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

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Renewable Electricity Futures: 

Operational Analysis of the 

Western Interconnection at Very 

High Renewable Penetrations

Gregory Brinkman

National Renewable Energy Laboratory

Technical Report

NREL/TP-6A20-64467

September 2015
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

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Renewable Electricity Futures: 

Operational Analysis of the 

Western Interconnection at 

Very High Renewable 

Penetrations

Gregory Brinkman

National Renewable Energy Laboratory

Prepared under Task No. SA15.0410

Technical Report

NREL/TP-6A20-64467

September 2015
NOTICE

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Available electronically at SciTech Connect http:/www.osti.gov/scitech

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

OSTI http://www.osti.gov

Phone: 865.576.8401

Fax: 865.576.5728

Email: reports@osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5301 Shawnee Road

Alexandria, VA 22312

NTIS http://www.ntis.gov

Phone: 800.553.6847 or 703.605.6000

Fax: 703.605.6900

Email: orders@ntis.gov

Cover Photos by Dennis Schroeder: (left to right) NREL 26173, NREL 18302, NREL 19758, NREL 29642, NREL 19795.

NREL prints on paper that contains recycled content.
iii

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Acknowledgments

We thank Mark Ahlstrom (WindLogics), Jon Black (ISO New England), Bob Hess (Salt River 

Project), Gary Jordan (GE Energy), Brendan Kirby (Consult Kirby), and Alex Papalexopoulos

(ECCO International) for their help in reviewing the methods, assumptions, and report for this 

study. We also thank Michael Milligan, Trieu Mai, Paul Denholm, and David Mulcahy (NREL) 

for comments and input, and Ookie Ma and Sam Baldwin (U.S. Department of Energy) for their 

support.
iv

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table of Contents

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

2 Methods................................................................................................................................................. 3

2.1 Data Sources.................................................................................................................................. 4

2.2 Assumptions.................................................................................................................................. 5

2.3 Scenarios....................................................................................................................................... 9

2.4 Sensitivities................................................................................................................................. 13

3 Scenario Results................................................................................................................................. 14

3.1 Reliability.................................................................................................................................... 14

3.2 Generation ................................................................................................................................... 15

3.3 System Dispatch.......................................................................................................................... 16

3.4 Curtailment and Prices................................................................................................................ 20

3.5 Penetration of Variable Generation............................................................................................. 26

3.6 Gas Fleet Operation..................................................................................................................... 27

3.7 Transmission Utilization ............................................................................................................. 29

3.8 Production Costs ......................................................................................................................... 31

4 Sensitivity Results.............................................................................................................................. 32

4.1 Flexibility of the Thermal Fleet................................................................................................... 34

4.2 Flexibility from Interchange........................................................................................................ 35

4.3 Flexibility from Storage .............................................................................................................. 38

5 Conclusions ........................................................................................................................................ 40

References................................................................................................................................................. 42

Appendix.................................................................................................................................................... 44
v

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

List of Figures

Figure 1. Additional transmission builds for this study ................................................................................ 8

Figure 2. Generation by type in the three scenarios, after curtailment ....................................................... 16

Figure 3. July dispatch stack, High scenario............................................................................................... 17

Figure 4. July dispatch stack, Higher Baseload scenario............................................................................ 17

Figure 5. July dispatch stack, Higher VG scenario..................................................................................... 17

Figure 6. March dispatch stack, High scenario........................................................................................... 18

Figure 7. March dispatch stack, Higher Baseload scenario ........................................................................ 18

Figure 8. March dispatch stack, Higher VG scenario ................................................................................. 19

Figure 9. Curtailment duration curve, High scenario.................................................................................. 21

Figure 10. Curtailment duration curve, Higher Baseload scenario............................................................. 21

Figure 11. Curtailment duration curve, Higher VG scenario...................................................................... 22

Figure 12. Price duration curve, High scenario .......................................................................................... 23

Figure 13. Diurnal curtailment in all scenarios........................................................................................... 24

Figure 14. Monthly curtailment in all scenarios......................................................................................... 25

Figure 15. Time series of fraction of generation from VG sources, High scenario .................................... 26

Figure 16. Duration curve of fraction of generation from VG sources, all scenarios................................. 27

Figure 17. Time series of gas generation in the High scenario................................................................... 28

Figure 18. Duration curve of gas generation in all three scenarios............................................................. 28

Figure 19. Time series of transmission along selected interfaces, High scenario....................................... 29

Figure 20. Duration curve of transmission along selected interfaces, High scenario ................................. 30

Figure 21. Generation by type in the sensitivities, after curtailment .......................................................... 33

Figure 22. Duration curve of gas generation in the sensitivities................................................................. 34

Figure 23. March dispatch stack, High LoFlex case................................................................................... 35

Figure 24. March dispatch stack, High LoFlex no DA case ....................................................................... 36

Figure 25. March dispatch stack, High LoFlex no DA/RT case................................................................. 36

Figure 26. March dispatch stack, High Storage case .................................................................................. 38

Figure 27. Diurnal storage dispatch, High and High Storage cases............................................................ 39

Figure A-1. Assumed transmission between “bubbles” in the Western Interconnection (prior to 

transmission buildout for this study)...................................................................................... 44

Figure A-2. Duration curve of transmission along selected interfaces, High LoFlex scenario .................. 45

Figure A-3. Duration curve of transmission along selected interfaces, High LoFlex no DA scenario....... 45

Figure A-4. Duration curve of transmission along selected interfaces, High LoFlex no DA/RT scenario. 46
vi

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

List of Tables

Table 1. Average Characteristics of Natural Gas Generation....................................................................... 4

Table 2. Penalties for Violating Constraints................................................................................................. 5

Table 3. Additional Transmission Builds for this Study............................................................................... 7

Table 4. Annual Possible Renewable Generation by Region and Type, High Scenario (TWh)................. 10

Table 5. Annual Possible Renewable Generation by Region and Type, Higher Baseload Scenario (TWh)11

Table 6. Annual Possible Renewable Generation by Region and Type, Higher VG Scenario (TWh)....... 12

Table 7. Penetration of Renewables, VG, and Curtailment for the Three Scenarios.................................. 15

Table 8. Fraction of Reserves Procured by Technology Type.................................................................... 19

Table 9. Average Annual Reserve Requirements....................................................................................... 20

Table 10. Curtailment during Zero-Price Hours throughout the Interconnection....................................... 23

Table 11. Transmission Utilization in the Three Scenarios........................................................................ 30

Table 12. Total Production Costs for the Three Scenarios ......................................................................... 31

Table 13. Assumptions for the Sensitivities in this Study .......................................................................... 32

Table 14. Total Production Costs for the Sensitivities................................................................................ 33

Table 15. Penetration of Renewables, VG, and Curtailment for the Three Scenarios................................ 33

Table 16. Total Production Costs for the Sensitivities................................................................................ 37
1

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

1 Introduction

The Renewable Electricity Futures Study (RE Futures) was an analysis of the costs and grid 

impacts of integrating large amounts of renewable electricity generation into the U.S. power 

system (Mai et al. 2012). RE Futures examined renewable energy resources, technical issues 

regarding the integration of these resources into the grid, and the costs associated with high 

renewable penetration scenarios. These scenarios included up to 90% of annual generation from 

renewable sources (including hydropower), although most of the analysis was focused on 80% 

penetration scenarios. Hourly production cost modeling was performed to understand the 

operational impacts of high penetrations of variable renewable generation. One of the 

conclusions of the study was that further work was necessary to understand whether the 

operation of the system was possible at sub-hourly time scales and during transient events. This 

study aimed to address part of this by modeling the operation of the power system at sub-hourly 

time scales using newer methodologies and updated data sets for transmission and generation 

infrastructure. The goal of this work was to perform a detailed, sub-hourly analysis of very high 

penetration scenarios for a single interconnection (the Western Interconnection). The penetration 

levels studied here varied from 82% to 88% for a variety of scenarios. The sub-hourly analysis 

showed that operation is possible (this study did not perform a full reliability analysis) at these 

renewable penetration levels and curtailment could range from 5%-11%. Capital costs have been 

studied extensively in other RE Futures work (including Mai et al. 2012 and the updated 

scenarios in Mai et al. 2014); this work focused on operational impacts, and it helps verify that 

the treatment of operational impacts in the capacity expansion models are sufficient for 

optimizing capacity buildout. 

Several recent integration studies have analyzed wind and solar penetrations in the 30%–50% 

range with more detailed production cost modeling than was done for the original RE Futures 

study (Mai et al. 2012). Some of these studies have included sub-hourly production cost

modeling, including the Western Wind and Solar Integration Study Phase 2 (WWSIS-2) (Lew et 

al. 2013). Others included additional models to analyze sub-hourly issues, including the PJM 

Renewable Integration Study (PRIS) (GE Energy 2014a) and analysis titled Investigating a 

Higher Renewables Portfolio Standard in California (E3 2014). Other studies have included 

dynamic modeling to understand grid performance during transient events, including the Western 

Wind and Solar Integration Study Phase 3 (WWSIS-3) (Miller et al. 2014) and the Minnesota 

Renewable Energy Integration and Transmission Study (MRITS) (GE Energy 2014b).

However, a small number of studies have been done on real systems using the detailed

methodologies mentioned above, and few have looked at total renewable penetrations (including 

hydropower) over 80% for an entire interconnection. This study aimed to use detailed methods

(including sub-hourly production cost modeling) to analyze operations of the grid at sub-hourly 

time scales for penetration levels above 80% in the Western Interconnection. Because the 

scenarios included a large transformation of the grid (with a large majority of today’s fossilfueled generation replaced with renewable electricity), doing a dynamic study for these scenarios

is difficult. The results from dynamic studies depend very much on the layout of the generation 

on the grid, and most of the generator buildout for these scenarios is still uncertain. This study 

focused on the sub-hourly operations of the grid; dynamic studies would need to be done to 

verify reliability of these scenarios during contingencies.
2

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The scenarios analyzed for this study included a variety of generation infrastructure buildouts 

and power system operational assumptions, with three different portfolios of renewable 

generators. The High scenario had approximately 82% renewable generation after curtailment, 

which included 41% of its generation coming from variable generation (VG) sources like wind 

and solar photovoltaics (PV). The remaining renewable generation came from hydropower, 

geothermal, and concentrating solar power (CSP). The Higher Baseload scenario adds CSP and 

geothermal to the High scenario to make 88% renewable generation. This study also included a 

Higher VG scenario with added wind and solar PV generation to get to 86% renewable 

generation. Both Higher scenarios added the same amount of possible generation, but the Higher 

VG scenario showed more curtailment from the incremental generation, leading to lower 

penetration levels after curtailment.

