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

A Review of Variable Generation 

Integration Charges

K. Porter, S. Fink, M. Buckley, and J. Rogers

Exeter Associates

B.-M. Hodge

National Renewable Energy Laboratory

Technical Report

NREL/TP-5500-57583

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

A Review of Variable 

Generation Integration 

Charges 

K. Porter, S. Fink, M. Buckley, and J. Rogers

Exeter Associates

B.-M. Hodge

National Renewable Energy Laboratory

Prepared under Task No. OE10.5010

Technical Report

NREL/TP-5500-57583

March 2013
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iii

Acknowledgments

The authors wish to thank the U.S. Department of Energy Office of Electricity Delivery and 

Energy Reliability, through the National Renewable Energy Laboratory, for funding the research 

that went into this report. 

We also thank Victoria L. Ravenscroft of the Western Electricity Coordinating Council and 

Doug Larson of the Western Interstate Energy Board for reviewing the report. In addition, we 

thank Kent School of Xcel Energy, Ron Flood of Arizona Public Service, Phil DeVol of Idaho 

Power, Magdalena Rucker of B.C. Hydro, Ken Dragoon of Ecofys, Juergen Bermejo of the 

Bonneville Power Administration, Todd Guldseth of NorthWestern Energy, Clint Kalich of 

Avista Corp., and Dan Williams of Puget Sound Energy for reviewing sections of the draft 

report. We would also like to thank the following reviewers for their valuable feedback: Erik Ela 

and Aaron Bloom of the National Renewable Energy Laboratory, Larry Mansueti of the 

Department of Energy, Michael Goggin of the American Wind Energy Association, Rebecca 

Johnson of the Colorado Public Utilities Commission, Matt Hunsaker of the Western Electricity 

Coordinating Council, Mark Ahlstrom of WindLogics, Jay Morrison of the National Rural 

Electric Cooperative Association, and Brendan Kirby, an independent consultant. 
iv

List of Acronyms

ACE area control error

AGC automatic generation control 

APS Arizona Public Service

BA balancing authority 

BPA Bonneville Power Administration

CAISO California Independent System Operator 

CAPEX Capital Expansion Model 

CPS2 Control Performance Standards 

DERBS Dispatchable Energy Resource Balancing Service 

DOE U.S. Department of Energy 

EIS energy imbalance service

ERP electric resource plan

FCRPS Federal Columbia River Power System

FERC Federal Energy Regulatory Commission 

GOM Generation Optimization Model

HYSIM Hydro Simulation Model 

IPC Idaho Power Company 

IPUC Idaho Public Utilities Commission 

IRP integrated resource plan

ISD incremental standard deviation

LMP locational marginal price

LTAP long-term acquisition plan 

NAU Northern Arizona University

NERC North American Electric Reliability Corporation

NPPD Nebraska Public Power District

NREL National Renewable Energy Laboratory 

NWP numerical weather prediction

OATT Open Access Transmission Tariff

PGE Portland General Electric 

PSE Puget Sound Energy 

PSCo Public Service Company of Colorado 

PURPA Public Utility Regulatory Policies Act 

PV photovoltaic 

QF qualifying facility

REC renewable energy credit

SAR surrogate avoided resource

SCORE Statistically Corrected Output from Record Extension 

SPP Southwest Power Pool 

SPSC State-Provincial Steering Committee

VER variable energy resources

VERBS Variable Energy Resource Balancing Service

WAPA Western Area Power Administration

WACM Western Area Colorado Missouri Balancing Authority 

WECC Western Electricity Coordinating Council 

WGA Western Governors’ Association 
v 

WIEB Western Interstate Energy Board

WIT Wind Integration Team 

WWSIS Western Wind and Solar Integration Study
vi 
vii 

Table of Contents

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

FERC Actions Related to Variable Generation Integration Charges ..................................................... 4

Report Findings........................................................................................................................................... 6

APS............................................................................................................................................................. 26

Background .......................................................................................................................................... 26

Wind Integration Costs......................................................................................................................... 26

Assumptions .................................................................................................................................... 27

Determination of Wind Integration Costs............................................................................................ 28

Solar Integration Costs ............................................................................................................................ 29

Load and Solar Assumptions ............................................................................................................. 30

Determination of Incremental Reserve Costs....................................................................................... 30

BC Hydro.................................................................................................................................................... 33

Background ..........................................................................................................................................................33

Assumptions and Modeling..................................................................................................................................33

BPA............................................................................................................................................................. 36

Background ............................................................................................................................................ 36

Assumptions........................................................................................................................................... 37

Balancing Reserve Requirement Forecast ........................................................................................... 37

Cost Allocation................................................................................................................................. 39

BPA’s 2012–2013 Rate Case.................................................................................................................... 41

Idaho Wind Integration Charges.............................................................................................................. 44

Background ............................................................................................................................................ 44

Idaho Power Company ............................................................................................................................ 47

Idaho Power’s 2007 Wind Integration Study ....................................................................................... 47

Determination of Current Integration Charge ...................................................................................... 49

Idaho Power’s 2012 Wind Integration Study ....................................................................................... 49

Avista Corporation .................................................................................................................................. 50

Avista’s 2007 Wind Integration Study ................................................................................................ 50

Determination of Current Wind Integration Charge ............................................................................. 51

PacifiCorp .............................................................................................................................................. 51

2003 IRP.......................................................................................................................................... 52

2004 IRP.......................................................................................................................................... 53

2007 IRP.......................................................................................................................................... 53

2008 IRP.......................................................................................................................................... 54

PacifiCorp’s 2010 Wind Integration Resource Study ........................................................................... 54

PacifiCorp’s 2012 Wind Integration Resource Study ........................................................................... 55

Determination of PacifiCorp’s Wind Integration Charge in Idaho ......................................................... 56

NorthWestern Energy – Montana ............................................................................................................ 57

Background ............................................................................................................................................ 57

NorthWestern’s Electricity Supply Resource Procurement Plan .................................................................. 57

Determination of Regulation Service Requirements ................................................................................... 58
viii

PGE............................................................................................................................................................. 60

Background ............................................................................................................................................ 60

Assumptions........................................................................................................................................... 60

Modeling................................................................................................................................................ 62

PSCo – Wind.............................................................................................................................................. 62

Background ............................................................................................................................................ 62

Assumptions and Modeling: Wind Integration Charge................................................................................ 63

Assumptions and Modeling: Coal Cycling Costs........................................................................................ 65

PSCo – Solar.............................................................................................................................................. 66

Background ............................................................................................................................................ 66

Assumptions and Modeling...................................................................................................................... 66

PSE ............................................................................................................................................................. 67

Background ............................................................................................................................................ 67

Assumptions........................................................................................................................................... 68

Determination of Regulation Reserve Charges........................................................................................... 69

Status of PSE’s Integration Rate ............................................................................................................... 70

Westar Energy ........................................................................................................................................... 71

Background ............................................................................................................................................ 71

Assumptions and Methodology ................................................................................................................ 72

WACM......................................................................................................................................................... 75

Background ............................................................................................................................................ 75

Assumptions and Determination of Charge................................................................................................ 75

References................................................................................................................................................. 76
ix

List of Figures

Figure 1. APS estimated wind integration costs. ......................................................................29

Figure 2. Mean absolute error for PGE wind forecasts of one, two, and three days ahead. ....61

Figure 3. Westar wind regulation rate calculation methodology. ............................................74

List of Tables

Table 1. How Balancing Authorities Profiled in This Report ...................................................3

Table 2. Components of Estimated Integration Costs ...............................................................8

Table 3. Determination of Wind Integration Impacts ...............................................................9

Table 4. Integration Charges and Methodologies by Balancing Authority ............................12

Table 5. Variable Integration Rates Incorporated in IRPs and/or Competition Solicitations 

for Generation ...........................................................................................................19

Table 6. APS Wind Energy Penetration and Geographic Diversity .......................................27

Table 7. APS Operating Reserve Requirements .....................................................................31

Table 8. APS’s Estimated Solar Integration Costs .................................................................32

Table 9. Water/Wind Year Combinations Used in the GOM Modeling ................................34

Table 10. BPA Wind Integration Rates ....................................................................................37

Table 11. Forecast of Average Generation Capacity for the 2012–2013 Rate Period ..............38

Table 12. Variable Cost Categories ..........................................................................................41

Table 13. Revenue Forecast for BPA’s 2012–2013 Rate Case for Regulation, VERBS, 

and DERBS ...............................................................................................................42

Table 14. 2012–2013 Rates for Regulation, VERBS, and DERBS ..........................................43

Table 15. Summary of Wind Integration Discounts for Idaho Power and Avista ....................44

Table 16. Chronological Summary of PacifiCorp Wind Integration Cost Estimate .................52

Table 17. Example Total Incremental Wind Integration Cost Calculation ...............................65

Table 18. PSE Variable Energy Resource Rate Adjusted Variance Calculations ....................69
x 
1 

Introduction

The growth of wind and solar generation in the United States, and the expectation of continued 

growth of these technologies, particularly in response to state statutory requirements such as 

renewable portfolio standards, dictates that the future electric power system will be operated in a 

somewhat different manner due to the increased variability and uncertainty relative to existing 

load and generation. A small number of balancing authorities (BAs) have attempted to determine 

an “integration cost” to account for these changes to their current operating practices.

1 A 

balancing authority is the responsible entity that integrates resource plans ahead of time, 

maintains load-interchange-generation balance within a balancing authority area, and supports 

interconnection frequency in real time, all focusing on ensuring that electricity can be reliably 

generated and delivered at the bulk power (i.e., wholesale) level (NERC 2012). 

Some balancing authorities directly charge wind and solar generators for integration charges, 

whereas others, operated by electric utilities that also procure or build electric generation, add 

integration charges to projected costs of wind and solar in any integrated resource plans (IRPs) 

they may produce or in competitive solicitations for generation. 

This project originated when the Western Interstate Energy Board’s State-Provincial Steering 

Committee (SPSC) requested assistance from the U.S. Department of Energy (DOE) in 

compiling information on variable generation integration charges imposed by balancing 

authorities other than the California Independent System Operator (CAISO) in the Western 

electricity interconnection.

2 The SPSC is interested in creating a variable generation “dashboard” 

for the West that would show information on how wind and solar generation is being integrated 

into the electric grid by balancing authorities in the West, including integration charges, if 

applicable. 

