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

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Examination of Potential Benefits 

of an Energy Imbalance Market 

in the Western Interconnection

M. Milligan and K. Clark

National Renewable Energy Laboratory

J. King and B. Kirby

Consultants

T. Guo and G. Liu

Energy Exemplar

Technical Report

NREL/TP-5500-57115

March 2013
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Examination of Potential Benefits 

of an Energy Imbalance Market 

in the Western Interconnection

M. Milligan and K. Clark

National Renewable Energy Laboratory

J. King and B. Kirby

Consultants

T. Guo and G. Liu

Energy Exemplar

Prepared under Task Nos. DP08.7010, SM12.2011

Technical Report

NREL/TP-5500-57115

March 2013
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iii

Acknowledgments

The National Renewable Energy Laboratory gratefully acknowledges the support of Larry 

Mansueti, Jon Worthington, and the Department of Energy Office of Electricity Delivery and 

Energy Reliability as well as Kevin Lynn, Venkat Banunarayanan, and the U.S. Department of 

Energy Solar Energy Technologies Office SunShot Intitative for funding this work. 

The National Renewable Energy Laboratory also thanks Victoria Ravenscroft previously of the 

Western Interstate Energy Board of the Western Governors’ Association, Doug Larson of the 

Western Interstate Energy Board of the Western Governors’ Association, and the Public Utility 

Commissions Energy Imbalance Market task force for their organizational support and assistance in 

engaging stakeholders. 

And many thanks to the members of the technical review committee for their insightful comments 

and assistance. Participation in the technical review committee does not imply agreement with the 

project findings. The technical review committee included:

• Jamie Austin, Pacificorp

• Venkat Banunarayanan, U.S. Department of Energy Solar Program

• Steve Beuning, Xcel Energy

• Patrick Damiano, Columbia Grid

• Jeanine Divis, Arizona Public Service

• Brent Guyer, Avista Corp.

• Paul Humberson, Western Area Power Authority

• Brendan Kirby, Consultant

• JJ Jamieson, Versify Solutions

• Rebecca Johnson, Colorado Public Utilities Commission

• Gary Lawson, Sacramento Municipal Utility District

• Michael McGowan, Northwestern Energy

• Michelle Mizumori, Western Electricity Coordinating Council

• Carl Monroe, Southwest Power Pool

• Tony Montoya, Western Area Power Authority

• Jack Moore, Energy and Environmental Economics 

• Arne Olson, Energy and Environmental Economics

• Jim Price, California Independent System Operator

• David Schiada, Southern California Edison

• Justin Thompson, Arizona Public Service.
iv

Finally, the National Renewable Energy Laboratory thanks the many members of the broader 

stakeholder community for their comments on the interim presentations and draft report. Comments 

provided by reviewers represent the opinions of the individual reviewers, and their participation 

does not imply agreement with the project findings. Reviewers of the interim presentations and

draft report included:

• Elise Caplan, American Public Power Association

• David Crowell, Salt River Project

• Jeanine Divis, Arizona Public Service for the Southwest Variable Energy Resource 

Initiative

• Kris Mayes, Kris Mayes Law Firm for Interstate Renewable Energy Council

• Clint Kalich, Avista Corp.

• Brendan Kirby, consultant

• Jimmy Lindsay, Renewable Northwest Project

• Paul McCurley, National Rural Electric Cooperative Association

• Nancy Norris, Powerex

• Gregory Miller, Public Service of New Mexico

• Jim Price, California Independent System Operator

• Ken Rose, independent consultant

• Kevin Smith, Braun Blaising McLaughlin & Smith for Sacramento Municipal Utility 

District and the Balancing Authority of Northern California

• John Stout, Mariner Consulting Services

• Brian Whalen, NV Energy

• Tom Veselka, Argonne National Laboratory for Western Area Power Administration.

The National Renewable Energy Laboratory regrets any inadvertent omission of any project 

participants and contributors.
v

List of Acronyms

ACE area control error

ADI ACE Diversity Interchange

AESO Alberta Electric System Operator

AVA Avista

AZPS Arizona Public Service

BA balancing authority

BAA balancing authority area

BANC Balancing Area of Northern California

BAU business as usual

BCTC British Columbia Transmission Corp.

BPA Bonneville Power Administration

CAISO California Independent System Operator

CC combined cycle

CFE Comision Federal de Electricidad

CHPD Public Utility District No. 1 of Chelan County

CSP concentrating solar power

CT combustion turbine

DC direct current

DCOPF direct current optimal power flow

DOPD Public Utility District No. 1 of Douglas County

E3 Energy and Environmental Economics

EIM energy imbalance market

EPE El Paso Electric

FRCC Florida Reliability Coordinating Council

GCPD Public Utility District No. 1 of Grant County

GW gigawatt

GWh gigawatt-hour

IID Imperial Irrigation District

IPC Idaho Power Corp.

ISO independent system operator

ISO-NE ISO New England

ITAP Intra-Hour Transaction Accelerator Platform

LADWP Los Angeles Department of Water and Power

LMP locational marginal pricing

MAGIC Magic Valley

MRO Midwest Reliability Organization

NEVP Nevada Power

NPCC Northwest Power Coordinating Council

NREL National Renewable Energy Laboratory 

NWE Northwest Energy

NWMT Northwest Montana

NWPP Northwest Power Pool

PACE Pacificorp East

PACE_ID Pacificorp Idaho

PACE_UT Pacificorp Utah
vi

PACE_WY Pacificorp Wyoming

PACW Pacificorp West

PC planning case

PG&E Pacific Gas and Electric

PGN Portland General Electric

PNM Public Service Company of New Mexico

PMA power marketing administration

PSCO Public Service Company of Colorado

PSE Puget Sound Energy

PV photovoltaic(s)

PUD public utility district

RFC ReliabilityFirst Corp.

SCE Southern California Edison

SCED security-constrained economic dispatch

SCL Seattle City Light

SCUC security-constrained unit commitment

SDGE San Diego Gas and Electric

SERC Southeastern Electric Reliability Council

SMUD Sacramento Municipal Utility District

SPP Southwest Power Pool

SPPC Sierra Pacific Power Co.

SRP Salt River Project

SUNY State University of New York

SVERI Southwest Variable Energy Resource Initiative

TEP Tucson Electric Power

TEPPC Transmission Expansion Planning and Policy Committee 

TES thermal energy storage

TID Turlock Irrigation District

TPWR Tacoma Power

TRE Texas Reliability Entity

TREAS Treasure Valley

WACM Western Area Colorado Missouri

WALC Western Area Lower Colorado

WAPA Western Area Power Administration

WAUM Western Area Upper Missouri

WECC Western Electricity Coordinating Council

WWSIS Western Wind and Solar Integration Study
vii

Executive Summary

In the Western Interconnection, there is significant interest in improving approaches to wide-area 

coordinated operations of the bulk electric power system, in part because of the increasing 

penetration of variable generation. These approaches include, but are not limited to, area control 

error pooling (area control error diversity interchange), advanced approaches to dynamic scheduling 

(dynamic scheduling system), and an Intra-Hour Transaction Accelerator Platform. They also 

include more recent analysis and proposals from the Northwest Power Pool Market Assessment and 

Coordination Committee and the Southwest Variable Energy Resource Initiative. In addition, an 

energy imbalance market (EIM) has been proposed as a way to improve wide-area coordination. 

This study focused on that approach alone, with the goal of identifying the potential benefits of an 

EIM in the year 2020. 

The primary objective of an EIM is to quickly dispatch generation to meet load across a broad 

geographic region. The economic dispatch of the EIM would operate every 5 minutes, allowing for 

a more economic balancing than would result if regulating resources were used for all imbalance 

inside the hour. Part of the generation-load imbalance that needs to be addressed derives from the 

variability and uncertainty associated with wind and solar generation. An EIM takes advantage of 

the reduction in wind and solar generation variability that is achieved via the geographic diversity 

inherent across a wide area. An EIM also allows a broader geographic range of generation resources 

to contribute to the economic balancing of generation and load. Thus, the EIM is intended to 

provide better generation-load balancing by being both big and fast. Participation in the EIM would 

be voluntary—as determined by each balancing authority and the generation resources within each 

balancing authority area. 

A series of studies, largely requested or encouraged by Western state electricity regulators and other 

state officials, has focused on the potential impact of an EIM. In 2011, the Western Electricity 

Coordinating Council (WECC) evaluated a proposed EIM in partnership with Energy and 

Environmental Economics (E3). The study was based on the Transmission Expansion Planning and 

Policy Committee (TEPPC) Planning Case 0 (PC0), which included annual energy penetrations of 

8% wind and 3% solar in the year 2020.

i

A large industry group provided guidance. The study 

evaluated only the electricity production cost savings of the EIM based on hourly time-step 

simulations (i.e., capital and other costs were excluded). Societal benefits—those accruing to the 

entire interconnection—were defined as the reduction in electricity production cost because of

the EIM. 

In early 2012, a group of public utility commissioners in the West expressed interest in additional 

analyses of the potential operational benefits of an EIM. The Public Utility Commissions Energy 

Imbalance Market (PUC EIM) Group,ii facilitated by the Western Interstate Energy Board, was 

formed. The PUC EIM Group asked the U.S. Department of Energy’s Office of Electricity Delivery 

and Energy Reliability to fund the National Renewable Energy Laboratory to perform the work. 

 i

The wind and solar penetration levels in WECC’s TEPPC PC0 were estimates of the generation needed to meet 

individual state renewable portfolio standard requirements. See http://www.wecc.biz/library/StudyReport/Documents/

Assumptions%20Matrix%20for%20the%202020%20TEPPC%20Dataset.pdf. ii Additional information about the PUC EIM Group can be found at http://www.westgov.org/PUCeim. 
viii

Four key factors bound the scope of this study.

1. This analysis was designed as an extension of the WECC-E3 study and, therefore, adopted 

all the assumptions of that study. 

2. This study used an electricity production simulation model, PLEXOS, with 10-minute timestep capability, rather than the hourly time-step of the WECC-E3 study, to better represent 

the 5-minute dispatch interval of the proposed EIM. 

3. This study was limited to an evaluation of the potential operational savings of the EIM and 

did not include an assessment of EIM implementation or other costs. 

4. This study evaluated the potential benefits of an EIM with full participation and an EIM 

with a reduced level of participation and included selected sensitivity analyses. 

Decision-makers may want to consider additional factors outside the scope of this report to 

determine whether participation in an EIM would be advantageous for their individual balancing 

authority areas. 

Study Limitations

Modeling any large system, especially one with the physical characteristics and existing market 

relationships of the Western Interconnection, is complex. In addition, all studies have limitations

and are subject to input data assumptions and modeling approximations. For example, this study 

examines only the potential production cost savings of an EIM for a specific set of study cases 

based on the TEPPC PC0 model and assumptions. Limitations include the lack of:

• Bilateral power purchase agreement data

• Detailed operational constraints in the hydro generation models

• Capability to simultaneously model different dispatch intervals in different balancing 

authority areas

• Real-time quick-start generation commitment procedures.

There are also uncertainties surrounding:

• Future cooperation and/or subhourly dispatch across the interconnection

• The amount and location of variable generation

• Transmission system additions

• Generation additions and retirements

• Gas and coal prices

• The EIM participation level.

Lack of contractual data has a significant impact on the commitment and dispatch performed by the 

production simulation software. Without such data, the software develops a minimum production 

cost commitment and dispatch, subject only to generating unit operating limits, transmission path 

ratings, and other performance constraints. Therefore, all individual balancing authority area 

benefits should be considered rough estimates.
ix

Today’s Western Grid Operation

The Western Interconnection, shown as part of the overall U.S. power system in Figure i, is 

composed of more than 30 balancing authorities. Superimposed on this structure are other levels of 

coordination, such as reserve-sharing groups that coordinate contingency reserve obligations, not 

shown on the map. There are also several subregional transmission planning groups—such as the 

group formed by Columbia Grid, Northern Tier Transmission Group, and WestConnect—that

coordinate transmission plans.

Source: North American Electric Reliability Corp.

Figure i. North American Electric Reliability Corp. regions and balancing authorities

Unit commitment and economic dispatch are not performed uniformly across the West; however, 

the objectives are the same. The process of committing generating units and dispatching their 

output optimizes for the least-cost generation dispatch to meet load, given the various physical, 

contractual, and institutional constraints inherent in any electric power system. 

In general, each balancing authority performs its own unit commitment the day before real-time 

operation. On the day of real-time operation, balancing authorities dispatch the committed 

generation to meet the actual load in a number of ways. California and Alberta have large 

independent system operators with centrally organized electricity markets that include fast (e.g., 5-

minute) economic dispatch. Other balancing authorities that own generation can dispatch units 

within the hour, either systematically (e.g., every 15 or 30 minutes) or on an as-needed basis. Still 
x

other balancing authorities may use self-schedules from independent generation owners and bilateral 

agreements for energy exchange on an hourly basis. Such hourly interchange schedules typically 

operate with a 20-minute period at the top of the hour to allow units to move to their new operating 

points. However, there are exceptions. For example, Bonneville Power Administration and the 

California Independent System Operator are running field trials of shorter interchange intervals. 

And, as noted above, there are other efforts to better coordinate operations across wider areas.

What Is the Proposed EIM?

The primary objective of the proposed EIM is to quickly dispatch generation to meet load across a 

broad geographic region. The EIM would perform a regional security-constrained economic 

dispatch,

iii for all participating generation, every 5 minutes solely to manage imbalances between 

generation and load and relieve transmission constraints. The EIM assumes each participant will 

continue to provide sufficient resources to cover its own obligations (i.e., commit sufficient 

generation to meet load, reserve requirements, and interchange agreements). EIM power flows 

would receive the lowest transmission service priority. Therefore, EIM flows would not displace 

reserved transmission service. 

Unlike other regional markets in which transmission service for market delivery is provided under a 

regional network service tariff, EIM flows would be accompanied by an imputed service 

compensation after the fact to participating transmission providers. At this stage of development, 

the specific terms for the transmission service revenue target and revenue allocation among 

participating transmission providers have not been established.

Figure ii illustrates the timeline of the security-constrained economic dispatch of an EIM. It would 

take 10 minutes from the time a system snapshot is taken until units have moved to their new set 

point. This process would repeat every 5 minutes.

Figure ii. EIM schedule for calculating dispatch set points 

and moving generation within 10 minutes 

 iii Security-constrained economic dispatch is defined as “the operation of generation facilities to produce energy at the 

lowest cost to reliably serve consumers, recognizing any operation limits of generation and transmission facilities.” See 

http://www.ferc.gov/industries/electric/indus-act/joint-boards/south-recom.pdf.
xi

Study Methods

The data requirements for this study included load, wind power, and solar power profiles and 

forecasts. The 2006 time series of load, wind power, and solar power profiles were used so common 

weather impacts would be maintained. The 2006 data were mapped to the simulation year 2020. 

WECC provided hourly load profile data projected for 2020 based on the 2006 load shapes, from 

which Pacific Northwest National Laboratory synthesized 10-minute load profiles. Wind data were 

obtained from NREL’s Western Wind and Solar Integration Study database (3TIER 2010). Solar 

data were developed by NREL. As noted previously, the variable renewable scenarioiv was defined 

by WECC TEPPC PC0 and includes approximately 8% wind and 3% solar penetration (by energy) 

in the Western Interconnection.

There were two primary analytical components to this study. First, a statistical data analysis was 

performed to determine the flexibility reserves required to meet the variability and uncertainty of 

wind and solar generation. Second, production simulation analysis was performed to evaluate 

grid operation over a full year for various study scenarios to identify potential EIM operational 

savings benefits. 

Flexibility reserves are a new type of reserve specifically designed to address the variability and 

uncertainty of wind and solar generation. They are separate and distinct from the reserves the power 

system already requires to address load variability and contingencies (Ela et al. 2011). Similar 

resources can fulfill both needs and come from the same resource pool (e.g., conventional

generation and responsive load), but this analysis does not use contingency or other existing 

reserves to provide flexibility reserves. Flexibility reserves are in addition to those reserves.

Flexibility reserves are a function of the time-synchronized expected variability of wind and solar 

power, which is, in turn, a function of wind or solar output. For example, if wind power output is at 

or near maximum, then there is relatively little variability and, therefore, relatively small flexibility 

reserve requirements. Conversely, if wind power output is in the middle of the operating range, then 

its variability is higher, and the flexibility reserve requirements are higher. The flexibility reserve 

requirements are therefore calculated for every hour of the year for each study scenario. 

For this study, the flexibility reserve requirements were divided into three classes based on the type 

of resources required to fulfill them:

1. Regulation covers fast changes of wind and solar power within the forecast interval. These 

changes can be up or down and happen minute-to-minute. This class of flexibility reserve 

covers minute-to-minute wind and solar variability and short-term forecast errors. 

Regulation requires resources on automatic generation control. 

2. Spinning reserves cover larger, less-frequent variations primarily caused by longer-term 

forecast errors. Spinning reserves are provided by resources (generation and responsive 

load) that are spinning and can fully respond within 10 minutes. These resources do not 

necessarily require automatic generation control. 

 iv This study analyzed only variable renewable generation (i.e., wind and solar). Other renewable generation (e.g., 

geothermal, hydro, and biomass) are included in TEPPC PC0 but were not germane to this analysis.
xii

3. Non-spinning and supplemental reserves cover large, slower-moving, infrequent events such 

as unforecasted ramping events. Non-spinning reserves can be available within 10 minutes 

and can come from quick-start resources and responsive load. Supplemental reserves can be 

made available within 30 minutes.

Because of software limitations, flexibility regulation and flexibility spinning reserves are 

represented as additional spinning reserves in the simulations. Non-spinning reserves could not 

be represented.

Production simulation analysis simulates actual bulk power system operations using timesynchronized load, wind, and solar data for each balancing authority; the associated flexibility 

reserve requirements; existing contingency reserve requirements; the TEPPC PC0 transmission 

system topology and constraints (e.g., path limits and nomograms); and operating characteristics for 

each unit in the generation portfolio. The simulation software solves the cost-minimization problem 

while respecting various input constraints. PLEXOS production simulation software was used for 

this study because of its hourly and subhourly simulation time-step capability. The subhourly 

capability was used with a 10-minute time-step to match the load, wind, and solar input data 

resolution and approximate the EIM’s dispatch interval of 5 minutes.

Production simulations produce an enormous volume of output data, which include generator 

commitment and dispatch, emissions, costs, and transmission path flows for each time-step of the 

year. Production costs are a key simulation output and consist of the fuel and variable operations 

and maintenance costs for the generation fleet. Fixed costs (e.g., power plant construction costs) are 

not included. 

To evaluate the potential operational savings of an EIM, two simulations were required: a businessas-usual (BAU) case and an EIM case. The societal (total throughout the West) savings from the 

EIM is the difference in production cost between these two cases, as shown in Figure iii.

Figure iii. EIM benefit formula

Additional analysis was required to determine how these societal benefits should flow to individual 

EIM participants. In the WECC-E3 study, a method was developed to evaluate how those benefits 

would be allocated. This method was referred to as the Benefits Allocation Roadmap. The 

calculations are based on the specific results from the production cost modeling and additional 

information, such as total load served and generation owned, supplied by the participants. 

Individual balancing authorities can potentially refine the allocation results by accounting for

confidential bilateral and other contracts not included in the production simulations.

The study examined several scenarios representing different EIM participation levels, hourly and 

10-minute BAU cases, alternative natural gas prices, and reduced flexibility reserve requirements.

EIM 

Benefit

Production 

cost of BAU

Production 

cost of EIM
xiii

Flexibility Reserve Reduction 

Flexibility reserve requirements can be reduced by an EIM. Figure iv shows the average flex 

reserve requirements under alternative EIM scenarios and dispatch interval/forecast lockdown 

assumptions. The right panel shows the flex reserves calculated for a range of BAU dispatch 

intervals (10–60 minutes) and forecast lockdown periods (10–40 minutes). The forecast lockdown 

period is the time between the last available forecast and actual operation. The middle panel shows 

the impact of three subregional EIMs on flexibility reserve requirements. The left panel shows the 

impact of a full EIM on flexibility reserve requirements. Flexibility reserve requirements decrease 

with shorter dispatch interval/forecast lockdown times and with larger EIMs.

Figure iv. Effect of dispatch interval and aggregation size on reserve requirements
xiv

Faster Dispatch 

Faster dispatch intervals for generating plants can reduce total production costs. Today, it is 

common in the West for dispatch and interchange functions to be performed hourly. However, 

some areas are experimenting with subhourly dispatch, and Federal Energy Regulatory Commission

Order 764 stipulates that 15-minute schedules be offered. Figure v shows the total production cost 

difference—approximately $1.3 billion—between an hourly BAU case and a 10-minute BAU case. 

(Note that the y-axis minimum on this and subsequent figures is not zero. The total y-axis range 

remains the same across all figures for ease of comparison.) 

Figure v. Potential impact of BAU assumptions on total production cost

 19,000

 20,000

 21,000

 22,000

Total Production Cost ($M)

Hourly BAU

10-minute BAU

~ $1.3 B
xv

Full-Footprint EIM 

Full EIM participation can reduce total production costs. The total production costs for the hourly 

and 10-minute BAU cases and the associated 10-minute full-EIM cases are shown in Figure vi. 

Note that each EIM case uses the unit commitment developed by its associated BAU case. Full EIM 

participation includes all balancing authority areas in the Western Interconnection except the 

California and Alberta independent system operators.

The full EIM with the hourly BAU commitment results in a savings of $294 million/year over the 

hourly BAU case. The full EIM with the 10-minute BAU commitment results in a savings of $146 

million/year over the 10-minute BAU case. 

Figure vi. Full-footprint EIM results under alternative BAU and commitment assumptions

 19,000

 20,000

 21,000

 22,000

Hourly 10-minute

Total Production Cost ($M)

BAU

~ $300 M Full EIM

~ $150 M

~ $1.3 B
xvi

Reduced EIM Participation

The production cost savings from an EIM can vary with participation level. The total production 

costs for the hourly and 10-minute BAU cases and the associated 10-minute reduced-participation 

EIM cases are shown in Figure vii. Note that each EIM case uses the unit commitment developed 

by its associated BAU case. For the reduced-EIM participation cases, which were requested by 

the PUC EIM Group, Bonneville Power Administration and two of the three Western Area Power 

Administration balancing authority areas are omitted. Several public utility districts, along with 

Seattle City Light and Tacoma Power, are embedded in Bonneville Power Administration and,

therefore, were also removed from EIM participation. 

The reduced EIM with the hourly BAU commitment results in a savings of $276 million/year over

the hourly BAU case. The reduced EIM with the 10-minute BAU commitment results in a savings 

of $95 million/year over the 10-minute BAU case. These savings are less than those achieved with 

full EIM participation.

Figure vii. Reduced-footprint EIM benefits for hourly and 10-minute unit commitment

 19,000

 20,000

 21,000

 22,000

Hourly 10-minute

Total Production Cost ($M)

BAU

Reduced Participation EIM

~ $300 M

~ $100 M

~ $1.3 B
xvii

Low Natural Gas Prices

The production cost savings from an EIM can also vary with natural gas price. A nominal natural 

gas benchmark price of $7.28/MMBtu, consistent with the TEPPC 2020 planning case, was used 

for most of the analysis. By today’s standards, this is a high price. Therefore, a lower price of 

$4.50/MMBtu was used to evaluate the impact of lower natural gas prices on EIM benefits. The 

latest Energy Information Administration forecast shows approximately $4.60/MMBtu (2011 

dollars) natural gas prices for the electric power sector from 2016 on (U.S. Energy Information 

Administration 2012). The total production costs for the hourly BAU case and the associated 10-

minute full-EIM case with the lower gas price are shown in Figure viii. The full EIM benefit is 

$281 million/year, which is a slight reduction from the $294 million/year of operational benefit 

achieved at the higher gas price.

Figure viii. Comparison of EIM benefits using the hourly BAU/EIM and $4.50/MMBtu natural gas

 15,000

 16,000

 17,000

 18,000

Total Production Cost ($M)

Hourly BAU with lower gas price

Full EIM with lower gas price

~ $300 M
xviii

Summary of West-Wide Results

This study shows an annual West-wide operating benefit of between $146 million and $294 million

for the EIM with full participation. An additional benefit of approximately $1.3 billion is associated 

with moving from an hourly dispatch interval to a 10-minute dispatch interval. Therefore, the total 

benefit of a faster dispatch interval and shared flexibility reserves could be as high as $1.46 billion.

Summaries of these West-wide results are shown in Figure ix and Table i.

Figure ix. Comparison of West-wide EIM benefits

Table i. West-Wide Annual EIM Benefits

Case Annual

Savings 

($ Millions)

Full EIM with hourly BAU commitment compared with hourly BAU 294

Full EIM with 10-minute BAU commitment compared with 10-minute BAU 146

Reduced EIM with hourly BAU commitment compared with hourly BAU 275

Reduced EIM with 10-minute BAU commitment compared with 10-minute BAU 95

Lower-gas-price, full EIM with hourly BAU commitment compared with hourly BAU 281

10-minute BAU compared with hourly BAU 1,312

294 275 281 

146 

95 

1,312 

 -

 200

 400

 600

 800

 1,000

 1,200

 1,400

Full EIM Reduced

Participation EIM

Full EIM at Lower

Gas Price

Faster Dispatch

Total Production Cost Savings ($M)

Hourly

10-minute

From hourly to 10-minute
xix

The largest benefit ($1.3 billion/year) is achieved by moving from hourly to 10-minute dispatch 

across the entire Western Interconnection. This faster dispatch is a main component of the EIM. 

The other component is sharing flexibility reserves across a wide area to take advantage of the 

reduced wind and solar variability associated with geographic diversity. Therefore, the 10-minute 

BAU could be considered a step along the path to the full-EIM implementation in which fast 

dispatch is adopted before the rest of the EIM. The potential benefits associated with the rest of the 

EIM range from $95 million/year to $294 million/year, depending on the BAU dispatch interval, 

EIM participation level, and natural gas price assumption. The lower estimate with full-EIM 

participation ($146 million/year) is based on the assumption that all balancing authority areas 

practice 10-minute dispatch in the BAU case. The upper estimate with full-EIM participation ($300 

million/year) is based on the assumption of hourly dispatch in the BAU case. With a lower level of 

participation, the annual benefit of the EIM ranges from $95 million to $275 million. The potential 

benefits of all EIM variations compared with an hourly BAU case are approximately $300 

million/year.

Individual Balancing Authority Area Results 

EIM benefits were allocated to individual balancing authority areas by calculating an adjusted 

production cost for each balancing authority area. This method has its roots in work performed at 

the Southwest Power Pool (2005) and in the Eastern Wind Integration and Transmission Study 

(EnerNex Corp. 2010) and was refined by E3 (2011). It takes into account not only the change in 

production cost but also the changes in imports and exports and calculates an adjusted production 

cost accordingly. The adjusted production cost decreased in the majority (21 of 29) of the balancing 

authority areas, showing a potential EIM benefit. Conversely, eight balancing authority areas

showed an increase in adjusted production cost for a potential EIM cost.

Lack of contractual data among generators, transmission providers, and load serving-entities has a 

significant impact on the commitment and dispatch performed by the production simulation 

software. Without such data, which can show how much spare transmission capacity is available, 

the software develops a minimum production cost commitment and dispatch, subject only to 

generating unit operating limits, transmission path ratings, and other performance constraints. The 

EIM modeled in this study assumes that physically available transmission capacity can be used for 

EIM transactions but with a lower priority than all other transmission uses. Physical limitations on 

transmission use are included in the modeling; however, contractual information is not. Therefore, 

all individual balancing authority area benefits should be considered rough estimates.