All scenarios assumed that all coal generation in the Western Interconnection was retired and 

that no additional fossil-fueled generation was built after 2022, which is the year the input data 

used for the study represents. The assumption of coal retirements is consistent with the policies 

that could drive a very high renewable penetration future, such as carbon limits or costs. 

Additional sensitivities analyzed the impact of institutional constraints that did not allow the full 

flexibility of the gas generation fleet to be utilized. These cases required that the gas generators 

had to be notified of start times in the day-ahead time frame and that minimum online times were 

more consistent with coal generation. Other sensitivities studied the impact of inefficient power 

exchange between balancing authorities in the day-ahead or real-time markets. We also ran a 

case that included 10 gigawatts (GW) of additional storage generation to help increase flexibility 

and mitigate curtailment.

The PLEXOS simulation tool was used for the production cost modeling in this study. 

Transmission was modeled zonally and new transmission infrastructure was built based on the 

congestion modeled for the High scenario. The real-time dispatch was performed every five

minutes for a single year that represents the meteorology for 2006. Most of the data came from 

the Western Electricity Coordinating Council’s 10-Year Regional Transmission Plan 2020 Study 

Report (WECC 2011), with updates coming from the WWSIS-2 study (Lew et al. 2013).

The primary conclusion of this study is that sub-hourly operation of the grid is possible with 

renewable generation levels between 80% and 90%. Dynamic studies will need to be done to 

understand any impacts on reliability during contingencies and transient events. This moredetailed analysis supports similar conclusions about the feasibility of hourly operation in the RE 

Futures study. Curtailment was between 5% and 11% (of total potential generation from wind, 

solar, and hydropower) if there was no significant friction between balancing authorities. This 

result is also consistent with the RE Futures study. Market friction1 between balancing 

authorities can lead to substantial cost and curtailment increases, while storage or similar 

technologies could help mitigate curtailment and reduce costs.

Section 2 describes the methods used, including details regarding the assumptions, data sources, 

and scenarios. Section 3 describes the results for the core scenario (High, Higher Baseload, 

 1 Market friction between balancing authorities is when the exchange of power between regions is sub-optimal due 

to institutional barriers or other non-technical reasons. For example, one region may have low-cost generation 

offline while another region has high-priced generation operating because the regions were not exchanging 

information prior to scheduling the resources.
3

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

and Higher VG). Section 4 describes the assumption sensitivity runs, and Section 0 reports 

the conclusions.

2 Methods

This study used and built upon the methods developed and used for Phase 2 of the Western Wind 

and Solar Integration Study (WWSIS-2) by Lew et al. (2013). Many of the methods and data 

sources are consistent with those used for WWSIS-2. WWSIS-2 included an extensive technical 

review process that involved technical review of all data, methods, and assumptions from a wide 

variety of stakeholders. Many of the assumptions were also vetted by the Western Electricity 

Coordinating Council (WECC) and their stakeholders as part of developing the Common Case 

by the Transmission Expansion Planning Policy Committee (TEPPC). All assumptions for this

study were reviewed by the smaller technical review committee noted in the Acknowledgements 

section, above.

This section describes some of the methods and modeling techniques used, including the data 

sources and assumptions. It also describes the scenarios and sensitivities that were implemented 

for this study.

The foundational model used for this analysis is the PLEXOS unit commitment and dispatchmodeling tool (Guo 2014). PLEXOS is a tool that uses mixed-integer programming (MIP) to 

optimize the commitment and dispatch of an electric power system, subject to constraints on 

load, transmission, and generation. The power system is optimized economically to serve load 

with the minimal possible operating cost, subject to the constraints provided. The operational 

costs include fuel costs, variable operation and maintenance, and startup costs. The PLEXOS 

model is run simultaneously for the entire interconnection, which represents either a single 

system operator or individual balancing authorities or markets that have no friction between 

them. One sensitivity for this study explored how results could change with additional friction 

between the balancing authorities (see Section 4.2 for results).

Load, wind, solar, and hydropower profiles were based on the meteorology for 2006 to keep the 

profiles meteorologically consistent with each other. Load was grown to 2020 (based on 

assumptions from the WECC TEPPC Common Case) and then fixed at 2020 levels. Although 

changes to load after 2020 are likely, refining load assumptions was not a main goal of the study. 

Because renewable penetrations are expressed as a fraction of load, conclusions drawn from the 

sub-hourly modeling done for these scenarios should be consistent with scenarios that considered 

higher loads with similar fractions served by renewable generating sources. Although the 

modeling runs used 2006 meteorology and load was grown to 2020, no specific year was 

associated with the study. The study is assumed to occur far enough in the future to allow time 

for significant transmission infrastructure expansion to occur, renewable generation to be built, 

and existing coal generation to retire.

Reserve requirements were calculated for the High scenario (described in Section 2.3) based on 

the methodology used for WWSIS-2 (Chapter 5 of Lew et al. 2013 describes the method in 

detail), and they were implemented with the data from the High scenario for this study. This 

calculated contingency, regulation, and flexibility reserve requirements for all reserve-sharing 

zones (the zones used are the same as the transmission zones shown in Figure 1) throughout the 
4

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Western Interconnection. The reserve requirements were based on the possible 1-hour (for 

flexibility) and 10-minute (regulation) variability and uncertainty in wind, solar, and load 

forecasts. The reserve requirements calculated with data from the High scenario were kept 

consistent for the other scenarios so that differences were from the assumptions that were 

changed between the scenarios, not different reserve requirements.

The model will randomly assign curtailment to generators during times when all generators see 

zero prices. To maintain consistency in the results presented, curtailment was assigned (after the 

optimization) to PV and wind during each five-minute interval, based on the ratio of PV to wind 

during that five-minute interval. For example, if there were six GW of wind and eight GW of 

solar at a certain time, seven megawatts (MW) of curtailment were assigned as three MW of 

wind curtailment and four MW of solar curtailment. 

In this paper, cases with different renewable buildouts are referred to as scenarios, including the 

High, Higher Baseload, and Higher VG scenarios. These scenarios are described in Section 2.3. 

Cases with changes to the assumptions are referred to as sensitivities, and these help us 

understand the impacts of assumptions or differences in technologies on the system that we are 

studying. The sensitivities are described in Section 2.4.

2.1 Data Sources

The PLEXOS database used for this study is similar to the one used for WWSIS-2. It was 

originally based on the WECC TEPPC 2020 Common Case, with a number of key changes 

applied to refine some of the assumptions (e.g., startup costs, heat rates, reserves modeling) 

and make others consistent with TEPPC 2022 (e.g., transmission limits, unit retirements, and 

gas prices).

The non-renewable generating fleet was assumed to be the same as WWSIS-2, except for 

retirement of all coal generators and the San Onofre Nuclear Generating Station, which retired 

after the WWSIS-2 data set was constructed. Startup costs and part-load heat rates were 

developed as part of WWSIS-2, and those costs were implemented in this database. The average 

characteristics of gas combined cycle (CC) and gas combustion turbine (CT) generators in the 

data set are shown in Table 1.

Table 1. Average Characteristics of Natural Gas Generation

Type 

Minimum 

Generation 

(as a % of 

Maximum 

Capacity)

Ramp Rate 

(%/min)

Heat Rate 

at Full Load 

(mmBtu/M

Wh)

Non-Fuel 

Start Cost 

($/MW

capacity)

Start Fuel 

(mmBtu/MW

capacity)

Non-Cycling 

Variable 

Operation and

Maintenance 

($/MWh)

Gas CC 52% 0.9 7.6 81 0.24 1.1

Gas CT 38% 4.5 10.7 67 1.1 0.8

MWh= megawatt-hours

mmBtu = million British thermal units

Changes to renewable generators in the database are described in Section 2.3.
5

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Load growth was described above. It consisted of actual profiles for 2006 with growth applied 

through 2020 by WECC TEPPC for their Common Case assumptions.

Five-minute generation profiles of wind for the selected sites originally from the WWSIS-1 data

set were revised and updated for WWSIS-2 to fix several anomalies, and these were used in this 

study, along with the associated wind forecast data. The wind data were based on a numerical 

weather prediction model to create wind speeds for a 2-km interval grid in the U.S. portion of the 

Western Interconnection.

Solar five-minute generation profiles for selected sites were based on hourly 10-km interval grid 

solar irradiance data from satellite estimates. Sub-hourly variability was added via an algorithm 

from Hummon et al. (2012), based on the spatial variability of irradiance at every time interval. 

Solar forecasts were from 3TIER numerical weather prediction simulations in WWSIS-1 (3TIER 

2010).

2.2 Assumptions

This section describes some of the important assumptions in the modeling.

The assumptions regarding hydropower generation are similar to those used for WWSIS-2 and 

the WECC energy imbalance market study (Milligan et al. 2012). Approximately one-third of the 

hydropower generation identically matches the generation from 2006, while two-thirds can be 

dispatched by the model within monthly maximum power, minimum power, and total energy 

limits. These constraints limit hydro flexibility significantly, as can be seen in some of the 

dispatch stacks in Section 3, where hydro generation does not highly correlate with gas CC 

generation. These constraints are consistent with the WECC TEPPC Common Case assumptions, 

although they are not identical because PLEXOS operates generators with long-term storage like 

hydro with different types of constraints compared to the models that WECC has used.