 

1 The electric power industry has long dealt with the variability and uncertainty of both load and generation. 

Variability and uncertainty are addressed through ancillary service requirements, as required by FERC Order 888 to 

address unexpected short-term changes in generation and load; contingency reserves to ensure that electric reliability 

is maintained in the case of unexpected loss of generation or transmission; and capacity reserves or planning 

reserves to ensure that projected electric demand in the future is met through new generation, transmission, or 

demand-side management or demand response. Although actual practices differ considerably by state and by region, 

one common practice is that load pays for ancillary services, contingency reserves, and capacity or planning 

reserves. With integration charges or costs, the discussion among industry participants is whether the variability or 

uncertainty introduced by higher levels of variable generation is great enough that any additional costs attributed to 

meeting increased variability or uncertainty should be reflected, either through direct charges to variable generation 

or indirectly through integrated resource plans or competitive bidding solicitations. The purpose of this report is to 

summarize the integration charges in place to date, either directly or indirectly, and to compare and contrast the 

methodologies used to estimate those integration charges. This report does not take a position on whether, or how, 

integration charges should be defined, measured or assigned. 2 The SPSC is organized and staffed by the Western Interstate Energy Board (WIEB), which is the “energy affiliate” 

of the Western Governors’ Association (WGA). The SPSC is the result of a grant awarded by DOE in 2010 under 

the American Recovery and Reinvestment Act of 2009 to the WGA to, among other duties, enhance the states’ 

capacity to effectively participate in interconnection-wide transmission planning being undertaken under a 

companion DOE grant to the Western Electricity Coordinating Council (WECC). These state participation activities 

under the DOE grant are occurring under the SPSC. Before the creation of the SPSC, similar functions were carried 

out by WIEB’s Committee on Regional Electric Power Cooperation (CREPC), which still exists and functions 

together with the SPSC.
2 

This report reviews the balancing authorities that have calculated variable generation integration 

charges and broadly compares and contrasts the methodologies they used to determine their 

specific integration charge. The practice of estimating variable generation integration costs is 

still evolving, although it has progressed significantly during the past decade (Milligan 2011). 

The practice has been a dynamic subject area, as there is disagreement within the industry as to 

the proper methodology that should be used. In short, there is no one generally accepted 

methodology to calculate variable generation integration costs. 

The report also profiles each balancing authority we discussed and how it derives wind and solar 

integration charges. Two tables were compiled on the specifics of each wind and solar 

integration charge, showing integration rates that are directly charged to wind and solar 

generators and those that are factored into IRPs or solicitations for generation. The information 

was gathered through an extensive literature review, with individual sections sent to each 

balancing authority for review. Not all provided comments, and these are noted where 

applicable. 

This report profiles 12 balancing authorities that have taken some action to account for variable 

generation impacts on their system. Seven impose integration charges on wind and/or solar 

generation, and six incorporate estimated integration costs in their resource planning or 

competitive solicitations for generation, or both.3 PacifiCorp is listed in both categories, as Idaho 

allows PacifiCorp to deduct $6.50/MWh from avoided cost payments made to wind facilities that 

are registered as qualifying facilities under the Public Utility Regulatory Policies Act (PURPA). 

PacifiCorp also factors the estimated integration charges into its integrated resource planning

process. All 12 balancing authorities estimate integration charges for wind; whereas only three 

estimate such charges for solar (Arizona Public Service [APS], Bonneville Power Administration 

[BPA], and Public Service Company of Colorado [PSCo]). Other than Westar, all of the 

balancing authorities profiled are located in the WECC area.

4 See Table 1. 

 

3 As this report was being finalized, we were notified of a wind integration service charge imposed by the Nebraska 

Public Power District (NPPD). The charge is $3.31/MWh for both regulation and supplemental reserves, 

$2.72/MWh for regulation reserves only (if supplemental reserves are self-supplied), or $0.59/MWh for 

supplemental reserves only (if regulation reserves are self-supplied). Due to limited information regarding the 

determination of NPPD’s wind integration service charge, it will not be discussed in any further detail in this report. 

4 A map of the WECC balancing authorities can be found at www.wecc.biz/library/WECC%20Documents/

Publications/WECC_BA_Map.pdf. 
3 

Table 1. How Balancing Authorities Profiled in This Report

Incorporate Wind and Solar Integration Charges

Integration Charges Imposed 

on Wind and/or Solar Generation

Estimated Integration Costs Factored Into IRPs

or Competitive Solicitations for Generation

Avista Corporation* Arizona Public Service

Bonneville Power Administration BC Hydro

Idaho Power* NorthWestern Energy

PacifiCorp* PacifiCorp

Puget Sound Energy** Portland General Electric

Westar Public Service Company of Colorado

Western Area Colorado Missouri Balancing 

Authority

*Allowed by Idaho PUC to deduct integration charges from payments made to wind qualifying facilities under PURPA.

**Conditionally approved by the Federal Energy Regulatory Commission and now in settlement proceedings with 

interveners. 

In general, variable generation integration costs are estimated by quantifying a certain set of 

system impacts. The system impacts analyzed by the balancing authorities reviewed in this report 

include different reserves such as regulation, load-following, and contingency reserves; unit 

commitment; and opportunity costs; among other impacts. How reserves are defined and used can 

vary significantly from region to region, and a multitude of terms are used to define comparable or 

similar reserves. A general description of these terms is provided below for reference:

• Regulation generally deals with the random, minute-to-minute variability of loads and 

generation (Milligan 2011).5

• Load-following typically deals with slower trends that extend from minutes to hours 

(Milligan 2011).

• Contingency reserves often encompass a series of reserves that must be maintained to 

provide fast and sustained response to a system emergency. This may include spinning, 

non-spinning, and supplemental reserves; ranging from seconds to hours (Milligan 2011). 

• Unit commitment is the longer-term, often day-ahead process, the balancing authority’s 

use to schedule generators based on forecasts of expected load and variable generation 

(Milligan 2011).

• Opportunity costs—as used by BC Hydro for example—are the costs of forgoing lowprice imports or high-price exports due to reserves being held to cover variable

generation uncertainty. 

 

5 “Regulation” can be a significantly different service depending on the scheduling interval of the balancing 

authority. In areas with 5-minute energy scheduling, regulation is a fast service that deals with minute-to-minute 

variability. In areas that have only hourly scheduling, regulation is typically based on a longer interval (e.g., 

90-minute service).
4 

FERC Actions Related to Variable Generation 

Integration Charges

The Federal Energy Regulatory Commission (FERC) must approve all variable generation 

integration charges that are imposed by FERC-jurisdictional balancing authorities. Provided 

below is a summary of the guidelines set forth by FERC for proposing such a charge, and a 

chronological summary of how these guidelines were established. Notable among these 

guidelines are the subhourly scheduling and power production forecasting requirements, as 

described herein. 

In November 2010, FERC issued a proposed rule that would require transmission providers to 

offer the option of scheduling transmission services at 15-minute intervals. In addition, the 

proposed rule would incorporate provisions into the pro forma Large Generator Interconnection 

Agreement that require generators using variable energy resources (VERs) to provide 

transmission owners with certain meteorological and forced outage data to support power 

production forecasting. Finally, FERC proposed to amend the pro forma Open Access 

Transmission Tariff (OATT)

6 to add a generic ancillary service rate schedule, Schedule 10—

Generator Regulation and Frequency Response Service. Ancillary services refer to those services 

that are necessary to support the transmission of capacity and energy from resources to loads 

while maintaining reliable operation of a balancing authority’s transmission system in 

accordance with good utility practice (NERC 2012). 

FERC sought to add a new rate schedule to the pro forma OATT that complements the generator 

imbalance service provided under Schedule 9 of the pro forma OATT. FERC stated that, to meet 

their obligations to offer generator imbalance service under Schedule 9, FERC-jurisdictional 

transmission providers must hold unloaded resources in reserve to respond to moment-tomoment variations attributable to generation. The proposed rule recognized this de-facto 

obligation and proposed to establish a generic rate schedule (Schedule 10) through which FERCjurisdictional transmission providers may recover the costs of providing this service (FERC 

2012b). FERC preliminarily found that clarifying the manner by which FERC-jurisdictional 

transmission providers may recover the costs associated with fulfilling their obligation to offer 

generator regulation service would remove barriers to the integration of VERs by eliminating 

FERC-jurisdictional transmission providers’ uncertainty regarding cost recovery. 

However, FERC in its final rule (Order No. 764) ultimately declined to adopt a new Schedule 10, 

deciding to instead evaluate proposed charges on a case-by-case basis. FERC received numerous 

comments urging flexibility in the design of capacity services needed to integrate VERs into 

transmission systems, suggesting that the proposed pro forma generator regulation service may 

not be the most efficient and economical service with which to integrate VERs. FERC did, 

however, provide a framework for transmission providers to use as a guideline in developing 

such charges, and established some general principles to evaluate individual proposals, as 

described further below. 

 

6 The pro forma OATT refers to FERC’s standardized transmission tariff that provides for open access transmission 

rights, as mandated by FERC in Order No. 888.
5 

FERC will continue to evaluate proposals to recover capacity costs incurred to provide 

Schedule 9 generator imbalance service on a case-by-case basis. To provide guidance in the 

development of proposals for generator regulation services, FERC responded to individual 

commenter concerns regarding the design of a generator regulation service charge. The 

subsequent paragraphs provide an overview of the primary principles that FERC included in the 

final rule. 

FERC stated that FERC-jurisdictional transmission providers may include opportunity costs for 

generator regulation service under certain circumstances. FERC also addressed the appropriate 

design of the volumetric component of a generator regulation service that would allow FERCjurisdictional transmission providers to require different transmission customers (or generator 

classes) to purchase or otherwise account for different quantities of regulation reserves based on 

cost-causation principles. For instance, distinguishing customers into classes for the purpose of 

requiring them to purchase or otherwise account for different quantities of generation regulating 

reserves should be done only to the extent that such classes and distinctions among classes are 

reasonably related to operational similarities and differences among those resources. 

To the extent a FERC-jurisdictional transmission provider proposes to break customers into 

specific groups based on operational characteristics, FERC expected FERC-jurisdictional 

transmission providers to provide detailed explanations as to why such classifications are 

appropriate if and when they propose to allocate different generating regulation reserve 

obligations to different customer classes. 

If a FERC-jurisdictional transmission provider proposes to differentiate among customers (or 

customer classes) in determining their relative regulating reserve responsibilities, the FERCjurisdictional transmission provider must demonstrate to FERC that the overall quantity of 

regulating reserve it requires of its transmission customers’ accounts for diversity benefits 

among all resources and loads, and the allocations to individual customers (or customer 

classes) of their proportionate share is based on the operational characteristics of such 

customers (or customer classes). 

FERC notes that weather events such as droughts may affect the required quantity of generator 

regulating reserves that the FERC-jurisdictional transmission provider must have in reserve more 

or less during one portion of the year versus another portion of the year. In such cases, FERC 

believed these events, though perhaps characterized as anomalies, should be included in the data 

set so that the quantity and costs of such reserves are more reflective of actual system operations. 

In designing any proposals for generator regulation service charges, FERC stated that a FERCjurisdictional transmission provider should consider the extent to which transmission customers 

are using intra-hour scheduling in evaluating whether to require different transmission customers 

to purchase or otherwise account for different quantities of generator regulating reserves. 

FERC noted that it recognizes that it is contentious to condition the allocation of different 

quantities of regulation reserves to different transmission customers on the FERC-jurisdictional 

transmission provider developing and deploying power production forecasting. Nevertheless, 

FERC stated that it expects the implementation of power production forecasting will be 

addressed in any proposal to require different transmission customers to purchase or otherwise 
6 

account for different quantities of generator regulating reserves. FERC further stated that FERCjurisdictional transmission providers should make the results of any centralized forecast used by 

the FERC-jurisdictional transmission provider available through a secure information exchange 

to VER generators providing related data. In addition, FERC-jurisdictional transmission 

providers proposing to require different transmission customers to purchase or otherwise account 

for different quantities of generator regulating reserves should explain in their proposals how 

forecasting results will be shared. 

