 

Figure 167. Wind and PV 4HA forecast error as a function 

of wind and solar output level for the High Wind Scenario
199 

Figure 168. Wind plus PV 4HA forecast error as a function 

of net load level for the High Wind Scenario

High Wind Scenario – Total Forecast Error vs Net Load