Table 2 shows the penalties for violating constraints within the model. These penalties govern 

which constraints are violated first during times of system stress. For these runs, flexibility,

contingency, and regulating reserves will be unserved, in that order, prior to the model choosing 

not to serve load. The constraints on transmission limits and monthly hydropower limits are 

higher, so that the model will have to violate a reserve or load constraint first. This makes 

tracking of any unserved load or reserves easier. No unserved load occurs in any of the scenarios 

run for this study. Only planned outages are modeled; forced outages and contingencies are not 

modeled.

Table 2. Penalties for Violating Constraints

Interface Penalty for Violating 

Constraint ($/MWh)

Flexibility reserves 3,900

Contingency reserves 4,000

Regulating reserves 4,100

Load 6,000
6

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

As described in Section 2.3, we assumed for this study that all coal units in the interconnection 

are retired. This is consistent with a future in which carbon emissions are constrained and 

renewable generation is incentivized. Because of the assumption of extensive growth in 

renewable generation capacity, no new replacement generation is necessary for the retired coal 

units, and all load and 99.999% of reserves are served. Section 4.1 shows the possible impacts of 

assuming that the gas CC fleet has flexibility parameters that make it more like coal.

The assumed natural gas price average of $4.60/mmBtu is consistent with WWSIS-2 and the 

WECC TEPPC 2022 Common Case. We assumed no carbon price or negative bidding by 

renewable generators in order to be consistent with the original RE Futures study (Mai et al. 

2012) and make interpreting results easier. Higher gas prices and carbon prices will cause price 

differentials between the cases to be higher.

This study uses a zonal transmission topology similar to that used in WWSIS-2. The zonal 

framework allows us to build significant amounts of renewable generation without making 

detailed upgrades to the transmission system for each one. Though this study is not intended to 

be a detailed transmission study, it is important to reasonably model transmission congestion so 

that the modeling does not over- or under-estimate the potential flexibility and curtailmentreducing benefits of transmission. To do that, we assume the transmission system is built out 

from the current planned system to help move renewable generation throughout the Western 

Interconnection.

The methodology followed for the transmission expansion is similar to what was used in 

WWSIS-2 (see Section 3.3 in Lew et al. 2013), but it is modified for the renewable buildout for 

the High scenario. The other scenarios are similar enough that the same transmission buildout 

was used for all scenarios in order to ensure consistency. The transmission expansion is done by 

iteratively running a simplified version of the PLEXOS model and building transmission along 

each interface if the congestion is significant enough to justify expansion. After enough iterations 

are complete, the model will no longer find any interfaces with enough congestion to justify new 

transmission.

The threshold used for deciding to build transmission in this study (and WWSIS-2) is a shadow 

price of $10/MW at every hour, averaged through the year. The shadow price is the production 

cost benefit that would be achieved by increasing the flow limit by one MW, and it is an output 

from PLEXOS. This is equivalent to $87,600/MW per year. If this level of congestion is reached 

along an interface, we add 500 MW to the flow limit.

Simplifications are made to the PLEXOS model to reduce run times (which take up to one week 

for each year of simulated time for this study) for the transmission-building runs only, so that 

many iterations can be run. These simplifications include two-hour time resolution and a singlepass commitment and dispatch step with no forecast error. These are unlikely to significantly 

impact the congestion patterns that determine the shadow prices.

For the transmission expansion PLEXOS runs, wind, PV, and geothermal are assumed to bid

-$25/MWh into the market to simulate an incentive to avoid curtailment and incentivize 

transmission intended to alleviate curtailment. This is different from the lack of negative bidding 

or carbon price in the scenarios and sensitivities in this study, as described above.
7

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

A map of original transmission, which is based on topology from the WECC Load and 

Resources Subcommittee as adjusted in WWSIS-2, is in the appendix. The capacity built for this 

study based on this methodology is shown in Table 3 and Figure 1. The High scenario is 

described in Section 2.3, and it is useful in interpreting the results of the transmission expansion. 

The bulk of the transmission expansion occurs in the northern portion of the interconnection. The 

southern portion is already quite well connected between New Mexico, Arizona, southern 

Nevada, and southern California. Also, the buildout in these areas is mostly solar, which is often 

built in regions with significant load. Much of the buildout in the northern regions includes lines 

that connect high-wind regions to high-load regions (e.g., Montana to the Northwest and 

northern California to San Francisco) where there is a lot of load but very little renewable 

development in the scenarios for this study.

Table 3. Additional Transmission Builds for this Study

Interface Capacity Built for this Study (MW)

Arizona to California, South 500

Arizona to Colorado 2,000

Arizona to Los Angeles Department of Water and Power 500

Arizona to Utah 500

California, North to Nevada, North 3,000

California, North to San Francisco 4,500

Colorado to Montana 500

Idaho to Nevada, North 500

Idaho to Utah 1,000

Imperial Irrigation District to San Diego 500

Montana to Northwest 1,500

Nevada, North to Northwest 500

Nevada, South to Utah 500

Utah to Wyoming 2,500

Total 18,500
8

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 1. Additional transmission builds for this study

1500

0 0

0

500

0

0

500

1000 2500

0

3000

0

0

4500

0 0

500

0 0

0 500

0

0

0

0 500

0

500

0

0

0

500

0

Pacific

Northw est

Northern

California

SF

SMUD

LADWP

San 

Diego

Southern 

California

Northern

Nev ada

Idaho

Montana

Wy oming

Colorado

New 

Mex ico

Southern 

Nev ada

Arizona

IID

Utah

MAPP
9

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2.3 Scenarios

The scenarios were created for this study using a qualitative method combining the results from 

previous RE Futures work and available data from WWSIS-2, in consultation with the technical 

review committee for this study. Sites in this study included solar from the WWSIS-2 “High 

Solar” scenario and wind from the “High Wind” scenario, and some sites that were not included 

in that study. In addition to these additional renewable generators, all scenarios assume that all 

coal generators in the Western Interconnection retire, consistent with the carbon-constrained 

world that might lead to very high renewable penetrations.

The High scenario was intended to represent a penetration similar to the 80% cases in the RE 

Futures study. In that study, all scenarios that allowed additional power transfer between 

interconnections projected much higher penetrations in the Western Interconnection. For 

operational analysis of a single interconnection, it is more interesting to study a case in which the 

interconnection is more islanded, as was the case in the “Constrained Transmission” scenarios in 

RE Futures. Such cases allow the study of a system where curtailment, variability, minimum 

generation issues, and other potential issues are contained within the interconnection rather than 

exported to a neighboring interconnection.

The Higher scenarios were intended to study a system with even higher renewable penetration 

levels. The Higher Baseload scenario examined a system that is similar to the High scenario but 

has 68 terawatt-hours (TWh) of additional CSP and geothermal that operate more like baseload 

generators in today’s system. The CSP has six hours of thermal storage, so it can operate more 

like baseload generation, but it cannot operate all night and all day. The Higher VG scenario 

included a 68-TWh increase in possible generation from wind and PV.

Assessing these systems in terms of a planning reserve margin is difficult due to the extensive 

variable generation in each case. However, the results showed that the total peak gas generation 

varied from 48 GW in the Higher Baseload scenario to 57 GW in the High scenario. Seventyfive GW of gas CC and CT generation were available during the summer in all cases (and all of 

the coal has been retired). This represents 18–27 GW of generating capacity beyond what is 

needed to supply peak load, or 13%–19% of peak load. While this is not directly comparable to 

planning reserve margin (due to differing treatments of hydropower, storage, and planned 

maintenance), it is representative of a system with sufficient capacity based on the modeling for 

this study and is similar to planning reserve margins in today’s systems.

The scenarios produced after-curtailment renewable penetrations of 81.5% (High scenario) to 

87.6% (Higher Baseload scenario) as a percentage of electricity demand (see Section 3.2 and 

Table 7). Table 4, Table 5, and Table 6 show the annual possible renewable generation by region 

and the type of renewable generation for the High, Higher Baseload, and Higher VG scenarios. 

In all of the scenarios, the wind-to-PV ratio was slightly higher than 2:1. The bulk of the wind 

generation was added to the northern portions of the interconnection, while the majority of the 

PV was added in the southern portions. Generators were assigned to regions based on which 

substation they tie into the grid, and we assumed each generator tied into the nearest 230-kilovolt

(kV) substation. This may mean, for example, that generation that is physically in the Imperial 

Irrigation District service territory might be classified in southern California if the nearest 230-

kV substation is outside the district.
10

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 4. Annual Possible Renewable Generation by Region and Type, High Scenario (TWh)

Region CSP Geothermal Hydropower PV Wind Total Renewable Load

Arizona 23.7 0.0 9.3 29.4 14.2 76.5 93.3

California, North 0.0 11.1 22.9 3.2 6.8 43.9 68.8

California, South 16.4 28.3 5.8 24.8 20.3 95.5 114.6

Colorado 2.7 0.0 3.8 10.3 56.8 73.5 78.2

Idaho 0.0 0.0 12.5 0.0 4.3 16.8 22.6

Imperial Irrigation District 2.3 0.0 0.4 2.6 3.4 8.7 4.6

Los Angeles Department of Water and Power 2.3 0.0 1.2 5.0 0.0 8.4 29.4

Montana 0.0 0.0 4.4 0.1 24.4 28.8 11.0

Nevada, North 0.2 9.2 0.1 6.9 10.0 26.4 12.2

Nevada, South 1.8 0.0 0.0 8.1 4.6 14.5 27.1

New Mexico 1.4 2.3 0.1 8.1 18.3 30.1 25.6

Northwest 0.0 2.1 123.2 2.2 41.3 168.7 163.8

San Diego 0.0 0.0 0.0 1.1 0.1 1.2 23.2

San Francisco 0.0 0.0 0.0 1.3 0.0 1.3 46.1

Sacramento Municipal Utility District 0.0 0.0 7.0 0.6 1.8 9.4 18.5

Utah 0.0 2.3 0.4 6.6 4.6 13.9 37.2

Wyoming 0.0 0.0 0.0 2.0 24.3 26.3 13.7

Total 50.6 55.3 191 112 235 644 790
11

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 5. Annual Possible Renewable Generation by Region and Type, Higher Baseload Scenario (TWh)