FERC declined to require transmission customers delivering from a VER to submit transmission 

schedules according to the FERC-jurisdictional transmission provider’s forecast; asserting that 

requiring a transmission customer to submit transmission schedules for VER deliveries according 

to a centralized forecast would cloud the delineation between the obligations of the VER and the 

obligations of the FERC-jurisdictional transmission provider with respect to the provision of 

transmission service. FERC also specified that the FERC-jurisdictional transmission provider

retains the risk and responsibility for inaccurate procurement of reserve requirements; whereas the 

transmission customer retains the financial risk and responsibility for inaccurate schedules. 

Finally, FERC emphasized that these principles are intended to provide a framework to assist 

FERC-jurisdictional transmission providers in developing proposals for generator regulation 

service should they desire to do so. FERC also noted that it does not intend these guidelines to 

preclude a FERC-jurisdictional transmission provider from making an alternative proposal under 

section 205 of the Federal Power Act (FERC 2012b). 

Report Findings

This report finds significant differences in how the studies are prepared in support of variable 

generation integration charges, including but not limited to study methodology, assumptions, 

reserve definitions, and the data that is collected and utilized. These findings indicate that 

integration studies in support of variable generation integration charges are at an early stage and 

are more art than science, as is often the case in electric utility ratemaking and its regulation. 

Examples include the following: 

• Differences in what each integration study is assessing (different reserves including 

regulation and load following, unit commitment, opportunity costs, contingency reserves, 

and impacts of variable generation on cycling of conventional generation units). 

• Determining the integration impact of wind (comparing wind to a flat block or ideal 

generator; comparing load and net load; comparing reserve requirements with and 

without wind; comparing subhourly wind output to subhourly persistence wind forecasts; 

and applying ad hoc “wind integration” factors). 

• Including or not including variable generation forecasts (mostly wind). Those that do 

include variable generation forecasts typically emphasize intra-day forecasts, not 

day-ahead forecasts. 

• Restricting availability of reserves to a subset of available generation. A small number of 

integration cost studies limit the supply of reserves to an individual generator or subset of 

available generation. Not surprisingly, these studies typically find that the amount of 

available reserves becomes scarce as more variable generation is incorporated, as this 
7 

approach inflates the actual need for incremental reserves by ignoring the aggregation 

effects from geographically dispersed wind and solar generators. 

These findings are supported by recent research that suggests that although it is easy in concept 

to define integration costs, it is very difficult in practice to calculate or measure integration costs 

in a meaningful way. This difficulty arises because of challenges in defining what to compare 

and the complexity of the multiple interactions between generation resources as their output is 

varied to maintain reliability. In addition, the integration studies focus on whether increased 

operating reserves are needed for variable generation but do not consider other possible 

mechanisms such as demand response, considering future changes in generation mix, or 

implementing grid operating reforms such as virtually or physically expanding the size of the 

balancing authority and moving to shorter generator dispatch and transmission scheduling 

intervals (Milligan 2011). These observations are summarized in more detail below. 

Methodologies and Tools for Determining Integration Rates Are Unique to Each Balancing 

Authority. Each balancing authority has a different resource mix (flexible versus inflexible), size 

of balancing area, access to ancillary services, and access to transmission with other balancing 

authorities. There are few commonalities and similarities to how each balancing authority 

estimates integration rates. Some examine multiple scenarios with different levels of geographic 

diversity for wind and different levels of water availability for hydro generation (BC Hydro). 

Some use combinations of optimization models, hydro-simulation models, and capital expansion 

models (BC Hydro); whereas others rely on a unit commitment or dispatch model that may be 

either off-the-shelf or developed internally (Avista, PSCo).

Types of Reserves or Reserve Impacts Included in Integration Rates. Balancing authorities 

include different reserves or system impacts as part of their integration rate (see Table 2). About 

half of the balancing authorities include load-following in estimated cost (BC Hydro, Portland 

General Electric [PGE] for both hour-ahead and intra-hour, APS, Avista, PacifiCorp, and BPA). 

A smaller number include contingency reserves (APS for solar, Avista, PacifiCorp, and PGE) 

and energy imbalances (BC Hydro, BPA, and PacifiCorp). Two balancing authorities include 

unit commitment impacts (APS and PSCo). BC Hydro incorporates opportunity costs from lost 

market transactions because of holding increased reserves with higher levels of wind capacity.

7

PSCo includes coal cycling, gas scheduling, and gas storage. Nearly every balancing authority 

includes regulation as part of the estimated integration cost, with five balancing authorities 

considering only regulation (Idaho Power Company [IPC], NorthWestern Energy, Puget Sound 

Energy [PSE], Westar, and Western Area Power Administration [WAPA]).

 

7 In PacifiCorp’s draft 2012 Wind Integration Resource Study, opportunity costs are defined as the value of a lost 

sale at a given generation station that must be backed down to meet reserve requirements. 
8 

Table 2. Components of Estimated Integration Costs

Regulation LoadFollowing

Contingency 

Reserves

Energy 

Imbalance

Unit 

Commitment 

Impacts

Opportunity 

Costs

Coal Cycling 

and Natural 

Gas Storage

APS X X X X 

Avista X X X 

BC Hydro X X X X 

BPA X X X 

Idaho Power X 

NorthWestern X 

PacifiCorp 

2007, 2008 X X 

PacifiCorp 

2010 X X X X 

PacifiCorp 

2012 X X X X X 

PGE X X X X 

PSCo (Solar) X 

PSCo (Wind) X X X 

PSE X 

WACM X 

Westar X 

Determining Integration Impacts. Balancing authorities use a variety of methods for estimating 

the impact of integrating additional wind (see Table 3). Three balancing authorities (Idaho 

Power, PSCo, and PacifiCorp’s 2010 IRP) compare wind with a flat block of energy. Note that 

PSCo divides the flat block into on-peak and off-peak for regulation. Three other balancing 

authorities (BC Hydro, APS, and Avista Corporation) compare wind with an ideal generator (i.e., 

a non-variable generator). Two balancing authorities (PacifiCorp in its 2007 IRP and PSCo for 

regulation) compare load with net load (load minus wind generation). 

Other balancing authorities employed different techniques altogether. PGE estimated the impacts 

of wind integration by simulating scenarios with and without incremental reserve requirements 

for wind, with the cost of wind integration defined as the savings in system operating costs that 

would result if wind placed no incremental requirements on system operations. Westar tracks the 

changes in wind output from one 10-minute interval to the next from five wind projects in 

Westar’s service territory. Westar also collects the difference in load from load forecasts; in 

generation between actual output and dispatch instructions; and in generators providing 

frequency response service, also as the difference between actual output and dispatch 

instructions. PSE used a methodology similar to Westar’s but compared 10-minute wind output 

to hourly persistence forecasts for wind, load, and other generation. NorthWestern Energy 

applies an 18% “wind integration factor” to the amount of existing wind capacity to determine 

how much additional regulation is needed, without describing how the 18% was determined. 

WAPA’s Western Area Colorado Missouri Balancing Authority (WACM) adds installed wind 

capacity to a rolling 12-month average of the system peak, then divides that sum by the annual 

revenue requirement necessary to provide regulation. 
9 

Table 3. Determination of Wind Integration Impacts

Compare With 

Flat Block

Compare With 

Ideal Generator

Compare With 

Net Load

Other 

Methodology

APS X 

Avista X 

BC Hydro X 

BPA X 

Idaho Power X 

NorthWestern X 

PacifiCorp X X 

PGE X 

PSCo X X 

PSE X 

WACM X 

Westar X 

Variable Generation Forecasts. Similar to determining wind integration impacts, balancing 

authorities use a myriad of different approaches for wind forecasting, and most are focused on 

the intra-day timescale, not the day-ahead timescale. PSE, BPA, and PacifiCorp (2010 IRP) used 

various forms of persistence forecasting. BC Hydro, PGE, and PSCo used varying types of dayahead forecasts. For wind, APS compares a perfect forecast with that of the simulated wind 

output produced by a contractor. Idaho Power used a function of simulated wind output ranging 

from 65 to 115 minutes before real-time. Avista assumed a range of wind forecasting errors from 

perfect forecast to 30% forecasting error. Three balancing authorities—NorthWestern Energy, 

Westar, and WACM—did not incorporate wind forecasting at all. 

Most integration studies assume that wind forecasting errors follow a normal distribution and 

allow for the utilization of multiple standard deviations (usually two or three times standard 

deviation) to estimate the variability and uncertainty impacts of wind. More recent research 

suggests that for time ranges from 6 to 48 hours ahead, wind forecast errors associated with 

numerical weather prediction (NWP) models are normal, but errors associated from wind power 

are not normal. Instead, the errors are somewhat skewed and may have more pronounced peaks 

and longer tails than normal distributions. The wind power forecast error distributions can vary 

substantially depending on the size of the wind plant or balancing area, the forecasting method 

used, and the timescale that is measured. Not accurately estimating the distribution of wind

power forecast errors can lead to either under- or over-estimating the frequency of large errors in 

wind power forecasting that in turn could raise system costs through under- or over-scheduling 

reserves, increased out-of-merit dispatch, and, possibly, events that could endanger system 

reliability (Hodge 2012).

Consideration of Solar. Only three utilities estimated solar integration costs, and all three made 

some approximations due to a lack of solar data and/or lack of experience with solar integration. 

APS contracted with Black & Veatch to conduct a solar integration cost study, which was 

released in November 2012. Black & Veatch utilized output and variability at existing Arizona 

solar facilities to estimate solar variability. However, for areas without actual data, Black & 
10 

Veatch made estimates using solar production modeling and statistical variability modeling. As 

adjusted by the sun’s position in the sky, Black & Veatch used persistence forecasts to estimate 

the difference between predicted and actual solar output in 2020 and 2030. The solar integration 

costs were calculated based on the impact of incremental operating reserve costs and incremental 

system energy costs between load only and load and solar. 

BPA assumed that solar would require only half as much balancing capability as wind because 

solar plants generate power only during the day. BPA also assumed a perfect forecast for solar 

and therefore did not include an energy imbalance component in the solar integration charge. 

BPA stated that it assumed perfect schedules for solar projects because it does not currently have 

enough solar projects scheduling on its system to provide data for scheduling accuracy. 

PSCo used solar forecasting to estimate solar integration costs. The day-ahead solar forecasts 

compiled averaging solar insolation for that hour in a month (i.e., solar forecast for any day in 

May at 3 p.m. is the average insolation for 3 p.m. for every day in May). PSCo used its unit 

commitment and dispatch model to estimate the unit commitment impacts of solar but did not 

attempt to estimate solar integration costs due to additional regulation that may be needed, or the 

impacts of solar on gas supply or operations and maintenance on existing conventional 

generation plants. 

Sources of Variable Generation Data. Five balancing authorities (APS, BC Hydro, Idaho Power, 

PGE, and PSCo) relied upon wind data created through mesoscale wind resource modeling. 

BPA, PSE, and Westar used historical wind data; whereas Avista used past wind speed data from 

one wind plant and anemometer readings from five sites and applied them to a wind turbine 

power curve. For its 2010 IRP, PacifiCorp relied on a combination of historical wind production 

data and the statistical relationship between pairs of sites to fill in data gaps.