Individual balancing authority areas could refine the allocation results by accounting for 

confidential bilateral and other contractual mechanisms. For example, if a balancing authority area

has contractual obligations to sell a given amount of energy during a year at a specified price, the 

revenue from those sales will not be affected by potential EIM transactions. Likewise, if a 

balancing authority area holds contracts for purchases at a specified price, the EIM would have no 

impact on that cost. 

Future Work 

As in any complex modeling and analysis of future conditions, additional questions surfaced as the 

work progressed. Therefore, additional analysis on the following topics is recommended:
xx

• Power purchase agreements, if they could be made available

• Alternative nonvariable generation mixes, including alternative assumptions regarding coal 

plant retirements

• Alternative wind and solar energy penetration levels and locations

• Alternative EIM participation scenarios

• Multiple EIMs

• Alternative fuel price and/or emissions prices and regulations

• Alternative seams management with EIM nonparticipants to explore nonparticipant benefits 

• Broader use of subhourly scheduling (e.g., Joint Initiative Intra-Hour Transaction

Accelerator Platform, Federal Energy Regulatory Commission Order 764)

• Alternative future transmission projects

• Improved generation modeling (e.g., unit-specific rather than generic data).

This project pushed the state of the art for electricity production simulation modeling to the limit, 

evaluated potential EIM benefits under a range of conditions, and identified areas for future 

research. Further analysis and model refinements are recommended to more fully assess the impact 

of the proposed EIM on the Western Interconnection. Such an effort could provide additional 

insight into the modeling of a large, complex system such as the Western Interconnection and the 

potential benefits of various operational changes.
xxi

Table of Contents

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

1.1 Today’s Western Grid Operations................................................................................................... 2

1.2 Why an EIM?................................................................................................................................... 3

1.3 Overview of the Proposed EIM ....................................................................................................... 3

1.4 Prior EIM Analyses.......................................................................................................................... 6

2 Study Approach.................................................................................................................................... 7

2.1 Input Data ........................................................................................................................................ 9

2.2 Reserves Calculation Method ........................................................................................................ 16

2.3 Modeling Limitations..................................................................................................................... 22

2.4 Study Cases.................................................................................................................................... 24

2.5 Production Simulation Model........................................................................................................ 32

3 Impact of an EIM ................................................................................................................................. 44

3.1 Reserve Calculation Results .......................................................................................................... 44

3.2 PLEXOS Model Alignment to WECC-E3 Results........................................................................ 48

3.3 Full EIM......................................................................................................................................... 53

3.4 Gas Price Sensitivity...................................................................................................................... 60

3.5 Reduced-Participation EIM Sensitivity ......................................................................................... 63

3.6 Additional Sensitivity Cases.......................................................................................................... 66

3.7 Comparison of Hourly and 10-Minute BAU Cases....................................................................... 68

4 Allocation of EIM Benefits to Participants ....................................................................................... 71

4.1 Allocation Method ......................................................................................................................... 71

4.2 Allocation of Benefits for EIM Under Hourly BAU ..................................................................... 73

4.3 Allocation of Benefits for EIM Compared With the 10-Minute BAU .......................................... 82

5 Conclusions ........................................................................................................................................ 88

6 References .......................................................................................................................................... 91
xxii

List of Figures

Figure 1. Operation timeline for the EIM toolkit ..........................................................................4

Figure 2. EIM schedule for calculating dispatch set points and moving generation within 

10 minutes......................................................................................................................5

Figure 3. Cycle of calculations and unit ramping .........................................................................5

Figure 4. The EIM footprint, which effectively pools variability.................................................6

Figure 5. Overview of the study approach ....................................................................................7

Figure 6. EIM benefit formula ......................................................................................................8

Figure 7. Example correction of the third-day anomaly in the Western Wind and Solar 

Integration Study wind data set....................................................................................12

Figure 8. Actual load, hourly average, and interpolated load for 2009.......................................15

Figure 9. Imposing the 2009 load variability on the 2020 interpolated load ..............................15

Figure 10. Example persistence forecast for 10-minute dispatch .................................................18

Figure 11. Short-term forecast error standard deviation as a function of wind production 

level..............................................................................................................................18

Figure 12. Hour-ahead forecast error standard deviation as a function of wind production 

level..............................................................................................................................20

Figure 13. Effect of hourly dispatch and 40-minute forecast lead on forecast error.....................21

Figure 14. Effect of dispatch interval and aggregation size on reserve requirements...................22

Figure 15. PLEXOS security-constrained unit commitment and economic dispatch algorithm ..33

Figure 16. Hydro dispatch comparison between this study and TEPPC results...........................40

Figure 17. WECC BAA map with subregional groups.................................................................43

Figure 18. Reserve requirements for sample BA for three study scenarios..................................45

Figure 19. Flex reserve duration plot for sample BA for three study scenarios............................46

Figure 20. Flex reserve requirements duration for full-EIM footprint for three study 

scenarios.......................................................................................................................47

Figure 21. Flex reserve requirements as a function of dispatch interval and forecast lead 

time ..............................................................................................................................48

Figure 22. Comparison of total energy production between PLEXOS and GridView in the 

BAU alignment case ....................................................................................................49

Figure 23. Comparison of total production cost between PLEXOS and GridView in the 

BAU alignment case ....................................................................................................50

Figure 24. Comparison of total energy production between PLEXOS and GridView for the 

full-EIM alignment case ..............................................................................................51

Figure 25. Comparison of total production cost between PLEXOS and GridView for the 

full-EIM alignment case ..............................................................................................52

Figure 26. Comparison of total production cost for the full-EIM participation and the hourly 

dispatched BAU...........................................................................................................54

Figure 27. Comparison of annual energy production by generation class in the hourly 

dispatched BAU and full EIM cases............................................................................56

Figure 28. Comparison of total production cost for the full-EIM participation and the 

10-minute dispatched BAU..........................................................................................58

Figure 29. Comparison of annual energy production by generation class in the 10-minute 

dispatched BAU and full-EIM cases............................................................................59

Figure 30. Comparison of total production cost by generation class for reduced-gas-price 

hourly BAU and EIM cases.........................................................................................61
xxiii

Figure 31. Comparison of generation for reduced-gas-price cases...............................................62

Figure 32. LMP map for hourly BAU case...................................................................................77

Figure 33. LMP map for full-EIM case with hourly BAU unit commitment ...............................79

Figure 34. LMP map of LMP changes from hourly BAU to full EIM with hourly unit 

commitment .................................................................................................................80

Figure 35. LMP map for 10-minute BAU case .............................................................................84

Figure 36. LMP map for full-EIM case with 10-minute BAU unit commitment .........................86

Figure 37. LMP map of LMP changes from 10-minute BAU to full EIM with 10-minute 

unit commitment ..........................................................................................................87
xxiv

List of Tables

Table 1. BAAs Defined for This Study .......................................................................................10

Table 2. Variable Generation Penetration (by Energy) for Each BAA.......................................11

Table 3. Case-Naming Convention .............................................................................................25

Table 4. Summary of Study Cases ..............................................................................................31

Table 5. Order of Constraint Relaxation .....................................................................................34

Table 6. Nomograms That Are Enforced ....................................................................................36

Table 7. Hurdle Rates Between BAAs........................................................................................37

Table 8. Regional Coal Prices.....................................................................................................39

Table 9. Hydro Energy Represented by Each Type of Model ....................................................39

Table 10. Hoover Hydro Power Plant Joint Ownership................................................................41

Table 11. Colstrip Thermal Power Plant Joint Ownership............................................................41

Table 12. BAAs Defined for This Study .......................................................................................42

Table 13. Annual Summary of Flex Reserve Requirements for Sample BA for Three Study 

Scenarios .......................................................................................................................45

Table 14. Summary of Flex Reserves Requirements for the Full EIM Footprint for Three 

Study Scenarios.............................................................................................................46

Table 15. Energy Production by Generation Class for the BAU Alignment Case.......................49

Table 16. Production Cost by Generation Class for the BAU Alignment Case............................50

Table 17. Energy Production by Generation Class for the Full-EIM Alignment Case................51

Table 18. Production Costs by Generation Class for the Full-EIM Alignment Case...................52

Table 19. EIM Production Cost Savings Compared With BAU Alignment Case .......................53

Table 20. Production Cost and Savings for Full EIM Over Hourly Dispatched BAU ................55

Table 21. Generation by Resource Type for the Hourly Dispatched BAU and 

Full-EIM Cases..............................................................................................................56

Table 22. Change in Emissions for Hourly Dispatched BAU and Full-EIM Case ......................57

Table 23. Production Costs and Savings for Full EIM Over 10-Minute Dispatched BAU .........58

Table 24. Energy Production by Generation Class for the 10-Minute Dispatched BAU and 

Full-EIM Cases..............................................................................................................59

Table 25. Change in Emissions for 10-Minute Dispatch BAU and Full EIM .............................60

Table 26. Production Costs and Savings for Full EIM Over Hourly Dispatched BAU With 

Low Gas Prices..............................................................................................................61

Table 27. Energy Production by Generation Class for Full EIM Over Hourly Dispatched 

BAU With Low Gas Prices...........................................................................................62

Table 28. Change in Emissions for Low-Gas-Price Sensitivity Cases.........................................63

Table 29. Production Cost and Savings by Generation Class for Reduced-Participation EIM 

Over Hourly Dispatched BAU ......................................................................................64

Table 30. Annual Energy Production by Generation Class for Reduced-Participation EIM 

Compared With Hourly Dispatched BAU.....................................................................64

Table 31. Change in Emissions for Hourly Dispatch BAU and Reduced-Participation EIM......65

Table 32. Production Cost and Changes for Reduced-Participation EIM and 10-Minute 

Dispatched BAU............................................................................................................65

Table 33. Change in Emissions for 10-Minute Dispatch BAU and Reduced-Participation 

EIM................................................................................................................................66

Table 34. Effect of Reduced Flex Reserve Requirements on Production Cost.............................67
xxv

Table 35. Comparison of Production Costs for 1-Hour Time-Step Simulation Versus 

10-Minute Time-Step Simulation of Full EIM..............................................................68

Table 36. Comparison of Production Costs and Savings by Generation Class for 1-Hour and 

10-Minute Dispatched BAU Cases ...............................................................................69

Table 37. Total Energy Production by Generation Class for 1-Hour and 10-Minute 

Dispatched BAU Cases.................................................................................................69

Table 38. Savings Associated With Fast Dispatch and EIM.........................................................70

Table 39. Approximate Production Savings Associated With Faster Dispatch and EIM............70

Table 40. Allocation of EIM Benefits (With the Hourly BAU Unit Commitment) to EIM 

Participants....................................................................................................................74

Table 41. Allocation of EIM Benefits (With the Hourly BAU Unit Commitment) to EIM 

Nonparticipants .............................................................................................................76

Table 42. Average Weighted LMP for Hourly BAU Case ...........................................................78

Table 43. Average Weighted LMP for Full-EIM Case With Hourly BAU Unit Commitment ...79

Table 44. Average Weighted LMP Changes From Hourly BAU to Full EIM With Hourly 

Unit Commitment..........................................................................................................81

Table 45. Allocation of EIM Benefits (With the 10-Minute BAU Unit Commitment) to EIM 

Participants....................................................................................................................83

Table 46. Allocation of EIM Benefits (With the 10-Minute BAU Unit Commitment) to EIM 

Nonparticipants .............................................................................................................84

Table 47. Average Weighted LMP for 10-Minute BAU Case......................................................85

Table 48. Average Weighted LMP for Full-EIM Case With 10-Minute BAU Unit 

Commitment..................................................................................................................86

Table 49. Average Load-Weighted LMP Changes From 10-Minute BAU to Full EIM With 

10-Minute Unit Commitment........................................................................................88

Table 50. Summary of EIM Savings.............................................................................................89
xxvi

List of Equations

Equation 1. Calculation of short-term wind standard deviation................................................19

Equation 2. Calculation of regulation flexibility reserve requirement......................................19

Equation 3. Sample calculation of hour-ahead wind standard deviation ..................................20

Equation 4. Calculation of spinning flexibility reserve requirement ........................................20

Equation 5. Calculation of non-spinning flexibility reserve requirement.................................21
1

1 Introduction

In early 2012, a group of public utility commissions in the West expressed interest in additional 

analysis of an energy imbalance market (EIM) through an ad hoc group formed under the 

auspices of the Western Governors’ Association’s Western Interstate Energy Board. In turn, that 

group asked the U.S. Department of Energy to fund the National Renewable Energy Laboratory

(NREL) to conduct such analysis, which culminated in this report. This study extends prior work 

but leans heavily on the assumptions in the Western Electricity Coordinating Council (WECC) 

and Energy and Environmental Economics (E3) studies (2011b and 2011c), which, in turn, used 

the assumptions of the WECC Transmission Expansion Planning and Policy Committee 

(TEPPC) Planning Case 0 (PC0) for the year 2020.

This study focuses on the potential operational benefits of an EIM in 2020 by using production 

simulation software to simulate future scenarios. Production simulations determine operating 

costs, which include fuel, variable operating and maintenance costs, startup costs, and other 

variable costs. The capital costs of generation and transmission infrastructure, the costs of 

implementing an EIM (e.g., hardware, software, and communications), and any other direct or 

indirect costs of an EIM, which may or may not be significant, are not included. Additional 

detail on the limitations of production simulation model accuracy is provided in Section 2.3.

Modeling any large system, especially one with the physical characteristics and existing market 

relationships of the Western Interconnection, is complex and difficult to capture appropriately. 

Therefore, the results of this study must be interpreted as one possible outcome, not as a 

definitive forecast. 

This study evaluates potential EIM benefits across the Western Interconnection (societal 

benefits) and on a balancing authority area-by-balancing authority area (BAA) basis. It explores:

• The benefit of an EIM using 10-minute economic dispatch

• Alternative business-as-usual (BAU) cases (one assuming hourly dispatch and the other 

assuming 10-minute dispatch)

• Alternative EIM participation levels

• Alternative flexibility reserve requirements

• Alternative natural gas prices.

Three potential operational benefits of the proposed EIM are:

1. The larger electrical footprint results in the reduction of variability and uncertainty in 

load and solar and wind energy because of increased geographic diversity.

2. The larger footprint allows access to a wider selection of generation, which can result in 

more cost-effective dispatch.

3. Five-minute dispatch allows for economic adjustments of generation at the time they are 

needed, which allows ramping to be distributed among more generators and across the 

hour. General operating practice today in the West constrains ramping to the 20-minute 

window surrounding the top of the hour, though that may change with Federal Energy 

Regulatory Commission Order 764.
2

A detailed analysis of the ramping and flexibility reserve implications of several configurations 

of an EIM is provided in an earlier study (King et al. 2012). This report builds on that work and

uses the flexibility reserve calculations to perform a full security-constrained unit commitment 

and economic dispatch to provide insight into the potential operational benefit of an EIM under 

alternative participation levels.

1.1 Today’s Western Grid Operations

The Western Interconnection is composed of approximately 30 BAAs—most of which do not 

participate in centrally organized electricity markets that perform economic dispatch on 5- or 10-

minute time-steps. The exceptions are market areas in California and Alberta that are full-fledged

independent system operators (ISOs) and not the focus of this study. The nonmarket areas of the 

Western Interconnection do have some market mechanisms, primarily bilateral, that are used by 

many entities on a regular basis. Much of the interchange energy is exchanged via long-term 

bilateral contracts and delivered in hourly blocks, which corresponds to the general practice of 

hourly interchange between balancing authority areas. 

Security-constrained economic dispatch (SCED) is not performed uniformly in each Western 

BAA, just as it is not uniform among regional transmission organizations and ISOs. Balancing 

authorities (BAs) that own generation can dispatch units within the hour, either systematically or 

on an as-needed basis. An example of this is Public Service Company of Colorado. Conversely, 

some BAs do not have the institutional means to request dispatch service from units that are 

electrically within the BAA but not owned by the BA. One example of this is Bonneville Power 

Administration (BPA), which can control hydro generation subject to physical and 

environmental constraints but is unable to access thermal generation within its BAA on a 

subhourly basis.

Interchange schedules typically operate hourly, with a 20-minute period at the top of the hour in 

which units move to their new operating points. BPA and California Independent System 

Operator (CAISO) recently began a 30-minute scheduling field trial with the objective of 

demonstrating the benefit of faster scheduling steps to help manage wind variability and 

uncertainty surrounding large wind exports. The initiative spreads ramping more evenly across 

the hour instead of restricting all ramping activity to the top of the hour.

Other mechanisms allow for similar schedule adjustments within the hour. As part of their Joint 

Initiative, WestConnect, Columbia Grid, and Northern Tier Transmission Group defined the 

Intra-Hour Transaction Accelerator Platform (ITAP), which allows for bilateral, on-demand 

schedule changes on the half-hour. Subscribers to ITAP can create a schedule modification with 

short notice once a counterparty is identified and agrees to the change. Other aspects of the Joint 

Initiative include a dynamic scheduling system, which allows participants to create a dynamic 

schedule on short notice, and the area control error (ACE) Diversity Interchange (ADI), which 

nets regulation across the participating BAAs.
3

More recently, the Northwest Power Pool began a Market Assessment and Coordination 

Committee Initiative1 to analyze (1) regulation sharing, (2) expanded use of an ITAP platform, 

(3) dynamic scheduling, (4) intra-hour pre-scheduling, (5) flexible bilateral contracts, and (6) a 

potential EIM in the Northwest Power Pool footprint. Even more recently, seven utilities in the 

Southwest formed the Southwest Variable Energy Resource Initiative.

In June 2012, the Federal Energy Regulatory Commission issued Order 764, which requires, 

among other things, that transmission operators offer 15-minute scheduling. Because this ruling 

has not yet been implemented in Western BAAs, it is not clear whether it will induce sufficient 

counterparties, including wind and solar generation owners, to change scheduling practice from 

1-hour blocks to 15-minute schedules. However, some entities in the West believe that, in the 

future, 15-minute schedules will be the norm.

1.2 Why an EIM?

As penetrations of variable generation increase, there is interest in the West in exploring options 

to efficiently manage it. There are many possible options, including those described above. 

Outside of the organized markets of California and Alberta, there are no regional transmission 

operators in the West. However, the WECC Seams Issues Subcommittee proposed in 2010 an 

EIM similar to the energy imbalance service adopted by the Southwest Power Pool (SPP). 

1.3 Overview of the Proposed EIM2

The EIM uses SCED to provide two functions:

• Balancing service

This service redispatches generation every 5 minutes to maintain balance between 

generation and load. For deliveries scheduled in advance, the effect is that the market 

supplies deviations from schedules in generator output and errors in load schedules.

• Congestion redispatch service

This service redispatches generation to relieve overload constraints on the grid. 

Information provided to the EIM from the enhanced curtailment calculator ensures 

correct allocation of the costs of redispatch.

An enhanced curtailment calculator, which allocates transmission service curtailments based on 

service priority for power flow impacts on the grid, would evaluate flows and pass relevant 

curtailment information to the EIM.

Federal Energy Regulatory Commission pro forma tariff schedules 4 (energy imbalance) and 9 

(generation imbalance) provide the approach used by the WECC BAs for balancing services. The 

proposed EIM replaces part of the BA services and results in a “virtual consolidation” because of 

a wide-area SCED that covers imbalances. The congestion redispatch service is new to the 

nonmarket portions of the Western Interconnection. 

 1 See http://www.westgov.org/PUCeim/meetings/present/nwpp.pdf. 2 This section is adapted from (E3 2011b)..
4

The EIM design includes a feature different from most regional markets in the United States in 

which internal resources are subject to a “must offer” requirement. Instead, the default operating 

assumption is that each market participant provides sufficient resources to cover its own 

obligations (as is the case today) and the regional economic dispatch is provided by any resource 

that voluntarily offers responsive capability and is cleared by the SCED process. Most 

transmission service deliveries would continue to use traditional reserved transmission service. 

The EIM, however, would not use pre-reserved transmission. Instead, the EIM flow would 

receive the lowest transmission service curtailment priority. By this mechanism, EIM flows 

would not displace reserved transmission service. 

Unlike other regional markets in which transmission service for market delivery is provided 

under a regional network service tariff, the EIM flows would be accompanied by an imputed 

service compensation after the fact to participating transmission providers. At this stage of 

development of the efficient dispatch toolkit, the specific terms for the transmission service 

revenue target and revenue allocation among participating transmission providers have not 

been established.

The EIM function adds operational steps to the practices of the Western Interconnection. 

Functionally, the operating steps for the proposed EIM track closely with the operating process 

established in the SPP in its Energy Imbalance Service market. Figure 1 illustrates the timeline of 

the proposed efficient dispatch toolkit. 

Figure 1. Operation timeline for the EIM toolkit

Schedule Day-Ahead and 

up to 30 Minutes Prior to

Operating Hour 

Real-Time 

Dispatch Post-Operating 

Time

 Forecast and

unit commit

 Generators

self-schedule

 Generators 

voluntarily submit

offers

 Demand-side

management

resources 

voluntarily submit

offers

 Prepare and 

finalize predispatch schedules

 Perform securityconstrained 

economic dispatch 

to keep balance

 Provide redispatch

if any congestion

occurs

 Send dispatch

set points to

generators

 Coordinate any

contingency

reserve deployments

with EIM dispatch

Market Participant: EIM Market Operator

 Gather meter

data to support

settlements

 Provide 

settlement statement

and invoices

Transmission Provider

 Provide meter

data to support

settlements
5

Figure 2 shows the sequence of taking the system data, calculating the expected conditions and 

required set points for the next interval, communicating those set points to generators and 

responsive loads, and ensuring responsive resources move to the new set points—all in 10 minutes.

Figure 2. EIM schedule for calculating dispatch set points 

and moving generation within 10 minutes

Figure 3 shows how continually repeating the process shown in Figure 2 results in meeting a new 

system dispatch point every 5 minutes based on information that is only 10 minutes old.

Figure 3. Cycle of calculations and unit ramping 
6

The EIM would effectively implement some aspects of a virtual BAA across all or some of the 

Western Interconnection. California and Alberta would not be included because they already 

have centrally organized energy markets. Imbalances would be netted out, much as they would 

be in a single BAA. As proposed, the EIM does not result in a coordinated unit commitment, nor 

does it pool regulation, which remains a service at the local BA level. However, the netting of 

energy imbalance—which includes impacts of load, solar, and wind energy—is expected to be 

significant. Figure 4 illustrates the concept, with each of the small bubbles representing a single 

BAA. The arrows between the BAAs indicate bilateral energy flows that would still occur. 

Under an EIM, however, only the net imbalance of the EIM footprint must be managed. This

results in less net variability within the local BAAs and less ramping across the footprint.

Figure 4. The EIM footprint, which effectively pools variability

1.4 Prior EIM Analyses

There have been other analyses of a proposed Western EIM. 

NREL examined the ramping and flexibility reserve implications of an EIM using a 30% wind 

energy penetration from the Western Wind and Solar Integration Study (King et al. 2011) and 

performed a follow-up study using data from WECC’s PC0 with 8% wind energy and 3% solar 

energy (King et al. 2012). A simplistic analysis, using representative pricing for the various types 

of flexibility reserves, provided a rough estimate of the reserve deployment savings. 

WECC commissioned E3 to undertake a production simulation analysis of the proposed EIM. 

NREL provided the chronological flexibility reserves to E3, and these reserves were modeled as 

a constraint in the GridView model (E3 2011). The WECC-E3 study found the benefit of the 

EIM to be $141.4 million, assuming full participation from all nonmarket areas of the West. This 

study also found that removing Pacific Northwest, BC Hydro, and Western Area Power 

Administration (WAPA) from the EIM reduced the total benefit to $54 million. Full coordination 

with CAISO had an estimated benefit of $182 million.

EIM Footprint

EIM Tool: 

SCED

Intra-hour variability is captured and allocated

in realtime within the entire region, limited by 

Diversity benefit the physical capability of the wires.

reduces operating 

costs for balancing
7

Some entities performed follow-up analyses of the WECC-E3 study, but these are generally not 

publically available.

2 Study Approach 

Although greater detail is presented later, this overview provides perspective on the extensive 

analysis and modeling performed for this project.

The original EIM benefits study, undertaken by E3 on behalf of WECC, was built on TEPPC 

PC0. PC0 was developed by various WECC stakeholders and includes future trajectories for 

generation and transmission build-out, plant retirements, and wind-solar energy siting and 

penetration. The wind-solar energy build-out used in this study is thus a product of the TEPPC 

process, which resulted in the location of potential wind and solar plants in the BAAs of the 

Western Interconnection. Ten-minute wind and solar energy production models were developed 

at NREL and based on various weather system models.

The analysis in this study required several steps, as shown in Figure 5. Generally, the analysis 

was divided into (1) data analysis, including the development of flexibility reserve requirements, 

and (2) production simulation modeling. These are represented by the two columns in the figure. 

Figure 5. Overview of the study approach

Historical wind and solar data

Statistical analysis

Equations that predict next-period 

variability based on current values of 

wind and solar

Analysis data for study, 

wind and solar profiles

Hourly reserve time series

synchronized to load, wind, and solar

Production cost modeling GridView 

and PLEXOS

Production modeling results analysis,

including production cost savings

and BAA benefits

Definition of flex reserve sharing 

areas (e.g., BAA or EIM)

NREL Reserves Analysis WECC-E3 and NRELBenefits Studies
8

Production simulation models, such as PLEXOS, calculate power system operating costs, which 

include fuel, variable operating and maintenance costs, startup costs, and other variable costs. 

The capital costs of generation and transmission infrastructure, the costs of implementing an 

EIM (e.g., hardware, software, and communications), and any other direct or indirect costs of an 

EIM are not included. These models solve a cost-minimization problem, subject to the large 

number of physical and institutional constraints of the power system. 

Before the simulation tool, PLEXOS, was run, the flexibility reserve requirements were 

calculated from the wind and solar power time series data. This flex reserve, which is a relatively 

new concept, is separate and distinct from contingency reserve and is based on the variability and 

uncertainty of the wind/solar generation. Flexibility reserves are also a function of geographic 

aggregation and the dispatch time-step. The process is described in more detail in Section 2.2, 

but it essentially resulted in hourly or 10-minute reserve requirements for flex regulation and flex

spin, which were then input to PLEXOS. For the BAU cases, the process was repeated for every 

BAA. For the EIM cases, it was performed for the specific EIM participation level under study. 

The current practices for holding and using contingency reserves are not affected by these 

flexibility reserves, and contingency reserves cannot be called on to help manage the wind/solar 

power variability or uncertainty.

Once the flexibility reserves were input to PLEXOS, a day-ahead, security-constrained unit 

commitment (SCUC) and SCED were calculated for the entire Western Interconnection. This 

effort used wind and solar power forecasts much as they would be used in actual unit commitment 

decisions. However, units committed day-ahead cannot generally be de-committed, which reflects 

actual constraints on these units. Finally, a real-time economic dispatch that used all the 

information from the prior commitment was run. In real time, “actual” wind and solar power data 

were used for the dispatch, whereas forecasts of wind and solar power were used in the 

commitment. This reflects reality. Day-ahead, actual variable generation output is not known with 

certainty, so real-time operation is constrained by the units that have been previously committed. 