Region CSP Geothermal Hydropower PV Wind Total Renewable Load

Arizona 43.8 0.0 9.3 29.4 14.2 96.7 93.3

California, North 0.0 16.2 22.9 3.2 6.8 49.0 68.8

California, South 30.4 41.3 5.8 24.8 20.3 122.5 114.6

Colorado 4.9 0.0 3.8 10.3 56.8 75.8 78.2

Idaho 0.0 0.0 12.5 0.0 4.3 16.8 22.6

Imperial Irrigation District 4.3 0.0 0.4 2.6 3.4 10.6 4.6

Los Angeles Department of Water and Power 4.3 0.0 1.2 5.0 0.0 10.4 29.4

Montana 0.0 0.0 4.4 0.1 24.4 28.8 11.0

Nevada, North 0.4 13.5 0.1 6.9 10.0 30.8 12.2

Nevada, South 3.4 0.0 0.0 8.1 4.6 16.0 27.1

New Mexico 2.5 3.3 0.1 8.1 18.3 32.3 25.6

Northwest 0.0 3.0 123.2 2.2 41.3 169.6 163.8

San Diego 0.0 0.0 0.0 1.1 0.1 1.2 23.2

San Francisco 0.0 0.0 0.0 1.3 0.0 1.3 46.1

Sacramento Municipal Utility District 0.0 0.0 7.0 0.6 1.8 9.4 18.5

Utah 0.0 3.4 0.4 6.6 4.6 15.0 37.2

Wyoming 0.0 0.0 0.0 2.0 24.3 26.3 13.7

Total 94.0 80.8 191 112 235 712 790
12

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 6. Annual Possible Renewable Generation by Region and Type, Higher VG Scenario (TWh)

Region CSP Geothermal Hydropower PV Wind Total Renewable Load

Arizona 23.7 0.0 9.3 35.5 17.0 85.3 93.3

California, North 0.0 11.1 22.9 3.9 8.1 45.8 68.8

California, South 16.4 28.3 5.8 30.0 24.1 104.5 114.6

Colorado 2.7 0.0 3.8 12.5 67.5 86.5 78.2

Idaho 0.0 0.0 12.5 0.0 5.1 17.6 22.6

Imperial Irrigation District 2.3 0.0 0.4 3.1 4.0 9.8 4.6

Los Angeles Department of Water and Power 2.3 0.0 1.2 6.0 0.0 9.5 29.4

Montana 0.0 0.0 4.4 0.1 29.0 33.5 11.0

Nevada, North 0.2 9.2 0.1 8.3 11.9 29.7 12.2

Nevada, South 1.8 0.0 0.0 9.8 5.4 17.0 27.1

New Mexico 1.4 2.3 0.1 9.8 21.8 35.3 25.6

Northwest 0.0 2.1 123.2 2.6 49.1 176.9 163.8

San Diego 0.0 0.0 0.0 1.3 0.1 1.4 23.2

San Francisco 0.0 0.0 0.0 1.6 0.0 1.6 46.1

Sacramento Municipal Utility District 0.0 0.0 7.0 0.7 2.1 9.8 18.5

Utah 0.0 2.3 0.4 8.0 5.5 16.2 37.2

Wyoming 0.0 0.0 0.0 2.4 28.9 31.3 13.7

Total 50.6 55.3 191 135 280 712 790
13

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

2.4 Sensitivities

The sensitivities for this study were intended to help analyze what would happen if certain 

assumptions were different or certain technologies existed.

The High LoFlex sensitivity assumed that gas CC generation must be committed in the dayahead market, and it had a minimum run time of 24 hours. This made the gas CC generation 

operate more like coal plants today, and so this sensitivity approximates what might happen if 

the coal does not retire or if rigid markets do not allow for accessing all of the flexibility 

technically available from gas CC generators. However, we did not change the fuel type or any 

costs associated with the generators for direct comparison with the High scenario.

Two sensitivities—the High LoFlex no DA (no day-ahead commitment market) and High 

LoFlex no DA/RT (no day-ahead commitment or real-time dispatch market)—investigate

conditions where there is very significant friction between the balancing authorities in the West. 

To implement this, we added $40/MWh hurdle rates between the different transmission zones in 

the model. This hurdle rate is quite high, and it produced significant changes to how the units 

were dispatched (see section 4.2). This sensitivity studies a case where balancing authorities 

attempt to supply their own power, even if zero-marginal cost renewable power is available in a 

neighboring region. In the no DA case, this friction was only implemented in the DA market. In 

the RT market, power can flow between regions again without friction. However, the units have 

been committed with friction, and many unnecessary units will be online. In the no DA/RT case, 

there is friction in both the DA and RT markets. We used $40/MWh hurdle rates to study a 

situation where balancing authorities actually attempt to schedule enough generation to meet 

load in their own regions. A hurdle rate of $40/MWh is significant enough to change dispatch 

and cause regions to commit generators even when neighboring regions have power available. 

Smaller hurdle rates typically used in modeling (e.g., $5/MWh–$20/MWh) may have minor 

impacts on dispatch and may prevent arbitrage between gas CC and gas CT generation, but they 

would not cause any redispatch between renewables and gas due to the difference in marginal 

cost of generation. While this case introduced more friction than really exists between balancing 

authorities, it can help us understand the potential impacts of cooperation between balancing 

authorities compared to cases where there is less cooperation.

The High Storage sensitivity examined the potential benefits of adding generic storage to the 

system. The storage was added in balancing authorities near the regions with the most 

curtailment. The breakdown of the new storage added was 5 GW in Arizona, 2.5 GW in 

Colorado, and 2.5 GW in the Northwest. The storage was assumed to have 80% round-trip 

efficiency and 12 hours of storage. The full 12 hours was not necessary in the model most of 

the time, and 6–8 hours of storage would probably have been sufficient to provide most of 

the benefit (see Figure 27).
14

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3 Scenario Results

This section presents the results of the PLEXOS production simulations for the three scenarios. 

Results for the sensitivities are provided in the Section 4.

All modeling runs were performed with a unit commitment (using four-hour-ahead forecasts 

[4HA] for wind and solar generation) and real-time (RT) time horizons. Day-ahead (DA) 

commitment was not done for the main scenarios because coal units (which have long start times 

that drive the need for DA commitment) were assumed to be retired for the main scenarios 

(sensitivities were performed with a DA market). This left only gas combined-cycle as long-start 

units, and these often have start times less than 4 hours. Hourly time resolution was used for the 

4HA simulations; five-minute time resolution was used for the RT simulation. The results 

presented are for the RT simulations, as these simulations model what would actually occur in 

the power system. The results presented are for the U.S. portion of the Western Interconnection; 

ties with Canada and Mexico are relatively modest and conclusions can be drawn based on the 

performance of the grid in the United States.

There are several important things to remember when interpreting these results. Regarding costs, 

all results are presented for the assumed natural gas price of $4.60 per million British thermal 

units (mmBtu) and zero cost for carbon emissions. Higher gas prices and carbon prices will 

cause price differentials between the cases to be higher in most cases. However, higher gas 

prices or carbon prices are unlikely to cause significant changes to dispatch, as the model rarely 

chose to operate significant amounts of gas and curtail renewables at the same time.

The model randomly assigns curtailment to generators during times when all generators see zero 

prices. To maintain consistency in the results presented, curtailment was assigned (after the 

optimization) to PV and wind during each five-minute interval based on the ratio of PV to wind 

during that five-minute interval. For example, if there were six GW of wind and eight GW of 

solar at a certain time, seven MW of curtailment were assigned as three MW of wind curtailment 

and four MW of solar curtailment. This ensures that each unit of solar and wind have an equal 

probability of curtailment during zero-price hours and that comparison of annual generation 

between wind and solar is consistent.

3.1 Reliability

The primary purpose of unit commitment and dispatch modeling using PLEXOS is to understand 

economics, commitment, dispatch, and transmission flows. However, the model can give some 

indications to the reliability of the system. In the scenarios (and sensitivities) run for this study, 

no unserved load was projected during any of the five-minute time intervals. In the scenarios, 

over 99.999% of the reserves were served, indicating that a reserve shortage that could lead to 

unserved load during a contingency would be very unlikely.

However, PLEXOS is not a reliability model and it does not consider certain reliability issues. 

For example, previous studies (e.g., WWSIS-3) have examined dynamic performance of a grid 

with high penetrations of variable renewables. Some of the conclusions from WWSIS-3 will be 

discussed in later sections, but dynamic performance and voltage stability have not been modeled 

in this work.
15

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3.2 Generation

One of the main goals of this study was to understand the system impacts of a high penetration of 

renewables. Table 7 shows the penetration of renewables (including hydropower), variable 

generation renewables, and curtailment in the scenarios. Wind and PV sources are counted as 

VG; no other sources are. Renewable penetration ranges from 81.5% to 87.6% in the scenarios, 

while curtailment ranges from 6.7% to 11.4% of the possible renewable generation. This result is 

consistent with the original hourly modeling of the RE Futures scenarios, which showed 8%–

10% curtailment as a fraction of wind, solar, and hydropower for the 80% penetration scenarios.

The potential VG penetration (if there was no curtailment) is 47.2% in the High and Higher 

Baseload cases and 56.4% in the Higher VG case. Although the Higher Baseload case has the 

same VG capacity as the High case, more VG is curtailed due to the existence of CSP with 

thermal storage and the geothermal generation. This generation displaced some of the VG that 

was generated in the High case. Although the amount of possible incremental generation in the 

Higher cases (compared to the High case) is the same, the renewable penetration is two 

percentage points higher in the Higher Baseload case than it is in the Higher VG case because 

more additional curtailment of renewables occurs in the Higher VG case. Slightly more than half 

of the incremental energy (compared to the High case) is curtailed in the Higher VG case. 