Exclusion of Generation Plants. Various balancing authorities limited the available generating 

plants able to provide reserves or balancing services. Idaho Power limited the source of 

regulation to the Hells Canyon hydro project. PGE excluded some generation projects, asserting 

that they were not capable of providing reserves or balancing services. NorthWestern Energy 

limited its integration analysis toward determining how much additional regulation will need to 

be provided from a single natural gas plant. PSE used a portfolio of generation plants consisting 

of four coal units, four combined-cycle natural gas plants, the utility’s share of hydro from the 

Columbia River, and two of its own hydro plants. 

Consideration of Hydro. Balancing authorities also utilize different methods for forecasting 

hydro output and for scheduling hydro. BPA models the average of the top 120-hour peaking 

capacity based on the 1958 water year. As noted earlier, Idaho Power modeled only the 

operations of one hydro plant in its 2007 study with three different water years—1998 (good), 

2000 (normal), and 2005 (poor). Avista also used three different water levels in its analysis (low, 

medium, and high). BC Hydro incorporated water data from 10 years and matched it up with 

simulated wind data from 1998 to 2006. The 10 water years (1964 to 1974) are considered 

representative of the full 60 years used in the hydro model, as they contain a range of water 

conditions including normal, dry, and wet water years. For determining the opportunity costs of 

lost market transactions, BC Hydro limited the ability to spill water at one hydro project, citing 
11 

restrictions from water licenses, flood control, downstream user requirements, and the Columbia 

River Treaty. 

Scenarios. Unlike variable generation integration studies, nearly every balancing authority 

profiled did not assess the effects of changes in resource mix over time, improvements in wind 

forecasting accuracy, changes in scheduling from hourly to subhourly, or expanding balancing 

area cooperation. Some balancing authorities evaluated different scenarios of increasing levels of 

wind or solar generation but largely assumed that the current scheduling practices, balancing area 

structure, and existing resource mix would stay intact. Exceptions include PSCo, which did 

multiple sensitivities ranging from varying gas costs, geographic diversity of wind projects, 

incremental pumped hydro storage, wind forecasting, demand response, and carbon prices, and 

PacifiCorp, which included carbon costs in its earlier wind integration analyses but not in their 

2010 IRP. 

Table 4 and Table 5 on the following pages summarize variable generation integration charges 

that are charged to variable generation, and variable generation integration rates that are 

incorporated into resource plans and/or competitive solicitations for generation. After these 

tables, profiles of each balancing authority that has set a variable generation integration rate are 

presented.
12 

Table 4. Integration Charges and Methodologies by Balancing Authority

Idaho Power Company Avista Corporation PacifiCorp

Total Average Load (MW) 1,680 1,096 9,431 (peak load for 2011)

Total Generation 

Capacity (MW) 3,276 1,791 10,597

Variable 

Generation 

Capacity (MW)

Wind 499 35 2,135

Solar 0 0 9i

Total 499 35 2,144

Percent of Variable 

Generation Exported Unknown, but assumed to equal 0%ii Unknown, but assumed to equal 0%iii Unknown, but assumed to be small

Amount of Variable 

Integration Charge

8% of the published avoided-cost rate for 

wind qualifying facilities (QFs) under 

PURPA (capped at $6.50/MWh).

7% of the published avoided-cost rate for 

wind qualifying facilities under PURPA 

(capped at $6.50/MWh).

$6.50/MWh

Charge Assessed on QF Wind Generators QF Wind Generators QF Wind Generators

Status of Charge 

In effect since February 2008. Original 

eligibility cap of 10 MW was reduced to 

100 kW, effective December 14, 2010. 

In effect since February 2008. Original 

eligibility cap of 10 MW was reduced to 

100 kW, effective December 14, 2010.

In effect since February 2008. Original 

eligibility cap of 10 MW was reduced to 

100 kW, effective December 14, 2010.

Regulatory Background and 

Establishment of Current 

Wind Integration Charge

IPC’s published avoided-cost rates are 

adjusted downward to reflect the 

company’s estimate of the costs of 

integrating wind energy. Adjustments are 

applied in three tiers, increasing at 

higher levels of wind capacity. The 

integration charge is calculated as a 

percentage (7%, 8% or 9%) of the 

current 20-year, levelized, avoided-cost 

rate, and subject to a cap of $6.50/MWh. 

Between 2008 and 2012, non-levelized 

avoided cost rates vary between $59.17 

and $66.82/MWh. Rates differ for light 

load and heavy load months. IPC’s wind 

integration charge was established in 

February 2008 (Idaho Public Utilities 

Commission (IPUC) Order No. 30488, 

Case No. IPC-E-07-03), as the result of 

a Settlement Stipulation approved by 

IPUC. In February 2007, IPC submitted 

its wind integration study to the IPUC, 

Avista’s published avoided-cost rates are 

adjusted downward to reflect the 

company’s estimate of the costs of 

integrating wind energy. Adjustments are 

applied in three tiers, increasing as total 

wind capacity in the system grows. The 

integration charge is calculated as a 

percentage (7%, 8% or 9%) of the 

current 20-year, levelized, avoided-cost 

rate, and subject to a cap of $6.50/MWh. 

Between 2008 and 2012, non-levelized 

avoided cost rates vary between $60.35 

and $67.90/MWh. Rates differ for light 

load and heavy load months. Avista’s 

wind integration charge was established 

in February 2008 (IPUC Order No. 

30500, Case No. AVU-E-07-02), as the 

result of a Settlement Stipulation 

approved by IPUC. In April 2007, Avista 

submitted its wind integration study to 

the IPUC, proposing a charge of 12% of 

PacifiCorp’s published avoided-cost 

rates are reduced by $6.50/MWh to 

account for the costs of integrating wind 

energy. Between 2008 and 2012, the 

non-levelized avoided cost rates vary 

between $59.67 and $67.28/MWh. Note 

that rates differ for light load and heavy 

load months. The current wind 

integration charge was approved by the 

IPUC in March 2010 (Order No. 31021, 

Case No. PAC-E-09-07). In April 2007, 

PacifiCorp proposed a wind integration 

adjustment of $5.04/MWh to be applied 

as a decrement to the published 

avoided-cost rates payable to wind QFs, 

based on PacifiCorp's 2004 IRP

(adjusted for inflation). In October 2007, 

PacifiCorp and the parties entered into a 

Settlement Stipulation, agreeing to an 

integration charge of $5.10/MWh, the 

estimate provided in PacifiCorp’s 2007 
13 

Idaho Power Company Avista Corporation PacifiCorp

proposing a charge of $10.72/MWh. In 

October 2007, IPC presented an 

updated cost of $7.92/MWh for up to 

600 MW of wind on its system. Also in 

October that year, IPC and the parties 

entered into the Settlement Stipulation, 

resulting in the current integration 

charge.

the published avoided-cost rate. In 

October 2007, Avista and the parties 

entered into the Settlement Stipulation, 

resulting in the current integration 

charge.

IRP. The rate of $5.10/MWh became 

effective in February 2008; however, 

PacifiCorp later requested that the IPUC 

allow a rate of $9.96/MWh. The IPUC 

denied that request, but allowed 

PacifiCorp to increase it to $6.50/MWh.

Methodology Used to 

Estimate Wind Integration 

Costs

IPC completed its initial wind integration 

study in February 2007. The study 

defines an integration cost as the 

economic impact of wind generation 

variability and uncertainty on the utility’s 

system. IPC states that higher regulating 

reserves for various amounts of wind 

capacity constrain hydro operations at 

IPC’s Hells Canyon Complex. As such, 

scenario-based modeling was used to 

estimate a range of costs. Main inputs 

include three different hydrologic 

conditions and four levels of wind 

integration (300, 600, 900, and 

1,200 MW). The IPC study initially 

presented a wind integration cost of 

$10.72/MWh, which was the midpoint 

between costs associated with the then 

currently contracted 384 MW of wind 

capacity and the additional cost that 

would be incurred at a penetration level 

of 600 MW. After holding two public 

workshops, IPC revised six specific 

modeling assumptions, resulting in an 

estimated wind integration cost of 

$7.92/MWh at a penetration level of 

600 MW. Among other things, the 

modeling revisions included greater 

geographic diversification of wind 

resources and a regressive forecasting 

Avista’s wind integration study was 

completed in March 2007. The study was 

prepared by the same group that 

prepared IPC’s study (EnerNex 

Corporation), so the methodology used 

to estimate the cost of wind integration is 

similar. Four wind penetration levels 

(100, 200, 400, and 600 MW), along with 

three water level scenarios, were 

modeled. Total reserve obligations were 

estimated using historical utility data, and 

incremental regulation and load-following 

reserves were calculated by identifying 

the amount of reserves needed to meet 

load variability alone, then performing 

the same analysis but netting wind 

generation against load when performing 

the calculations. The results of Avista’s 

study were estimated wind integration 

costs expressed in terms of a 

percentage of market prices. Avista’s 

base case result was that the cost of 

integrating up to 400 MW of wind 

capacity was approximately 12% more 

than the cost of integrating other nonwind resources.

Avista said they will apply the 7% 

discount as a starting point in negotiating 

contacts with larger wind projects.

PacifiCorp’s most recent estimate of the 

cost to integrate wind energy is based on 

its 2010 Wind Integration Resource 

Study. Their estimate is based on the 

cost of incremental operating reserves 

and incremental system balancing costs 

required for wind integration. The 

amount of operating reserves required 

for different penetration levels of wind, 

and the estimated cost of holding those 

reserves on the system was modeled by 

PacifiCorp using existing operating 

reserve requirements for load and 

production data, subdivided into 

regulation and load following. The load 

data was the baseline case (zero wind 

generation) in each scenario, whereas

coincident wind data was added in 

increasing levels of wind penetration 

capacity to gauge the change in 

operating reserves demand. The 

differences in system cost between the 

two simulations were divided by the total 

volume of wind generation in each 

scenario to derive the estimated 

operating reserve costs for wind 

integration. The majority of integration 

costs (i.e., about 90%) are associated 

with the incremental operating reserves; 

however, PacifiCorp also modeled the 
14 

Idaho Power Company Avista Corporation PacifiCorp

Methodology Used to 

Estimate Wind Integration 

Costs

method, instead of persistence. IPC is 

updating the 2007 study and held a 

workshop in April 2012 to share the 

preliminary results and solicit feedback 

from interested parties.

incremental system balancing costs 

associated with wind integration. 

Similarly, the change in system costs 

between the two model simulations was 

used to isolate the wind integration cost 

due to system balancing. Note that 

PacifiCorp is currently in the process of 

updating the 2010 study. PacifiCorp 

most recently held a stakeholder meeting 

in May 2012, and currently expects to 

complete its updated wind integration 

study by fall 2012.
15 

Westar Energy PSE BPA

Total Average Load (MW) 5,549 (peak load) 2,570 6,200

Total Generation 

Capacity (MW) 7,100 3,000 18,400iv

Variable 

Generation 

Capacity (MW)

Wind 295 369 4,421

Solar 0 0 13

Total 295 369 4,434

Percent of Variable 

Generation Exported Unknown 26%v 80%

Amount of Variable 

Integration Charge

3.47% * the amount of generating 

capacity within the Westar balancing 

area * (1, 2, 3, 4, or 5).