This security-constrained unit commitment and economic dispatch simulation minimized

operational costs within the given constraints. To evaluate the potential operational savings of 

the EIM, two simulations were required: a BAU case and an EIM case. The societal (total West) 

savings from the EIM is the difference in production cost between these two cases, as shown in

Figure 6.

Figure 6. EIM benefit formula

The E3 modeling and assumptions were adopted for the NREL study as completely as possible. 

The E3 study assumed that BAU operation in the Western Interconnection in 2020 would be 

based on 10-minute dispatch within BAAs and that interchange would continue to occur largely 

as it does today. The E3 model used an hourly time-step; thus, the equivalent cases in this study 

also modeled 10-minute dispatch at an hourly time-step. This was done by calculating the 

flexibility reserves (described below) assuming 10-minute dispatch.

EIM 

Benefit

Production 

cost of BAU

Production 

cost of EIM
9

To answer additional questions about the potential benefit of the EIM, the NREL study added 

10-minute dispatch cases with 10-minute time-steps. This 10-minute modeling time-step 

approximates the operation of the proposed EIM, which would operate on 5-minute time-steps. 

Thus, there are two types of cases: those run with an hourly time-step to match WECC-E3 and 

those that are actually simulated with a 10-minute time-step. In the discussion of the 10-minute 

cases below, it will be necessary to keep in mind these variations.

Further description—of the input data, reserves calculation method, production simulation 

modeling, and study cases—of the study approach and assumptions is provided in the 

following sections.

2.1 Input Data

The data requirements for this study included load, wind power, and solar power profiles and

forecasts. The 2006 time series load, wind power, and solar power profiles were used so common 

weather impacts would be maintained. The 2006 data were mapped to the simulation year 2020. 

In this report, “profile” is the actual power production from a wind or solar plant, and “forecast”

is the forecasted power production. Both the profiles and forecasts are synthesized and are either 

hourly or 10-minute values for the entire year, depending on the modeling case in question. 

WECC provided hourly load profile data projected for 2020 based on the 2006 load shapes, from 

which Pacific Northwest National Laboratory synthesized 10-minute load profiles. 

Wind data were obtained from NREL’s Western Wind and Solar Integration Study database

(3TIER 2010). Solar data were developed by NREL (Orwig et al. 2011). The renewable scenario 

was defined by the WECC TEPPC PC0 and includes approximately 8% wind and 3% solar 

penetration (by energy) in the Western Interconnection.

The BAAs were also defined in accordance with the TEPPC case and are shown in Table 1. Table 

2 shows the variable generation for each BAA in the study. 
10

Table 1. BAAs Defined for This Study

BAAs BAAs

Alberta Electric System Operator (AESO) Imperial Irrigation District (IID)

Arizona Public Service (AZPS) Los Angeles Department of Water and Power (LADWP)

Avista (AVA) Nevada Power (NEVP)

Balancing Area of Northern California (BANC) Northern Nevada [Sierra Pacific Power Co. (SPPC)]

Sacramento Municipal Utility District (SMUD) Northwest Energy (NWE)

Turlock Irrigation District (TID) Northwest Montana (NWMT)

Bonneville Power Administration (BPA) Western Area Upper Missouri (WAUM)

PUD No. 1 of Chelan County (CHPD) Pacificorp East (PACE)

PUD No. 1 of Douglas County (DOPD) Pacificorp Idaho (PACE_ID)

PUD No. 1 of Grant County (GCPD) Pacificorp Utah (PACE_UT)

Seattle City Light (SCL) Pacificorp Wyoming (PACE_WY)

Tacoma Power (TPWR) Pacificorp West (PACW)

British Columbia Transmission Corp. (BCTC) or 

BC Hydro Portland General Electric (PGN)

California Independent System Operator (CAISO) Public Service Company of Colorado (PSCO)

Pacific Gas and Electric (PG&E) Public Service Company of New Mexico (PNM)

Southern California Edison (SCE) Puget Sound Energy (PSE)

San Diego Gas and Electric (SDGE) Salt River Project (SRP)

Comision Federal de Electricidad (CFE) Tucson Electric Power (TEP)

El Paso Electric (EPE) Western Area Colorado Missouri (WACM)

Idaho Power Corp. (IPC) Western Area Lower Colorado (WALC) 

Far East (FAR EAST)

Magic Valley (MAGIC)

Treasure Valley (TREAS)
11

Table 2. Variable Generation Penetration (by Energy) for Each BAA

Load 

(GWh)

Solar 

(GWh)

Solar 

Penetration

Wind 

(GWh)

Wind 

Penetration

Total Variable 

Generation 

(GWh)

Variable 

Generation

Penetration

APS 35,874 3,280 9% 507 1% 3787 11%

AVA 15,010 0 0% 914 6% 914 6%

BPA 57,572 0 0% 16,680 29% 16680 29%

CFE 17,209 0 0% 22 0% 22 0%

CHPD 4,062 0 0% 0 0% 0 0%

DOPD 2,137 0 0% 0 0% 0 0%

EPE 10,628 84 1% 0 0% 84 1%

GCPD 5,180 0 0% 0 0% 0 0%

IID 4,694 0 0% 1,913 41% 1913 41%

IPC 19,540 0 0% 886 5% 886 5%

LDWP 32,480 653 2% 1,838 6% 2491 8%

NEVP 28,208 2,463 9% 0 0% 2463 9%

NWMT 11,439 0 0% 2,600 23% 2600 23%

PACE 55,897 0 0% 7,123 13% 7123 13%

PACW 20,665 14 0% 1,524 7% 1539 7%

PG&E 115,478 3,167 3% 2,858 2% 6026 5%

PGN 23,466 18 0% 2,012 9% 2030 9%

PNM 16,158 674 4% 2,352 15% 3026 19%

PSC 49,461 2,143 4% 7,777 16% 9920 20%

PSE 26,353 0 0% 3,012 11% 3012 11%

SCE 114,892 12,283 11% 11,098 10% 23381 20%

SCL 10,882 0 0% 0 0% 0 0%

SDGE 24,409 1,874 8% 1,753 7% 3627 15%

SMUD 18,478 0 0% 163 1% 163 1%

SPPC 12,714 0 0% 346 3% 346 3%

SRP 40,248 543 1% 334 1% 877 2%

TEP 16,421 951 6% 108 1% 1059 6%

TIDC 3,136 0 0% 0 0% 0 0%

TPWR 5,412 0 0% 0 0% 0 0%

WACM 29,653 53 0% 1,152 4% 1204 4%

WALC 7,523 154 2% 0 0% 154 2%

WAUW 631 0 0% 0 0% 0 0%

Total 835,911 28,355 3% 66,972 8% 95327 11%
12

2.1.1 Wind Profiles and Forecasts

This study used hourly and 10-minute wind power profiles as well as hourly day-ahead 

wind forecasts.

As part of the NREL Western Wind and Solar Integration Study, 3TIER Group developed a large 

wind speed and wind power database (2010). It applied a numerical weather prediction model to 

re-create the weather and synthesize high-resolution wind speed and power data (every 10 

minutes for a 3-year period on a 2-km spatial resolution) across the Western Interconnection. 

The resulting data set captures the chronological behavior of the wind that would be seen at 

locations around the West. This high-resolution data set was then used to construct the wind 

scenario for this study. 

The numerical weather prediction model of the Western Interconnection contained geographic 

and temporal seams that could not be entirely eliminated. This resulted in unrealistic wind 

energy ramps near the temporal boundaries, which occurred every 3 days. To make the reserves 

and ramping analysis complete, a continuous annual record was needed, so a method to smooth 

those ramps below statistical significance was required. To do this, the wind data were analyzed 

in detail surrounding the anomalies. 

The anomalies occurred at approximately the same time, 3 p.m., every third day, starting with the 

first day of data for all wind plants in the data set. Anomalous data were seen up to 3 hours 

before this time and 3 three hours after—a side effect of the blending of model runs to mitigate 

the seams. These anomalous data caused 10-minute ramps more than double that seen anywhere 

else in the data sets. Figure 7 shows a scatter plot of 10-minute interval changes verses the 

interval number of a 3-day period. The red dots show where the anomalous data are found. The 

spikes near 90 on the x-axis show the peak interval changes on the first day. The similar time, 3 

p.m., on the second and third days are near 230 and 380 and do not show similar peaks.

Figure 7. Example correction of the third-day anomaly 

in the Western Wind and Solar Integration Study wind data set
13

The time range and magnitude of the anomalies were determined. Statistics for similar time 

periods not affected by the seam were computed. Several moving average filters were designed 

to push the magnitude of the anomalies below a threshold consistent with statistics from the 

nonaffected times. The blue dots show the results of the filtering. Although some artifacts of the 

filtering are observed, the overall shape of the envelope is similar to that of days 2 and 3.

Hourly wind power profiles were developed from the 10-minute profiles by calculating the 

average of six 10-minute intervals.

Day-ahead, hourly wind power forecasts were also synthesized from a numerical weather 

prediction model but used a different input data set to ensure independence from the synthesized 

wind power production profiles. The actual power profiles were driven by the National Center 

for Environmental Prediction–National Center for Atmospheric Research reanalysis data set, 

while the forecasts were driven by the Global Forecast System. One consequence of using 

different data sets was that the total annual energy of the wind forecasts was 10%–20% higher 

than the total annual energy of the power profiles. Removing this bias is beyond the scope of the 

current study.

The WECC TEPPC mapped each 2020 PC0 wind plant location to the best available match in 

this wind database and created aggregate profiles for the work. A total of 29,084 MW of wind 

plants were included in this case to achieve the 8% (by energy) wind penetration level. The 

TEPPC wind profiles were disaggregated back to the bus level by Pacific Northwest National 

Laboratory for use in this study.

2.1.2 Solar Profiles and Forecasts

This study used hourly and 10-minute solar power profiles and hourly day-ahead solar forecasts. 

Solar power profiles for this study were generated at NREL based on the hourly, satellite-derived 

data from State University of New York–Clean Power Research and a statistical model to 

synthesize subhourly variations (Orwig et al. 2011). Power production data were developed for 

multiple solar technologies, including 50-MW fixed photovoltaics, 50-MW one-axis tracking 

photovoltaics, and 100-MW concentrating solar power plants with and without thermal energy 

storage. The data were developed for 1,488 grid locations that correspond to Western Renewable 

Energy Zones.

Hourly solar power profiles for concentrating solar power were generated using the State 

University of New York irradiance data and then the System Advisor Model to convert 

irradiance to power. These 1-hour profiles were interpolated to generate 10-minute concentrating 

solar power profiles.

For photovoltaics, the procedure for developing the subhourly data included the following steps:

• Classify the cloud regime based on the State University of New York hourly data. 

• Use the 1-minute irradiance ground observations to build ramp distributions for each 

cloud regime.

• Synthesize 1-minute irradiance data for each selected grid cell.
14

• Filter the irradiance data to represent the spatial smoothing.

• Use PVWatts® to convert irradiance to power.

Both the 10-minute and hourly profiles used in this study were generated by averaging the 

1-minute data. 

Because solar power forecasting is a relatively immature field, the hourly solar power profiles 

were also used as the day-ahead forecasts. Thus, the solar forecasts were perfect in the unit 

commitment stage of the production simulations (Section 2.5). Persistence forecasts were used in 

the calculation of the flexibility reserves (Section 2.2).

The WECC TEPPC mapped each 2020 PC0 solar plant location to the best available match in 

this solar database and created aggregate profiles for the work. A total of 7023 MW of 

photovoltaic plants, 1835 MW of concentrating solar power with 6 hours of thermal energy 

storage, and 5741 MW of concentrating solar power without storage were included in this case to 

achieve the 3% (by energy) solar penetration level. The TEPPC solar profiles were disaggregated 

back to the bus level by Pacific Northwest National Laboratory for use in this study.

2.1.3 Load Profiles and Forecasts

Because no load data with 10-minute resolution were provided for the study year 2020, these

data were generated by Pacific Northwest National Laboratory using a method developed for the 

WECC Variable Generation Subcommittee study (Samaan 2012). The available load data for the 

32 balancing authorities include (1) hourly load for the year 2020 from PROMOD and (2) 

minute-by-minute load data for the year 2009. Therefore, the following procedures were applied 

to generate the required load data for the study year 2020. The idea was to impose the minute-tominute variability of the 2009 load data onto the 2020 data. The procedure was:

• Compute the hourly average load data time series for all 32 BAAs in 2009 with 1-minute 

resolution.

• Apply MATLAB’s nonlinear interpolation method to obtain a new interpolated load 

series, as shown in Figure 8.

• Calculate the error between the actual load and interpolated load, normalize it based on 

the peak load in 2009 for each BAA individually, and scale it by multiplying by the peak 

load in 2020.

• Interpolate the provided hourly load data in 2020 to obtain 1-minute resolution load data.

• Apply the scaled 2020 error to the 1-minute-resolution 2020 load data to obtain the 

desired load curves, as shown in Figure 9.

This procedure was applied to the 32 BAAs. For this study, the 1-minute data were averaged to 

create the needed 10-minute data.
15

Figure 8. Actual load, hourly average, and interpolated load for 2009

Figure 9. Imposing the 2009 load variability on the 2020 interpolated load

Error
16

2.2 Reserves Calculation Method

The increased variability and uncertainty from wind and solar power increases the need for 

operating reserves (e.g., flexible generation and responsive load) that help manage that

variability. The additional reserves required are calculated dynamically and are a function of the 

time-synchronized expected variability of wind and solar power. A method to estimate the 

requirements for regulation with wind variability was developed in the Eastern Wind Integration 

and Transmission Study (King et al. 2011, EnerNex Corp. 2010). That method was used to 

calculate the reserve requirements for this study. 

The technique uses statistical analysis of 3 years of simulated historical wind and solar 

generation to estimate the reserve requirements at periods both faster (to provide regulation) and 

slower (to follow longer unforecasted changes in variable generation output) than the dispatch 

interval. These flexibility reserves are in addition to, not instead of, existing contingency reserves

and reserve-sharing group arrangements. The flexibility reserves are calculated for each hour of 

the year to create a dynamic reserve that can be deployed to manage variability and uncertainty

over various time scales.

2.2.1 Definition of Flexibility Reserves

For this study, the flexibility reserve3 requirements were divided into three classes based on the 

type of resources required to fulfill them:

1. Regulation covers fast changes of wind and solar power within the forecast interval. 

These changes can be up or down and happen minute-to-minute. This class of flexibility 

reserve covers minute-to-minute wind and solar variability and short-term forecast errors. 

Regulation requires resources on automatic generation control. 

4. Spinning reserve covers larger, less-frequent variations primarily caused by longer-term 

forecast errors. Spinning reserve is provided by resources (generation and responsive 

load) that are spinning and can fully respond within 10 minutes. These resources do not 

necessarily require automatic generation control. 

5. Non-spinning and supplemental reserves cover large, slower-moving, infrequent events 

such as unforecasted ramping events. Non-spinning reserve can be available within 10 

minutes and can come from quick-start resources and responsive load. Supplemental 

reserve can be available within 30 minutes. 

Note that flexibility reserves are a new type of reserve, specifically designed to address the 

variability and uncertainty of wind and solar generation. They are separate and distinct from the 

reserves the power system already requires to address load variability and contingencies. The 

names are the same (regulation, spinning reserve, and non-spinning reserve) because the same 

types of resources are required to provide flexibility reserves and contingency reserves. 

Flexibility reserves are distinct because they address the variability and uncertainty of wind and 

solar generation—instead of conventional generation contingencies. Large wind and solar ramp 

events are similar to conventional contingencies in that they are large and infrequent. They are 

 

3 Use of the term flexibility reserves is not consistent with other recent studies such as the NREL Western Wind and 

Solar Integration Study Phase 2. That study uses the term to describe only one component of what is referred to here 

as flex reserves.
17

different because they are slower. Similar resources can fulfill both needs and come from the 

same resource pool (e.g., conventional generation and responsive load), but this analysis does not 

use contingency reserves to provide flexibility reserves. Flexibility reserves are in addition to 

contingency reserves. Unless specifically stated otherwise, all references in this report to 

reserves apply to flexibility reserves.

Longer-term (an hour or more) forecast errors can be managed by bringing additional generation 

on line, which is done via the unit commitment process. Faster-responding spinning and nonspinning reserves are required to bridge the time from when it becomes evident to the system 

operator that a large, slow ramping event is unfolding to when the additional resources are 

available. The use of slower-responding reserves would reduce reserve costs, but this benefit has 

not been quantified.

The total flexibility reserve requirement would be the simple sum of the three components:

regulation, spinning, and non-spinning flexibility reserves. However, limitations in production 

simulation tools affected how these reserves were used in the analysis. For instance, nonspinning reserves are not represented in production simulation software, so that class of 

flexibility reserves was not included. In addition, production simulations can only approximate 

the provision of regulation services because the time frame (e.g., ones to tens of seconds) is 

outside the applicability of the tool. Therefore, both flexibility regulation and flexibility spinning 

reserves were represented as spinning reserves in the simulation software for this study.

Note that these flexibility reserve calculations incorporate only wind and solar variability. Load 

also varies but was not included in these calculations. It was assumed that load variability is 

covered by existing reserve requirements. This is a conservative assumption because the net load 

(load minus wind minus solar) variability would be less than the simple sum of load, wind, and 

solar variability. Therefore, a flexibility reserve calculation covering net load would be less than 

the simple sum of the wind and solar flexibility reserve calculations below and the existing 

reserves that cover load variability (Milligan 2003).

2.2.2 Regulation Flexibility Reserve Requirement Calculation

Regulation flexibility reserves cover short-term variability. Such variability is challenging 

because it is difficult to fully anticipate the fluctuations and schedule changes that must be 

covered with regulation flexibility reserves. Minute-to-minute fluctuations are uncorrelated 

among individual variable generation plants (Ela et al. 2011). As such, these fluctuations result 

in only a small contribution to regulation requirements and are neglected in this analysis. In a 

system with 10-minute or faster dispatch time-steps, it is common to use a persistence forecast. 

Such a forecast predicts constant wind or solar output for the next interval, based on the past 10 

to 20 minutes. An example of a persistence forecast is shown in Figure 10. Although the forecast 

remains constant, the wind and solar power will vary over the 10 minutes. Thus, the short-term 

forecast error (the difference between the actual wind and solar power output and the forecasted 

value) drives the regulation component of the flexibility reserve requirement. 
18

Figure 10. Example persistence forecast for 10-minute dispatch

In this study, a statistical approach was used to characterize the short-term variability in the wind 

and solar data and estimate the reserve requirements. The first step was to calculate the 

difference in aggregate wind (or solar) power output between one 10-minute interval and the 

next. This essentially represents the short-term forecast error between the actual output and a 10-

minute persistence forecast. Next, the errors were sorted into deciles based on the 10-minute 

wind (or solar) power production at the same time as the error. Using the Eastern Wind 

Integration and Transmission Study (EnerNex 2010) assumption that the short-term forecast 

error is normally distributed over a large geographic footprint, the standard deviation (sigma, or 

σ) of these 10-minute errors was calculated. One standard deviation covers about 68% of 

normally distributed errors, two standard deviations cover about 95%, and three standard 

deviations cover greater than 99%. 

The variability of wind output, as measured by this standard deviation, is shown by the blue line 

in Figure 11. Each marker on that line represents the standard deviation of a particular decile of 

wind production. The variability is a function of wind production level, with the most variability 

in the middle of the operating range and the least variability at maximum or minimum output. 

Figure 11. Short-term forecast error standard deviation as a function of wind production level
19

The next step was to find an analytical representation of the data. The red line in Figure 11 can 

be approximated by a polynomial curve that is fit to the data (Equation 1), providing an 

approximation to the data. This equation can be used to determine the variability associated with 

any given short-term wind production level. 

Equation 1. Calculation of short-term wind standard deviation

σWST (short– term wind)

= − 6.72E − 06 ∙ (short– term wind)2 + 0.0437 ∙ (short– term wind)

+ 26.74

 

A similar procedure can be used to obtain an equation that describes the short-term variability of 

solar output as a function of production level. The two equations are used to calculate the 

standard deviation of the wind and solar variability for each interval under a given scenario. 

The final step was to calculate a regulation flexibility reserve requirement to cover the expected 

wind and solar variability, with “expected” defined as 99.7% or 3 sigma of the normally 

distributed variability. This calculation is shown in Equation 2. Three sigma was selected to 

ensure that the variability of wind and solar generation would not have a negative impact on 

control performance scores.

Equation 2. Calculation of regulation flexibility reserve requirement

regulation flexibility reserve requirement

= 3 ∙ �(σWST (short– term wind))2 + (σSST(short– term solar))2

Different versions of the above figures and their associated equations were developed for each of 

the study cases, such that the flexibility reserve correctly reflected the desired wind and solar 

penetration, BAA or EIM footprint, dispatch interval, and forecast lead time. All variations on 

these figures and equations were developed from the full 10-minute wind and solar data sets. The 

input to these equations (i.e., short-term wind or short-term solar production level) was a 10-

minute value for simulations with a 10-minute time-step and an hourly value for simulations with 

an hourly time-step.

2.2.3 Spinning and Non-Spinning Flexibility Reserve Requirement Calculation

The spinning and non-spinning flexibility reserves cover hour-ahead wind and solar forecasting 

errors. This component is calculated in a similar manner to the short-term forecast error 

described above. Figure 12 shows the standard deviation of the hour-ahead forecast errors (blue 

line) in the wind data set. The errors were sorted into deciles based on the hourly production, and 

each diamond marker represents the standard deviation of a particular decile.
20

Figure 12. Hour-ahead forecast error standard deviation as a function of wind production level

The red line in Figure 12 is a curve fit polynomial (Equation 3) that approximates the data. This 

equation can be used to determine the standard deviation of the forecast error associated with a 

previous hour’s production (persistence forecast). 

Equation 3. Sample calculation of hour-ahead wind standard deviation

σHAWind(hourly wind)

= − 2.985E − 05 ∙ (hourly wind)2 + 0.1895 ∙ (hourly wind) + 103.2

A similar procedure can be used to obtain an equation that describes the hour-ahead forecast 

error of solar output as a function of production level. The two equations were used to calculate 

the standard deviation of the wind and solar forecast error for each interval under a given study 

scenario. Each study scenario had its own set of equations based on the number of BAAs 

represented and the aggregate wind and solar in each BAA.

Both spinning and non-spinning flexibility reserves cover the probable wind and solar forecast 

error. Spinning reserves cover one standard deviation of the forecast errors, or approximately 

68%. Non-spinning reserves cover two standard deviation of the forecast errors, or 

approximately 95%. Equation 4 shows the calculation for the spinning reserves, and Equation 5 

shows the calculation for the non-spinning reserves. 

Equation 4. Calculation of spinning flexibility reserve requirement

spinning flexibility reserve requirement

= � σHAWind(previous hour wind)2 + σHASolar(previous hour solar)2
21

Equation 5. Calculation of non-spinning flexibility reserve requirement

non-spinning flexibility reserve requirement

= 2 ∗ � σHAWind(previous hour wind)2 + σHASolar(previous hour solar)2

Three-sigma total coverage was selected to ensure that the uncertainty associated with variable 

generation will not negatively affect control performance scores. The 1 sigma-2 sigma split for 

spinning and non-spinning resources was determined through an analysis of variable generation 

ramping data and the timeframes in which those ramps occur.

2.2.4 Effect of Forecast and Dispatch Timing on Forecast Error and 

Reserve Requirements

The discussion in the previous sections assumes a particular timing for forecast and dispatch. 

The short-term error example assumed that the forecast would be made 10 minutes before the 

beginning of the dispatch interval, which would last 10 minutes before a new dispatch would 

take effect. The method developed can be used to evaluate other timing as well.

Figure 13 shows how a different set of timings can affect forecast errors. In this example, the 

forecast for the next hour-long dispatch period is taken 40 minutes before the beginning of that 

period. That forecast is assumed valid throughout the entire hour of the dispatch. The difference 

between the hourly forecast and the actual wind and solar profile is a variation on the short-term 

forecast error shown in Figure 10. 

Figure 13. Effect of hourly dispatch and 40-minute forecast lead on forecast error

Using the forecast error determined from the hourly persistence forecast, the flexibility reserve 

requirements can be calculated using the reserves method described above. These requirements 

are much higher because the reserves must of sufficient magnitude to cover the variability seen 

in an hour instead of 10 minutes, as seen in the earlier example.
22

Figure 14 shows the impact of geographic size and dispatch frequency on flexibility reserve 

requirements. The left box shows the flexibility reserve need from the full-participation EIM at 

alternative dispatch and forecast time-steps. The middle box shows the reserve implication of 

three regional EIM implementations, and the right box shows BAU. The time-steps are shown in 

the legend by two numbers. The first number is the dispatch time-step, and the second number is 

the forecast lead time or notification. For example 30-10 is a 30-minute dispatch with 10-minuteahead forecast and notification. This graph shows that large BAAs with fast economic dispatch 

have smaller flexibility reserve requirements than small BAAs with slow economic dispatch.

Figure 14. Effect of dispatch interval and aggregation size on reserve requirements 

2.3 Modeling Limitations 

It is not possible to model the Western Interconnection exactly as it will operate in 2020—either 

with or without the EIM. Limitations on the modeling include:

• A lack of data on bilateral power purchase agreements in place today or that will be in 

place in 2020. Lack of contractual data between generators, transmission providers, and 

load-serving entities has a significant impact on the commitment and dispatch performed 

by the production simulation software. Without such data, the software develops a 

minimum production cost commitment and dispatch, subject only to generating unit 

operating limits, transmission path ratings, and other performance constraints. Even if 

these agreements were made available for this study, it is possible that the terms of the 

energy exchanges could change by 2020. Thus, even if current agreements were modeled 

precisely, there is uncertainty regarding their future form. Because power purchase 

information is not available, the generally accepted approach is to use hurdle rates to 

provide transactional friction and limit economic power flows. The WECC-E3 study 

developed hurdle rates for each pair of BAAs so that flows in the model were closely 

aligned with actual flows from 2006. Those hurdle rates were also used in this analysis.
23

• Uncertainty regarding the use of other new or proposed tools to better coordinate widearea operations in the West. Some of these tools, such as the dynamic scheduling system

and ITAP, are designed to be used on an as-needed basis and therefore cannot be 

systematically captured in a modeling framework. Further, no data regarding the extent to 

which ITAP will be used were available for this study. This study focuses on the potential 

benefits of the EIM and does not evaluate other means of operational coordination that 

may emerge before 2020.

• Uncertainty regarding the impact of Federal Energy Regulatory Commission Order 764. 

This order requires transmission operators to offer 15-minute (or faster) transmission 

scheduling. However, the ruling does not appear to require the use of 15-minute 

schedules, only that transmission operators offer 15-minute scheduling to generators—

including wind and solar generation owners. Therefore, it is not possible to know which 

entities will use these faster schedules or the extent to which the schedules will be used. 

The specific dispatch interval assumptions for both the BAU and EIM cases evaluated in 

this study are described in Section 2.4.

• Uncertainty regarding the amount and location of variable generation such as wind and 

solar energy. These assumptions are key drivers of the study results, and although 

alternative wind-solar scenarios were not evaluated with PLEXOS, prior work on 

flexibility reserves has shown a significant impact of penetration on reserve requirements 

(King et al. 2012). This study adopted the assumptions from TEPPC PC0 and the WECCE3 study, which were vetted by WECC’s public stakeholder process. The 8% wind and 

3% solar penetrations for 2020 were included in these assumptions.