Approximately one-third of the incremental energy is curtailed in the Higher Baseload case

(although VG is curtailed, not the incremental baseload renewable generation). A full capital cost 

analysis would be needed to understand the relative costs and benefits of the tradeoffs between 

storage, baseload renewables, and variable generation.

These results also demonstrate that VG curtailment depends on more than just VG penetration; it 

also depends on the characteristics of the other generating capacity in the system. In this case, 

nuclear (approximately 7 GW remain at Palo Verde, Diablo Canyon, and Columbia power 

plants), hydropower, geothermal, and CSP were all dispatched prior to VG, and VG curtailment 

went up when the penetration of geothermal and CSP went up.

Table 7. Penetration of Renewables, VG, and Curtailment for the Three Scenarios

Scenario Actual 

Penetration 

of VG

Possible 

Penetration of 

VG (before 

Curtailment)

Actual 

Penetration 

of 

Renewables

Curtailment 

as Fraction 

of Possible 

VG

Curtailment as 

Fraction of 

Possible 

Renewables

High 41.3% 47.2% 81.5% 12.4% 6.7%

Higher Baseload 37.5% 47.2% 87.6% 19.2% 9.3%

Higher VG 44.6% 56.4% 85.5% 19.6% 11.4%
16

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 2 shows the annual generation from different generator types in the three scenarios. 

Nuclear and hydropower were identical in all cases, while the non-hydropower renewables 

changed depending on the scenario. Wind and PV generation were smaller in the Higher 

Baseload case due to the curtailment. Most of the extra renewable generation in the Higher cases 

displaced gas combined-cycle (CC) generation, as the CTs remained online mostly during brief 

periods for flexibility during certain hours of the day.

Figure 2. Generation by type in the three scenarios, after curtailment

3.3 System Dispatch

Figure 3 through Figure 5 show seven days of dispatch during July, a high-load period that 

shows no curtailment in any of the cases. The dispatch of the system was similar in all of the 

cases. Nuclear and geothermal provided baseload power, while the wind, PV, and CSP reduced

the peak power requirement by approximately 50 GW each day. The remaining flexibility was 

provided by hydropower ramping between overnight and daytime hours and gas combustion 

turbines (CTs) coming online to provide power at sunset. With high PV penetration, sunset was

consistently when the gas fleet was needed to provide the most power. During summer, the 

diurnal shape of the dispatch was very consistent, and most generator types did a single period 

of ramping up and down each day.
17

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 3. July dispatch stack, High scenario

Figure 4. July dispatch stack, Higher Baseload scenario

Figure 5. July dispatch stack, Higher VG scenario
18

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

During March, the dispatch of the system looks quite different. Figure 6 through Figure 8 show 

the dispatch of the system during March. Curtailment was significant, particularly on March 29 

in all scenarios. The dispatch of most generator types did not follow a consistent diurnal pattern. 

During March, not much flexibility was available in the hydropower fleet, although it did ramp 

up and down several times each day, particularly in the High scenario. Very little gas was online 

during most of the week for any of the scenarios. Because this is a time of high curtailment, 

the biggest difference between the Higher scenarios and the High scenario is the amount of 

curtailment during most hours. In spite of the high curtailment and low gas generation, there was 

some CT usage during the morning of March 25 and the evening of March 30.

Figure 6. March dispatch stack, High scenario

Figure 7. March dispatch stack, Higher Baseload scenario
19

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 8. March dispatch stack, Higher VG scenario

Reserves are relatively simple for the system to procure in all of the cases. In most regions (and 

all scenarios), reserves had no additional cost for more than three-fourths of the hours. At very 

high instantaneous penetrations, wind and solar curtailment can provide reserves to the system. 

At moderate levels of penetration, enough gas and other generation were online to provide 

reserves. Table 8 shows which technologies provided contingency and regulating reserves to the 

system on average for the three scenarios. In all cases, the majority of the reserves were provided 

by renewables (hydropower, CSP, and curtailed wind and PV). The remaining reserves were 

nearly equally split between storage and thermal generation.

Table 8. Fraction of Reserves Procured by Technology Type

Type High Higher Baseload Higher VG

Hydropower 32% 32% 32%

Storage 19% 18% 18%

Gas CC 13% 9% 11%

CSP 12% 16% 12%

Wind and PV 11% 13% 14%

Other thermal 8% 7% 7%

Gas CT 3% 3% 4%

Demand response 2% 2% 2%
20

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Similar resources provided flexibility reserves as well. These reserves were released in the realtime dispatch and allowed to provide energy or other ancillary services. The amount of reserves 

required were based on the methodology described in Section 2 is shown in Table 9. The 

flexibility reserve requirement was slightly higher than the contingency requirement, which was 

3% of load in most balancing authorities in the model. The regulation reserve requirement was 

approximately 1.6% of load while the flexibility reserve requirement was approximately 3.4% 

of load.

Table 9. Average Annual Reserve Requirements

Type Reserve Requirement (MW)

Regulation 1,450

Contingency 2,760

Flexibility 3,090

3.4 Curtailment and Prices

This section analyzes in detail the VG curtailment seen during the sample weeks in Section 3.3. 

Figure 9 through Figure 11 show a duration curve of curtailment in the scenarios. Although the 

plots are labeled with hours in the x-axis for clarity, this is a result of modeling with five-minute 

resolution. In the High scenario, curtailment peaked at 56 GW throughout the Western 

Interconnection. In the Higher Baseline and Higher VG, curtailment peaked at 71 GW and 

73 GW, respectively. The shape of the curtailment was similar in all scenarios, with the majority 

of the curtailment occurring during a small number of hours, similar to mid-day on March 29 in 

the dispatch stacks in Section 3.3. Curtailment followed a similar pattern in each region of the 

scenarios, with the larger curtailments generally happening in regions with more installed VG 

capacity.
21

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 9. Curtailment duration curve, High scenario

Figure 10. Curtailment duration curve, Higher Baseload scenario
22

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 11. Curtailment duration curve, Higher VG scenario

Analyzing the curtailment can help understand potential solutions to reduce curtailment. For 

these scenarios, the transmission expansion described in Section 2.2 has been included. Because 

the majority of the curtailment occurs during a small number of hours, potential solutions would 

have to be able to handle a significant amount of renewable power during those hours. Figure 12

shows the average price duration curve for the regions of the study. In each region, there are 

close to 2,000 hours of zero price, and because these hours partially coincide, there are 

approximately 1,800 hours of zero price interconnection-wide. Curtailment during these hours 

would not be mitigated even if there were no transmission constraints. 

Table 10 shows the percentage of curtailment that occurs during hours where the price is zero 

throughout the interconnection. Depending on the scenario, 66%–80% of curtailment occurs 

during these hours, and no amount of transmission expansion (unless it connects with other 

interconnections) will relieve this curtailment.
23

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 12. Price duration curve, High scenario

Table 10. Curtailment during Zero-Price Hours throughout the Interconnection

Scenario Hours During which Average Price in 

Western Interconnection is Near $0/MWh

Percentage of Curtailment that 

Occurs During Zero-Price Hours

High 1,780 hours 66%

Higher Baseload 2,640 hours 75%

Higher VG 2,610 hours 80%
24

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 13 shows the diurnal pattern of the curtailment (averaged throughout the year) for the 

scenarios. It followed approximately the same shape as the solar PV profiles. Although the 

curtailment appeared to be primarily caused by PV, there was still a significant amount of wind 

online during times of curtailment. More than half of the curtailment was assigned to wind 

(based on the amount of power coming from wind and PV during times of curtailment), although 

there was more than twice as much wind generation as PV generation in all scenarios. Successful 

curtailment reduction technology would shift demand from evening (or possibly morning) hours 

to mid-day. Storage or demand response could play this role, and to this end, a simple storage 

sensitivity was analyzed (see Section 4.3). The ideal curtailment reduction would be able to shift 

the load by at least 6 hours, to increase the load between hours 9 and 15 and reduce the load 

outside those hours. Although the added storage devices had 12 hours of storage, 6 hours of 

storage was enough to achieve most of the curtailment benefit.

Figure 13. Diurnal curtailment in all scenarios
25

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 14 shows the monthly distribution of curtailment. Approximately two-thirds of 

curtailment occurred during January through May in all scenarios, and almost half occurred

during March, April, and May. The monthly curtailment shapes can help identify when the 

curtailment is occurring seasonally, and they can identify whether there are possible seasonal 

solutions to help with curtailment. Although short-term (6–12 hour) storage can help with the 

diurnal shape, other solutions may help with the seasonal nature. Any storage for this purpose 

would have to have very large energy reservoirs. However, maintenance scheduling could play a 

role in seasonal curtailment reduction, and shutting down nuclear or other baseload generation 

seasonally (for longer than standard maintenance periods) could also reduce curtailment.

Figure 14. Monthly curtailment in all scenarios
26

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3.5 Penetration of Variable Generation

This section examines VG penetrations throughout the interconnection. The metric is the fraction 

of generation at a given five-minute interval that comes from VG. Figure 15 shows a time series 

for the VG penetration for the entire year. As loads get higher during the summer, VG 

penetrations get lower. The highest VG penetrations occur during the fall, as hydropower is

lower in the fall compared to the spring. The peak VG penetrations were limited by the 

constraints on the system, not by VG capacity. For example, there was enough wind and PV on 

March 29 to supply all load on the system, but the hydro, geothermal, and nuclear generation 

were dispatched prior to VG. Nuclear is considered must-run generation in the modeling, and 

hydro and geothermal resources have constraints that prevent shutdown in the model (and in 

reality). There was also a very small amount of gas online during those hours.

Figure 15. Time series of fraction of generation from VG sources, High scenario
27

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 16 shows a duration curve of the VG penetration in the three scenarios. Because system 

limitations—not VG supply—constrain the peak VG penetration, the three scenarios have similar 

peak penetrations. The Higher Baseload scenario had slightly lower VG penetration, on average, 

than the High scenario because of higher curtailment needed to make room for the additional 

geothermal and CSP generation.