1. For yearly delivery, $53,358.74/MW

2. For monthly delivery, $4,446.56/MW

3. For weekly delivery, $1,026.13/MW

4. For daily delivery, $205.23/MW

5. For hourly delivery, $12.83/MW 

$1.55/kW-month of transmission 

reservation capacity for generators with 

hourly scheduling intervals

• 30% discount available for 30-minute 

scheduling intervals

• 50% discount available for 15-minute 

scheduling intervals

Wind: $1.23 per kW-month

Solar: $0.21 per kW-month

Charge Assessed on

Wind generation and other nondispatchable resources that export 

power outside of Westar balancing area 

or to the SPP energy imbalance market

Exporting variable energy generators All wind and solar generators

Status of Charge In effect

In effect, but subject to refund pending 

FERC’s final approval of the settlement 

agreement

In effect

Methodology Used to 

Estimate Wind Integration 

Costs

FERC approved Westar’s request to 

impose a wind integration charge in 

March 2010. Westar’s integration charge 

is an interim measure until SPP 

implements its locational marginal price 

(LMP)-based market in 2014. Westar 

uses a portfolio approach to assess 

different generator regulation charges for 

dispatchable and intermittent generation. 

The charge for dispatchable resources is 

calculated using 1.46% and the charge 

for non-exporting resources is calculated 

at 1.35%. For the wind integration 

charge, Westar used data from three

PSE compares historical scheduling and 

forecast data for wind generation to 

actual output to determine the regulation 

capacity needed to balance deviations in 

wind output within the hour. PSE then 

performs a portfolio analysis to account 

for any offsetting variability from load and 

dispatchable generation. Then, based on 

winds proportionate effect on system 

variability, PSE calculates the 

percentage of a customer’s transmission 

schedule reservation that a transmission 

customer exporting energy outside of 

PSE’s BA, needs to purchase to account 

BPA calls its integration rate the Variable 

Energy Resource Balancing Service 

(VERBS) and determines the rate as part 

of its biannual rate cases. BPA first 

forecasts its total balancing reserve 

capacity quantity that will be needed 

during the two-year rate period. The 

model then allocates the total balancing 

reserves into incremental and 

decremental components of regulating 

reserves, following reserves, and 

imbalance reserves requirements for 

load, non-federal thermal generation 

(Dispatchable Energy Resource 
16 

Westar Energy PSE BPA

Methodology Used to 

Estimate Wind Integration 

Costs 

wind farms to create a single portfolio 

value for each 10-minute interval. Westar 

then compared the portfolio observation 

for each 10-minute interval to the 

previous interval to create values that 

represented the interval deviations. To 

calculate the regulation percentage, 

Westar uses two times the standard 

deviation of the portfolio deviations and 

divides it by the total nameplate capacity 

of all the wind sites. The current 

regulation percentage is 3.47% times the 

amount of generation and inside the 

Westar BA times the applicable charge. 

The amount of generation inside the BA 

is equal to nameplate capacity minus the 

amount of the generator capacity that the 

customer is self-supplying regulation for 

minus the amount of generating capacity 

that is supplying power to load inside the 

Westar BA. Westar updates the 

regulation requirement percentage 

annually.

for the variability of the wind resource. In 

its June 2011 FERC filing, PSE 

estimated that exporting wind customers 

need to purchase an amount of 

regulation equal to 16.77% of the 

transmission reserve capacity. In the 

same filing, PSE requested authority to 

increase its regulation service cost from 

$5.50/kW-month to $12.39/kW-month, 

therefore, 16.77% of that would be 

$2.08/kW-month. Note that PSE's 

proposed methodology is based on the 

Westar methodology recently approved 

by FERC. On October 20, 2011, FERC 

issued an order accepting and 

suspending PSE’s filing for 5 months, 

and establishing a hearing and 

settlement procedure. In September 

2012, PSE filed a Stipulation and Offer of 

Settlement for approval by FERC. The 

pending Settlement contains a 1.21% 

purchase obligation and a capacity 

charge of $10.50/kW-month in Schedule 

3. The Schedule 13 generator regulation 

charge for VERs exporting energy 

outside PSE’s BA is set at a “Base Rate” 

of $1.55/kW-month for non-dispatchable 

generators that submit schedules on an 

hourly basis. If a non-dispatchable 

generator is able to submit schedules in 

30-minute intervals, the VER will receive 

a 30% discount. If a non-dispatchable 

generator is able to submit schedules in 

15-minute intervals, the VER will receive 

a 50% discount. PSE indicated that the

charges for regulation service are based 

on a black box, negotiated value, from 

an unspecified volumetric purchase 

Balancing Service, or DERBS) and, 

variable generation, based on their 

contributions to total requirements. BPA 

estimates the cost of providing reserves 

under two cost categories—embedded 

costs and variable costs. Embedded cost 

is the revenue requirement needed by 

BPA’s 10 largest hydro projects to 

provide all types of reserves. BPA 

calculates an embedded unit-cost, which 

for the 2012–2013 rate period was 

$6.69/kW-month. For the VERBS rate 

(as for the other rates), the unit cost is 

then multiplied by the incremental 

reserve requirement capacity amounts 

for the three VERBS components to 

estimate the total embedded costs. 

Variable costs consist of various losses 

associated with a loss of efficiency due 

to BPA needing to hold a sufficient 

amount of machine capability in a state 

of readiness to meet balancing needs. 

These losses are calculated for Stand 

Ready Costs and Deployment Costs 

using energy shift losses, efficiency 

losses, cycling and spill losses. Variable 

costs are similarly allocated amongst the 

various rate components. The total costs 

are then divided by the average 

estimated MW of wind capacity in the 

BPA area over the rate period to create 

the VERBS rate components. The 

VERBS rate is therefore is made up of 

three distinct components—regulation, 

following, and imbalance summed up to 

create the single VERBS rate of 

$1.23/kW-month. The solar rate is 

estimated to be half of the derived 
17 

Westar Energy PSE BPA

Methodology Used to 

Estimate Wind Integration 

Costs

obligation for regulation service and a 

fixed capacity charge. 

VERBS regulation and following 

components.
18 

WAPA’s Colorado Missouri Balancing Authority

Total Average Load (MW) Marketing area within WAPA that serves parts of Colorado, Wyoming, 

Missouri Nebraska and Kansas. 

Total Generation 

Capacity (MW) 10,505 (for all of WAPA)

Variable 

Generation 

Capacity (MW)

Wind 1,030

Solar N/A

Total Assumed to be small

Percent of Variable 

Generation Exported Unknown

Amount of Variable 

Integration Charge

1. Hourly, $0.000458/kW-hour 

2. Daily, $0.011/kW-day

3. Weekly, $0.076/kW-week

4. Monthly, $0.331/kW-month

Charge Assessed on Load and variable generation

Status of Charge In place since 2006

Methodology Used to 

Estimate Wind Integration 

Costs

For variable generation within WAPA, nameplate capacity of variable 

generation added to rolling 12-month average of system peak. Total 

Annual Revenue Requirement for regulation service divided by this 

value to determine regulation service rate.

Revenue requirement for regulation based on annual costs of plants 

providing regulation and costs of buying regulation on the wholesale 

market.

WAPA no longer provides regulation and frequency response to 

variable generation being exported out of WAPA. Variable generation 

being exported out of WAPA must be dynamically scheduled or selfsupply or receive regulation service from a third party. 

i According to PacifiCorp’s 2011 IRP, as of year-end 2010, PacifiCorp had 2,419 net metering customers throughout its six-state territory, generating more than 

10,000 kW using solar, hydro, wind, and fuel cell technologies. About 92% of customer generators are solar-based; therefore Exeter has included 9 MW of solar 

generation in the table. 

ii Wind exports are assumed to equal zero because Idaho Power purchases wind generation through power purchase agreements.

iii Wind exports are assumed to equal zero because Avista purchases wind generation through power purchase agreements.

iv The majority of BPA’s capacity is hydro power and this figure represents the 2011 operating year sustained 1-hour peak capacity adjusted for average water 

conditions. 

v According to PSE’s FERC filing (Revisions to Open Access Transmission Tariff, Docket No. ER11-3735, June 6, 2011), PSE provides regulation service to one 

96 MW facility exporting out of its BA and owns one 273 MW facility within its BA used to serve native load. Therefore, Exeter estimates that 26% of wind energy in 

PSE’s BA is exported ((273+96)/96 = 0.26). 
19 

Table 5. Variable Integration Rates Incorporated in IRPs and/or Competition Solicitations for Generation

PSCo (Wind Integration) PacifiCorp PGE

Average Load (MW) 3,878–4,340 9,431 (2011 peak load) 2011 winter: 2,612

2011 summer: 2,233

Total Generation 

Capacity (MW) 7,922 10,597 2,766

Variable 

Generation 

Capacity (MW)

Wind 1,768.5 2,135 450

Solar 200 0 0

Total 1,968.5 2,135 450

Percent of Variable 

Generation Exported Assumed to be zero or very small Assumed to be zero or very 

small

Small amount exported through the BPA-CAISO 

dynamic scheduling pilot

IRP/ERP/Competitive 

Solicitation PSCo 2011 ERP 2011 IRP 2009 IRP (2011 Update)

Estimated Integration 

Cost

Total average wind integration cost excluding 

coal cycling estimated at $3.68/MWh at 2 GW of 

wind and $4.09/MWh at 3 GW of wind. 

Incremental integration cost excluding coal 

cycling estimated at $4.32/MWh for a 200 MW 

wind project added to a 2 GW wind portfolio at 

gas price of $5.06/MMBtu. 

Average coal cycling and curtailment costs 

estimated at $0.77/MWh with wind curtailment 

and $0.83/MWh with coal deep cycling (both at 

2 GW of wind) and $1.03/MWh with wind 

curtailment and $1.08/MWh with coal deep 

cycling (both at 3 GW of wind). 

$9.70/MWh (2010$) $9.15/MWh (2014$, included in 2011 IRP update)

Incorporation Method

Incremental integration and coal cycling costs 

will be added to utility-build wind plant prices or 

bid prices from competitive suppliers for wind 

power. Value set to reflect differing integration 

costs as affected by natural gas prices and the 

amount of existing wind capacity. 

Incorporated in resource 

expansion optimization

modeling for comparing 

wind with other demand 

and supply resource 

options

Incorporated in resource plan modeling for 

comparing wind with other demand and supply 

resource options

For wind integration costs without coal cycling, 

studied 2 GW and 3 GW by 2018 scenarios, 

with multiple sensitivities ranging from varying 

gas costs, geographic diversity of wind projects, 

incremental pumped hydro storage, carbon, 

wind forecasting, demand response, and carbon 

prices. 

Defined wind integration 

costs as from increased 

operating reserves 

(regulation and load 

following, both up and 

down) and system 

balancing from day-ahead 

Wind integration cost estimate represents PGE’s 

estimated cost of using its own generating 

resources to integrate 850 MW (450 MW existing, 

400 MW new) of wind by 2014. Additional 

reserves incorporated into model for hour-ahead 

uncertainty of wind; within-hour load following for 

wind; and generation resource requirements for 
20 

PSCo (Wind Integration) PacifiCorp PGE

Methodology Used to 

Estimate Wind 

Integration Costs 

Integration costs defined as including regulation, 

system operations and gas storage. Regulation 

amount determined through a comparative 

statistical analysis of net load and load, then 

multiplying by the regulation cost in PSCo’s 

OATT ($6.740/kW-month, $80.88/kW-year). 