• Uncertain transmission additions. New transmission may alleviate existing transmission 

constraints or allow more access to remote but economic generation. In addition, EIM 

transactions would flow at the lowest level of transmission service and therefore be 

subject to curtailment. If transactional curtailment were to be significantly more (or less) 

than expected, this would reduce (or increase) the value of the EIM. TEPPC PC0 

assumptions regarding future transmission were incorporated.

• Uncertain generation additions and retirements between now and 2020. Specifically, 

there is significant uncertainty regarding coal retirements—which may be stimulated by 

new Environmental Protection Agency rules regarding emissions. These retirements 

would alter the future nonvariable generation mix, its flexibility characteristics, and fuel 

costs and therefore impact study results. The TEPPC PC0 assumptions regarding future 

generation were incorporated.

• Potential changes in the way hydro generation will be operated (e.g., because of 

drought) in the future. Hydro modeling is described in Section 2.5.2.3. The PLEXOS 

hydro model results were somewhat closer to the WECC TEPPC results than were the 

WECC-E3 results.
24

• Software limitations. Currently, CAISO and AESO operate 5-minute economic dispatch 

with compensation from energy markets. The rest of the interconnection generally 

operates on hourly schedules. The current generation of production simulation models 

cannot model different dispatch intervals within a single simulation. Thus, it is not 

possible to simultaneously model the 5-minute market and hourly nonmarket areas of the

Western Interconnection.

• Uncertainty about gas and coal prices. The EIM will result in more efficient dispatch, 

thus reducing generation on the margin of the dispatch curve. When fuel prices are 

unknown, the value of the new dispatch is not known with certainty. Two gas price 

scenarios were evaluated.

• Uncertainty regarding which BAAs and generators would participate in the EIM. Both 

full and reduced EIM participation were evaluated. 

• Ten-minute resolution load, wind, and solar data. The EIM would operate at a 5-minute 

time-step, so the use of 10-minute data gives slightly conservative results. A longer 

dispatch interval means higher flex reserve requirements for the EIM scenario and,

therefore, less of a difference between it and the BAU case. 

• The fact that quick-start units, similar to other thermal units, are committed in the dayahead unit commitment step of PLEXOS simulations. That commitment is maintained 

throughout the SCED step. This results in committed combustion turbines (CTs)

operating at minimum generation during the real-time dispatch. An improved model of 

quick-start CTs would allow these units to start and stop during real time rather than 

maintaining the day-ahead commitment and enforcing minimum generation operation.

Each of these items will have an impact on the BAU simulation results, the EIM simulation 

results, and the benefit calculation of the EIM. One set of study assumptions and modeling 

approximations, as described in this section, was used in this study to represent the key elements 

of the BAU and EIM study scenarios. Other assumptions and approximations could be analyzed 

in future studies. Therefore, the results of this study must be interpreted in the proper context as 

one possible outcome and not as a definitive forecast. 

2.4 Study Cases

Study cases were developed to identify the potential benefits of an EIM under various system 

conditions and assumptions and to confirm the consistency of this study’s results with those of 

the E3 study. For clarity, the cases are grouped in the following sections according to their 

purpose. Thus, the E3 alignment cases are described in Section 2.4.1, and the EIM benefit cases 

are described in Section 2.4.2.

To facilitate the following discussion, a naming convention for the simulation cases was 

developed. To minimize confusion, both the overall description of the case in question and the 

case name is used in the discussion. For example, E3BAU is a BAU case that aligns with the 

WECC-E3 study and uses an hourly time-step in the production simulations, whereas PLHBAU 

is a new PLEXOS BAU case that uses an hourly time-step.
25

Table 3. Case-Naming Convention

Part 1 Part 2 and/or 3

E3 Aligns with the WECC-E3 study, 

hourly time-step

BAU Business as usual

PL PLEXOS case, 10-minute timestepEIM1 EIM using PLHBAU day-head commitment

PLH PLEXOS case, hourly time-step EIM2 EIM using E3BAU day-ahead commitment

PMA1 Federal power marketing administrations excluded 

from EIM, using PLHBAU day-head commitment 

PMA2 Similar to PMA1 but with E3BAU day-head 

commitment

RES Reduced reserve

GAS Lower gas price

2.4.1 E3 Alignment Cases

Two cases were developed to check the alignment of the results of this study, which used 10-

minute simulations and PLEXOS software, with those of the E3 study, which used 1-hour 

simulations and GridView software. Regardless of the simulation time-step, the alignment cases 

are referred to as 10-minute cases because the flexibility reserves were calculated assuming a 10-

minute dispatch interval and a 10-minute forecast lead time. 

The first case, E3BAU is an hourly simulation with few changes to Western Interconnection 

operating procedures between now and 2020. Thus, it is termed a BAU case and acts as a 

benchmark for comparison with the case of the full EIM footprint. This case includes:

• Hourly simulation time-step

• Hourly dispatch of generation

• 10-minute lockdown or lead time for forecasts

• 10-minute flex reserve requirements. 

In this case:

• Individual BAs perform their own unit commitment to meet their own load and reserve 

requirements with their own generation

• Individual BAs perform their own dispatch to balance load and generation

• All BAA hurdle rates are implemented.

The forecast lead time, also referred to as the forecast lockdown time, is the time period between 

when the forecast for the load and variable generation is fixed for a given dispatch interval and 

the beginning of that dispatch interval. In this study, the forecast for variable generation is taken 

as a persistence forecast. That is, the value of wind or solar generation is assumed to be constant 

over the dispatch interval as measured at the forecast lead time. 
26

The hourly unit commitment is developed from the hourly wind forecast, hourly actual solar,

and hourly actual load. Thus, the solar and load forecasts used are perfect. Each BA commits 

its units to meet its forecasted net load (actual load minus forecasted wind minus actual solar) 

and reserve requirements. The economic dispatch step uses hourly actual wind, solar, and load 

data. It results in a chronological hourly dispatch and interchange results for the study year. 

Each BA dispatches its units to meet its actual net load (actual load minus actual wind minus 

actual solar) and reserve requirements. 

Reserve requirements include the standard contingency reserve and the new flexibility reserve 

necessary to accommodate the variability and uncertainty of wind and solar power. These flex 

reserve requirements are calculated using the hourly wind or solar production level as an input to a 

case-specific set of equations based on the 10-minute data, as described in the previous section. As 

noted above, the flexibility reserve calculations must be performed for each region or aggregation 

of regions studied. For this BAU case, each BA within the study footprint is responsible for 

managing the variability within its boundaries. These calculations are performed on the data 

specific to each BAA, such as the aggregate wind and solar production profiles. These profiles are 

aggregated from individually modeled plants and thus fully represent the geographic and temporal 

diversity of their BAA. The result of this analysis is 8760 hours of combined flex regulation and 

flex spin for each BA, which are used in the production simulation analysis.

The second alignment case, E3EIM, represents the operation of the EIM with full participation. 

The full EIM footprint consists of the Western Interconnection without the areas that already 

have markets in place (CAISO and AESO). A comparison of this case with the benchmark, 

E3BAU, will show the potential EIM benefits under study assumptions similar to those of the E3 

study. This case includes:

• Hourly simulation time-step

• Hourly dispatch of generation

• 10-minute lockdown or lead time for forecasts

• 10-minute flex reserve requirements. 

In this case:

• Individual BAs perform their own unit commitment to meet their own load and reserve 

requirements with their own generation

• Dispatch of imbalance is performed across the full EIM footprint to balance load and 

generation

• The hurdle rates between the BAAs within the EIM footprint are removed. Hurdle rates 

remain between the EIM participants (e.g., SRP) and nonparticipants (e.g., CAISO).

The flex reserve requirements are calculated using the hourly wind or solar production level as 

an input to a set of equations specific to the 10-minute data for the entire EIM footprint.
27

2.4.2 EIM Benefit Cases

The majority of cases were developed to explore the potential benefits of an EIM. Given the 

inherent difficulty of predicting the future, the primary objective in designing these cases was to 

cover a range of possible scenarios. 

One series of cases uses the E3BAU case, described in the previous subsection, as a benchmark. 

The three other cases in this series represent system operation with full EIM participation, 

reduced EIM participation, and reduced flexibility reserves. Regardless of the simulation timestep, these cases are referred to as 10-minute cases because the flexibility reserves are calculated 

assuming a 10-minute dispatch interval and a 10-minute forecast lead time.

The PLEIM2 case examines the operation with full EIM participation, which means the Western 

Interconnection without the areas that already have markets (i.e., CAISO and AESO). A 

comparison of this case with the benchmark, E3BAU, will show the potential benefit of the full 

EIM footprint for one set of assumptions. It includes:

• 10-minute simulation time-step

• 10-minute dispatch of generation

• 10-minute lockdown or lead time for forecasts

• 10-minute flex reserve requirements.

In this case:

• The same unit commitment as E3BAU is used

• Dispatch is performed across the full EIM footprint to balance load and generation

• The hurdle rates between the BAAs within the EIM footprint are removed. Hurdle rates 

remain between the EIM participants and nonparticipants. 

For this and other EIM cases, the wind and solar production profiles for each BAA are further 

aggregated into profiles for a given EIM footprint. Again, the geographic and temporal diversity 

of the data are preserved. A different set of combined flex regulation and flex spin reserves will 

result for this case. Aggregating the variable generation reduces the overall variability of the 

combined regions. This leads to lower aggregate reserve requirements.

The PLEIM-PMA2 case was designed to explore the sensitivity of the potential EIM benefits to 

reduced participation in the EIM. The full EIM included participation of the entire Western 

Interconnection except those areas that already have markets. Requested by the Public Utility 

Commission Energy Imbalance Market (PUC EIM) Group, this reduced footprint excludes BPA 

and two of the three Western Area Power Administration (WAPA) BAs. Several entities 

(CHPUD, DOPUD, GCPUD, Seattle City Light, and Tacoma Power) embedded in BPA are also 

excluded. This case includes:

• 10-minute simulation time-step

• 10-minute dispatch of generation

• 10-minute lockdown or lead time for forecasts

• 10-minute flex reserve requirements. 
28

In this case:

• The same unit commitment as E3BAU is used

• Dispatch is performed across the reduced EIM footprint to balance load and generation

• The hurdle rates between the BAAs within the EIM footprint are removed. Hurdle rates 

remain between the EIM participants and nonparticipants.

The final case in this series, PLRES, carries reduced flexibility reserves across the full EIM 

footprint. Only the regulation component of the flexibility reserves is used. (This assumes flex

spin can be released in real time.) This case, developed at the suggestion of industry 

stakeholders, provides information about the sensitivity of results to the potential release of nonspin, which contrasts with other cases. This case includes:

• 10-minute simulation time-step

• 10-minute dispatch of generation

• 10-minute lockdown or lead time for forecasts

• 10-minute flex reserve requirements for regulation only.

In this case:

• The same unit commitment as E3BAU is used

• Dispatch is performed across the full EIM footprint to balance load and generation

• The hurdle rates between the BAAs within the EIM footprint are removed. Hurdle rates 

remain between the EIM participants and nonparticipants.

A second series of cases begins with a different benchmark case: PLHBAU. PLHBAU is an 

hourly simulation with few changes to Western Interconnection operating procedures between 

now and 2020. It is referred to as an hourly case because the flexibility reserves are calculated 

assuming an hourly dispatch interval and a 40-minute forecast lead time. This hourly BAU case 

and the 10-minute BAU case (E3BAU, described above) act as bookends to the range of possible 

scenarios. This case includes:

• Hourly simulation time-step

• Hourly dispatch of generation

• 40-minute lockdown or lead time for forecasts

• Hourly flex reserve requirements. 

In this case: 

• Individual BAs perform their own unit commitment to meet their own load and reserve 

requirements with their own generation

• Individual BAs perform their own dispatch to balance load and generation

• All BAA hurdle rates are implemented.
29

The forecast lead time, also referred to as the forecast lockdown time, is the time period between 

when the forecast for load and variable generation is fixed for a given dispatch interval and the 

beginning of that dispatch interval. In this study, the forecast for variable generation is taken as a 

persistence forecast. That is, the value of wind or solar generation is assumed to be constant over 

the dispatch interval as measured at the forecast lead time. For this hourly BAU case, the forecast 

lockdown was 40 minutes. Therefore, this forecast will be 1 hour and 40 minutes out of date by 

the end of the dispatch interval. 

The hourly unit commitment is developed from the hourly wind forecast, hourly actual solar,

and hourly actual load. Thus, the solar and load forecasts used are perfect. Each BA commits 

its units to meet its forecasted net load (actual load minus forecasted wind minus actual solar) 

and reserve requirements. The economic dispatch step uses hourly actual wind, solar, and load 

data. It results in a chronological hourly dispatch and interchange results for the study year. 

Each BA dispatches its units to meet its actual net load (actual load minus actual wind minus 

actual solar) and reserve requirements. 

Reserve requirements include the standard contingency reserve and the new flexibility reserve 

necessary to accommodate the variability and uncertainty of wind and solar. These flex reserve 

requirements are calculated using the hourly wind or solar production level as an input to a casespecific set of equations, as described in the previous section. As noted above, the flexibility 

reserve calculations must be performed for each region or aggregation of regions studied. For this 

BAU case, each BA within the study footprint is responsible for managing the variability within 

its boundaries. These calculations are performed on the 10-minute data specific to each BAA, 

such as the aggregate wind and solar production profiles as well as the forecast lead time. These 

profiles are aggregated from individually modeled plants and thus fully represent the geographic 

and temporal diversity of their BAA. The result of this analysis is 8760 hours of combined flex 

regulation and flex spin for each BA. This is used in the production simulation analysis.

Two EIM cases are included in this series. PLEIM1 examines operation with full EIM 

participation. It is similar to PLEIM2 in its assumptions but starts with a different unit 

commitment. It includes:

• 10-minute simulation time-step

• 10-minute dispatch of generation

• 10-minute lockdown or lead time for forecasts

• 10-minute flex reserve requirements.

In this case:

• The same unit commitment as PLHBAU is used

• Dispatch is performed across the full EIM footprint to balance load and generation

• The hurdle rates between the BAAs within the EIM footprint are removed. Hurdle rates 

remain between the EIM participants and nonparticipants.
30

The PLEIM-PMA1 case was designed to explore the sensitivity of the potential EIM benefits 

to reduced participation in the EIM. This reduced footprint excludes BPA, its embedded 

utilities (CHPUD, DOPUD, GCPUD, SCL, and TPWR), and two of the three WAPA BAs. It is 

similar to the PLEIM-PMA2 case in its assumptions but starts with a different unit 

commitment. This case includes:

• 10-minute simulation time-step

• 10-minute dispatch of generation

• 10-minute lockdown or lead time for forecasts

• 10-minute flex reserve requirements. 

In this case:

• The same unit commitment as PLHBAU is used

• Dispatch is performed across the reduced EIM footprint to balance load and generation

• The hurdle rates between the BAAs within the EIM footprint are removed. Hurdle rates 

remain between the EIM participants and nonparticipants.

A final pair of sensitivity cases was designed to evaluate the impact of reduced natural gas prices 

on potential EIM benefits. The WECC TEPPC 2020 planning case that is the basis of this 

analysis uses a gas price of $7.28/MMBtu. A lower price of $4.50/MMBtu (Henry Hub 

benchmark) was used for the final cases. This lower gas price is consistent with the newer 

TEPPC 2022 cases, which included a natural gas price of $4.60/MMBtu. The first case, 

PLGASBAU, is similar to the PLHBAU case but with a lower gas price. This case includes:

• Hourly simulation time-step

• Hourly dispatch of generation

• 40-minute lockdown or lead time for forecasts

• Hourly flex reserve requirements. 

In this case:

• Individual BAs perform their own unit commitment to meet their own load and reserve 

requirements with their own generation

• Individual BAs perform their own dispatch to balance load and generation

• All BAA hurdle rates are implemented.

The second case, PLGASEIM, examines operation with full EIM participation. A comparison of 

this case to the benchmark, PLGASBAU, will show the potential benefit of full EIM 

participation at the lower gas price. This case includes:

• 10-minute simulation time-step

• 10-minute dispatch of generation

• 10-minute lockdown or lead time for forecasts

• 10-minute flex reserve requirements.
31

In this case:

• The same unit commitment as PLGASBAU is used

• Dispatch is performed across the full EIM footprint to balance load and generation

• The hurdle rates between the BAAs within the EIM footprint are removed. Hurdle rates 

remain between the EIM participants and nonparticipants.

2.4.3 Case Summary

The above cases are summarized in Table 4. Each column is identified by an individual case 

name (e.g., E3BAU), and each row shows the assumptions and modeling details built into that 

case (e.g., simulation time-step).

Table 4. Summary of Study Cases

E3

BAU

E3

EIM

PL

EIM2

PLEIMPMA2 PLRESPLH

BAU

PL

EIM1

PLEIMPMA1PLGAS

BAU

PLGAS

EIM

Simulation

time-step

(minutes)

60 60 10 10 10 60 10 10 60 10

Generation

dispatch

(minutes)

60 60 10 10 10 60 10 10 60 10

Forecast

lead time

(minutes)

10 10 10 10 10 40 10 10 40 10

Flex reserve 

requirement 

(minutes)

10 10 10 10 10 60 10 10 60 10

Reserve 

requirements Full Full Full Full Regulationonly Full Full Full Full Full

Commitment — E3BAU E3BAU E3BAU E3BAU — PLHBAU PLHBAU — PLGAS

BAU

Dispatch to 

balance

Within 

BAAs

Full 

EIM

Full 

EIM

Reduced 

EIM Full EIM Within BAAs Full EIM Reduced EIM

Within 

BAAs

Full 

EIM

Hurdle rates 

outside EIM Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Hurdle rates 

within EIM — No No No No — No No — No

Gas price High High High High High High High High Low Low
32

2.5 Production Simulation Model

A production cost model simulates the operation of the interconnected generation and 

transmission system by solving at each time interval the least-cost solution to generating 

sufficient energy to meet demand. The solution is influenced by transmission constraints [direct 

current (DC) optimal power flow (DCOPF) on the nodal system representation], generator 

operation properties (e.g., start-up costs, ramp rates, and maintenance outages), and reserve 

requirements for meeting intra-interval generation-demand imbalances. Operators run production 

cost models for planning purposes (e.g., to test capacity expansion feasibility) and during 

daily/hourly operation to schedule generating units.

For this study, a commercial production simulation tool, PLEXOS, was used. The production 

simulations were performed by the software developer, Energy Exemplar Ltd. PLEXOS has 

hourly and subhourly simulation capability. Both were used in this study. Hourly simulations 

were used to emulate current operating practices in the Western Interconnection, and subhourly 

simulations were used to capture the impact of subhourly operations as well as the subhourly 

variability and uncertainty of wind and solar.

A multistage simulation process was used to emulate power system operation. The first stage 

developed an initial profile for hydro generation based on monthly energy production while 

respecting minimum and maximum capacity. The second stage mimicked the hourly day-ahead 

SCUC, and the third stage mimicked the real-time SCED using the unit commitment schedules 

and hydro profiles from the prior stages. The time interval for the first and third stages was either 

hourly or 10 minutes, depending on the study scenario. The second stage or unit commitment 

used a 1-hour time interval.

2.5.1 PLEXOS Software Overview

PLEXOS has three levels of simulation: a long-term plan for capacity expansion simulations; a 

medium-term schedule for optimizing hydro storage, fuel supplies, or emissions; and a shortterm schedule for chronological unit commitment and dispatch. The latter two capabilities were 

used in this study.

The medium-term schedule was used to develop hourly (or 10-minute) hydro profiles based on 

monthly energy requirements and unit minima and maxima. This logic performs the cooptimization of energy and ancillary services for an entire month at the regional level. The hours 

in a month are grouped into 90 time blocks in the descending order of a load duration curve, with 

each time block an interval in the optimization. The outputs of this step are hydro generation 

profiles that honor the monthly hydro energy constraints. Chronological hydro unit constraints, 

such as ramp rate limitations, are not enforced in this part of the process. 
33

The short-term schedule was used once for the SCUC to emulate the day-ahead commitment 

process and a second time for the SCED to emulate real-time operation. The SCUC-SCED 

simulation algorithm is illustrated in Figure 15. 

Figure 15. PLEXOS security-constrained unit commitment and economic dispatch algorithm

The unit commitment-economic dispatch logic performs the energy-ancillary services cooptimization by mixed integer programming, while enforcing all resource and operation 

constraints. The unit commitment-economic dispatch algorithm commits and dispatches 

resources to balance system energy demand and meet system reserve requirements. The hydro 

generation profiles developed in the first step are input to this step in the simulation process. The 

hydro schedules may be modified in this second step to respect chronological hydro unit 

constraints (e.g., ramp rates) or to respond to price signals. 

The resource schedules from the unit commitment-economic dispatch logic are passed to the 

network application logic. The network application logic solves the DCOPF to enforce the power 

flow limits (i.e., transmission line or interface limits) and nomograms (i.e., limits based on a 

specific relationship between generation, load, transmission topology, and/or interface power 

flows). The network application logic also performs a contingency analysis for defined 

contingencies, although none were defined for this study. If there are transmission limit 

violations, the limits are passed to the unit commitment-economic dispatch logic for a re-run. 

The iteration continues until all transmission limit violations are resolved. Thus, the cooptimized solution of energy-ancillary services-DCOPF is reached.

DC-OPF solves network power 

flow for the resource 

schedules passed from unit

Performs contingency power 

flow analysis

Enforces transmission line

limits, interface limits, and 

nomograms

Energy-ancillary service cooptimization by mixed integer 

programming

Enforces resource chronological

constraints, transmission 

constraints passed from network

applications, and other constraints

Solutions include resource online status, loading levels, and

ancillary service provisions

Unit CommitmentEconomic Dispatch Resource 

Schedule

Violated 

Transmission

Constraints

Network Applications

commitment-economic 

dispatch
34

Shortages of generating capacity, over-supply of energy, and transmission congestion are 

recognized and measured in terms of unserved energy, spilled energy, and reserve shortfall. 

When this occurs, the energy balance constraint or reserve requirement constraint is relaxed. The 

order of the relaxation of these constraints is determined by the slack penalty prices of the 

constraints. The constraints with a smaller slack penalty price are relaxed first. As shown in 

Table 5, the order of relaxation is: (1) spill energy, (2) contingency spinning reserve shortfall, (3) 

flexibility reserve shortfall, and (4) unserved energy. 

Table 5. Order of Constraint Relaxation

Constraints Slack Variables Penalty Price

Energy balance Spill energy -$20/MWh

Contingency reserve Spin shortfall $250/MWh

Flexibility reserve Flexibility shortfall $400/MWh

Energy balance Unserved energy $500/MWh

In this study, the wind and solar generation were treated as noncurtailable.

The day-ahead SCUC optimizes over 24 hours, with an hourly interval. Input data include:

• Forecasted wind generation profiles

• Actual load and solar generation profiles (i.e., perfect forecasts)

• Detailed generator characteristics

• Contingency reserve, regulation reserve, and flexibility reserve for each specified BAA 

or group of BAAs

• Transmission hurdle rates between BAAs

• Detailed nodal transmission network of the Western Interconnection.

The day-ahead SCUC simulation results in an hourly unit commitment and resource schedule for

the entire interconnection. Each BA commits enough online capacity to cover its own load and 

reserve requirements at any hour. 

For hourly cases, the real-time SCED optimizes over the hour with no look-ahead. For 10-minute

cases, the real-time SCED optimizes over 10 minutes with a look-ahead of five 10-minute

intervals. Input data for either include:
35

• The actual load, wind, and solar profiles

• Unit commitment schedules from the day-ahead SCUC

• Detailed generator characteristics

• Contingency reserve, regulation reserve, and flexibility reserve for each specified BAA 

or group of BAAs

• Transmission hurdle rates between BAAs

• The detailed nodal transmission network of the Western Interconnection.

The real-time SCED simulation results in either an hourly or 10-minute dispatch for 

the interconnection.

The flexibility reserves to cover the renewable variability and uncertainty are included in both 

the day-ahead SCUC and the real-time SCED. For the day-ahead simulations, the flexibility 

reserves are defined at the BAA level. For the 10-minute real-time simulations, the flexibility 

reserves are defined at the BAA level for the BAU scenario and at the EIM footprint level for 

EIM scenarios.

2.5.2 System Model

The WECC TEPPC 2020 PC0 system model was used in this study. It was converted directly 

from the TEPCC PROMOD format to PLEXOS format. This model includes the transmission 

and generation systems as well as the regional definitions used for contingency reserves. Each is 

described in more detail below. The PLEXOS version of this model is essentially the same as the 

original TEPPC model. In addition, study assumptions were drawn from the E3 study to enable 

comparisons of results. Modifications made for the purposes of this study are reported below.

2.5.2.1 Transmission System Model

The transmission network model represents the entire Western Interconnection and includes:

• More than 17,500 nodes

• More than 22,590 transmission lines and transformers 

• 1043 transmission lines and transformer limits that are enforced

• 44 phase shifters modeled as control variables

• More than 120 interfaces whose limits are enforced 

• 8 nomograms, including several multisegment ones

• 39 regions.

The interfaces that were enforced and their limits are shown in the appendix. The nomograms are 

shown in Table 6. 
36

Table 6. Nomograms That Are Enforced

Nomogram Description

COB Limits Alturas Project and COI interfaces from north to south

COI Three-segment nomogram that limits COI interface as a function of Northern 

California hydro generation 

IPP DC Limits IPP DC intertie flow north to south as a function of generation at the 

north end

John Day vs COI + PDCI Three-segment nomogram of the interfaces north of John Day, COI, Alturas 

Project, Midpoint-Summer Lake, and Pacific DC Intertie

John Day vs COI Three-segment nomogram of the interfaces north of John Day, COI, Alturas 

Project, and Midpoint-Summer Lake

John Day vs PDCI Three-segment nomogram of the interfaces north of John Day, Pacific DCIntertie, and Midpoint-Summer Lake

SCE Import SCE under-frequency nomogram

TOT 4AB2 TOT 4A and 4B nomogram

The regional demands from the WECC TEPPC database include the transmission losses. Therefore, 

the PLEXOS simulations do not model transmission losses explicitly. Rather, the transmission 

losses are calculated and reported based on the power flows in the transmission facilities.

Hurdle rates between BAAs were used to provide the necessary transactional friction to 

approximate historical power flows across various paths. The hurdle rates (shown in Table 7) 

were defined by the earlier E3 study, which also performed the calibration of simulated power 

flows to historical power flows (E3 2011b). These same hurdle rates were used in this study.

For the BAU scenarios, all BAA hurdle rates were honored. For the EIM scenarios, the hurdle 

rates between the BAAs within the EIM footprint were removed. Hurdle rates remained between 

the EIM participants (e.g., SRP) and nonparticipants (e.g., CAISO).
37

Table 7. Hurdle Rates Between BAAs

There are many transmission rights in the Western Interconnection. Only those transmission 

rights with complete information were modeled. The transmission reconfiguration used to model 

these transmission rights is described below. These changes were necessary to allow the unit 

commitment to be performed accurately in the affected areas. 