Figure 16. Duration curve of fraction of generation from VG sources, all scenarios

This study focused primarily on the system operation of a grid with high levels of renewable 

penetration. The scenarios modeled had very high penetrations of inverter-based technologies 

(PV and wind), at times approaching 75%. This compares to the “Extreme” sensitivity that was 

studied for dynamic stability in Phase 3 of the Western Wind and Solar Integration Study 

(WWSIS-3). Systems operating with high levels of inverter-based technologies (such as wind 

and PV) are “challenged to provide fast, confident control during faults and other disturbances”

(Miller et al. 2014 p. 106). The WWSIS-3 authors suggest that while maintaining reliability in a 

scenario with very high instantaneous penetrations of inverter-based technologies it might be 

necessary to install or convert existing units to synchronous condensers, which contribute to 

system inertia and dynamic stability. Further exploration of these scenarios at this time scale is 

beyond the scope of this analysis.

3.6 Gas Fleet Operation

Analyzing the operation of the gas fleet provides insight into the scenarios and into how the 

model provides capacity and flexibility when needed. Figure 17 shows the generation from the 

gas units in the High scenario. The left side of the figure shows gas CC generation. The more 

efficient CC units were committed occasionally during the October through June, sometimes 

providing very little power but occasionally providing significant amounts of power for periods 

of a day or two. During the July through September, the CC units were committed and provided 

at least 20 GW for the entire period. The CT generation was more distributed throughout the 

year. Although the CT units are used somewhat more frequently and at higher levels during the 

summer, there are times during the rest of the year when they are frequently used during peak 

periods. The CT units were often online for just a few hours at around sunrise or sunset, as 
28

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

shown in Section 3.3. Often the peak in net load at sunset was a short occurrence, with load 

declining later in the evening and the CTs shutting down.

Figure 17. Time series of gas generation in the High scenario

In the Higher Baseload and Higher VG scenarios, the time series of dispatch was similar to the 

High scenario, with a little less use of the gas CCs. Figure 18 shows a duration curve of the three 

scenarios. As the usage of CTs is primarily for flexibility during brief periods, the usage of these 

is not significantly impacted between the scenarios. Some CC generation was displaced in the 

Higher Scenarios, with more displaced in the Higher Baseload scenario due to the VG 

curtailments in the Higher VG scenario.

Figure 18. Duration curve of gas generation in all three scenarios
29

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3.7 Transmission Utilization

As has been seen in previous studies (e.g., RE Futures [Mai et al. 2012]), the transmission 

system is mostly used bi-directionally. In most cases, power is not simply transmitted in one 

direction at all times to ship power from one renewable resource location to load. Figure 19

shows the annual time series of transmission along six key interfaces in the Western 

Interconnection. Along most of these interfaces, the lines may be congested in opposite 

directions in the same day. Bi-directional flow means that different types of power (e.g., PV, 

wind) flow to different regions depending on the output and loads at different hours. It also 

enables the regions to balance load using the least-expensive possible generation. For example, 

this can include a region with high PV penetration shipping power out during the day and 

importing power from an adjoining region with high wind penetration during the evening. These 

changes in direction often follow a diurnal cycle along with the PV output and load.

The interface between northern and southern California is commonly congested today, but with 

the new PV generation in southern California and new transmission going into northern 

California in the scenarios, this interface was rarely congested in our results.

Figure 19. Time series of transmission along selected interfaces, High scenario
30

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 20 shows a duration curve of the same transmission flows. Congested flows with the 

interfaces at the flow limits show up as flat lines in the top left or bottom right corners of the 

plot. There is a wide variety of flows across all these interfaces, although several of them flow 

the same direction most of the time (e.g., Los Angeles Department of Water and Power imports 

from the Northwest and the Northwest imports from Montana). Table 11 shows the transmission 

utilization between the scenarios. The time series and duration curves were very similar for the 

three scenarios, and the transmission utilization was slightly higher in the cases with higher 

renewable generation.

Figure 20. Duration curve of transmission along selected interfaces, High scenario

Table 11. Transmission Utilization in the Three Scenarios

Scenario Transmission Utilization

High 0.44

Higher Baseload 0.46

Higher VG 0.46

Transmission utilization is defined as the fraction of available transmission capacity that is used, 

averaged throughout the year.
31

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

3.8 Production Costs

Previous RE Futures reports (Mai et al. 2012 and 2014) performed detailed cost analyses, 

including operational and capital costs. This study focused on operational impacts of high 

renewable penetrations, and Table 12 shows the sum of these costs from the sub-hourly 

modeling. The vast majority of the operational costs were for fuel, while startup costs and 

variable operation and maintenance (VO&M) costs made up 2.4% and 4.9% of the total costs.

In the Higher Baseload case, total production cost was reduced by $1.5 billion due to the addition 

of 68 TWh of CSP and geothermal generation. This included a $50 million reduction in startup 

costs due to less reliance on gas generation. In the Higher VG case, total production cost was 

reduced by $860 million due to the addition of 68 TWh of wind and solar PV. The production 

cost change was lower due to the additional curtailment in the Higher VG case compared to the 

Higher Baseload case. This included a $20 million increase in startup costs. A full capital cost 

analysis would help analyze the tradeoffs between capital costs and operational costs for VG, 

baseload renewables, storage, and other technologies. Although gas generation was displaced, 

the additional flexibility required by the incremental VG caused the startup costs to increase 

by 8% more than the High scenario.

Because most of the generation comes from sources with negligible operating costs, the 

production cost was less than $10/MWh in each scenario. This was much lower than the cost of 

natural gas combined cycle generation, which is approximately $35/MWh using the assumed gas 

price of $4.60/mmBtu. For reference, the production cost for the Western Interconnection with 

0%, 11%, and 33% wind and solar penetrations from the WWSIS-2 study was approximately 

$18 billion, $15 billion, and $11 billion (respectively). Although the gas price was the same, 

several differences in the model made the generation fleet not directly comparable (e.g., all coal 

generation was retired for this study).

Table 12. Total Production Costs for the Three Scenarios

Scenario Fuel Cost 

(million $)

VO&M Cost 

(million $)

Start Cost 

(million $)

Total 

Production Cost 

(million $)

Production 

Cost per MWh 

($/MWh)

High 8,657 465 225 9,348 9.6

Higher Baseload 7,252 415 178 7,844 8.0

Higher VG 7,809 432 244 8,485 8.7
32

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4 Sensitivity Results

This section describes the results of the sensitivity cases that explored the impact of generator 

flexibility, transmission interface efficiency, and storage on the High scenario. The impact of 

generator flexibility was tested in the High LoFlex case (Section 4.1) by requiring that gas CC 

generators are committed in a day-ahead (DA) market and have minimum run times of 24 hours. 

This sensitivity could also represent a case where markets are not flexible enough to fully utilize 

the capabilities of the generators. For example, an inflexible market may commit units with fourhour start times in a DA market. The impacts of transmission flexibility were tested in the High 

LoFlex no DA and no DA/RT cases (Section 4.2). These cases were comparable to the High 

LoFlex case, but very high hurdle rates ($40/MWh) were added to the DA market (in the no DA 

case) and to both the DA and the RT markets in the no DA/RT case. These hurdle rates create 

friction between markets and cause regions to commit primarily local generators and avoid interregional transmission except in cases where the electricity prices differ by more than $40/MWh 

(e.g., local CC generation will be committed instead of importing free electricity). This case is 

extreme case and unlikely to occur throughout the interconnection, but it provides information on 

how much benefit could come from increasing the ability to transfer power efficiently between 

regions. The impacts of storage were simulated in a case that adds a large amount of storage 

(10 GW) to the three regions with highest curtailment (Section 4.3). Table 13 shows the changes 

to the assumptions for each sensitivity.

Table 13. Assumptions for the Sensitivities in this Study

Region

DA or 4HA 

Commitment of Gas 

CC Generators

Min. Run 

Time for CC 

Generators

DA/4HA 

Hurdle 

Rates

RT Hurdle 

Rates

Additional 

Storage 

Generation

High 4HA 8 hours 0 0 0

High LoFlex DA 24 hours 0 0 0

High LoFlex 

no DA DA 24 hours $40/MWh 0 0

High LoFlex 

no DA/RT DA 8 hours $40/MWh $40/MWh 0

High Storage 4HA 8 hours 0 0 10 GW

While the remainder of this section contains tables and figures that apply to all of the 

sensitivities, Sections 4.1 through 4.3 describe the results of each sensitivity in detail. Figure 21

shows the annual generation by type in each of the sensitivities. The generation fleet was the 

same in each sensitivity, with the exception of some of the properties of the gas CC generators in 

the High LoFlex case and the additional storage in the High Storage case. Table 14 shows the 

annual production cost in each sensitivity, which is useful for understanding the value of 

different types of flexibility. Table 15 shows how curtailment was affected in each of the 

sensitivities, and how that affects the renewable penetration. Figure 22 presents a duration curve 

of the gas fleet in the different sensitivities to show how the gas fleet responded to the different 

conditions and assumptions.
33

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 21. Generation by type in the sensitivities, after curtailment

Table 14. Total Production Costs for the Sensitivities

Scenario Fuel Cost 

(Million $)

VOM Cost 

(Million $)

Start Cost 

(Million $)

Total 

Production 

Cost (Million $)

High 8,657 465 225 9,348

High LoFlex 8,991 466 243 9,700

High LoFlex no DA 9,748 492 171 10,410

High LoFlex no DA/RT 11,349 546 171 12,066

High Storage 8,260 458 130 8,848

Table 15. Penetration of Renewables, VG, and Curtailment for the Three Scenarios

Scenario Actual 

Penetration 

of VG

Actual 

Penetration of 

Renewables

Curtailment as 

Fraction of 

Possible VG

Curtailment as 

Fraction of 

Possible 

Renewables

High 41.3% 81.5% 12.4% 6.7%

High LoFlex 41.0% 81.0% 12.0% 6.5%

High LoFlex no DA 39.0% 78.4% 16.2% 8.9%

High LoFlex no DA/RT 32.4% 71.7% 30.6% 16.8%

High Storage 42.2% 83.0% 9.4% 5.1%
34

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 22. Duration curve of gas generation in the sensitivities

4.1 Flexibility of the Thermal Fleet

The High LoFlex explored the impact of less flexibility in the thermal fleet. This could represent 

either a case where the coal fleet does not experience significant retirements or a case in which 

institutional constraints (e.g., bilateral contracts, market structures that do not include any intraday re-commitments) reduce the ability of the thermal fleet to be able to provide flexibility that is 

technically possible.