Cost then divided by predicted annual wind 

production for each scenario.

Five-step process used for estimating system 

operation cost for wind integration. Step 1 is 

projecting day-ahead unit commitment plan 

every hour for 2018. Step 2 simulates serving 

daily load with actual load data and produces 

hourly system operating cost. For both of those 

steps, two proxies of hourly wind shapes used—

flat block that distributes wind power evenly over 

each hour of the 24-hour period, and on/off peak 

proxy that distributes wind power over an onpeak block and an off-peak block. Step 3 has a 

new unit commitment with a day-ahead wind 

forecast, whereas Step 4 does economic 

dispatch with hourly wind profiles. Step 5 is 

calculating wind integration costs, which is the 

difference between the system production costs 

in Steps 2 (load dispatch) and Step 4 (load 

dispatch with wind), then dividing by annual 

wind energy production.

Gas storage integration costs determined by the 

largest over- and under-nomination amounts for 

natural gas (to set demand charge) and total 

annual over- and under-nomination amounts (to 

set commodity charges). Demand and 

commodity charges totaled and the value that 

requires the greatest storage system demand 

used to estimate the average gas storage wind 

integration cost. 

For coal cycling, spreadsheet model developed 

with load forecast before and after a userspecified level of wind generation to derive the 

number and intensity of coal cycles. Cost per 

coal unit cycle derived from previous work done 

by Aptech Engineering for Xcel. Wind 

curtailment costs estimated by forecasts of coal 

load and forecast errors. 

Uses four wind scenarios of 

0; 425; 1,372 and 

1,833 MW. Determine 

quantity of operating 

reserves needed at each 

scenario. For operating 

reserve costs, compared 

each wind profiles in each 

scenario with ideal wind 

profiles where wind 

generation is averaged 

across on- and off-peak 

blocks. System cost 

differences between the two 

divided by total wind 

generation. For system 

balancing costs, compare 

unit commitment costs with 

day-ahead load and wind 

forecasts with actual load 

and wind. Difference in 

system operating costs 

between actual wind 

profiles and with unit 

commitment adjusted to 

load and wind forecast 

error, divided by total wind 

generation. 

within-hour regulation for wind. Subdivided 

integration costs into day-ahead uncertainty (for 

day-ahead wind forecast error); hour-ahead 

uncertainty (for hour-ahead wind forecast error); 

intra-hour load following; and within-hour 

changes of wind generation from wind schedules 

(regulation). 

Used constrained optimization model that 

accounts for three kinds of reserves: regulation, 

load-following, and contingency reserves. Model 

run in three stages corresponding to day-ahead, 

hour-ahead, and intra-hour; commitments made 

in prior stages carry through to the next stage as 

constraints. Total system operating costs at the 

third stage used in determining wind integration 

costs. Wind integration costs derived by running 

model with and without incremental reserve 

requirements for wind and dividing by total wind 

generation. Integration cost estimates affected by 

limited number of PGE generating plants that 

could provide reserves and that PGE’s 

generation portfolio is short of total load 

requirements. Incorporating variable generation 

requires PGE generating resources to provide 

reserves rather than energy and increases PGE’s 

reliance on wholesale market purchases.

Earlier integration cost estimate of $11.04/MWh 

reduced with assumption that PGE would deploy 

new flexible thermal resources, i.e., two 100 MW 

GE LMS 100 simple cycle combustion turbines. 
21 

PSCo (Wind Integration) PacifiCorp PGE

Methodology Used to 

Estimate Wind 

Integration Costs 

prices, renewable energy credit (REC) prices 

and CO2 emission costs. Generating resources 

selected to meet load forecast and number of 

coal cycles by unit was estimated. Wind 

curtailment costs also added. Model run twice, 

with and without wind, with cost difference 

representing cycling and wind curtailment costs.
22 

NorthWestern Energy - Montana PSCo (Solar Integration) BC Hydro

Average Load (MW) 743 3,878-4,340 6,400

Total Generation 

Capacity (MW) 549 7,922 11,300

Variable 

Generation 

Capacity (MW)

Wind 141 1,768.5 246 in operation; 534 MW under contract

Solar 0 200 0 

Total 141 1,968.5 780

Percent of Variable 

Generation Exported Unknown Assumed to be zero, or very small None

IRP/ERP/Competitive 

Solicitation

2011 Electricity Supply Resource 

Procurement Plan PSCo 2011 ERP BC Hydro Wind Integration Study (value for the forthcoming 2012 IRP)

Estimated Integration 

Cost $11.28/MWh

Average solar integration costs 

ranged from $1.25/MWh to 

$6.06/MWh. Actual integration costs 

that PSCo will impose dependent on 

natural gas prices and amount of 

existing solar capacity.

$10/MWh (for the 2012 IRP)

Incorporation Method

Value serves as a Wind Integration 

Pricing Signal, and is applied to wind in 

the electric supply portfolio

Incremental integration costs will be 

added to bid prices from competitive 

suppliers for solar capacity. Value set 

to reflect differing integration costs as 

affected by natural gas prices and the 

amount of existing solar capacity. 

Wind integration cost included in evaluating 

generation bids and in comparing wind with 

other resources in long-term resource 

planning 

Methodology Used to 

Estimate Wind or Solar 

Integration Costs 

Applies 18% of installed wind capacity to 

regulation. Incremental regulation needs 

beyond load-only requirement of 60 MW 

assigned to wind. For 141 MW of wind 

online, 25 MW of additional regulation is 

estimated. 80% of costs of Dave Gates 

natural gas plant assigned to energy 

supply, and of that, about 14% 

Solar integration charges estimated in 

2009 study that focused on system 

dispatch inefficiencies from solar 

forecast errors. No integration costs 

estimated for solar from regulation, 

impacts on gas supply nominations, 

impacts on O&M on existing 

conventional generation units, 

Wind integration cost considered the sum of 

variability cost, incremental operating 

reserves cost (regulation, load-following and 

imbalance reserve capacity), and day-ahead 

wind opportunity cost (opportunity cost for 

maintaining capacity to address day-ahead 

wind forecast error). 

Studied two years—2011 and 2021; three 
23 

NorthWestern Energy - Montana PSCo (Solar Integration) BC Hydro

Methodology Used to 

Estimate Wind or Solar 

Integration Costs 

($5.575 million) is assigned to wind. 

Integration cost is from wind’s share of 

revenue requirement of natural gas plant 

divided by total wind generation. 

transmission expansion costs or 

energy trading inefficiencies. Six 

scenarios were developed ranging 

from 200 to 800 MW of solar, with at 

least 200 MW coming from a solar 

thermal parabolic trough plant with 

four hours of thermal energy storage. 

Integration cost is determined by 

running a unit commitment model 

twice—once for day-ahead forecasted 

load and actual load, the second with 

day-ahead solar forecasts and actual 

solar generation. The difference in 

costs between the two model runs, 

divided by solar generation, is 

considered the solar integration cost. 

Average solar integration costs 

ranged from $1.25/MWh to 

$6.06/MWh, with integration costs 

roughly increasing at $1/MWh with 

each 100 MW of additional solar 

capacity. Solar day-ahead forecasts 

compiled averaging solar insolation 

for that hour in a month (i.e., solar 

forecast for any May day at 3 p.m. is 

the average insolation for 3 p.m. for 

every day in May). 

wind penetration levels of 15%, 25%, and 

35% and low-diversity and high-diversity 

wind.

Incremental reserve requirements 

determined with statistical analysis of 

10 years of historic 1-minute load data and 

simulated 1-minute wind data, with reserve 

requirements set at three standard 

deviations. Variability impacts estimated by 

difference between wind as an ideal 

generator and wind profiles. Optimization 

model used to estimate incremental reserve 

and variability costs for each wind scenario 

and water-wind year, with 10 water-wind 

years (not synchronous) studied.

Day-ahead wind opportunity costs defined as 

the value of reduced trade opportunities in 

the 8- and 16-hour trading blocks to maintain 

flexibility in response to day-ahead wind 

forecast errors. System flexibility defined at 

three standard deviations (99.7%). Two 

methods modeled for flexibility—trade and 

generation schedule changes, and a 

combination of spilling water, wind 

curtailment and trade and generation 

schedule changes. Imbalance and loadfollowing reserves allowed to be used for 

providing flexibility. Three water/wind years 

(normal, high and dry water years) modeled, 

with the opportunity cost of each determined 

on a daily basis. Most economical method on 

a daily basis is selected.
24 

APS (Wind Integration) APS (Solar Integration)

Average Load (MW) 7,236 (peak load—all-time high) 7,236 (peak load—all-time high)

Total Generation 

Capacity (MW) 8,696 8,696

Variable 

Generation 

Capacity (MW)

Wind 289 289 

Solar 241 241 

Total 530 530

Percent of Variable 

Generation Exported Assumed to be zero, or very small Assumed to be zero, or very small

IRP/ERP/Competitive 

Solicitation 2012 IRP Future IRP

Estimated Integration 

Cost $3.25/MWh 

Ranging from $1.53/MWh to $3.04/MWh, depending on the 

level of solar photovoltaic (PV) capacity on APS’s system and 

the level of compliance with the North American Electric 

Reliability Corporation’s (NERC) Control Performance 

Standards (CPS2). In a sensitivity analysis with higher gas 

prices and higher solar variability, solar integration costs were 

as high as $3.53/MWh.

Incorporation Method Incorporated in resource plan modeling for comparing wind 

resources with other demand and supply resource options

Incorporated in resource plan modeling for comparing solar 

resources with other demand and supply resource options
25 

APS (Wind Integration) APS (Solar Integration)

Methodology Used to 

Establish Wind or 

Solar Integration Cost 

Estimate

Estimated wind integration costs are based on a study 

performed at Northern Arizona University (NAU) under the 

direction of Dr. Tom Acker. Scenario-based modeling was used 

in the study, which examines four wind energy penetration 

levels (1%, 4%, 7%, and 10%) and three variations of 

geographic diversity (low, medium, and high). The 

4% penetration level, along with the medium geographic 

diversity assumption, is used for the base case scenario. Wind 

integration costs are estimated by simulating APS system 

operation and planning for one typical year. Modeling was used 

to determine the operating costs for the system excluding the 

effects of wind variability and uncertainty, and comparing those 

results to a scenario which includes the operating costs for the 

system with wind (including the effects of its variability and 

uncertainty). Estimated wind integration costs are then 

deduced as the difference between the costs computed in the 

two simulations. The study year was selected as 2010. 

Historical load data for APS in 2004 was scaled to match the 

expected load and energy required in 2010, maintaining the 

hour-to-hour shape of the load and its correlation to the 

weather. A reasonable set of wind power plants in Arizona 

were simulated, using a mesoscale weather model, 2004 

historical weather data, and a wind power prediction model. 

This provided wind power data that is time-synchronized with 

the load data, maintaining any correlation inherent between the 

two. APS will reevaluate these figures as renewable 

penetration increases and more experience is gained in dealing 

with the integration of intermittent or variable generation.