From BAA To BAA

$/MWh

From BAA To BAA

$/MWh

Forward Backward Forward Backward

Alberta British Columbia 4.72 3.63 New Mexico EPE 5.43 5.63

Alberta NWE 4.72 3.63 New Mexico WALC 5.43 3.64

AVA British Columbia 4.07 3.63 NNV California 6.04 3.88

AVA BPA 4.07 3.26 NNV LADWP 40 9.68

AVA PACW 4.07 5.06 NNV NVP 6.04 3.03

AVA PGN 4.07 1.62 NWE AVA 14.72 4.07

AZPS California 9.62 3.88 NWE BPA 14.72 3.26

AZPS IID 2.12 4.13 NWE PACE 14.72 5.06

AZPS LADWP 9.62 9.68 NWE WACM 12.22 7.27

AZPS New Mexico 2.12 5.43 PACE AZPS 12.56 3.62

AZPS SRP 2.12 2.98 PACE California 40 9.68

AZPS TEP 2.12 4.88 PACE IPC 5.06 3.86

AZPS WALC 2.12 3.64 PACE LADWP 40 9.68

BPA BANC 8.94 5.99 PACE NVP 12.56 2.03

BPA British Columbia 3.26 3.63 PACE NNV 5.06 6.04

BPA California 11.44 7.29 PACE WACM 10.06 7.27

BPA LADWP 8.94 9.68 PACE WALC 12.56 2.64

BPA NNV 6.44 6.04 PACW California 10.06 3.88

BPA PACW 3.26 5.06 PACW PGN 5.06 1.62

BPA PGN 3.26 1.62 PSCO

New 

Mexico 9.22 5.43

BPA PSE 3.26 0.96 PSCO WALC 11.72 3.64

California BANC 3.88 5.99 SRP California 7.98 3.88

EPE California 20.13 10.88 SRP TEP 2.98 4.88

IID California 4.13 3.88 SRP WALC 2.98 3.64

IPC AVA 11.36 4.07 TEP EPE 4.88 5.63

IPC BPA 11.36 3.26 TEP New Mexico 2.38 5.43

IPC NNV 11.36 6.04 WACM New Mexico 14.77 5.43

IPC PACW 11.36 5.06 WACM PSCO 14.77 4.22

IPC PGN 11.36 1.62 WACM WALC 14.77 3.64

LADWP California 9.68 3.88 WALC California 8.64 3.88

NVP California 8.03 3.88 WALC IID 3.64 4.13

NVP LADWP 8.03 9.68 WALC LADWP 8.64 9.68

NVP WALC 3.03 3.64 WALC TEP 3.64 4.88
38

2.5.2.2 Modifications to the Transmission Network

SMUD has transmission rights to the California-Oregon Transmission Project. To capture that in 

the simulations, the California-Oregon Transmission Project from Captain Jack (BPA Bus 

45035) to Olinda (PG&E Bus 30020) was reconfigured to terminate at an adjacent SMUD bus 

(Olinda West 37565). This line is subject to the hurdle rates between BPA and SMUD in the 

BAU and reduced-EIM participation cases but is not subject to the hurdle rate in the full-EIM 

participation case. 

CAISO has transmission rights to the Pacific Northwest DC tie. To capture that in the 

simulations, one of the two DC lines from Celilo (BPA) to Sylmar (LADWP) was reconfigured 

to terminate at Sylmar (Bus 24147). Because this is an SCE bus and SCE is part of CAISO, this 

method represents CAISO’s transmission rights. Each DC line has a rated capacity of 1550 MW. 

The BPA-LADWP DC line is subject to the hurdle rate between BPA and LADWP in the BAU 

and the reduced-EIM participation cases but is not subject to the hurdle rate in the full-EIM 

participation case. The BPA-SCE DC line is subject to the hurdle rate between BPA and CAISO 

in all cases.

CAISO also has transmission rights to the Intermountain DC tie. The DC line from Intermountain 

(Bus 26114 LADWP) to Adelanto (Bus 26003 LADWP) was split into two DC lines. One DC line,

with a capacity of 2064 MW, still terminates at the LADWP Adelanto bus. The second DC line,

with a capacity 1526 MW, terminates at Lugo, which is an SCE bus (24086). The IntermountainLADWP DC line is not subject to the hurdle rate in any case. The Intermountain-SCE DC line is 

subject to the hurdle rate between LADWP and CAISO in all cases.

IID has transmission rights to the Southwest Power Link. To capture this in the simulations, a 

fictitious DC line was created with the capacity of 195 MW from SRP (Bus 15090) to IID (Bus 

21025). The fictitious line is in the interface AZPS-IID, which was subject to the SRP-IID hurdle 

rate in the BAU case and is not subject to this hurdle rate in the EIM cases.

2.5.2.3 Generation System Model

There are also more than 2200 generators representing the Western Interconnection in the 

database. All generator characteristics were modeled in the PLEXOS simulations: minimum and 

maximum capacity, minimum up and down times, maximum ramp rates up and down, startup 

costs, etc. 

The variable renewable resources (i.e., wind and solar) were modeled with either the forecasted 

or actual generation profiles, as appropriate for either the unit commitment or economic 

dispatch modules.

All thermal generators were modeled with heat rate and designated fuels with specified fuel 

prices and variable operating and maintenance charges. All heat rate and ramp rate data were

generic, rather than unit-specific, as supplied by WECC TEPPC PC0.

The combined-cycle (CC) plants were modeled at the plant level, consistent with the original 

WECC TEPPC database. This means a single generator with appropriate data represented the 

combination of steam and CT generators that make up a CC plant. 
39

In the PLEXOS database, the Henry Hub gas price of $7.28/MMBTU (2009 dollars) was used 

to derive the regional gas prices on a monthly basis in the same manner as in the E3 study. For 

the lower gas price sensitivity scenario, $4.50/MMBTU (2009 dollars) at Henry Hub was used 

to derive the regional gas prices. The regional coal prices are shown in Table 8. No carbon tax 

was used.

Table 8. Regional Coal Prices

Region $/MMBTU

AESO 1.49

APS 1.88

BPA 2.12

FAR EAST 1.49

LDWP 1.21

NEVP 1.73

NWMT 1.04

PACE_UT 1.21

PACE_WY 0.98

PG&E_BAY 1.73

PG&E_VLY 1.73

PGN 2.12

PNM 1.38

PSC 1.42

SCE 1.73

SPPC 1.73

SRP 1.88

TEP 1.88

WACM 1.42

WALC 1.88

Two types of hydro models were used in the production simulation. The first represents a hydro 

plant’s annual production as a fixed generation profile. The second represents a more 

dispatchable hydro plant with a PLEXOS model that approximates the hydro-thermal 

coordination approach used to dispatch hydro in the Western Interconnection. The amount of 

hydro energy represented as fixed hourly generation profiles and the amount dispatched by 

PLEXOS is shown in Table 10.

Table 9. Hydro Energy Represented by Each Type of Model

Fixed Profile Dispatchable

GWhr 58,696 188,169

% 24% 76%

The fixed hydro generation profiles were included in the TEPPC model and directly converted 

into PLEXOS. 
40

The dispatchable hydro schedules were developed in a multistage process. First, the mediumterm schedule was used to develop hourly (or 10-minute) hydro profiles based on monthly 

energy requirements and unit minima and maxima. This logic performs the co-optimization of 

energy and ancillary services for an entire month at the regional level. The hours in a month are 

grouped into 90 time blocks in the descending order of a load duration curve, with each time 

block an interval in the optimization. The output of this step is hydro generation profiles that 

honor the monthly hydro energy constraints. Chronological hydro unit constraints, such as ramp 

rate limitations, are not enforced in this part of the process. Second, the short-term schedule was 

used for the SCUC-SCED to commit and dispatch resources to balance the system energy 

demand and meet the system reserve requirements. The hydro generation profiles developed in 

the first step are input to this step in the simulation process. The hydro schedules may be 

modified in this second step to respect chronological hydro unit constraints (e.g., ramp rates), 

respond to price signals, or respect any transmission limits. The dispatchable hydro model was 

tuned to better match prior TEPPC simulation results, as shown in Figure 16.

Figure 16. Hydro dispatch comparison between this study and TEPPC results

To calculate the societal benefit of the EIM, detailed information regarding plant ownership and 

power purchase agreements are not critical inputs. However, because this study calculates the 

benefit of the EIM to each BAA, more specific data are required. There are many jointly owned 

generation resources in the Western Interconnection. When a generation resource is outside the 

owner’s BAA, additional modeling detail is required to ensure that the simulated commitment 

and dispatch of that unit is realistic. Complete ownership information was provided for only four 

generating plants: BPA, Hoover, Colstrip, and Mid-Columbia. The joint-ownership models for 

those plants are described below.

Because the owners of the BPA hydro power plants are already within the BPA BAA, there was 

no need to model the ownership explicitly to achieve a realistic commitment and dispatch.
41

The owners of the Hoover hydro power plant are listed in Table 10. The ownership is officially 

defined as a percentage of the entire power plant capacity and energy. The assignment of 

individual unit ownership is for the convenience of the modeling.

Because the Hoover plant is connected radially to the rest of the system, it was decided that each 

unit could be connected to the nearest bus of the owner through a fictitious DC line with minimal 

impact to surrounding power flows. The unit capacity is included in the owner’s commitment 

and reserve requirement constraints, and the power flows from Hoover hydro plant to the owners 

are not subject to the BAA hurdle rates.

Table 10. Hoover Hydro Power Plant Joint Ownership

Region Hoover Unit % Ownership 

WALC HOOVERA3_1 6%

APS HOOVERA4_1 6%

SRP HOOVERA5_1, HOVRA8A9_A8 9%

NEVP HOOVERA6_1, HOOVERA7_1, HOVRA1A2_A1, HOVRA1A2_A2 25%

LADWP HOVRA8A9_A9, HOVRN1N2_N1, HOVRN1N2_N2, 16%

CAISO HOVRN3N4_N3, HOVRN3N4_N4, HOVRN5N6_N5, HOVRN5N6_N6, 

HOVRN7N8_N7, HOVRN7N8_N8 37%

The owners of the Colstrip Thermal Power Plant are listed in Table 11. For this plant, the 

ownership is defined as a percentage of individual units. Because of the interconnected 

transmission network around the Colstrip plant, no fictitious DC lines were used to connect these 

units to their owner’s system. The potential impact of such fictitious lines on parallel power 

flows was deemed too high. The unit capacity is included in the owner’s commitment and 

reserve requirement constraints. However, the power flows from Colstrip to the owners are 

subject to any BAA hurdle rates.

Table 11. Colstrip Thermal Power Plant Joint Ownership

Owner % Ownership Units

NWMT 50 Colstrip #1 and #2

PSE 50

AVA 15

Colstrip #3 and #4

NWMT 30

PACE 10

PGN 20

PSE 25

Information about the joint ownership of the Mid-Columbia hydro power plants was also 

provided. The majority (50%–75%) of the owners of those units—BPA, SCL, TPWR, CHPD, 

DOPD, and GCPD—are within the BPA BAA. The remainder (25%–50%)—AVA, PACW, 

PGN, and PSE—are outside the BPA BAA. No fictitious DC lines were used to connect these 
42

hydro units to the minority owners. Therefore, the unit capacity is included in the owner’s 

commitment and reserve requirement constraints. The power flow from Mid-Columbia to the 

majority owners is not subject to BAA hurdle rates. However, the power flow to the minority 

owners is subject to any BAA hurdle rates.

2.5.2.4 Regional Definitions

For this study, 24 individual BAAs were represented across the Western Interconnection, as 

shown in Table 12 (repeated from Table 1). Several are aggregations of the 39 original load 

regions in the database. For example, the BPA area includes five embedded utilities—Chelan 

County Public Utility District, Douglas County Public Utility District, Grant County Public 

Utility District, Seattle City Light, and Tacoma Power—and is treated as a single large entity. 

BAAs that did not contain load were consolidated with the nearest BAA with load.

Table 12. BAAs Defined for This Study

BAAs BAAs

Alberta Electric System Operator (AESO) Imperial Irrigation District (IID)

Arizona Public Service (AZPS) Los Angeles Department of Water and Power (LADWP)

Avista (AVA) Nevada Power (NEVP)

Balancing Area of Northern California (BANC) Northern Nevada [Sierra Pacific Power Co. (SPPC)]

Sacramento Municipal Utility District (SMUD) Northwest Energy (NWE)

Turlock Irrigation District (TID) Northwest Montana (NWMT)

Bonneville Power Administration (BPA) Western Area Upper Missouri (WAUM)

PUD No. 1 of Chelan County (CHPD) Pacificorp East (PACE)

PUD No. 1 of Douglas County (DOPD) Pacificorp Idaho (PACE_ID)

PUD No. 1 of Grant County (GCPD) Pacificorp Utah (PACE_UT)

Seattle City Light (SCL) Pacificorp Wyoming (PACE_WY)

Tacoma Power (TPWR) Pacificorp West (PACW)

British Columbia Transmission Corp. (BCTC) or 

BC Hydro Portland General Electric (PGN)

California Independent System Operator (CAISO) Public Service Company of Colorado (PSCO)

Pacific Gas and Electric (PG&E) Public Service Company of New Mexico (PNM)

Southern California Edison (SCE) Puget Sound Energy (PSE)

San Diego Gas and Electric (SDGE) Salt River Project (SRP)

Comision Federal de Electricidad (CFE) Tucson Electric Power (TEP)

El Paso Electric (EPE) Western Area Colorado Missouri (WACM)

Idaho Power Corp. (IPC) Western Area Lower Colorado (WALC) 

Far East (FAR EAST)

Magic Valley (MAGIC)

Treasure Valley (TREAS)
43

Figure 17 shows a map of the BAAs. 

Figure 17. WECC BAA map with subregional groups

2.5.2.5 Reserve Sharing Groups and Contingency Reserves

In the E3 study, seven contingency reserve regions were formed from the 39 regions:

• AESO

• BANC (includes SMUD and TID)

• BCTC (or BC Hydro)

• California (includes PG&E, CFE, SCE, and SDGE)

• Northwest (includes FAR EAST, MAGIC, PACE, SPPC, TREAS, AVA, BPA, CHPD, 

DOPD, GCPD, NWMT, PACW, PGN, PSE, SCL, TPWR, and WAUM)

• Rockies (includes PSCO and WACM)

• Southwest (includes AZPS, EPE, NEVP, PNM, SRP, TEP, WALC, IDD, and LADWP).

The contingency reserve requirement was set to 4% of the hourly regional load. These seven

contingency reserve regions and the contingency reserve requirement were also used in this 
44

study. In all cases, these contingency reserve requirements are enforced based on the reserve 

sharing groups listed above. 

3 Impact of an EIM

3.1 Reserve Calculation Results

The reserve calculation method described in the previous section was used to generate appropriate 

hourly or 10-minute flexibility reserve data for each case to correctly reflect the desired wind and 

solar penetration, BAA or EIM footprint, dispatch interval, and forecast lead time. 

For BAU cases, each BA is responsible for balancing the variability within its own boundaries. 

The ownership of load and conventional generation is based on the ownership of the bus where 

the load or generation resource is connected. The ownership of variable generation resources is 

based on the TEPPC 2020 PC0 specifications, and these resources are used as a basis for flex 

reserve requirements within the BAA. These assignments are not necessarily boundary-based. 

The owning BA is responsible for balancing resources assigned to it, whether local or remote. 

Each BA is responsible for both day-ahead scheduling and “real-time” dispatch of enough 

resources to cover its flexibility needs on its own. There is no pooling of flexibility reserves or 

real-time assistance to meet flexibility needs. 

For EIM cases, each BA commits enough generation to cover its load and flexibility requirements 

in the day-ahead commitment. In the “real-time” economic dispatch, however, the flexibility 

requirements are pooled across all participants in the EIM. The resources of any of these may be 

economically dispatched to cover those requirements. This pooling of requirements and resources, 

along with the faster 10-minute economic dispatch cycle, gives rise to the EIM savings.

Figure 18 illustrates the impact of the study scenario on the magnitude of the flexibility reserve 

requirement for an example BAA. The flexibility reserve requirement combines the regulation 

component and spin component into a single flex reserve. Flex reserves, as well as the wind and 

solar production, for three cases are plotted in this figure.

The highest reserves are for the PLHBAU case, which assumes an hourly dispatch and a 40-

minute lockdown on the forecast information. The middle curve is for the E3BAU case, which 

assumes a 10-minute dispatch but no EIM. The lowest curve is for the reserves calculated for the 

full EIM footprint, PLEIM. Because the reserve requirements were calculated for the entire 

footprint, rather than on a BAA-by-BAA basis, they needed to be allocated to the BAA level for 

this plot. The asterisk indicates that this allocation was adapted from an Oak Ridge National 

Laboratory report (Kirby and Hirst 2000) and further developed by NREL (King et al. 2012). 

The allocation attempts to divide the total requirement for the EIM to the participants in a fair 

way, based on the variability each BAA contributes to the whole.

In all three cases, the reserves have the same general shape because that is dictated by the wind 

and solar production profiles. As demonstrated in the previous section, the reserve requirements 

for wind tend to be greatest in the middle range of production and lowest at low and high 

production. Solar flex reserve requirements have a slightly different pattern. For solar, the 

maximum requirements happen at low to medium output, particularly at sunrise and sunset. 

Similar to wind, the lowest requirements for solar are seen near peak production.
45 

Figure 18. Reserve requirements for sample BA for three study scenarios

A comparison of annual average and annual maximum flex reserves for the EIM and two BAU 

cases is shown in Table 13. The reserve requirements associated with implementing the EIM 

relative to an hourly BAU (PLHBAU) are reduced by about 77%, or 730 MW versus 166 MW. 

Moving from the 10-minute BAU (PLBAU) to the EIM results in a reserve requirement 

reduction of about 44%, or 298 MW versus 166 MW. 

If the 10-minute BAU is treated as a step along the path to the EIM, then the reduction in 

dispatch interval from the hourly to the 10-minute BAU case reduces the reserve requirement 

from 730 MW to 298 MW. The additional step of implementing the EIM reduces the 10-minute 

BAU reserve requirement of 298 MW to 166 MW. The consequences of these reserve 

requirement reductions on production cost savings are discussed more in following sections. 

Table 13. Annual Summary of Flex Reserve Requirements 

for Sample BA for Three Study Scenarios 

PLHBAU Flex Reserves 

(MW)

E3BAU Flex Reserves 

(MW)

PLEIM* Flex Reserves 

(MW)

Average 730 298 166

Maximum 1123 446 485

*Reserves calculated for EIM footprint and allocated to sample BA using Oak Ridge National Laboratory-NREL 

procedure

0

500

1000

1500

2000

2500

3000

0:00 12:00 0:00 12:00 0:00 12:00 0:00

Production or Flex Reserves (MW)

Time of Day

Wind Prod.

Solar Prod.

Reserves

PLHBAU
46

A duration plot of the hourly flex requirements for the three study scenarios for the sample BA is 

shown in Figure 19. This duration plot shows the number of hours that the flex requirement is at 

a particular value or higher. For instance, the PLEIM requirements are expected to be above 200 

MW for about 2750 hours per year. A change in the slope of a curve at either end of the plot 

indicates a small number of hours have significantly different flex reserve requirements 

compared with the rest of the year.

Figure 19. Flex reserve duration plot for sample BA for three study scenarios

To see a fuller picture of the reserves, the following tables and figures focus on the full-EIM 

footprint rather than on a sample BA. Reductions in the annual average and annual maximum 

flex reserve requirements for the entire footprint are shown in Table 14. The average flex 

requirement for the EIM (PLEIM) is about 79% less than the hourly BAU case (PLHBAU) and 

56% less than the 10-minute BAU case (E3BAU).

Again, a large savings is associated with moving from the hourly dispatched BAU (PLHBAU) to 

the 10-minute dispatch BAU (PLBAU): 2790 MW. If the E3BAU is again considered a step 

along the path to the EIM implementation, this is the majority of the total flex reduction of 4187 

MW from PLHBAU to the EIM. This shows that the majority of savings are associated with the 

faster dispatch component of the EIM rather than the wide-area pooling of variability that the

EIM also provides.

Table 14. Summary of Flex Reserves Requirements 

for the Full EIM Footprint for Three Study Scenarios

PLHBAU Flex Reserves 

(MW)

E3BAU Flex Reserves 

(MW)

PLEIM Flex Reserves 

(MW)

Average 5275 2485 1088

Maximum 8626 4071 1578

0

200

400

600

800

1000

1200

0

500

1000150020002500300035004000450050005500600065007000750080008500

Flex Reserves (MW)

Hours

PLHBAU

E3BAU

PLEIM*
47

Figure 20 shows a duration plot of the flex reserve requirements across the entire EIM footprint

for the three cases. Note the overall reduction in flex reserve requirements, as well as a flattening 

of the curve, as a faster dispatch interval and the EIM are implemented. The PLEIM curve, with 

full EIM participation, shows no increase in slope at the y-axis. This flatter curve indicates a 

reduction in the number of hours with a significantly different flex reserve requirement from the 

rest of the year. 

Figure 20. Flex reserve requirements duration for full-EIM footprint for three study scenarios

The dispatch interval and forecast lead time are important influences on the reserve 

requirements. The dispatch interval is how frequently new dispatch instructions are sent to the 

generation fleet. This corresponds to how frequently any economic dispatch application is run. 

The forecast lead time, also referred to as the forecast lockdown time, is the time period from

when the forecast for the load and variable generation is fixed for a given dispatch interval to the 

beginning of that dispatch interval. In this study, the forecast for variable generation is taken as a 

persistence forecast. That is, the value of wind or solar generation is assumed to be constant (as 

measured at the forecast lead time) over the ensuing dispatch interval.

The EIM is designed to have a 5-minute dispatch interval with a 5-minute forecast lead time. In 

this study, which has 10-minute resolution wind and solar data, it is represented as a 10-minute 

dispatch with a 10-minute lead time. Current practices for dispatch and lead time vary across the

Western Interconnection, with the most conservative practices having an hourly dispatch interval 

and a forecast lead time of as much as 40 minutes. The dispatch interval and forecast lead time 

have significant influence on flexibility reserve requirements, as shown in Figure 21. 

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

Flex Reserves (MW)

Hours

PLHBAU

E3BAU

PLEIM
48

This figure shows the average flex reserve requirements for a number of dispatch interval and 

forecast lead time combinations. The flex reserve requirement for the hourly dispatch with 40-

minute lead time is more than three times larger than that of the 10-minute dispatch interval 

with a 10-minute lead time. In this study, these two conditions are the bookends for 

determining EIM benefits.

Figure 21. Flex reserve requirements as a function 

of dispatch interval and forecast lead time

3.2 PLEXOS Model Alignment to WECC-E3 Results

As this study grew out of the earlier WECC-E3 EIM benefits study (E3 2011c), cases were 

designed to explore the alignment of the study results. Both studies were based on the WECC 

TEPPC 2020 PC0 model in Ventyx PROMOD format. The E3 study used ABB GridView 

software, and this study used PLEXOS software. The primary goal of the alignment cases was to 

compare the study results and understand the differences. 

The cases used to examine the alignment of the two models were the BAU case, E3BAU, and the 

full-EIM case, E3EIM. Both use an hourly time-step in the simulation but assume 10-minute 

dispatch in the flex reserve calculations. The details of each of these cases are presented in 

Section 2.4. 

An important difference between the two models is the handling of losses. In the WECC-E3 

GridView model, losses were determined as part of the production cost model and therefore 

included in the total generation. In the PLEXOS model, losses were determined after the 

production simulations (i.e., post-processed from the production simulation results). These postprocessed losses were then added to the total energy production from the PLEXOS model to 

compare results. The cost of these post-processed losses is calculated at the average production 

cost of the BAA in which the losses occur.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

10/10 30/10 30/20 30/30 60/20 60/30 60/40

Average Flex Reserve Requirement (MW)

Dispatch Interval/Forecast Lead Time
49

Figure 22 compares the total energy production as calculated by PLEXOS and GridView for the 

BAU alignment case. The “Losses” bar represents the transmission losses post-processed from 

the PLEXOS solution that are embedded in the production calculation for the WECC-E3 

solution. The “Other” bar represents generation by nuclear, biomass, geothermal, gas steam, 

petroleum coke, and demand-side management resources. The two cases have nearly identical 

total energy. The biggest generation differences are observed for CC and coal. 

Figure 22. Comparison of total energy production 

between PLEXOS and GridView in the BAU alignment case

As shown in Table 15, the difference in CC energy production is 15 TWh, or about 1.5% of the 

total energy production. The difference in coal energy production is 11 TWh, or about 1% of the 

total energy production. 

Table 15. Energy Production by Generation Class 

for the BAU Alignment Case

PLEXOS 

Generation 

(TWh)

WECC-E3 

Generation 

(TWh)

Difference 

(TWh)

CC 189 204 15

Coal 298 309 11

Hydro 254 255 1

CT 26 27 1

Solar 32 32 0

Wind 80 82 2

Other 144 145 1

Losses 35 0 -35

Total 1057 1055 -3

1,057 TWh 1,055 TWh

0

200

400

600

800

1000

1200

PLEXOS WECC/E3

Generation (TWh)

Losses

Other

Wind

Solar

CT

Hydro

Coal

CC
50

Figure 23 compares the total production cost as calculated by PLEXOS and GridView for the 

BAU alignment cases. The difference between the two cases is about $600 million, or 3%. Table 

16 breaks down the production cost by generation class. The largest difference is observed for 

the CC generation. The production cost difference between the PLEXOS and GridView cases for 

the CC generation is about $1.1 billion, or 5% of the total production cost.

Figure 23. Comparison of total production cost 

between PLEXOS and GridView in the BAU alignment case

Table 16. Production Cost by Generation Class for the BAU Alignment Case

PLEXOS 

Generation 

($ millions)

WECC-E3 

Generation 

($ millions)

Difference 

($ millions)

CC 10,547 11,672 -1,125

Coal 5,020 5,211 -191

CT 2,047 1,960 87

Other 2,061 2,033 27

Losses 593 0 593

Total 20,268 20,876 -609

A similar comparison was made between the full-EIM alignment case (E3EIM) and the 

equivalent WECC-E3 case. This EIM alignment case was simulated with a 1-hour time-step to 

match the WECC-E3 analysis. Note that the alignment case (E3EIM) is used only for this 

comparison and is not part of the EIM evaluation. Most of the analysis (described below) uses 

10-minute time-step simulations for EIM cases.

$20.3 billion $20.9 billion

0

5

10

15

20

25

PLEXOS WECC/E3

Production Cost 

($billion)

Losses

Other

Wind

Solar

CT

Hydro

Coal

CC
51

Figure 24 and Table 17 compare the energy production by class of generation for the EIM 

alignment cases. The total production differs by about 5 TWh hours, or about 0.5%. Similar to 

the BAU case alignment, the majority of the difference between the models is in the CC and 

coal production. 

Figure 24. Comparison of total energy production 

between PLEXOS and GridView for the full-EIM alignment case

Table 17. Energy Production by Generation Class 

for the Full-EIM Alignment Case

PLEXOS 

Generation 

(TWh)

WECC-E3 

Generation 

(TWh)

Difference 

(TWh)

CC 185 202 -16.5

Coal 303 313 -10.1

Hydro 254 255 -0.8

CT 26 27 -0.9

Solar 32 32 0.0

Wind 80 82 -2.5

Other 143 145 -2.2

Losses 38 0 38.3

Total 1061 1056 5.3

1,061 TWh 1,055 TWh

0

200

400

600

800

1000

1200

PLEXOS WECC/E3

Generation 

(TWh)

Losses

Other

Wind

Solar

CT

Hydro

Coal

CC
52

Figure 25 and Table 18 compare the production costs for the EIM alignment cases. As was the 

case with the BAU comparisons, the production costs for the PLEXOS solution are less than 

those for the WECC-E3. The difference is about $600 million, or about 3%.