The High LoFlex case assumed that all gas CC generators must be committed during a dayahead unit commitment (instead of 4HA commitment), requiring the use of forecasts with longer 

lead times and less accuracy. It also assumed the gas CC units have minimum run times of 

24 hours. This makes the CC units similar to a typical coal unit, although we left all costs (fuel 

costs, VO&M, and startup costs) identical to the other scenarios so that the results from the High 

LoFlex case could be compared directly with the High case. This case gives us insights into the 

potential impacts of having a less flexible thermal fleet.

Figure 21 shows that the annual dispatch of the system was quite similar in the High LoFlex and 

High cases. The additional constraints on the gas generators caused the system to use less gas CC 

generation and more gas CT generation. This is probably due to the higher forecast error in the 

DA market, and the system calls upon CT generation during times when the CC generation is 

under-committed. Figure 23 shows the sample week of dispatch in March, and the only 

noticeable difference between the High LoFlex and High cases is the addition of gas CT 

generation during morning and evening of days (it replaces gas CC generation). This same 

pattern is shown in the total annual numbers. Figure 22 shows another way of viewing this 

change, where the gas CT generation operated at higher levels for more hours of the year.

The difference between the High and High LoFlex cases was more noticeable in the production 

costs in Table 14. The reduction in flexibility cost the system $350 million, which represents a 

3.8% increase in production costs, or $0.48 per MWh delivered. Approximately half of this 
35

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

production cost difference is explained by switching from gas CC to gas CT generation; the other 

half is due to less efficient operation of the gas CC fleet (e.g., operation at part-load).

The High LoFlex case shows slightly lower curtailment than the High case (see Table 15); this is 

because the model chooses to back down CSP dispatch slightly. This reduction of CSP output 

was not counted towards curtailment in Table 14 because it is difficult to classify for CSP, which 

has six hours of thermal storage. However, if counted, it would eliminate the difference in 

curtailment between the High (6.7%) and the High LoFlex (6.5%) cases.

Figure 23. March dispatch stack, High LoFlex case

4.2 Flexibility from Interchange

This section explores the impact of reducing the ability of the model to efficiently exchange 

power between regions in the DA and RT markets. The inefficiency and friction are introduced 

by adding hurdle rates to the model. Hurdle rates add to the optimization cost in the PLEXOS 

model for every MWh that is transmitted over a given path. For these runs, to encourage regions 

to supply their own electricity when possible, we added very high ($40/MWh) hurdle rates. The 

hurdle rates were added in only the DA market for the no DA case and in both the DA and RT 

markets for the no DA/RT case. When added only in the DA market, the hurdle rates imitate a 

system where regions try to self-commit their generation but there is efficient real-time trading 

for energy imbalances. Currently, there is not a DA or RT market between all of the balancing 

authorities in the Western United States. The California Independent System Operator (CAISO) 

is the largest DA market, while there is a new energy imbalance market among CAISO, 

PacifiCorp, NV Energy, Arizona Public Service, and Puget Sound Energy in the West that 

allows for real-time trading within a market framework (California ISO 2015). There are varying 

amounts of friction between other balancing authorities in the West.

The framework for comparison of these cases is the High LoFlex sensitivity because its dispatch 

system and day-ahead market provided a clearer platform for comparison of interchange 

flexibility than the 4HA market (which would provide more flexibility than what currently exists 

in some regions) that is used the High scenario. These cases explored a situation where there is a 
36

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

combination of inefficiencies in the dispatch of different regions due to inefficiencies between 

markets and inability to access all potential generator flexibility.

Figure 21 shows the impacts of reducing transmission flexibility on annual generation from each 

type of generator. Because regions are self-supplying, the gas CC commitment and generation 

went up significantly in both cases. This was primarily at the cost of increased curtailment, 

although the additional gas CC commitment does lead to a reduced usage of gas CT peaker 

generation. The overcommitment of gas CC generation is also noticeable in Figure 24 and Figure 

25, when gas CC generation is committed even though it is unnecessary and curtailment is high. 

For example, on March 28 and March 29, there were two days of almost continuous curtailment 

while gas CC generation simultaneously generated 5–15 GW. This did not happen in the High 

LoFlex case because the power was efficiently shipped from areas with excess renewable 

generation to areas with load.

Figure 22 shows that even in the no DA case, gas CC generation never went below 5 GW, while 

all of the cases that included efficient exchange of power had gas CC generation going down to 

approximately 1 GW for many hours of the year. The no DA/RT case rarely had gas CC 

generation below 10 GW.

Figure 24. March dispatch stack, High LoFlex no DA case

Figure 25. March dispatch stack, High LoFlex no DA/RT case
37

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

The production cost changes due to transmission friction are shown in Table 14. The hurdle rate 

costs are included in the PLEXOS optimization, but they are not included in the production costs 

in Table 14. The increase in costs was due only to the inefficient dispatch because of the friction 

from the hurdle rates. The lack of DA coordination increased production costs by $700 million 

compared to the High LoFlex case, which is $0.96/MWh delivered and a 7.3% increase in total 

production costs. The lack of DA and RT coordination cost the model $2.4 billion more than the 

High LoFlex case, which is $3.2/MWh delivered and a 24% increase in production costs. These 

are extreme cases, and the $40/MWh caused the model to generate with gas CCs prior to 

importing free power. However, it demonstrates the impact of market friction is significant 

enough to cause changes in unit commitment patterns.

Table 15 shows that transmission friction has a significant impact on curtailment and renewable 

penetrations, also. Curtailment went from 7% in the High LoFlex case to 9% in the case with no 

DA coordination, to 17% in the case with no DA or RT coordination. This is because gas CC 

units operated in regions adjacent to other regions that were curtailing electricity. This result

shows that reaching renewable penetrations of 80% and higher could be quite difficult without 

either balancing areas coordinating transmission or some other mitigating mechanism (e.g., more 

storage or only local renewables) in place.

Table 16 shows the contrast in transmission utilization between the High LoFlex and the two 

cases with friction between regions. In the LoFlex no DA case, transmission utilization was 

reduced to 0.37 (from 0.44), but the model continued to transfer a significant amount of power 

after the overcommitment of the gas CC generation. In the LoFlex no DA/RT case, transmission 

utilization was reduced to 0.09, as renewables were curtailed while gas CC generation in 

neighboring regions ran at its full capacity. There was not enough incentive in the LoFlex no 

DA/RT case even to turn down the overcommitted gas CC generation to reduce curtailment in 

adjacent regions. Flow duration curves for the six selected interfaces in these sensitivities are 

shown in the appendix.

Table 16. Total Production Costs for the Sensitivities

Scenario Transmission 

Utilization

High LoFlex 0.44

High LoFlex no DA 0.37

High LoFlex no DA/RT 0.09

Transmission utilization is defined as the fraction of available transmission capacity that 

is used, averaged throughout the year.
38

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4.3 Flexibility from Storage

This section explores the impact of 10 GW of additional storage. This storage was placed near 

the three regions of the model that were experiencing the most curtailment: Arizona (5 GW,

which should help reduce curtailment in southern California), Colorado (2.5 GW), and the 

Northwest (2.5 GW). The relatively modest impact of the storage on annual generation (see 

Figure 21) mainly came from additional wind and solar PV generation displaced gas CCs (and, 

to a lesser extent, gas CTs). Figure 22 shows the modest reductions in usage of CCs and CTs in 

the High Storage case.

The redispatch can also be seen during the evening hours in the March dispatch stack (Figure 

26). The storage charging and generating can be seen in Figure 27, which shows the average 

diurnal dispatch of storage. Storage dispatch has a very consistent shape, with charging occurring 

during mid-day hours when solar PV generation is at its peak, and most generation occurring in 

the evening hours after sunset. The storage was not used to absorb high-frequency variability; as 

the consistent diurnal shape shows, it was used to charge— and generate—for approximately six

consecutive hours most days. Most of the cost reduction could probably be realized with 6-hour 

devices instead of the 12-hour devices modeled here, as Figure 27 shows that typical days don’t 

use the full 12 hours of storage capacity. The capacity factor of all storage in the Western 

Interconnection was 14% in the High Storage case, compared to 11% in the High scenario. The 

higher capacity factor is probably because the new storage is more efficient than the old storage.

Figure 26. March dispatch stack, High Storage case
39

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 27. Diurnal storage dispatch, High and High Storage cases

Table 15 shows that adding 10 GW of storage reduced curtailment from 6.7% to 5.1% as a 

fraction of total renewable generation. This is a curtailment reduction of approximately 10 TWh 

(one gigawatt-hour [GWh] of annual curtailment reduction per MW of storage capacity). 

However, approximately one-third of the curtailment reduction was offset by the additional 

storage losses in this case compared to the High scenario. Curtailment peaked at over 50 GW in 

the High scenario, and curtailment was higher than 10 GW approximately 17% of the time. 

While there is very little economic case to be made for mitigating peak curtailment hours, the 

10 GW of storage did significantly reduce overall curtailment.

Table 14 shows that production costs were reduced by $500 million in the High Storage case, 

which represents a 5.3% reduction in system-wide production costs or $0.68 per delivered MWh. 

Almost $100 million of the total reduction came from reduced startup costs. The reduced starts 

are primarily during the evening peak hours. The remaining benefit came from reduced fuel 

costs, which were primarily caused by the reduction in curtailment. There was also some 

arbitrage between CC and CT usage.