To comply with Arizona’s Renewable Energy Standard, APS 

anticipates increasing solar PV resources on its system to 

1,038 MW by 2020 and 1,669 MW by 2030. The APS Solar 

Study provides an estimate of the anticipated incremental cost 

to provide the reserve capacity and energy services necessary 

to integrate the projected levels of solar PV into the APS 

system during the 10-minute operating time frame. In addition 

to a base case scenario, the study included sensitivity cases 

with assumptions involving greater solar variability, higher gas 

prices, and varying levels of compliance with NERC’s CPS2. 

Black & Veatch developed a spreadsheet model to calculate 

the variability of load and solar generation on a 10-minute time

step throughout the year. Where available, Black & Veatch 

used output and variability at existing Arizona solar facilities to 

develop a deterministically-derived quantity of solar variability.

For areas without actual data, estimated output was applied 

using solar production modeling and statistical variability 

modeling. Black & Veatch utilized a two-step methodology to 

calculate the reserve quantity and costs associated with 

integrating the anticipated levels of solar PV. The first step was 

to estimate the amount of incremental upward and downward 

regulating reserves required as a result of the forecasting 

errors from the solar PV penetration levels in each case. The 

second step involved modeling the cost impact to the system 

using production cost modeling software to estimate the system 

energy cost differential of providing the regulating energy 

margin. 
26 

APS 

Background

APS does not directly assess an integration charge on VERs operating in its service territory. 

APS does, however, incorporate estimated variable generation integration costs in its resource 

planning model for comparing variable generation with other demand and supply resource 

options as part of its IRP. APS submitted its most recent IRP to the Arizona Corporation 

Commission in March 2012. As stated in the 2012 IRP, system integration costs may be incurred 

by the operation of some non-dispatchable resources, including wind and solar. At higher levels 

of wind and solar capacity, additional operating reserves may be needed on the rest of the system 

to effectively follow APS load and meet WECC reliability requirements (APS 2012a). 

In APS’s 2012 IRP, a system integration cost of $3.25/MWh was added to wind generation 

based on the results of APS’s 2007 Wind Integration Cost Impact Study, discussed in further 

detail below (APS 2012a). Because solar generation in Arizona is more predictable than wind, 

APS assumed a lower integration cost for solar (without storage) of $2.50/MWh, per the WGA’s

Western Renewable Energy Zone Generation and Transmission Model (Black & Veatch 2009). 

In the 2012 IRP, APS indicated that it would reevaluate these figures as renewable penetration 

increases and more experience is gained in dealing with the integration of variable generation 

(APS 2012a). APS noted that it was undertaking an effort to update its estimated cost of solar 

integration; and in November 2012, APS released its Solar Photovoltaic (PV) Integration Cost 

Study, prepared by Black & Veatch. Based on the level of solar integration and the assumptions 

used, estimated solar energy integration costs included in this study ranged from approximately 

$1.50/MWh to about $3.50/MWh (Black & Veatch 2012). An overview of the assumptions and 

methodology used in this study is provided at the end of this chapter. 

Note that for the resource modeling used in its 2009 Resource Plan Report, APS included an 

estimated wind integration cost, also based on the results of the 2007 study prepared by NAU. 

APS did not incorporate an estimated integration cost for solar resources, but noted that in the 

future and at high penetration levels, it may be appropriate to include an integration cost for solar 

resources (APS 2009). 

Wind Integration Costs 

APS’s wind integration study, Final Report: Arizona Public Service Wind Integration Cost 

Impact Study (NAU Report), was published in September 2007. The study effort was led by 

NAU on behalf of APS, and is the product of a joint effort between NAU, 3Tier, EnerNex, and 

APS, who all provided detailed analysis for the study. Tom Acker of NAU aggregated all of the 

analyses and wrote the final report. The objective of the study was to simulate APS system 

operation and planning for one typical year and estimate the incremental system costs for 

integrating wind generation. The basic methodology is summarized as follows: 

• Determine the operating costs for the system, excluding the effects of wind variability 

and uncertainty 

• Determine the operating costs for the system with wind, including the effects of 

variability and uncertainty
27 

• Derive the integration costs as the difference between the costs computed in these two 

simulations (NAU 2007)

Assumptions

A range of wind energy penetration levels and geographic diversity in wind power production 

were considered, as shown in Table 6. All wind energy penetration levels listed refer to the 

expected APS energy production and peak load in 2010—the analysis year selected for the study. 

The 4% wind energy penetration level, with medium geographical diversity, was considered the 

“base case” in the study. Wind power plants were sited to achieve a prescribed level of 

geographic diversity (i.e., high, medium, or low), and to locate wind power plants at sites within 

the zones where adequate wind power potential existed as predicted by the wind simulation, 

which was conducted by 3TIER (NAU 2007). 

Table 6. APS Wind Energy Penetration and Geographic Diversity

Wind Energy Scenarios Installed Capacity (MW) by

Geographic Diversity Level

Energy

Penetration 

Level (%)

Penetration by 

Capacity (%) High Medium Low

1 1.5 108

4 5.9 510 468 468

7 10.4 864

10 14.8 1260

Note: Scenarios that have no figure under “Installed Capacity” were not modeled 

as a part of this study.

Source: NAU, 2007.

Actual year 2004 hourly load data was employed in conjunction with simulated wind power 

production data over the same period. The study year was selected as 2010 so that the analysis 

could be conducted while retaining maximum certainty about the projected APS loads and 

generation resources. Thus, the 2004 actual loads were scaled up to the level expected in 2010 

(NAU 2007). 

In the NAU Report, integration costs are based on the incremental costs associated with unit 

commitment in the day-ahead and hour-ahead time frames, load-following within the hour, and 

regulation for minute-to-minute fluctuations. The modeling software utilized by APS at the time 

of the study would not permit any change in the actual wind that showed up during the day of 

operation from that which was forecast day ahead. The practical implication is that the actual 

wind (from the simulation) had to be used for the forecasted wind, and that the impact of 

different wind forecasts could not be directly investigated (e.g., a professional forecast versus a 

persistence forecast versus a perfect forecast, etc.). To account for uncertainty in the day-ahead 

wind forecast in the day-ahead optimization, the modeling software allowed a “firmness” factor 

to be applied to the wind energy. The firmness factor allows a fixed percentage from 0% to 

100% of the forecasted wind generation for the day to be considered “firm” in the day-ahead 

optimization (NAU 2007).

The wind power simulation was conducted by 3TIER, using a mesoscale weather model 

employing 2004 historical weather data as an input to maintain a high correlation between the 
28 

simulations and the actual weather. Wind speeds were simulated with 3TIER’s mesoscale model 

for 1996 to 2006, with particular focus on 2003, 2004, and 2005. The time step of the mesoscale 

simulation was 10 minutes. This resolution in time was selected because it allows study of intrahour wind variations, and could be easily modified for an hourly power system simulation. Using 

the output of the mesoscale modeling as an input, wind power plant output was computed using 

3TIER’s Statistically Corrected Output from Record Extension (SCORE) methodology (NAU 

2007). 

SCORE was developed specifically to predict the magnitude and variability of the output from a 

wind power plant. A GE 1.5-MW turbine with a 77-meter rotor diameter and 80-meter hub 

height was the reference turbine model employed in this simulation. The time step of the wind 

plant simulation was also 10 minutes (NAU 2007). Using the SCORE methodology, a wind 

power plant was composed of nine separate groupings of turbines, typically 36 MW per group of 

turbines (i.e., 24 turbines per group), allowing a maximum of 324 MW per site. This approach 

allowed wind power plants of varying sizes to be located at different sites, by choosing any 

number of groups of turbines. Strings of turbines were placed with a minimum spacing of four 

rotor diameters between turbines on the same string and a minimum spacing of 10 rotor 

diameters between the rows of turbines. Wind power output from 10 different sites were 

employed for the high geographic diversity case. For the medium-diversity cases, output from 

three sites centrally located in Arizona was utilized. For the low-diversity case, output from only 

two wind power plants was employed. Note that the 10-minute wind power output from the 

SCORE methodology was aggregated into hourly power sequences for each scenario for input 

into the APS power system model (NAU 2007). 

Determination of Wind Integration Costs 

The approach in conducting the APS integration study was to simulate system planning, 

operational activities, and decisions over the course of one typical year. In APS’s case, this 

entailed running the modeling software, RTSim—the tool used by APS on a daily basis to model 

their system planning and operations, using an hourly time step. The simulation performs an 

optimal commitment of available generating units in the day-ahead time frame, ensuring there is 

adequate generation available to cover the next day’s load, the variations in the load (e.g., 

ramps), and setting aside sufficient reserves. As the simulation proceeds into the day of 

operation, units that were committed for use during the day are re-optimized and recommitted on 

an economic basis in the hour-ahead timeframe when the expected load, generation, and wind is 

more certain. The units available during the hour (i.e., real-time) must follow the load swings 

within the hour and hour-to-hour (load following), as well as minute-to-minute fluctuations 

(regulation). After simulating the system operation for a year, an overall cost to run the system 

and meet the load is determined, including all market transactions (NAU 2007). 

To assess the incremental cost to integrate the wind energy, the system operation was first 

simulated with some baseline set of resources that includes wind power, but in a way that 

attempted to remove the effect of its uncertainty and variability. This was done by assuming that 

wind power output was known perfectly in advance and that it added no variability to the control 

area, and where next-day load was the only uncertainty (NAU 2007). The system was then 

simulated again with the actual characteristics of wind energy, accounting for its uncertainty 

(inaccuracy in prediction) both day-ahead and hour-ahead, and accommodating for its variability, 

as predicted by the 3TIER modeling. The cost incurred during this simulation was then 
29 

subtracted from the cost incurred with the baseline resources to estimate the overall integration 

costs (NAU 2007). 

For the base case of 4% wind energy (medium diversity), the total integration cost was estimated 

at $3.25/MWh, varying from $0.91/MWh for 1% wind energy to $4.08/MWh for 10% wind 

energy (NAU 2007). Hour-ahead uncertainty, as employed by APS’s modeling tool RTSim for 

in-the-day commitment of generating units, was the largest component of integration cost. This 

quantity is effectively a type of operating reserve (load following), and can be significant in 

magnitude relative to the other reserve amounts attributable to wind generation—see Figure 1 

(NAU 2007). 

Source: NAU, 2007

Figure 1. APS estimated wind integration costs

Solar Integration Costs 

In its March 2012 IRP, APS incorporated a solar integration cost of $2.50/MWh in its resource 

planning model to compare solar resources with all other demand and supply resource options. 

APS’s estimated solar integration cost is based on the WGA’s Western Renewable Energy Zone 

Generation and Transmission Model. The integration cost assumptions provided in that model 

are $2.50/MWh for solar thermal and solar PV, and $0.00/MWh for solar thermal with storage.8

 

8

 APS includes a $0 integration cost for solar thermal with storage because there is no minute-by-minute variability 

because it operates with traditional steam turbine generators with voltage regulators and other traditional controls, 

thereby meeting WECC requirements.
30 

According to the Methodology and Assumptions document that accompanies the model, these 

costs were provided as starting point assumptions and may be adjusted by the user (Black &

Veatch 2009). 