Figure 25. Comparison of total production cost 

between PLEXOS and GridView for the full-EIM alignment case

Table 18. Production Costs by Generation Class

for the Full-EIM Alignment Case

PLEXOS 

Generation 

($ millions)

WECC-E3 

Generation 

($ millions)

Difference 

($ millions)

CC 10,345 11,524 -1,179

Coal 5,069 5,265 -197

CT 2,031 1,926 105

Other 2,021 2,019 2

Losses 637 0 637

Total 20,104 20,735 -631

$20.1 billion $20.7 billion

0

5

10

15

20

25

PLEXOS WECC/E3

Production Cost 

($billion)

Losses

Other

Wind

Solar

CT

Hydro

Coal

CC
53

The potential EIM benefit was calculated as the production cost savings between the BAU and 

the full-EIM alignment cases, as shown in Table 19. These results are comparable given the 

differences in the models.

Table 19. EIM Production Cost Savings 

Compared With BAU Alignment Case

Production Cost ($ million)

BAU EIM

EIM 

Savings

PLEXOS 20,268 20,104 164

WECC-E3 20,876 20,735 141

Multiple factors drive the differences between the simulation results. First, the PLEXOS model 

and software are inherently different from the ABB GridView model and software. For example, 

the software packages use different models for complex generation types such as CC modes. 

Second, there are small differences in the base system models such as coal plant retirements and 

wind generation. Third, transmission losses are handled in different ways in the two models. The 

combination of these factors was judged to explain the difference observed between the 

simulation results. Notwithstanding these differences in generation and production costs, the 

models were determined to be similar enough to compare results.

3.3 Full EIM

The purpose of this study is to determine the potential benefits of an EIM that would cover 

various portions of the Western Interconnection. The EIM analyzed in this section contains all 

load-serving BAAs in the Western Interconnection except those included in CAISO and AESO. 

Section 2.5.2.4 details the BA included in each case of the study.

To evaluate the potential savings associated with the EIM, a BAU case needs to be established as 

a benchmark. That BAU case should reflect the system conditions (e.g., dispatching practices 

and variable generation) that would exist without an EIM. The EIM case will share most 

characteristics with the BAU but will also model the dispatch timing and market rules of the 

EIM. The savings are then calculated as the difference in variable production costs between the 

BAU and the EIM cases. Fixed costs are not considered as part of this study and are not

addressed by the EIM.

There are many scheduling practices across the BAs in the Western Interconnection. It was 

not within the scope of this study to collect and analyze each of these or to accurately model 

them within the simulation. Instead, a bookend approach was used with two BAU cases. One 

represents the most conservative scheduling practice currently in use, and the other represents 

a less-conservative approach. The more-conservative approach uses a full hourly dispatch and 

a 40-minute lockdown or lead time for the persistence forecasts of variable generation. The 

less-conservative bookend anticipates the continuous evolution of operating practices 

independent of the EIM initiative and uses a 10-minute dispatch with a 10-minute lead time 

on the forecast. Section 3.1 above showed that the flexibility reserve requirements for the 
54

hourly dispatch with 40-minute lead time can be as much as three times greater than that for 

the 10-minute dispatch case. 

Neither condition represents the system exactly as it operates now or will operate in the future. 

The reality will lie somewhere between these two points. Two BAU cases provide a range of 

operating conditions and, therefore, a range of potential EIM savings. It also allows the savings 

associated with the shorter dispatch interval to be separated from those associated with an EIM 

operating cooperatively across multiple BAAs. That issue is discussed in Section 3.7. The 

potential savings of an EIM compared with the two BAU cases is discussed below.

3.3.1 EIM Benefits From an Hourly Dispatched BAU Case

The hourly dispatched BAU case (PLHBAU) assumes that the generation throughout the 

Western Interconnection is dispatched once an hour and the forecasts for that dispatch are fixed 

40 minutes prior to the beginning of the dispatch interval. The EIM case is constrained to use the 

day-ahead unit commitment determined by this hourly BAU case. This means that all units 

committed in the day-ahead unit commitment for the hourly BAU case must stay committed in 

the EIM case.

The total production cost for full-EIM participation compared with the hourly BAU is shown in 

Figure 26. The full EIM reduces total annual production costs by $294 million, or about 1.4%. 

The transmission losses for these cases are post-processed and therefore added to the generation 

stack to show the total energy production.

Figure 26. Comparison of total production cost 

for the full-EIM participation and the hourly dispatched BAU

0

5

10

15

20

25

PLHBAU PLEIM1

Production Cost 

($ billion)

Losses

Other

Wind

Solar

CT

Hydro

Coal

CC
55

Detailed differences in production cost by generation class are shown in Table 20. Most of the 

savings are due to a 3% reduction in CC plant production. The cost of coal-supplied electricity

increases by about 1% because it is displacing the CC plants. Losses are greater with the EIM

because there is more long-distance power flow. The increased losses reduce the EIM savings by 

$20 million.

The change in production costs is seen for all BAAs in the study, not just those participating in 

the EIM. In this case, CAISO, AESO, and CFE also see changes of approximately $100 million 

in savings. The remainder of the savings is seen by participants in the EIM.

Table 20. Production Cost and Savings 

for Full EIM Over Hourly Dispatched BAU

PLHBAU 

($ million)

PLEIM1

($ million)

EIM 

Savings 

($ million)

EIM 

Savings 

(%)

CC 10,519 10,216 303 2.9%

Coal 4799 4843 (44) -0.9%

CT 3541 3541 1 0.0%

Other 2121 2067 54 2.6%

Losses 600 620 (20) -3.4%

Total 21,580 21,286 294 1.4%

The change in energy output from the different types of resources is also of interest. Figure 27

shows the annual energy production by generation class for the BAU and EIM cases. As one 

would expect, the total energy for the two cases is nearly identical, differing only by the larger 

losses in the EIM case. The differences are clearer in Table 21, which shows the detailed annual 

energy production by generation class. CC generation decreased by 6 TWh for the year, while 

coal generation increased by the same amount. The less-expensive coal resources are displacing 

the more-expensive CC plants in the EIM implementation, as shown in Table 20. The difference 

in coal production may be distorted by the hurdle rates. Some energy from coal plants may have 

transmission rights that are not subject to the hurdle rates in reality but are subject to them in the 

simulation of BAU. This may cause the production of those coal plants to be lower in the BAU, 

leading to a greater difference between the BAU and EIM than would otherwise occur. The data 

in the model were not sufficiently detailed to explore this effect.

Losses in the EIM case were also 4 TWh higher than in the BAU case, which reduced the savings.
56

Figure 27. Comparison of annual energy production 

by generation class in the hourly dispatched BAU and full EIM cases

Table 21. Generation by Resource Type 

for the Hourly Dispatched BAU and Full-EIM Cases

PLHBAU 

(TWh)

PLEIM1

(TWh)

EIM 

Savings 

(TWh)

EIM 

Savings 

(%)

CC 187 181 6 3.0%

Coal 286 292 -6 -2.2%

Hydro 253 254 0 -0.1%

CT 43 43 0 -0.2%

Solar 32 32 0 0.0%

Wind 80 80 0 0.0%

Other 143 142 1 0.7%

Losses 33 37 -4 -10.9%

Total 1056 1060 -4 -0.4%

0

200

400

600

800

1000

1200

PLHBAU PLEIM1

Generation 

(TWh)

Annual Generation Comparison for Full EIM over Hourly 

Dispatched BAU by Resource Type

Losses

Other

Wind

Solar

CT

Hydro

Coal

CC
57

Table 22 shows the emissions calculated for the BAU and EIM cases. The emission results 

reflect the increase in coal-fueled production discussed above. 

Table 22. Change in Emissions 

for Hourly Dispatched BAU and Full-EIM Case

CO2

(ktons)

NOx

(ktons)

SO2

(ktons)

PLHBAU 473,913 697 461 

PLEIM1 477,457 708 468 

Increase (ktons) 3544 11 6 

Increase (%) 0.7 1.5 1.4

3.3.2 EIM Benefits From a 10-Minute Dispatched BAU Case

The 10-minute dispatched BAU case, E3BAU, assumes that the generation throughout the Western 

Interconnection is dispatched every 10 minutes and the forecasts for that dispatch are fixed 10 

minutes prior to the beginning of the dispatch interval. The reserve requirements for this BAU case 

are substantially less than those for the hourly dispatch BAU case. The average flex reserve 

requirement is 53% less for the 10-minute dispatch BAU case than the hourly dispatch case. This 

BAU reflects a future in which all BAs are dispatching at 10 minutes but no EIM is in place. 

The EIM case is again constrained to use the unit commitment case as determined by the 10-

minute dispatch BAU case. This means that all units committed to run in the day-ahead unit 

commitment must stay committed in EIM real time. No additional units can be committed in real

time. All committed units are assumed to be spinning, synchronized, and operating at minimum 

generation except non-spinning reserves. (No quick-start units are modeled.)

The savings achieved by the EIM case compared with the 10-minute dispatch BAU case were

calculated as $148 million, or approximately 50% of the savings achieved in comparison with 

the hourly dispatch BAU case. The $148 million represents a 0.7% reduction in the overall 

production cost. 

Again, production cost changes are seen by all BAAs in the Western Interconnection, including 

those not participating in the EIM. In this case, the nonparticipants—CAISO, AESO, and CFE—

see approximately $75 million of the savings, with the remaining savings for the EIM participants.
58

Figure 28 shows the annual production costs for the EIM and BAU cases by generation class. 

The transmission losses for these cases are post-processed and therefore added to the generation 

stack to show the total energy production. Table 23 shows these results in more detail. Most of 

the savings are because of less use of CC units, with less-expensive coal units making up the 

difference. The cost of transmission losses increases with the EIM—in this case, by about $27 

million. This is a similar increase to that in the hourly dispatched BAU case.

Figure 28. Comparison of total production cost 

for the full-EIM participation and the 10-minute dispatched BAU

Table 23. Production Costs and Savings 

for Full EIM Over 10-Minute Dispatched BAU

E3BAU 

($ million)

PLEIM 

($ million)

EIM 

Savings 

($ million)

EIM 

Savings 

(%)

CC 10,547 10,357 190 1.8%

Coal 5020 5067 (47) -0.9%

CT 2047 2051 (4) -0.2%

Other 2061 2026 34 1.7%

Losses 593 620 (27) -4.6%

Total 20,268 20,122 146 0.7%

0

5

10

15

20

25

E3BAU PLEIM2

Production Cost 

($billion)

Total Annual Production Cost Comparion for Full EIM over 

10-Minute Dispatched BAU by Resource Type

Losses

Other

Wind

Solar

CT

Hydro

Coal

CC
59

Figure 29 shows the breakdown of energy production by generation class for these cases. As 

noted above, gas-fired CC units are displaced by less-expensive coal resources. The changes are 

relatively small and therefore difficult to discern in the figure. Table 24 shows the data in detail.

Figure 29. Comparison of annual energy production 

by generation class in the 10-minute dispatched BAU and full-EIM cases

CC use is reduced by 3.5 TWh (3500 GWh), and other generation (geothermal, biomass, steam, 

etc.) is reduced by 1 TWh. This is made up with an increase in coal resources of 4.8 TWh. 

Losses associated with the EIM are about 2.4 TWh, or about 7%, greater than those in the BAU 

case. The increase in losses is associated with the additional movement of energy from the 

lower-cost resources in the northeastern area of the Western Interconnection.

Table 24. Energy Production by Generation Class

for the 10-Minute Dispatched BAU and Full-EIM Cases

E3BAU 

(TWh)

PLEIM 

(TWh)

EIM Savings 

(TWh)

EIM 

Savings 

(%)

CC 189 186 4 1.9%

Coal 298 303 -5 -1.6%

Hydro 254 254 0 0.0%

CT 26 26 0 -0.1%

Solar 32 32 0 0.0%

Wind 80 80 0 0.0%

Other 144 143 1 0.7%

Losses 35 37 -2 -7.0%

Total 1057 1060 -3 -0.2%

0

200

400

600

800

1000

1200

E3BAU PLEIM2

Generation 

(TWh)

Annual Generation Comparison for Full EIM over 10-Minute 

Dispatched BAU by Resource Type

Losses

Other

Wind

Solar

CT

Hydro

Coal

CC
60

Table 25 shows the emissions calculated for the EIM and BAU cases. The emissions are slightly 

higher for the EIM because of the increased use of coal described above. 

Table 25. Change in Emissions 

for 10-Minute Dispatch BAU and Full EIM

CO2

(ktons)

NOx

(ktons)

SO2

(ktons)

E3BAU 478,749 713 483 

PLEIM2 481,585 722 487 

Increase (ktons) 2,836 9 5 

Increase (%) 0.6 1.2 1.0

3.4 Gas Price Sensitivity

Fuel prices are a key variable in production cost analyses. They are also difficult to forecast

because they can fluctuate widely in short periods of time. This is especially true for natural gas, 

though some claim that the wide availability of shale gas may provide relatively stable gas prices 

for decades.

The WECC TEPPC 2020 planning case, which is the basis for this analysis and was developed in 

2009, uses a benchmark price of $7.28/MMBtu. By today’s standards, this is a high price. In fact, 

the newer TEPPC 2022 cases use a reduced price of $4.60/MMBtu. Therefore, a lower price of 

$4.50/MMBtu (Henry Hub benchmark) was used in a pair of sensitivity cases to evaluate the 

effects of lower natural gas prices on potential EIM benefits. Note that these prices are in 2009 

dollars. The latest Energy Information Administration forecast shows approximately 

$4.60/MMBtu (2011 dollars) natural gas prices for the electric power sector from 2016 on (U.S.

Energy Information Administration 2012).

Both the hourly dispatched BAU and the full-EIM cases were modified to include the lower gas 

prices. This means that the low-gas-price BAU (PLGASBAU) case uses a 60-minute dispatch 

with a 40-minute forecast lockdown. The low-gas-price EIM case (PLGASEIM) includes full 

EIM participation. All BAAs—except those with existing markets (i.e., CAISO and AESO)—are 

participating in the EIM. 

The EIM case is again constrained to use the day-ahead unit commitment determined by the 

hourly BAU case. This means that all units committed in the day-ahead unit commitment for the 

hourly BAU case must stay committed in EIM. 

The savings associated with the EIM are approximately $281 million, about a 2% savings in 

production cost compared with the hourly BAU case. This can be compared with the equivalent 

nominal-gas-price scenario, in which the savings were approximately $294 million. 
61

Figure 30 shows the costs for the reduced-gas-price cases by resource type. The details of the 

production costs and savings are shown in Table 26. The majority of the cost reduction is still 

due to reduced use of CC plants. CC costs drop by $388 million, or about 5%, while coal 

generation increases by $113 million, or about 2%. 

Figure 30. Comparison of total production cost 

by generation class for reduced-gas-price hourly BAU and EIM cases

Table 26. Production Costs and Savings 

for Full EIM Over Hourly Dispatched BAU With Low Gas Prices

PLHGASBAU 

($ million)

PLGASEIM 

($ million)

EIM 

Savings 

($ million)

EIM 

Savings 

(%)

CC 7381 6993 388 5.3%

Coal 4529 4642 (113) -2.5%

CT 2564 2561 3 0.1%

Other 1929 1882 47 2.4%

Losses 434 478 (44) -10.1%

Total 16,837 16,556 281 1.7%

$16.84 billion $16.56 billion

0

5

10

15

20

PLGASBAU PLGASEIM

Production Cost 

($ billion)

Total Annual Production Cost Comparion for Reduced Gas Price 

Cases by Resource Type

Losses

Other

Wind

Solar

CT

Hydro

Coal
62

Figure 31 shows the change in energy production by generation class for the low-gas-price cases. 

The total energy is nearly identical for the BAU and EIM cases. The details are shown in Table 

27. CTs and CC plants are reduced—in this case, by a total of about 11 TWh, or 5%—and coal 

plant production increases by about 11 TWh, or 4%.

Figure 31. Comparison of generation for reduced-gas-price cases

Table 27. Energy Production by Generation Class 

for Full EIM Over Hourly Dispatched BAU With Low Gas Prices

PLHGASBAU 

(TWh)

PLGASEIM 

(TWh)

EIM Savings 

(TWh)

EIM 

Savings 

(%)

CC 200 190 11 5.4%

Coal 270 281 -11 -4.2%

Hydro 254 254 0 -0.1%

CT 46 46 0 -0.1%

Solar 32 32 0 0.0%

Wind 80 80 0 0.0%

Other 141 140 1 0.6%

Losses 36 36 -1 -1.5%

Total 1056 1060 -4 -0.4%

The savings for this low-gas-price sensitivity case (about $281 million annually) are slightly 

lower than those for the nominal-gas-price case, which were $294 million. One might expect the 

difference to be larger with lower savings for the reduced-gas-cost case. This is because the 

overall production costs are lower, and thus the savings should be lower.

0

200

400

600

800

1000

1200

PLGASBAU PLGASEIM

Generation 

(TWh)

Annual Generation Comparison for Reduced Gas Price Cases 

by Resource Type

Losses

Other

Wind

Solar

CT

Hydro
63

There is a larger shift from CC to coal generation in the low-gas-price case than in the nominalgas-price cases. In the BAU case, the combination of the low gas prices and hurdle rates leads to 

higher use of CC units—200 TWh versus 187 TWh for the nominal gas price. Remote coal units 

are displaced by local gas-fired units to a greater extent because average prices for CC

generation drop nearly $20/MWh with the lower gas price, making it a more attractive 

alternative than some remote coal generation with hurdle rates included. Therefore, overall coal 

use is less than in the nominal-gas-price case. 

However, coal use increases more from the low-gas-price BAU case to the low-gas-price EIM 

than it did in the nominal BAU and EIM cases. As shown in Table 27, coal increases 11 TWh 

when the EIM is implemented under the low-gas-price scenario. In the nominal-gas-price case, 

the coal increase was 6 TWh, as shown in Table 21. This occurs because the gas-fired generation 

is no longer competitive with remote coal generation when the hurdle rates are removed—even 

at the low gas price. This results in a larger shift to coal for the low-gas-price case. Again, there 

is still more energy generated by gas in the low-gas case than in the nominal case. It is the 

increase in coal when implementing the EIM that is greater.

The emissions cases reflect the change in the relative use of gas and coal in the BAU and EIM

cases, as shown in Table 28. Carbon is increased in the EIM case because of the shift from gas to 

coal discussed above.

Table 28. Change in Emissions 

for Low-Gas-Price Sensitivity Cases

CO2

(ktons)

NOx

(ktons)

SO2

(ktons)

PLGASBAU 463,556 668 435 

PLGASEIM 470,275 688 448 

Increase (ktons) 6719 20 13 

Increase (%) 1.4 2.9 2.9

3.5 Reduced-Participation EIM Sensitivity

Another sensitivity case was developed to evaluate the impact of reduced participation in the EIM. 

The full-EIM footprint includes participation by all BAAs in the Western Interconnection, except 

those areas with existing markets (CAISO and AESO). The sensitivity case reduced the level of 

participation by removing BPA and two of the three WAPA BAs. Several PUDs, as well as 

Seattle City Light and Tacoma Power, are embedded in BPA and, therefore, were also removed 

from EIM participation in this sensitivity case.

The reduced-participation sensitivity case was first compared with the hourly dispatched BAU 

case, PLHBAU. This BAU case uses an hourly dispatch with a 40-minute forecast lead time. The 

reduced-EIM sensitivity case was constrained to use the day-ahead unit commitment from the 

BAU case. This means all units committed as part of the solution to the hourly BAU case are
64

committed in the EIM. The comparison of these simulations provides the value of the EIM for 

the reduced-participation case relative to the hourly BAU case.

Table 29 shows the resulting production costs and savings by generation class. The overall 

production cost savings with reduced EIM participation is $276 million annually, or about 1.4%

of total production cost. For comparison, the savings associated with the full EIM was $294 

million. The reduced-EIM-participation case produces $18 million less benefit than the full-EIMparticipation case.

As in other cases, the savings are dominated by the reduction in CC use, which contributed $321 

million, or about 3% of total CC production cost, to the savings. Coal production costs increased 

by $81 million. Table 30 shows the details of the annual energy production by generation class. 

The reduction in CC is offset primarily by the increase in coal generation.

Table 29. Production Cost and Savings by Generation Class

for Reduced-Participation EIM Over Hourly Dispatched BAU

PLHBAU 

($ million)

PLEIMPMA1 

($ million)

EIM 

Savings 

($ million)

EIM 

Savings 

(%)

CC 10,519 10,198 321 3.1%

Coal 4,799 4,880 (81) -1.7%

CT 3,541 3,542 (1) 0.0%

Other 2,121 2,072 49 2.3%

Losses 600 612 (12) -2.0%

Total 21,580 21,305 276 1.3%

Table 30. Annual Energy Production by Generation Class

for Reduced-Participation EIM Compared With Hourly Dispatched BAU

PLHBAU 

(TWh)

PLEIMPMA1 

(TWh)

EIM 

Savings 

(TWh)

EIM 

Savings 

(%)

CC 187 181 6 3.2%

Coal 286 292 -6 -2.2%

Hydro 253 254 0 -0.2%

CT 43 43 0 -0.3%

Solar 32 32 0 0.0%

Wind 80 80 0 0.0%

Other 143 142 1 0.6%

Losses 33 34 -1 -3.1%

Total 1056 1060 -4 -0.4%
65

The change in emissions for the hourly BAU and reduced-participation EIM are shown in Table 

31. Again, there is an increase because of the increased use of coal, as seen above.

Table 31. Change in Emissions for Hourly Dispatch BAU 

and Reduced-Participation EIM

CO2

(ktons)

NOx

(ktons)

SO2

(ktons)

PLHBAU 473,913 697 461 

PLEIM-PMA1 477,530 708 472 

Increase (ktons) 3,618 11 11 

Increase (%) 0.8 1.6 2.3

The reduced-EIM-participation sensitivity case was also compared with the 10-minute 

dispatched BAU case, E3BAU. This BAU case uses a 10-minute dispatch with a 10-minute 

forecast lead time. As in other comparisons, the reduced-EIM sensitivity case was constrained to 

use the day-ahead unit commitment from the BAU case. This means all units committed for the 

hourly BAU case are committed in the EIM.

This case comparison gives a lower estimate of savings because the BAU already includes a 10-

minute dispatch. The total savings are about $95 million. For comparison, the savings for the full 

EIM relative to the 10-minute BAU were $146 million. The details of the production costs and 

savings are shown in Table 32. 

Table 32. Production Cost and Changes 

for Reduced-Participation EIM and 10-Minute Dispatched BAU

E3BAU 

($ million)

PLEIMPMA2

($ million)

EIM 

Savings 

($ million)

EIM 

Savings 

(%)

CC 10,547 10,400 146 1.4%

Coal 5020 5071 (50) -1.0%

CT 2047 2055 (8) -0.4%

Other 2061 2046 14 0.7%

Losses 593 600 (7) -1.2%

Total 20,268 20,173 95 0.5%
66

Table 33 shows that the EIM increases emissions. This is because of the increased use of coal in 

the EIM case.

Table 33. Change in Emissions for 10-Minute Dispatch BAU 

and Reduced-Participation EIM

CO2

(ktons)

NOx

(ktons)

SO2

(ktons)

E3BAU 478,749 713 483 

PLEIM-PMA2 480,740 720 488 

Increase (ktons) 1,991 6 6 

Increase (%) 0.4 0.9 1.2

3.6 Additional Sensitivity Cases

Additional sensitivity analyses of the impact of reduced flexibility reserve requirements and 

hourly simulation tools were performed. These cases were based on the full-participation EIM.

3.6.1 Reduced Flexibility Reserves Requirements in Economic Dispatch

This sensitivity case involved reducing the reserve requirements in the real-time economic 

dispatch phase of the simulations. The idea is that spinning reserves withheld as part of the unit 

commitment process could be released during the economic dispatch to compensate for any 

deviations from schedule.

This sensitivity case is based on the full-EIM case, which uses the 10-minute BAU unit 

commitment, as described in Section 3.3. This new case differs from the prior case in that only 

the regulation component of the flexibility reserves is maintained in the economic dispatch. The 

spinning reserves are released in real time. This scenario represents a potential operating 

practice. The day-ahead unit commitment ensures that the flex-spin requirements are available in 

case of a large wind or solar ramp or a deviation from forecast. However, if the flex spin is not 

needed in real time, there is no reason to keep it, and it can be released to help manage wind and 

solar variability. 
67

Table 34 shows the impact of the reduced reserve requirements on total production cost by 

comparing the EIM cases with and without the flex-spin component. The reserves reduction 

saves about $6.9 million of production cost—and about $7.6 million if transmission losses are 

taken into account. 

Table 34. Effect of Reduced Flex Reserve Requirements on Production Cost

Production 

Cost ($ k)

Cost of 

Transmission 

Losses ($ k)

Total Cost ($ k)

EIM case with flex-spin 

component (PLEIM2) 19,500,959 620,441 20,121,400 

EIM case with reduced 

reserves (PLRES) 19,494,066 619,758 20,113,824 

Savings 6893 683 7576

This study did verify that release of the flex spin would not result in significant reserve shortfalls 

in real time. As such, this sensitivity case demonstrates that the reserve assumptions for the main 

cases are conservative and some additional savings could be expected if the flex-spin release 

were done in all cases. The actual result would be different for each EIM case because there is a 

unique dispatch for each.

3.6.2 Effect of Simulating Short Dispatch Cycles With 1-Hour 

Time-Step Software 

Until recently, no commonly used production simulation software could simulate dispatch 

periods of less than 1 hour because they all used a 1-hour simulation time-step. Therefore, the 

WECC-E3 and other studies had to approximate subhourly dispatch by running an hourly timestep simulation with reserve requirements consistent with a 5- or 10-minute dispatch. As 

discussed Section 3.1, flexibility reserve requirements drop dramatically as the dispatch interval 

shortens. Hence, these hourly simulations do incorporate some of the effects of fast timing. 

However, these hourly methods likely understate the effects of intra-hour variability because 

they cannot account for the extra dispatch movements at the subhourly level. Therefore, they also 

likely understate production cost. 

PLEXOS is a relatively new tool that does have the ability to simulate a subhourly dispatch with 

a subhourly time-step. As part of the alignment with the WECC-E3 study (Section 3.2), a fullEIM case with10-minute dispatch and 10-minute flexibility reserves was simulated with a 1-hour 

time-step. A sensitivity case that used a 10-minute time-step was also developed. The same 

flexibility reserve requirements and unit commitment were used in both cases. 
68

Table 35 shows the production cost, cost of transmission losses, and total cost for each case. The 

1-hour time-step simulation understated the production cost by about $34 million. Because of a 

difference in the cost of transmission losses, the understatement of total cost was reduced to 

slightly more than $17 million. 

Table 35. Comparison of Production Costs for 1-Hour Time-Step Simulation 

Versus 10-Minute Time-Step Simulation of Full EIM

Production 

Cost ($ k)

Cost of 

Transmission 

Losses ($ k)

Total Cost 

($ k)

EIM case solved at 1-

hour time-step

(E3EIM)

19,466,671 637,296 20,103,967

EIM case solved at 

10-minute time-step

(PLEIM2)

19,500,959 620,441 20,121,400

Increase 34,288 -16,855 17,433

3.7 Comparison of Hourly and 10-Minute BAU Cases

Two BAU cases were developed for this study: a 1-hour and a 10-minute dispatch. Until now, 

comparisons were made between a BAU and an EIM case to estimate savings associated with the 

EIM. Comparing the two BAU cases is also of interest.