The $500 million cost reduction works out to approximately $50 of annual savings per kilowatt 

of incremental storage capacity. This production cost value for storage is consistent with 

previous studies at high VG penetration (e.g., Denholm et al. 2013). This value does not include 

any capacity credit, value for deferring transmission or distribution system costs, or other 

potential values not considered in an operational model. The value per kilowatt of incremental 

storage depends on storage capacity storage efficiency, and VG penetration. In previous studies 

storage shows decreasing returns such that a smaller increment of storage might have more value 

to the system (per MW).
40

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

5 Conclusions

In the RE Futures study (Mai et al. 2012), the production cost modeling of the operation of the 

grid was performed with an hourly time step. Operation of the simulated system was possible at 

the hourly time step, and curtailment was between 8% and 10% of wind, solar, and hydropower 

generation for scenarios that were close to 80%. RE Futures did not address any impacts that 

occur in a sub-hourly time scale. This report attempts to address impacts that may occur in the 

sub-hourly time frame by including real-time dispatch of the system with five-minute time 

resolution. Our work focused on the Western Interconnection, and it used methodologies and 

data sets that have been reviewed by stakeholders for several previous studies. Using more 

advanced modeling techniques such as improved transmission modeling, five-minute time 

resolution, and mixed integer programming optimization, we reinforce findings from RE Futures 

and show the system can be balanced at a 5-minute level. Other important conclusions from this 

work are:

• Curtailment is consistent with prior RE Futures study results and reliability metrics that 

can be obtained from sub-hourly unit commitment and dispatch modeling (e.g., unserved 

load, unserved reserves, planning reserve margin for the net load) did not indicate any 

reliability problems.

• Curtailment ranges from 5% to 11% for the 82%–88% penetration cases, assuming no 

significant friction exists between markets. This is consistent with the original RE 

Futures study (Mai et al. 2012), which reported 8%–10% curtailment for a scenario that 

was approximately 80% penetration.

• Although curtailment is not greatly impacted by modest changes to generator fleet 

flexibility, reducing the flexibility of the gas CC fleet cost the system $350 million ($0.48 

per delivered MWh).

• Simulation of friction between markets leads to increases in costs and curtailment. The 

lack of coordinated day-ahead planning could cost the simulated system over $700 

million ($0.96/MWh) and increased curtailment from 7% to 9%.

• In our results, transmission helps alleviate curtailment, at high penetrations of renewables 

and VG. However, 66%–80% of the curtailment occurs when curtailment is happening 

throughout the interconnection and more transmission cannot mitigate this curtailment.

• Storage can reduce curtailment by one GWh per MW of storage capacity, for a value of 

approximately $50/kW of storage capacity (or $0.68 per delivered MWh). Ten GW of 

storage could reduce curtailment from 7% of possible renewable generation to 5%.

• Marginal prices may be near zero a significant fraction of the time (20%–30% of the time 

in these scenarios) if annual renewable penetration is above 80%. Capacity factors at 

thermal power plants go down in the cases we analyzed. These changes reduce financial 

returns on investment in generation if markets are based on marginal energy prices alone. 

Other types of markets (e.g., capacity markets, more ancillary service markets, renewable 

energy markets) may be needed to provide financial incentives to ensure sufficient 

capacity exists on the grid in a high renewables future.
41

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

• Our scenarios and sensitivities are not optimized. Other configurations of resources 

(including enabling technologies, such as storage and demand response) may be optimal 

for reliability, curtailment mitigation, or both. Other practices, such as additional 

transmission connecting the Western Interconnection with the Eastern Interconnection or 

the Texas Interconnection may also be beneficial, but they were not studied here.

• Future work (as described in WWSIS-3) will need to be done to ensure that scenarios like 

those used in this study can be operated reliably during contingencies and transient 

events. System upgrades, such as synchronous condensers and shunt capacitors may be 

necessary to ensure transient stability.
42

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

References 

3TIER. 2010. Development of Regional Wind Resource and Wind Plant Output Datasets, 

NREL/SR-550-47676, Prepared by 3TIER for the National Renewable Energy Laboratory. 

Golden, CO: National Renewable Energy 

Laboratory. http://www.nrel.gov/docs/fy10osti/47676.pdf. 

California ISO. 2015. “Energy Imbalance Market Overview.” Accessed July 2, 

2015. http://www.caiso.com/informed/Pages/EIMOverview/Default.aspx.

Denholm, Paul, Jennie Jorgenson, Marissa Hummon, Thomas Jenkin, David Palchak, Brendan

Kirby, Ookie Ma, and Mark O’Malley. 2013. The Value of Energy Storage for Grid 

Applications. NREL/TP-6A20-58465. Golden, CO: National Renewable Energy 

Laboratory. http://www.nrel.gov/docs/fy13osti/58465.pdf.

E3 (Energy and Environmental Economics). 2014. Investigating a Higher Renewables Portfolio 

Standard in California. San Francisco: : Energy and Environmental 

Economics.. https://ethree.com/documents/E3_Final_RPS_Report_2014

_01_06_with_appendices.pdf .

GE Energy. 2010. Western Wind and Solar Integration Study. NREL/SR-550-47434. Prepared 

by GE Energy for the National Renewable Energy Laboratory. Golden, CO: National Renewable 

Energy Laboratory. http://www.nrel.gov/docs/fy10osti/47434.pdf.

GE Energy. 2014a. PJM Renewable Integration Study: Executive Summary Report. Prepared by 

GE Energy for PJM Interconnection, LLC. Schenectady, NY: GE Energy. http://www.pjm.com/

~/media/committees-groups/subcommittees/irs/postings/pris-executive-summary.ashx. 

GE Energy. 2014b. Minnesota Renewable Energy Integration and Transmission Study Final 

Report. Prepared by GE Energy for the Minnesota Utilities and Transmission Companies and the 

Minnesota Department of Commerce. Schenectady, NY: GE 

Energy. http://www.minnelectrans.com/documents/MRITS-report.pdf. 

Guo, Tao. 2014. Renewable Generation Integration Study by PLEXOS. Sacramento, CA: Energy 

Exemplar. http://energyexemplar.com/wp-content/uploads/2014/09/Renewable-GenerationIntegration.pdf.

Hummon, Marissa, Eduardo Ibanez, Gregory Brinkman, and Debra Lew. 2012. Sub-Hour Solar 

Data for Power System Modeling From Static Spatial Variability Analysis. NREL/CP-6A20-

56204. Golden, CO: National Renewable Energy 

Laboratory. http://www.nrel.gov/docs/fy13osti/56204.pdf. 

Lew D., G. Brinkman, E. Ibanez, A. Florita, M. Heaney, B.-M. Hodge, M. Hummon, G. Stark, J. 

King, S.A. Lefton, N. Kumar, D. Agan, G. Jordan, and S. Venkataraman. 2013. The Western 

Wind and Solar Integration Study Phase 2. NREL/TP-5500-55588. Golden, CO: National 

Renewable Energy Laboratory. http://www.nrel.gov/docs/fy13osti/55588.pdf.
43

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Mai, T., R. Wiser, D. Sandor, G. Brinkman, G. Heath, P. Denholm, D.J. Hostick, N. Darghouth, 

A. Schlosser, K. Strzepek. 2012. Exploration of High-Penetration Renewable Electricity 

Futures. Vol. 1 of Renewable Electricity Futures Study. NREL/TP-6A20-52409-1. Golden, CO: 

National Renewable Energy Laboratory. http://www.nrel.gov/analysis/re_futures/.

Mai, Trieu, David Mulcahy, M. Maureen Hand, and Samuel F. Baldwin. 2014. “Envisioning a 

Renewable Electricity Future for the United States,” Energy 65: 374–386.

Milligan, M., K. Clark, J. King, B. Kirby, T. Guo, G. Liu. 2012. Examination of Potential 

Benefits of an Energy Imbalance Market in the Western Interconnection. NREL/TP-5500-

57115. Golden, CO: National Renewable Energy 

Laboratory. http://www.nrel.gov/docs/fy13osti/57115.pdf. 

Miller, N., M. Shao, S. Pajic, and R. D’Aquila. 2014. Western Wind and Solar Integration Study 

Phase 3: Frequency Response and Transient Stability. NREL/SR-5D00-62906. Prepared by GE 

Energy for the National Renewable Energy Laboratory. Golden, CO: National Renewable 

Energy Laboratory. http://www.nrel.gov/docs/fy15osti/62906.pdf. 

WECC (Western Electricity Coordinating Council). 2011. 10-Year Regional Transmission Plan 

2020 Study Report: TEPPC 2010 Study Program. Salt Lake City: Western Electricity 

Coordinating Council. https://www.wecc.biz/Reliability/2011Plan_2020%20Study

%20Report.pdf.
44

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix

Figure A-1 shows the transmission capacity between regions prior to the transmission buildout 

for this study. Figures A-2 through A-4 show the duration curve of transmission flows across 

selected interfaces in the High LoFlex, High LoFlex no DA, and High LoFlex no DA/RT 

scenarios.

Figure A-1. Assumed transmission between “bubbles” in the Western Interconnection

(prior to transmission buildout for this study)

2000

325 400

1800

300

2200

4200

350

775 400

1400

100

360

650

1272

2750 1920

250

2858 664

3000 250

3883

3750

4785

2814 468

5582

1600

600

195

2440

150

1168

Pacific

Northw est

Northern

California

SF

SMUD

LADWP

San 

Diego

Southern 

California

Northern

Nev ada

Idaho

Montana

Wy oming

Colorado

New 

Mex ico

Southern 

Nev ada

Arizona

IID

Utah

MAPP
45

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure A-2. Duration curve of transmission along selected interfaces, High LoFlex scenario

Figure A-3. Duration curve of transmission along selected interfaces, High LoFlex no DA scenario
46

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure A-4. Duration curve of transmission along selected interfaces, High LoFlex no DA/RT 

scenario