One can think of the 10-minute BAU as a step along the path to the full-EIM implementation in 

which fast dispatch is adopted before the rest of the EIM. In that sense, movement to the 10-

minute BAU represents efficiency improvement, similar to the existing and emerging ITAP, 

dynamic scheduling system, and ADI. Although these methods are different from a 10-minute 

dispatch, they qualitatively represent efficiency improvements that would lessen the benefit of an

EIM. A comparison of the hourly and 10-minute BAU cases allows the savings associated with 

the shorter dispatch interval to be separated from those associated with an EIM operating 

cooperatively across multiple BAAs. In this comparison, all savings are associated with the 

reduction in reserve requirements and the faster dispatch of the system resources, not the 

aggregation of variability resulting from an EIM. Section 3.1 showed the reserves for a sample 

BA decreased 53% on average when operation changed from a 1-hour dispatch with a 40-minute 

forecast lockdown to a 10-minute dispatch with 10-minute lockdown. 

In this sensitivity analysis, the 10-minute and hourly BAU cases were compared. The unit 

commitment was optimized for each case. This case comparison illustrates the sensitivity of the 

results to the unit commitment. 
69

Table 36 shows the total production costs and savings by generation class for the two BAU 

cases. The total savings because of the faster dispatch are about $1.3 billion. The savings are 

primarily from the reduced use of CTs. 

Increases in coal generation make up the majority of the energy difference between the two 

cases, as shown in Table 37. Generation by CTs decreased by 16 TWh annually, and generation 

by coal increased by 13 TWh.

Table 36. Comparison of Production Costs and Savings by Generation Class

for 1-Hour and 10-Minute Dispatched BAU Cases

PLHBAU 

($ million)

E3BAU 

($ million)

Fast 

Dispatch 

Savings 

($ million)

CC 10,519 10,547 -28

Coal 4799 5020 -221

CT 3541 2047 1494

Other 2121 2061 60

Losses 600 600 0

Total 21,580 20,275 1306

Table 37. Total Energy Production by Generation Class 

for 1-Hour and 10-Minute Dispatched BAU Cases

PLHBAU 

(TWh)

E3BAU 

(TWh)

Fast 

Dispatch 

Savings 

(TWh)

CC 187 189 -2.6

Coal 286 298 -12.6

Hydro 253 254 -0.5

CT 43 26 16.4

Solar 32 32 0.0

Wind 80 80 0.0

Other 143 144 -0.6

Losses 33 35 -1.2

Total 1056 1060 -3.8
70

This comparison shows only the savings associated with the faster dispatch. The total savings 

from switching to faster dispatch and the EIM can be estimated by comparing the hourly 

dispatched BAU (PLHBAU) case with the full-EIM case that uses the 10-minute BAU 

commitment (PLEIM1). This comparison shows a total savings of $1.46 billion. Table 38 shows 

the production cost by class of generation for these two cases. A comparison of this table and 

Table 36 shows most of the savings are realized when going from hourly to 10-minute dispatch.

These savings can be compared with the savings associated with the EIM cases discussed in 

Section 3.3. The EIM using the hourly BAU commitment saw savings of $294 million, and the 

EIM with the 10-minute BAU commitment saw savings of $146 million.

Table 38. Savings Associated With Fast Dispatch and EIM

PLHBAU 

($ million)

PLEIM1

($ million)

Dispatch 

and EIM 

Savings 

($ million)

CC 10,519 10,357 162

Coal 4799 5067 -268

CT 3541 2051 1490

Other 2121 2026 94

Losses 600 620 -20

Total 21,580 20,122 1458

Table 38 shows the how production changes when change in dispatch is combined with the EIM. 

CT use drops, with coal and CC generation making up that energy.

Table 39. Approximate Production Savings 

Associated With Faster Dispatch and EIM

PLHBAU 

(TWh)

PLEIM1

(TWh)

Dispatch 

and EIM 

Savings 

(TWh)

CC 187 186 1.0

Coal 286 303 -17.4

Hydro 253 254 -0.4

CT 43 26 16.4

Solar 32 32 0.0

Wind 80 80 0.0

Other 143 143 0.4

Losses 33 37 -3.7

Total 1056 1060 -3.8

 
71

4 Allocation of EIM Benefits to Participants

The results presented in the previous section show the overall societal savings associated with an 

EIM. Additional analysis is required to determine how these benefits would flow to the EIM 

participants. In the WECC-E3 study, a method was developed to evaluate how those benefits 

would be allocated. This method was referred to as the Benefits Allocation Roadmap (E3 2011a). 

The calculations are based on the specific results from the production cost modeling and additional 

information, such as total load served and generation owned, supplied by the participants.

A number of factors affect the accuracy of roadmap calculations. First and foremost is the 

fidelity of the production cost model to the actual operations of the Western Interconnection. 

This study is based on the publicly available WECC TEPPC planning model for the year 2020. 

There are known limitations to this model—such as the simplification of the trading relationships 

between the BAs; the use of generic heat rates for generation units; and the inaccuracies inherent 

in forecasting future load, generation, and transmission system topology. Most of these 

limitations can be overlooked because the analysis compares two similar cases at a macro scale. 

These issues and how they affect the outcome are discussed in detail in the report from the 

WECC-E3 EIM benefits analysis study (E3 2011c). 

A critical factor in understanding the allocation of EIM benefits to the participants is the 

omission of bilateral contract information in the model. Lack of contractual data has a significant 

impact on the commitment and dispatch performed by the model. In all the EIM and BAU cases 

that were modeled and analyzed, PLEXOS minimized production cost subject to the many 

constraints imposed by unit operating limits, transmission limits, and other inputs and did not 

consider contracts between entities in the West because that information was not available. 

Therefore, the individual benefits presented below are based on an optimal dispatch of the BAU 

and EIM cases.

Individual BAs can potentially refine these allocation results significantly. For example, if a BA 

has contractual obligations to sell given amounts of energy during a year at a specified price, the 

revenue from those sales will not be affected by potential EIM transactions. Likewise, if a BA 

holds contracts for purchases at a specified price, the EIM would have no impact on that cost. 

Thus, individual BAs would likely have the ability to post-process the modeling data to account 

for bilateral and other contractual mechanisms that are not part of the model.

4.1 Allocation Method

The method developed for the WECC-E3 EIM benefits study has its roots in even earlier work. 

Similar methods were used in the Cost Benefits Analysis for the Southwest Power Pool (2005)

and, to some extent, in the Eastern Wind Integration and Transmission Study’s (2010) Adjusted 

Production Cost. 

The goal of the allocation procedure is to fairly allocate the savings (or cost) incurred because of

the implementation of an EIM. The first thought might be to just determine the change in 

production costs for each participant. There is more to the story, however. The implementation 

of an EIM results in changes in the import and export of energy between the participants. One 

participant may generate and export more energy in the EIM case than in the base case. 

Therefore, the increased production costs are due to the extra energy sold as well as to the EIM. 
72

Similarly, a participant may import more energy under an EIM. Its production cost is reduced, 

but it is also purchasing more energy. The allocation procedure must account for energy sales 

and purchases as well as production costs.

The approach used in the referenced studies has been called modified generation cost or adjusted

production cost, which is used in this report. The idea is to calculate the cost of serving 

participants’ load by netting out transactions and valuing imports and exports in such a way as to 

determine an effective production cost.

To properly value imports and exports, some pricing mechanism is necessary. The PLEXOS 

model economically dispatched generation and recorded prices at each node of the system. These 

are called locational marginal prices, or LMPs. Although the West does not use LMPs, the basic 

calculation of LMPs by PLEXOS provides a good way to represent the value of energy imports 

and exports4

.

The adjusted production cost for a BA is calculated as:

adjusted production cost = 

prorated production cost for all units owned or contracted by the BA

+ net imports priced at load-weighted LMP

- Net exports priced at generation-weighted LMP.

Production costs are calculated as the sum of prorated production costs for units owned or 

contracted by the BA, including units that are remote from the BAA but whose output is partially 

or fully owned or contracted by the BA. It also includes partially owned units within the BAA

boundaries. The ownership information was primarily derived from the TEPPC 2020 PC0 model,

with additional ownership information obtained from Energy Information Administration Form 

860 (2010) and stakeholders. Net imports and exports are calculated each interval (1-hour or 10-

minute) by determining the total generation associated with the BA and subtracting the total load 

responsibility of the BA. If the difference is positive, then excess energy is available in the BA,

and it is sold at that interval’s generator-weighted LMP for the BA. If the difference is negative,

there is less generation than required, and the difference is purchased at that interval’s loadweighted LMP for the BA. The load- and generation-weighted LMPs are calculated for each BA 

in each interval from the production simulation results.

The value of transmission losses must also be added to the above components because the 

generator production costs do not include these losses. The value of the losses is calculated by 

obtaining an interval’s (hour or 10-minute) losses for all transmission at 115 kV and above and 

pricing them at the interval’s average production cost for the area that owns the transmission.

 4 LMPs are used solely to illustrate simulation results. Although LMPs are also used in the operation of centrally 

organized electricity markets (e.g., regional transmission organizations and ISOs), the use of LMPs in this report 

does not imply the need for a regional transmission organization in the West.
73

Similar to the societal saving calculations, the adjusted production costs for each BA were 

calculated first for the BAU case and then for the EIM case. The allocated savings are the 

differences between the EIM and BAU adjusted production costs for each BA. In the following 

sections, each component of the adjusted production cost is calculated individually to illuminate 

the nature of the savings for each BA.

4.2 Allocation of Benefits for EIM Under Hourly BAU

The allocation of benefits for the hourly dispatched BAU and full-EIM case pair (presented in 

Section 3.3.1) is discussed in this section. The BAU case uses an hourly dispatch, with a forecast 

that is set 40 minutes prior to the beginning of the hour. All BAAs in the Western Interconnection 

participate in the EIM except those with existing markets (i.e., CAISO and AESO).

The allocation of benefits for all EIM participants is shown in Table 40. For each BA, the change 

in production cost, purchases, and exports as well as the adjusted production cost savings is 

shown. Savings are indicated by positive numbers, and cost increases are negative. Imports are 

reported as positive for reductions in import costs. Exports are reported as positive for increases 

in revenue. For instance, AZPS has a $27-million reduction in production cost for all generation, 

$14 million in additional purchases of energy, and $12 million less in export revenue. This 

results in a total adjusted production cost savings of $0.8 million. Avista has a $4.5-million 

reduction in production cost, $33.6 million less in import costs, and an $87-thousand increase in 

export revenue. This results in a total adjusted production cost savings of $38 million.
74

Table 40. Allocation of EIM Benefits (With the Hourly BAU Unit Commitment) to EIM Participants 

Reduction in 

Production 

Cost ($ k)

Decrease in 

Import Cost 

($ k)

Increase in 

Export 

Revenue ($ k)

Adjusted 

Production 

Cost Savings 

($ k)

APS 27,000 (14,000) (12,000) 1,000 

AVA 5000 34,000 0 38,000 

BCTC 6500 (4600) (27,900) (26,100)

BPA 40,900 0 (354,100) (313,300)

CHPD 0 (1500) (7900) (9400)

DOPD 0 (1200) (2700) (4,000)

EPE 12,800 (2700) (1300) 8700 

GCPD 0 2500 (4500) (1900)

IID 2400 (100) (16,900) (14,500)

IPC (2,300) 3400 (3000) (1900)

LADWP 20,400 32,300 (3500) 49,200 

NEVP 33,500 (10,100) (17,000) 6300 

NWE (23,600) 6200 41,100 23,700 

PACE (97,300) 19,500 156,800 79,000 

PACW 10,800 49,200 100 60,000 

PGN 11,100 64,600 0 75,700 

PNM (13,000) 800 17,600 5500 

PSCO 39,700 46,600 6600 92,900 

PSE (10,300) 107,600 300 97,500 

SCL 0 24,300 (200) 24,100 

SMUD 28,700 10,900 0 39,600 

SPPC 10,300 10,500 (100) 20,600 

SRP 51,900 4600 0 56,500 

TEP 13,200 7000 (4600) 15,600 

TID 3800 7500 0 11,300 

TPWR 0 8800 (500) 8300 

WACM (6500) 3000 3900 400 

WALC 11,000 (100) (32,100) (21,200)

WAUM 0 0 7400 7400 
75

The high reserve requirements of these cases lead to significant CT commitment to cover the 

flexibility needs of the BAAs. This is particularly true for BAAs with high solar penetration 

because those requirements are relatively more demanding. This leads to significant out-of-meritorder commitment because high-cost CTs are frequently not needed for energy because lower-cost 

units are available. They are committed and running at minimum generation only to provide 

necessary reserves. Because the day-ahead commitment decisions of the SCUC must be honored in 

the EIM to be sure the BAs are capable of self-sufficiency, those CTs remain committed in the 

EIM, which limits the savings possible in many BAAs. If these CTs were instead taken offline 

when not needed, and started when needed, then the results could change significantly.

There are several patterns in the results. First, there are BAs whose production costs are cut 

substantially because of the reduction of reserve requirements and the use of more-economical 

resources without significantly changing imports or exports. SRP is an example of this. Its 

production costs are reduced by $52 million with the EIM. A relatively small decrease in both 

import costs and export revenue leaves it with a net benefit of $56 million. 

Next, there are BAs with low-cost coal resources that increase exports with the reduced 

transactional friction between BAs in the EIM case. PACE is an example of this. Its production 

costs increase by $97 million, but its export revenues increase by $157 million. This results in an 

overall savings from the EIM of $79 million.

Another group of BAs sees decreases in production costs but increases in imports and/or 

decreases in export revenues. Typically, these are BAs with high renewable energy penetration 

levels that benefit from the reduced EIM reserve requirements. APS is a good example of such a 

BA. Its production cost is reduced by $27 million, purchases increase by $14 million, and sales 

decrease by $12 million. The net benefit to the BA is $1 million. 

Yet another group has relatively small changes in production cost but relatively large savings on 

purchased energy. The purchased energy savings are due to the reduction in overall LMPs across 

most of the Western Interconnection with the EIM. These BAs tend to be smaller areas that 

import a significant amount of their energy. PGN is an example. There is a reduction of $11

million in production cost but a larger decrease in purchases of $65 million. This gives a total 

savings of $76 million. SMUD also falls into this category, with a reduction of $29 million in 

production costs and $11 million in energy purchase costs, even though it imports an additional 

250 GWh in the EIM case. 

Finally, there are BAs that see small benefits or penalties to production cost but large decreases 

in export revenue. BPA is an example of this group. The value of BPA’s exports decline because 

of the overall decrease in LMP throughout the Western Interconnection and a slight rise in 

generator-weighted LMP in BPA. The value of the exports decreases by $354 million, while 

production cost savings are $40 million. This results in a net increase in costs of $314 million.

The EIM also impacts nonparticipants, such as the CAISO utilities, AESO, and CFE. Table 41

shows the results of the benefits allocation roadmap for those entities.
76

Table 41. Allocation of EIM Benefits 

(With the Hourly BAU Unit Commitment) to EIM Nonparticipants

Reduction in 

Production 

Cost ($ k)

Decrease in 

Import Cost 

($ k)

Increase 

in Export 

Revenue ($ k)

Adjusted 

Production 

Cost Savings 

($ k)

AESO (9100) (9400) (700) (19,200)

CFE 7700 (4900) (2200) 600 

PGE 43,200 63,000 (11,700) 94,600 

SCE 45,100 60,000 (6200) 98,900 

SDGE 17,200 12,700 (6500) 23,400 

About 60% of the societal benefit calculated as the production cost savings for the EIM belongs 

to the participants of the EIM. The other 40% is realized by nonparticipants, such as the CAISO 

utilities and AESO. To understand why such large benefits go to nonparticipants, it is important 

to understand that all entities are going from a 1-hour to a 10-minute dispatch interval. 

Therefore, they also see the benefits of faster schedules. Areas such as CAISO are also able to 

import more energy under the EIM at prices well below that of the CTs needed for the BAU 

flexibility reserve requirements. 

SCE, for instance, sees a $45 million reduction in production costs when 10-minute dispatch is 

implemented. This is primarily because of a reduction in CC use—about $35 million 

representing about 644 GWh of energy. There are also reductions in coal, biomass, and steam 

generation totaling 416 GWh. This results in an additional production cost savings of $14 

million. The energy is replaced through additional purchases and reduced sales of approximately 

1100 GWh for an additional savings of $60 million. This is possible because the price paid for 

all purchased energy falls by about $5/MWh on average.

To understand the factors driving the changes in generation patterns, it is useful to consider the 

LMP for the entire Western Interconnection and observe the LMP-driven flows between the 

BAs. To visualize this data, an LMP map was developed. Each map has a background that shows 

the average annual bus LMPs across the Western Interconnection, where the colors indicate the 

value of the LMP. These plots are frequently called “heat maps” because the color tells the 

“temperature” of the variable being plotted. For the annual average LMP, cooler colors indicate 

lower LMPs, and warmer colors indicate higher LMPs. The color scales and associated LMP 

values are shown on each plot. 

On top of the LMP map are bubbles for each BA in the PLEXOS model. The arrows between the

bubbles indicate the average hourly power flows between BAs. The width of the arrows is a coarse 

indication of the magnitude of the flow and gives a sense of the big picture. The detailed flow data 

are on top of the arrows. For clarity, flows between BAs of less than 100 MW are not shown.
77

Figure 32 shows the LMP map for the hourly BAU (PLHBAU) case. The LMPs are highest in 

California, with an average near $50/MWh. LMPs are lowest in the northeastern part of the

Western Interconnection, with an average in the low $20s/MWh in NWE. Elsewhere, the prices 

tend to be in the mid-$30s/MWh. There is a clear pattern of energy moving from low- to highprice areas, with movement generally from east to west and north to south.

Figure 32. LMP map for hourly BAU case 

The average generator-weighted and load-weighted LMPs for each of the BAs are shown in 

Table 42 for the hourly dispatched BAU case. The highest prices are seen in BANC, NEVP, and 

PSC. The lowest prices are seen in NWMT, WACM, and PACE. NWMT has low LMP because 

of the hydro, wind, and coal facilities. 

1-Hour BAU LMP

($/MWh)

55

50

45

40

35

30

25

BCTC AESO

NWE

AVA

BPA

PSE

PGE

PACW

BANC

CAISO

LADWP

IID

WACM

PSCO

WALC

IPC

PACE

APS

TEP

SRP

PNM

EPE

NEVP

891

626

178

1181

607

1111

SPP

153

602 100

116

105

891

179

857

777 2633

393

216

1139

2274 840

705

324

489

113

431 261

386

788

197

265

718

492

560

204

Hourly BAU

Annual Average Nodal LMP

And Regional Flows above 

100MW

Thickness of lines indicates magnitude of 

flow

173

101
78

Table 42. Average Weighted LMP for Hourly BAU Case

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

AESO 55.69 60.61 PGE 49.26 49.32

APS 32.82 33.30 PGN 36.16 38.49

AVA 31.61 34.51 PNM 31.79 32.32

BCTC 39.37 39.13 PSCO 39.91 41.09

BPA 35.20 35.73 PSE 33.84 39.62

CFE 51.32 51.32 SCE 45.65 48.20

CHPD 35.35 35.32 SCL 29.19 36.21

DOPD 35.35 35.24 SDGE 45.29 45.80

EPE 35.48 37.86 SMUD 47.91 48.21

GCPD 35.20 35.18 SPPC 38.48 38.46

IID 39.89 40.47 SRP 34.45 34.85

IPC 31.04 31.58 TEP 33.47 33.56

LADWP 41.09 43.10 TID 48.31 48.31

NEVP 43.22 44.48 TPWR 35.69 35.94

NWMT 21.05 20.40 WACM 27.98 27.81

PACE 27.79 27.98 WALC 35.79 35.71

PACW 35.46 37.10 WAUM 20.50 22.40

As shown in Figure 33, the effect of the full EIM is to reduce the range of LMPs across the 

Western Interconnection, raising the price in low-cost generation regions and lowering the price 

in others. The color scale is identical to the previous plot of the BAU case. Significantly higher 

prices are observed in NWE and, to a lesser extent, PACE and WACM. Table 43 shows the 

average generator-weighted and load-weighted LMP values for each area.
79

Figure 33. LMP map for full-EIM case with hourly BAU unit commitment

Table 43. Average Weighted LMP for Full-EIM Case 

With Hourly BAU Unit Commitment

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

AESO 55.79 60.69 PGE 44.52 44.62

APS 30.43 30.96 PGN 30.17 30.91

AVA 26.39 26.36 PNM 30.32 30.06

BCTC 37.31 37.00 PSCO 29.55 29.54

BPA 30.47 31.11 PSE 30.74 32.22

CFE 50.12 50.12 SCE 41.25 43.41

CHPD 30.95 30.92 SCL 25.58 32.05

DOPD 30.95 30.80 SDGE 40.80 41.23

EPE 31.18 31.66 SMUD 40.95 41.96

GCPD 30.72 30.71 SPPC 31.28 31.37

IID 36.01 36.33 SRP 31.36 30.80

IPC 30.33 30.42 TEP 30.85 30.72

LADWP 31.76 37.12 TID 42.47 42.47

NEVP 37.59 38.42 TPWR 31.23 31.65

NWMT 27.33 27.21 WACM 29.50 29.44

PACE 29.81 30.11 WALC 33.38 32.89

PACW 30.46 30.87 WAUM 27.58 28.72

Full EIM with 1-Hour BAU LMP

($/MWh)

55

50

45

40

35

30

25

BCTC AESO

NWE

AVA

BPA

PSE

PGE

PACW

BANC

CAISO

LADWP

IID

WACM

PSCO

WALC

IPC

PACE

APS

TEP

SRP

PNM

EPE

NEVP

839

615

116

1384

623

1463

238

SPP

182

775

181

102

864

182

857

911 2098

1124

1138

2273 1222

701

925

117

556

198

132

471 353

447

817

178

187

288

854

464

486

314

Case PLEIM with 1-Hr BAU

Annual Average Nodal LMP

And Regional Flows above 

100MW Thickness of lines indicates magnitude of 

flow

128

150
80

Figure 34 shows both the change in LMPs and the change in flows between the EIM and BAU 

cases. The color scale indicates the increase or decrease in the LMPs associated with the 

implementation of the EIM. Only power flow changes of more than 50 MW are shown. Flows 

that decrease are in red. In general, the flow increases from east to west. It follows both the lowto-high LMP differential and the removal of hurdle rates in the EIM case.

Although the average flow directly from BPA to CAISO decreases by 549 MW, that flow is 

made up with additional flow from BPA through LADWP and BANC. This is because the hurdle 

rate from BPA to CAISO is $11.44, while that from LADWP to CAISO is $9.68 and from

BANC to CAISO is $5.99. With the EIM in place, there are no hurdle rates from BPA to either 

LADWP or BANC, which makes these paths less expensive. In addition, significant energy 

comes in from NEVP, which also has a lower hurdle rate at $8.03 than the BPA path. 

Figure 34. LMP map of LMP changes from hourly BAU to full EIM with hourly unit commitment

Table 44 shows the change in annual average generator-weighted and load-weighted LMP values 

for each area. As shown in Figure 34, the largest changes are in the low-cost northeastern areas 

of NWE, PACE, and WACM. Without the EIM, these areas have long periods with very low 

LMP values because of large amounts of wind. With the EIM, more energy can move out of the 

northeastern region, which reduces the time when LMPs are below the marginal costs of the 

lowest-cost thermal generators in the region. In NWE, the LMP is below the marginal cost for 

Colstrip for about 2800 hours/year in the BAU case and around 1200 hours in the EIM case. 

Change in LMP ($/MWh)

1-Hour BAU to Full EIM

20

15

10

5

0

-5

-10

BCTC AESO

NWE

AVA

BPA

PSE

PGE

PACW

BANC

CAISO

LADWP

IID

WACM

PSCO

WALC

IPC

PACE

APS

TEP

SRP

PNM

EPE

NEVP

-63

203

68

106

352

186

SPP

88

81

-88

134 -535

732

382

64

601

67

103

92

136

-74

110

FULL EIM Compared to Hourly BAU

Annual Average Nodal LMP Changes 

And Regional Flows Increases above 50 MW

Thickness of lines indicates magnitude of change of flow

Red lines indicate reduction of flow in EIM Case

81

173

-52

-155

173

72
81

Table 44. Average Weighted LMP Changes 

From Hourly BAU to Full EIM With Hourly Unit Commitment

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted 

LMP 

($/MWh)

Area

GenerationWeighted 

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

AESO 0.10 0.08 PGE -4.74 -4.70

APS -2.39 -2.34 PGN -5.99 -7.58

AVA -5.22 -8.16 PNM -1.47 -2.26

BCTC -2.06 -2.13 PSCO -10.36 -11.55

BPA -4.74 -4.62 PSE -3.10 -7.40

CFE -1.20 -1.20 SCE -4.40 -4.79

CHPD -4.40 -4.41 SCL -3.61 -4.15

DOPD -4.40 -4.44 SDGE -4.49 -4.57

EPE -4.31 -6.20 SMUD -6.97 -6.25

GCPD -4.48 -4.47 SPPC -7.20 -7.08

IID -3.87 -4.14 SRP -3.10 -4.05

IPC -0.72 -1.16 TEP -2.62 -2.84

LADWP -9.33 -5.98 TID -5.83 -5.83

NEVP -5.63 -6.05 TPWR -4.46 -4.29

NWMT 6.28 6.81 WACM 1.53 1.64

PACE 2.01 2.13 WALC -2.41 -2.82

PACW -5.00 -6.23 WAUM 7.08 6.32

PSCO has a significant reduction in LMP because of the reduction of reserve requirements and 

the removal of hurdle rates to allow additional imports from WACM. 

The effect of the EIM is less noticeable in the southwest (APS, PNM, and SRP), where 

significant amounts of CTs are committed and running to cover the high reserve requirements 

associated with the high penetration of solar resources. For example, there are approximately 

100,000 hours of CT operation, mostly at minimum generation, in APS for both the hourly BAU 

and EIM cases. Note that the EIM case was constrained to use the hourly BAU commitment. 

Therefore, running high-cost resources at minimum generation limits the ability of the BA to 

find lower-cost energy from neighbors even with hurdle rates eliminated in the EIM. An 

improved model of quick-start CTs would allow these units to start and stop during real time 

rather than maintaining the day-ahead commitment and enforcing minimum generation.

LADWP sees a sizable reduction in LMP because of its relatively high LMP in the BAU case 

and the new resources available at much lower cost when hurdle rates are removed in the EIM

case. As shown in Figure 34, flows from BPA and the southwestern BAs into LADWP increase 

with the EIM. 
82

4.3 Allocation of Benefits for EIM Compared With the 10-Minute BAU

The allocation of benefits for the 10-minute dispatched BAU and full-EIM case pair (presented in 

Section 2.4.2) is discussed in this section. The BAU case uses a 10-minute dispatch with a forecast 

that is set 10 minutes prior to the beginning of a dispatch interval. All BAs in the Western 

Interconnection participate in the EIM except those with existing markets (i.e., CAISO and 

AESO). The changes from a 10-minute BAU to the EIM are likely to be more modest than for the 

hourly BAU because the savings associated with reserve reductions are much smaller. With the 

lower reserves in the 10-minute dispatch BAU case, the largest differences would be expected for 

BAs with higher variable generation penetrations and thus higher reserve requirements.

The allocation results for this pair of cases are shown in Table 45. Savings are indicated by

positive numbers, and cost increases are negative. Imports are reported as positive for reductions 

in import costs. Exports are reported as positive for increases in revenue.

The reductions in production costs are primarily due to the reduction of reserve requirements 

with the EIM. Also, there are additional exchanges of energy from lower-cost areas to highercost areas because of the elimination of hurdle rates in the EIM and the accompanying general 

reduction in LMP. 

Several patterns are evident in the results. The largest group sees small to moderate changes in 

production cost but a substantial reduction in purchased energy cost. This is because of a general 

lowering of prices across most of the Western Interconnection. PGN, PSE, and AVA are typical 

of this group. They tend to have fairly low levels of variable generation in their portfolios and 

import significant amounts of their energy requirements.

A group of BAs (NWE, PACE, and WACM) sees production cost rise because its members are 

low-cost producers and export more energy when hurdle rates are removed. The additional export 

revenues more than offset the increase in production cost, giving a net savings with the EIM.

PSCO and LADWP are examples of BAs that have lower production costs primarily because of 

the reduction of reserve requirements from the EIM. These BAs have significant variable 

generation assigned to their territories. The pooling in the EIM of variability from that variable 

generation leads to the reduction. They are also able to purchase additional energy in the EIM 

because of the elimination of hurdle rates with their neighbors.

PNM is typical of a group that sees relatively small change in production cost. In PNM’s case, 

there is a small increase in production cost because of an increase in exports. This increase in 

exports occurs because of the removal of hurdle rates with the EIM.
83

Table 45. Allocation of EIM Benefits 

(With the 10-Minute BAU Unit Commitment) to EIM Participants 

Reduction in 

Production 

Cost ($ k)

Decrease in 

Import Cost ($

k)

Increase 

in Export 

Revenue ($ k)

Adjusted 

Production 

Cost Savings 

($ k)

APS 10,400 6700 1500 18,700 

AVA 1800 22,100 200 24,000 

BCTC 2900 (7500) 8600 4000 

BPA 31,500 0 (206,600) (175,000)

CHPD 0 0 (5200) (5100)

DOPD 0 (400) (2000) (2400)

EPE 11,600 (4800) (700) 6000 

GCPD 0 1100 (2500) (1,300)

IID 400 0 (5600) (5300)

IPC (5700) 2100 7200 3600 

LADWP 13,700 16,100 (400) 29,400 

NEVP 8000 9300 6500 23,700 

NWE (19,200) 3800 42,200 26,800 

PACE (59,500) 8900 144,700 94,200 

PACW 4100 28,500 100 32,700 

PGN 10,000 41,900 0 51,900 

PNM (7200) 3000 16,400 12,200 

PSCO 38,400 29,200 3300 70,900 

PSE (9100) 77,000 400 68,300 

SCL 0 11,800 100 11,900 

SMUD 16,500 4900 (200) 21,200 

SPPC 6800 6300 (100) 13,100 

SRP 32,400 10,500 0 42,900 

TEP 0 7900 10,200 18,100 

TID 3500 2000 0 5400 

TPWR 0 4100 (100) 4000 

WACM (17,500) 9500 13,400 5400 

WALC (3500) 0 3400 (100)

WAUM 0 0 10,600 10,600 

Table 46 shows the allocation of benefits for BAs not participating in the EIM. As with the 

participant benefits, there is generally a decline in these benefits compared with the hourly 

dispatch case. Unlike the hourly dispatch case, in which the majority of savings were due to 

the reduction in reserve requirements in areas with substantial variable generation, the 

savings here are primarily due to the general lowering in the cost of energy throughout the

Western Interconnection.
84

Table 46. Allocation of EIM Benefits 

(With the 10-Minute BAU Unit Commitment) to EIM Nonparticipants

Reduction 

in Production 

Cost ($ k)

Decrease 

in Import Cost 

($ k)

Increase 

in Export 

Revenue ($ k)

Adjusted 

Production 

Cost Savings 

($ k)

AESO (25,600) 41,600 200 16,200 

CFE 8000 (9000) (2700) (3800)

PGE 30,100 52,900 (4800) 78,100 

SCE 39,200 19,100 (2100) 56,300 

SDGE 23,700 2900 (5500) 21,100 

The LMP map for the 10-minute BAU (E3BAU) case is shown in Figure 35. (A description of this 

type of figure and its interpretation is in Section 4.2.) It looks similar to the hourly BAU 

(PLHBAU) shown in Figure 32 and discussed in the previous section. However, a comparison of 

these two figures shows that the LMP values are higher in the 10-minute BAU than in the hourly 

BAU. In fact, they are higher in nearly every BA by about $6/MWh on average. Although it seems 

backward, the hourly BAU has lower LMPs because of the out-of-merit-order commitment of CTs 

to cover flex reserve requirements. There are enough CTs running at minimum load to push down 

the loading on more-efficient generators, making those the marginal units. 

Figure 35. LMP map for 10-minute BAU case 

Full EIM with 10-Min BAU LMP

($/MWh)

55

50

45

40

35

30

25

BCTC AESO

NWE

AVA

BPA

PSE

PGE

PACW

BANC

CAISO

LADWP

IID

WACM

PSCO

WALC

IPC

PACE

APS

TEP

SRP

PNM

EPE

NEVP

937

648

223

844

632

1318

SPP

140

715 124

107

862

216

832

843 2997

478

1049

2263 967

647

322

444

181

111

245 311

383

778

357

326

730

371

613

276

Case E3BAU with 10-Minute BAU

Annual Average Nodal LMP

And Regional Flows above 

100MW

Thickness of lines indicates magnitude of 

flow

105

107

114

161

174
85

Flows between BAs are, in general, a bit lower in the hourly dispatch case. This is because each 

BA has higher reserve requirements in the 1-hour case, which forces more units to be committed 

locally. In the 10-minute case with lower reserve requirements, fewer high-cost units are 

committed, and a more optimal dispatch is possible. 

Table 47 shows annual average generation-weighted and load-weighted LMP values for each of 

the study areas. Again, NWMT, PACE, IPC, WAUM, and WACM show the lowest average 

LMPs because of the mix of low-cost generation such as coal, hydro, and wind compared with 

the relatively low load levels found in these areas. The highest U.S. prices are found in PG&E at 

$53/MWh and BANC at $51/MWh.

Table 47. Average Weighted LMP for 10-Minute BAU Case

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

AESO 56.11 60.92 PGE 53.26 53.34

APS 38.39 38.67 PGN 37.77 40.28

AVA 33.13 36.31 PNM 36.53 36.94

BCTC 41.40 41.15 PSCO 41.71 42.92

BPA 37.08 37.67 PSE 35.38 41.55

CFE 54.48 54.46 SCE 49.63 52.12

CHPD 37.25 37.22 SCL 30.21 38.13

DOPD 37.24 37.14 SDGE 49.89 50.30

EPE 41.35 44.05 SMUD 50.62 50.98

GCPD 37.09 37.08 SPPC 40.64 40.63

IID 44.39 45.06 SRP 38.97 40.03

IPC 32.28 32.81 TEP 37.89 38.26

LADWP 44.64 46.35 TID 51.11 51.11

NEVP 46.83 48.31 TPWR 37.61 37.86

NWMT 21.94 21.42 WACM 29.34 29.15

PACE 28.91 29.08 WALC 39.73 39.82

PACW 36.50 38.21 WAUM 21.53 23.42

Figure 36 shows the LMP map and major energy flows for the EIM case with the 10-minute 

BAU commitment. The effect of the EIM is to even out the LMPs across the Western 

Interconnection by raising the price in low-cost generation regions and lowering the price in 

others. The color scale is identical to the previous plot of the BAU case. Note the significantly 

higher prices in NWE and WAUM and, to a lesser extent, PACE, WACM, and IPC. Table 48

shows the average generator-weighted and load-weighted LMP values for each area.
86

Figure 36. LMP map for full-EIM case with 10-minute BAU unit commitment

Table 48. Average Weighted LMP for Full-EIM Case With 10-Minute BAU Unit Commitment

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

AESO 56.78 61.50 PGE 51.19 51.27

APS 37.25 37.47 PGN 34.41 35.12

AVA 30.76 30.83 PNM 36.42 36.09

BCTC 40.92 40.63 PSCO 34.69 34.65

BPA 34.74 35.31 PSE 34.91 36.36

CFE 55.71 55.69 SCE 47.60 49.86

CHPD 35.19 35.13 SCL 29.20 36.20

DOPD 35.19 35.02 SDGE 47.35 47.77

EPE 38.57 39.83 SMUD 46.58 47.69

GCPD 34.97 34.93 SPPC 36.18 36.24

IID 42.54 42.86 SRP 37.69 37.37

IPC 34.80 34.85 TEP 37.37 37.40

LADWP 36.97 43.05 TID 48.26 48.23

NEVP 43.45 44.40 TPWR 35.45 35.82

NWMT 31.58 31.46 WACM 34.57 34.47

PACE 34.46 34.65 WALC 39.71 39.30

PACW 34.72 35.10 WAUM 31.87 32.81

Full EIM with 10-Min BAU LMP

($/MWh)

55

50

45

40

35

30

25

BCTC AESO

NWE

AVA

BPA

PSE

PGE

PACW

BANC

CAISO

LADWP

IID

WACM

PSCO

WALC

IPC

PACE

APS

TEP

SRP

PNM

EPE

NEVP

923

663

181

953

651

128

102

1503

249

SPP

176

816

177

108

813

249

847

949 2352

1173

1083

2269 1243

169

658

956

125

529

249

131

265 363

439

843

185

361

376

870

373

541

330

Full EIM with 10-Min BAU

Annual Average Nodal LMP

And Regional Flows above 

100MW Thickness of lines indicates magnitude of 

flow

121

102
87

Figure 37 shows the change in LMPs and average flows between BAAs between the 10-minute 

BAU case and the EIM case. Black lines show flows that increase with the EIM, and red lines 

indicated flows that decrease. The thickness of the lines roughly indicates the magnitude of the 

change in flow. Again, flow increases from the northeastern section of the Western 

Interconnection to the west and southwest. This indicates that lower-cost energy is flowing from 

areas of lower costs to areas of higher with the removal of hurdle rates with the EIM. There is a 

large decrease in sales from BPA to CAISO. This energy is made up through smaller increases in 

flows from a number of BAAs. This happens because the hurdle rate from BPA to CAISO is 

$11.44—higher than the hurdle rates from other BAAs.

Figure 37. LMP map of LMP changes from 10-minute BAU 

to full EIM with 10-minute unit commitment

As in the hourly dispatch case, nearly all BAs see lower LMPs with the EIM. The exceptions are 

the low-cost areas of NWMT, PACE, IPC, and WACM. These areas see substantial increases in 

LMP values. Table 49 shows the change in LMP values for each area from the 10-minute BAU 

case to the EIM case. 

Change in LMP ($/MWh)

10-Min BAU to Full EIM

20

15

10

5

0

-5

-10

BCTC AESO

NWE

AVA

BPA

PSE

PGE

PACW

BANC

CAISO

LADWP

IID

WACM

PSCO

WALC

IPC

PACE

APS

TEP

SRP

PNM

EPE

NEVP

109

58

185

181

SPP

74

53

107 -645

696

202

276

64

634

85

56

52

65

50

140

-72

54

Full EIM compared to 10-Minute BAU

Annual Average Nodal LMP Changes 

And Regional Flows Increases above 50 MW

Thickness of lines indicates magnitude of change of flow

Red lines indicate reduction of flow in EIM Case

77

78

-93 101

56

50
88

Table 49. Average Load-Weighted LMP Changes

From 10-Minute BAU to Full EIM With 10-Minute Unit Commitment

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

Area

GenerationWeighted

LMP 

($/MWh)

LoadWeighted

LMP 

($/MWh)

AESO 0.67 0.57 PGE -2.07 -2.07

APS -1.15 -1.19 PGN -3.35 -5.16

AVA -2.37 -5.47 PNM -0.11 -0.85

BCTC -0.48 -0.52 PSCO -7.03 -8.26

BPA -2.34 -2.35 PSE -0.47 -5.19

CFE 1.23 1.23 SCE -2.03 -2.25

CHPD -2.06 -2.09 SCL -1.01 -1.93

DOPD -2.06 -2.12 SDGE -2.54 -2.52

EPE -2.78 -4.23 SMUD -4.03 -3.28

GCPD -2.12 -2.14 SPPC -4.46 -4.39

IID -1.85 -2.19 SRP -1.28 -2.67

IPC 2.52 2.04 TEP -0.52 -0.86

LADWP -7.67 -3.30 TID -2.85 -2.88

NEVP -3.38 -3.91 TPWR -2.16 -2.04

NWMT 9.64 10.04 WACM 5.23 5.31

PACE 5.54 5.58 WALC -0.02 -0.51

PACW -1.79 -3.11 WAUM 10.34 9.39

5 Conclusions

This study was performed at the request of the Western Interstate Energy Board’s PUC EIM 

working group. The primary objective of the analysis was to build upon prior work by WECC 

and E3 to further evaluate the potential benefits of an EIM. 

Any study, including this one, necessarily involves assumptions and approximations to 

compensate for missing data, software limitations, and the inherent uncertainty of the future. 

Modeling any large system, especially one with the physical characteristics and existing market 

relationships of the Western Interconnection, is complex and difficult. One set of study 

assumptions and modeling approximations, as described in Section 2, was used in this study to 

represent the key elements of the BAU and EIM study scenarios. Other assumptions and 

approximations could be analyzed in future studies. Therefore, the results of this study represent 

a possible outcome rather than a definitive forecast. Flexibility reserve requirements, societal 

(system-wide) benefits, and individual BAA results were calculated.

Flexibility reserves are in addition to, not instead of, existing contingency reserve requirements 

and are intended to help manage wind, solar, and load variability. This additional reserve 

requirement is a function of the time-synchronized expected variability of wind and solar power, 

which is, in turn, a function of the wind and solar output. For example, if wind power output is at 
89

or near maximum, then there is relatively little variability and, therefore, relatively small 

flexibility reserve requirements. Conversely, if wind power output is in the middle of the 

operating range, then its variability is higher and the flexibility reserve requirements are higher. 

The flex reserve requirements decrease with shorter dispatch interval/forecast lockdown times 

and with larger EIM participation.

This study shows an annual societal operating benefit of between $146 million and $300 million

for the EIM with full participation. There is an additional benefit of approximately $1.3 billion 

associated with moving from an hourly dispatch interval to a 10-minute dispatch interval. Some 

of these large benefits may be achieved through implementation of Federal Energy Regulatory 

Commission Order 764 as well. Therefore, the total benefit of a faster dispatch interval and 

shared flexibility reserves could be as high as $1.46 billion. 

Table 50. Summary of EIM Savings

Case Savings 

($ millions)

Full EIM with hourly BAU commitment compared with hourly BAU 294

Full EIM with 10-min BAU commitment compared with 10-minute BAU 146

Reduced EIM with hourly BAU commitment compared with hourly BAU 276

Reduced EIM with 10-minute BAU commitment compared with 10-

minute BAU 95

Lower-gas price, full EIM with hourly BAU commitment compared with

hourly BAU 281

Reduced-reserve, full EIM with 10-minute BAU commitment compared 

with 10-minute BAU 7.6

10-minute BAU compared with hourly BAU 1,306

Full EIM with 10-minute BAU commitment compared with hourly BAU 1,458

The range of societal EIM benefits occurs because of the uncertainty surrounding future 

efficiency improvements with or without an EIM. The lower estimate ($146 million) is based on 

the assumption that BAs practice 10-minute dispatch internally; the upper estimate ($300 

million) is based on the assumption of hourly dispatch throughout. With a lower level of 

participation, the annual benefit of the EIM ranges from $94 million to $276 million. The EIM 

benefit varies with the level of participation and other factors, as described in this report.

Allocation of EIM benefits to individual BAAs was performed by calculating an adjusted 

production cost for each BAA. This method has its roots in work done in the Southwest Power 

Pool and in the Eastern Wind Integration and Transmission Study. It was refined by E3 for the 

WECC-E3 study. The method takes into account not only the change in production cost but also 

the changes in imports and exports and calculates an adjusted production cost accordingly. The 

adjusted production cost decreased in the majority of the BAAs (21 of 29), showing a potential 
90

EIM benefit. Conversely, eight BAAs showed an increase in adjusted production cost for a 

potential EIM cost.

Lack of contractual data has a significant impact on the commitment and dispatch performed by 

the production simulation software. Without such data, the software develops a minimum 

production cost commitment and dispatch, subject only to generating unit operating limits, 

transmission path ratings, and other performance constraints. Therefore, all individual BAA 

benefits should be considered rough estimates. 

Individual BAAs could refine the allocation results by accounting for confidential bilateral and 

other contractual mechanisms. For example, if a BAA has contractual obligations to sell a given 

amount of energy during the year at a specified price, the revenue from those sales will not be 

affected by potential EIM transactions. Likewise, if a BAA holds contracts for purchases at a 

specified price, the EIM would have no impact on that cost.

As with any complex modeling and analysis of future conditions, additional questions surfaced 

as the work progressed. Therefore, additional analysis on the following topics is recommended:

• Power purchase agreements, if they could be made available

• Alternative nonvariable generation mixes, including alternative assumptions regarding 

coal plant retirements

• Alternative wind/solar energy penetration rates and locations

• Alternative EIM participation scenarios

• Multiple EIMs

• Alternative fuel price and/or emissions prices and regulations

• Alternative seams management with EIM nonparticipants to explore nonparticipant 

benefits

• Broader use of subhourly scheduling (e.g., Joint Initiative ITAP and Federal Energy 

Regulatory Commission Order 764)

• Alternative future transmission projects

• Improved generation modeling (e.g., unit-specific, not generic data).

This project has pushed the state of the art for electricity production simulation modeling to the 

limit, evaluated potential EIM benefits under a range of conditions, and identified areas for 

future research. Further analysis and model refinements are recommended to more fully assess 

the impact of the proposed EIM on the Western Interconnection. Such an effort could provide 

additional insight into the modeling of a large, complex system such as the Western 

Interconnection and the potential benefits of various operational changes.
91

6 References

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NREL/SR-550-47676. Work performed by 3TIER. Golden, CO: National Renewable Energy 

Laboratory. http://www.nrel.gov/docs/fy10osti/47676.pdf.

E3. (2011a). “Technical Memo: Roadmap for Estimating Benefits for Individual EIM 

Participants.” San Francisco, CA: Energy and Environmental Economics Inc. 

http://www.wecc.biz/committees/EDT/EDT%20Results/

Participant%20Benefits%20Calculation/E3_EIM_Benefits_StudyRoadmap_for_Participant_Benefit_Estimation_2011-07-14.pdf. 

E3. (2011b). WECC EDT Phase 1 EIM Benefits Analysis and Results. Work performed by 

Energy and Environmental Economics Inc., San Francisco, CA. Western Electricity 

Coordinating Council. http://www.wecc.biz/committees/EDT/

EDT%20Results/E3_EIM_Benefits_Study-Phase_1_Report-20apr2011.pdf.

E3. (2011c) WECC EDT Phase 2 EIM Benefits Analysis and Results, October 2011 Revision. 

San Francisco, CA: Energy and Environmental Economics Inc.

http://www.wecc.biz/committees/EDT/EDT%20Results/ 

E3_EIM_Benefits_Study-Phase_2_Report_RevisedOct2011_CLEAN2[1].pdf. 

Ela, E.; Milligan, M.; Kirby, B. (2011). Operating Reserves and Variable Generation. A 

Comprehensive Review of Current Strategies, Studies, and Fundamental Research on the Impact 

That Increased Penetration of Variable Renewable Generation Has on Power System Operating 

Reserves. NREL/TP-5500-51978. Golden, CO: National Renewable Energy Laboratory. 

http://www.nrel.gov/docs/fy11osti/51978.pdf. 

EnerNex Corp. (2010). Eastern Wind Integration and Transmission Study. NREL/SR-5500-

47078. Work performed by EnerNex Corp., Knoxville, TN. Golden, CO: National Renewable 

Energy Laboratory. http://www.nrel.gov/docs/fy11osti/47078.pdf. 

King, J.; Kirby, B.; Milligan, M.; Beuning, S. (2011). Operating Reserve Implication of 

Alternative Implementations of an Energy Imbalance Service on Wind Integration in the Western 

Interconnection. NREL/TP-5500-52330. Golden, CO: National Renewable Energy Laboratory. 

http://www.nrel.gov/docs/fy12osti/52330.pdf.

King, J.; Kirby, B.; Milligan, M.; Beuning, S. (2012). Operating Reserve Reductions From a 

Proposed Energy Imbalance Market With Wind and Solar Generation in the Western 

Interconnection. NREL/TP-5500-54660. Golden, CO: National Renewable Energy Laboratory. 

http://www.nrel.gov/docs/fy12osti/54660.pdf.

Kirby, B.; Hirst, E. (2000). Customer-Specific Metrics for the Regulation and Load Following 

Ancillary Services. ORNL/CON-474. Oak Ridge, TN: Oak Ridge National Laboratory. 

http://www.consultkirby.com.
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Milligan, M. (2003). “Wind Power Plants and System Operation in the Hourly Time Domain.” 

Preprint. Prepared for WINDPOWER 2003, May 18–21, 2003. NREL/CP-500-33955. Golden, 

CO: National Renewable Energy Laboratory. http://www.nrel.gov/docs/fy03osti/33955.pdf.

Orwig, K.; Hummon, M.; Hodge, B.M.; Lew, D. (2011). “Solar Data Inputs for Integration and 

Transmission Planning Studies.” NREL/PO-5500-52985. Golden, CO: National Renewable 

Energy Laboratory.

Samaan, N.; Milligan, M.; Makarov, Y.V.; Hunsaker, M.; Nguyen, T.; Jin, C.; Diao, Ma, J.;

Hafen, R; Guo, X.; Lu, N. (2012) “Evaluation of Balancing Authorities Consolidation Benefits in 

the Western Interconnection Under High Wind and Solar Penetration.” Proceedings of 

WINDPOWER 2012; June 3–6, 2012, Atlanta, Georgia. 

Southwest Power Pool. (2005). Cost-Benefit Analysis Performed for the SPP Regional State 

Committee, Final Report. Boston, MA: Charles River Associates. http://www.spp.org/

publications/CBARevised.pdf.

U.S. Energy Information Administration. (2012). “Energy Prices by Sector and Source, United 

States.” http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2013ER&subject=0-

AEO2013ER&table=3-AEO2013ER®ion=1-0&cases=full2012-d020112c,early2013-

d102312a.
93

Appendix. Enforced Interface Limits 

Interface

Limit (MW)

Forward Backward

Alberta-British Columbia 700 720

Alturas Project 300 300

Bonanza West 785 none

Borah West 4057 none

Bridger West 3700 none

Eldorado-McCullough 500 kV 2598 2598

Eldorado-Mead 230 kV 1140 1140

Idaho-Montana 337 256

Idaho-Northwest 3500 2050

Idaho-Sierra 500 360

IID-SCE 600 none

Intermountain-Gonder 230 kV 200 none

Intermountain-Mona 345 kV 1400 1200

Lugo-Victorville 500 kV 2400 900

Midpoint-Summer Lake 1500 550

Montana-Northwest 2200 1350

Montana Southeast 600 600

North of John Day 8400 none

Northern New Mexico (NM2) 1800 none

Northwest-Canada 2000 3150

NW to Canada East BC 400 400

NW to Canada West BC 2750 2850

Pacificorp-PG&E 115 kV 100 45

PATH C 1400 1400

Pavant Intermountain-Gonder 230 kV 440 235

Perkins-Mead-Marketplace 500 kV 1923 1923

PG&E-SPPC 160 150

PV West 3600 none

South of San Onofre 2500 none

Southern New Mexico (nm1) 1048 1048

TOT 1A 800 800

TOT 2A 690 690

Tot 2A 2B 2C Nomogram 1570 1600

TOT 2B 780 850

TOT 2B1 560 600

TOT 2B2 265 300

TOT 2C 600 600

TOT 3 1800 1800
94

Interface

Limit (MW)

Forward Backward

TOT 4A 810 810

TOT 4B 680 680

TOT 5 1675 1675

West of Cascades-North 10200 none

West of Cascades-South 7700 none

Wyoming-Utah 1700 none

Z1-Hassayampa-N. Gila 1905 none

Z1-Palo Verde to Devers 2338 none

Z2-SDGE Import Limit 4000 none

Z2-SCIT 17700 17700

Z2-WOR 11823 none

Z4-Moenkopi-El Dorado 1900 1645

Z4-Navajo-Crystal 1900 1900

Z4-Perkins-Mead 1238 1238

Z6-Path 26 4000 3000

Z7-Miguel-Tijuana 912 912

Z7-Imperial Valley-La Rosita 797 797

Z7-Path 45 408 800

Z9-HA-Red Butte PS 300 300

Z9-Pinto-4 Corners PS 600 600

Z9-Shiprock-Lost Canyon PS 400 400

Z9-Sigurd-Glen Canyon PS 300 300

Alberta-Saskatchewan 150 150

Brownlee East 1850 none

California ISO-Mexico (CFE) 408 408

Cascade Crossing 2000 2000

Centennial 3000 none

Cholla-Pinnacle Peak 1200 none

COI 4800 3675

Combined 4a 4b 1096 none

Coronado-Silver King-Kyrene 1600 none

Coronado West 1494 1364

Crystal-Allen 950 950

Eagle Mtn 230 161 kV-Blythe 16 kV 72 218

East of Colorado River (EOR) 9300 none

Four Corners 345 kV/500 kV 1000 1000

Inyo-Control 115 kV Tie 56 56

IPP DC Line 2400 1400

Midway-Los Banos 5400 3265

Montana Alberta Tie Line 325 300

North of Hanford 4100 none

North of San Onofre 2440 none
95

Interface

Limit (MW)

Forward Backward

Northern-Southern California 4000 3000

Pacific DC Intertie South 2780 3100

pacific DC Intertie 3100 2780

SDG&E-Mexico (CFE) 408 800

Silver Peak-Control 55 kV 17 17

South of Alston 4100 none

Southern CA Imports 14750 none

Southern Navajo 3200 none

Southwest of Four Corners 2325 none

Sunrise Powerlink 1000 none

TOT 7 890 none

WALC Blythe-SCE Blythe 161 kV 218 218

West of Broadview 2573 none

West of Colorado River (WOR) 10623 none

West of Colstrip 2598 none

West of Crossover 2598 none

West of Hatwai 4277 none

West of John Day 3450 none

West of McNary 4500 none

West of Slatt 5500 none

WOR-n-El Dorado to Lugo 2754 none

WOR-n-McCullough to Victorville 2592 none

Z1-N. Gila-Imperial Valley 1905 none

Z1-Imperial Valley to Miguel 2200 none

Z1-Miguel Bank No. 1 1120 1120

Z1-Miguel Bank No. 2 1120 1120

Z1-North of Miguel 2000 none

Z2: South of Lugo 6100 6100

Z3-Eldorado-Lugo 1645 1645

Z3-Market Place-Adelanto 1636 1636

Z3-Mccullough-Victorville 1385 1385

Z3-Mohave-Lugo 1386 1386

Z4-Jojoba-Kyrene 1732 1732

Z4-Peacock-Mead 508 508

Z5-Navajo-Moenkopi 1411 none

Z5-South of Navajo 2264 none

Z6-East of PV 8010 8010

Z8-Crystal-H Allen 500 kV PS 1300 none

Z8-Crystal-H Allen 230 kV PS 950 None