In November 2012, APS released the Solar Photovoltaic (PV) Integration Cost Study (APS Solar 

Study), prepared by Black & Veatch. The objective of the APS Solar Study was to estimate the 

incremental operating reserves, and the associated costs, necessary to integrate projected solar 

PV development in the APS service territory in the years 2020 and 2030 (Black & Veatch 2012).

To comply with Arizona’s Renewable Energy Standard, APS anticipates increasing solar PV 

resources on its system to 1,038 MW by 2020 and 1,669 MW by 2030. The APS Solar Study 

provides an estimate of the anticipated incremental cost to provide the reserve capacity and 

energy services necessary to integrate the projected levels of solar PV into the APS system 

during the 10-minute operating time frame (Black and Veatch 2012). In addition to a base case 

scenario, Black & Veatch incorporated sensitivity cases with assumptions involving greater solar 

variability, higher gas prices, and varying levels of compliance with NERC’s CPS2. 

Load and Solar Assumptions

Black & Veatch developed a spreadsheet model to calculate the variability of load and solar 

generation on a 10-minute time step throughout the year. Load variability was developed by 

taking the APS projected load forecast for 2020 and 2030 and applying historic load variability 

to it, in order to forecast the 10-minute changes in load. A similar process was used to develop 

the solar variability estimates. First, hourly production estimates of solar output were developed 

for areas where APS envisions likely solar development in 2020 and 2030. A mix of solar PV 

generation technologies was modeled within these areas to reflect likely resource diversity.

Where available, Black & Veatch used output and variability at existing Arizona solar facilities 

to develop a deterministically-derived quantity of solar variability. For areas without actual data, 

estimated output was applied using solar production modeling and statistical variability 

modeling. Persistence forecasts (adjusted for the sun’s position) allowed Black & Veatch to 

develop a forecast of the difference between the predicted and actual output of the PV energy on 

the APS system in 2020 and 2030 (Black & Veatch 2012). 

Because many of the prospective sites for new solar projects were unknown, Black & Veatch 

developed assumptions for likely development areas with the assistance of APS. For utility-scale

solar plants, sites that are anticipated to host future solar projects were used to develop the solar 

resource profile. Undefined projects were divided amongst Phoenix, Yuma, Prescott, Gila Bend, 

and Palo Verde. These locations were picked because of their solar resource potential, 

transmission availability, likelihood for future development, and proximity to load. For 

distributed rooftop projects, 80% were located in the Phoenix metro area; the other 20% were 

evenly divided between Yuma, Flagstaff, and Prescott (Black & Veatch 2012).

Determination of Incremental Reserve Costs 

Black & Veatch utilized a two-step methodology to determine the reserve quantity and costs 

associated with integrating the anticipated levels of solar PV. The first step was to estimate the 

amount of incremental upward and downward regulating reserves required due to solar 

forecasting errors from the solar PV penetration levels in each case. The second step involved 

modeling the cost impact using the ABB/Ventyx ProMod production cost modeling software to 
31 

measure the energy cost differential of providing the needed amount of regulation (Black & 

Veatch 2012). 

To identify the reserves required to integrate the expected level of solar development, Black & 

Veatch estimated the expected incremental area control error (ACE) violations, then calculated 

the operating reserves necessary to minimize the violations to comply with NERC’s CPS2 and to 

conform to APS’s current operating levels. Black & Veatch considered the reserve requirements 

and cost to achieve CPS2 compliance at the 90%, 95%, and 99% monthly levels (Black & 

Veatch 2012). To calculate reserve requirements for any given level of NERC’s CPS2 

compliance, Black & Veatch developed a spreadsheet model. The required input was forecasted 

hourly loads and 10-minute expected generation for the entire year. After the load and solar data 

were entered into the model, the number of CPS2 violations was calculated. Violations due to 

load only were first calculated, assuming perfect solar forecasting. After the solar forecast 

variability was added, the incremental reserves that was needed beyond what was required for 

load only during daylight hours was determined. The forecast error is then calculated as the net 

difference between the actual load and solar generation and the forecasted load and solar 

generation and represents the incremental operating reserve requirements needed for solar PV 

integration (Black & Veatch 2012). 

The APS Solar Study focused on operating reserves that can respond to changes in system ACE 

in a 10-minute time frame to ensure reliable system operation. This includes spinning, 

non-spinning, and contingency reserves (see Table 7). APS expects that the GE LMS100 aeroderivative gas-fired peaking generator will be providing any additional needed reserve capacity. 

APS also stated that many of these units will be built within its planning horizon to 

accommodate future load growth and to provide firming-up capacity to compensate for 

variability of wind and solar generation. It is important to note that these units are already 

planned as future additions in APS’s 2012 IRP, so their capacity costs have already been 

included in APS’s system revenue requirements. Thus, it was assumed that there would be no 

additional capacity costs to provide the necessary regulation capacity in integrating solar energy 

generation into the APS system (Black & Veatch 2012). 

Table 7. APS Operating Reserve Requirements

Source: Black & Veatch, Solar Photovoltaic (PV) Integration Cost Study, 2012.

Note: “N-1” is a NERC operation principle that requires that an electric system be capable of withstanding the loss of 

any individual component without experiencing unacceptable system conditions. 
32 

Energy costs were divided into the dispatch costs, reflecting changes in the system energy output 

to accommodate the solar PV, and spinning reserve costs, which represent the higher cost to 

commit resources to make sure that there are enough operating generating resources to meet 

dispatch requirements. Note that the cost to maintain reserve capacity was referred to as the 

spinning reserve cost in the APS Solar Study. Black & Veatch utilized production cost modeling 

to estimate the energy costs from supplying the incremental reserves. As noted earlier, 

incremental capacity costs were not included. Three production costs model runs were run for 

each scenario: 

• Base Case with no incremental 10-minute reserve requirement

• Change Case #1: Increase load (in every hour when the solar PV is available) by the 

calculated amount of regulating reserve up, i.e., the amount of regulating reserve that can 

be dispatched upward 

• Change Case #2: Decrease load (in every hour when the solar PV is available) by the 

calculated amount of regulating reserve down, i.e., the amount of regulation reserve that 

can be dispatched downward (Black & Veatch 2012)

The three production cost runs were intended to determine the system energy cost differential by 

relying upon all generating units that were available for 10-minute reserves. The production cost 

runs with varying loads are meant to estimate the cost of the incremental energy required for 

ramping up and ramping down the system. The system energy differential cost is the difference 

between system costs to move the system up netted out by the system cost savings of moving the 

system down using the new defined level of regulation reserves (Black & Veatch 2012). The 

base case net solar integration costs are presented below, in Table 8. 

Table 8. APS’s Estimated Solar Integration Costs

Source: Black & Veatch, Solar Photovoltaic (PV) Integration Cost Study, 2012.

The sensitivity analysis revealed that the greater variability case did not have a significant impact 

on the estimated solar integration costs (i.e., less than $0.10/MWh). This is because the higher 

variability did not lead to a significantly greater amount of CPS2 violations. According to Black 

& Veatch, the high level of geographic diversity greatly smoothes out the generation profile and 
33 

leads to some projects varying in the up direction; whereas others vary downward, eliminating 

some of the net impact (Black & Veatch 2012). 

Higher gas prices had a more meaningful impact on the marginal energy and reserve costs, not 

surprisingly because the marginal generation resource is a natural gas-fired combustion turbine. 

Black & Veatch ran a sensitivity analysis that assumed a 30% increase in the price of natural gas. 

The marginal cost of integrating solar is lower than the 30% increase in the price of natural gas, 

with an approximate increase of 22% in 2020 and 15% in 2030 at a 99% CPS2 compliance level. 

The increases are about $0.25 to $0.50/MWh, depending on the time period and level of CPS2 

compliance (Black & Veatch 2012). 

BC Hydro

Background

BC Hydro began considering wind integration costs in a preliminary evaluation that was 

included in its 2008 long-term acquisition plan (LTAP). BC Hydro included a wind integration 

cost of $10/MWh in its 2008 LTAP analysis, and in its request for proposals for clean power that 

followed. In the 2008 assessment, BC Hydro determined the total wind integration cost as the 

sum of regulation and load-following reserve costs, and energy shift costs (i.e., the opportunity 

costs of forgoing low price imports or high price exports due to reserves being held to cover 

wind generation uncertainty). Scenarios were run at four levels of penetration (10%, 20%, 30%, 

and 40%), and with three aggregation scenarios, and analyzing the balancing reserve 

requirements at 2, 2.5, and 3 standard deviations. The Wind Integration Study included in BC 

Hydro’s draft 2012 IRP builds on that previous examination. The draft 2012 IRP also proposes a 

wind integration cost of $10/MWh to be included in evaluating generation bids and in comparing 

wind with other generating resources in long-term resource planning. The value will be updated 

as future wind studies are conducted (BC Hydro 2012b; BC Hydro 2012a; Rucker 2012; and BC 

Hydro 2009). 

Assumptions and Modeling

The Wind Integration Study examined multiple scenarios for the two fiscal years 2011 and 2021. 

Wind penetration was studied at three levels: 15% (approximately 1,500 MW), 25% 

(approximately 2,500 MW), and 35% (approximately 3,500 MW). In addition, scenarios with 

low-diversity and high-diversity wind were also examined, nested within the three penetration 

levels. Wind in the study was represented by a portfolio of theoretical wind projects that were 

identified in the May 2009 version of the BC Hydro Wind Data Study (DNV Global Energy 

Concepts, Inc. 2009). The wind integration costs ranged from $5/MWh to $19/MWh among the 

different scenarios. A cost of $10/MWh corresponded with the fiscal year 2011 economic 

dispatch, 15% scenario (BC Hydro 2012b; BC Hydro 2012a). 

The study used three models to determine wind integration cost: the Generation Optimization 

Model (GOM), the Hydro Simulation Model (HYSIM), and the Capital Expansion Model 

(CAPEX). GOM, a deterministic, linear optimization model, was used to calculate the

incremental operating reserve (regulation, load-following, and imbalance reserve capacity) costs, 

and to determine the day-ahead opportunity costs (opportunity cost for maintaining capacity to 

address day-ahead wind forecast errors). The HYSIM, a simulation model for the integrated BC 
34 

Hydro electric generation system with a monthly time step, was used to develop hydraulic 

boundary conditions (year-end and monthly target water levels) for GOM. CAPEX is a linear 

and mixed integer programming optimization model. This model is used by BC Hydro for 

economically dispatching resources over a planning time horizon. In the study, the CAPEX 

scenarios are synonymous with the low-diversity scenarios, because although not a direct 

intention of the model, these cases represent low-diversity levels of wind resources. Highdiversity scenarios were determined by assuming wind projects were equally proportioned across 

specified regions of the province (Peace, Southern Interior, North Coast, and Vancouver Island) 

(BC Hydro 2012b). 

Water data for the inflows used in the GOM modeling runs were based on 10 years (October 

1964 to September 1974) representative of water conditions throughout the full 60 years of water 

data used in HYSIM. For statistical analysis of operating reserve requirements, the load data was 

synchronized with the wind data. Wind and water years could not be directly synchronized, 

however, due to problems with data availability and differences in the definition of a year (the 

wind data begins in August; whereas water years begin in October). Therefore, the water/wind 

year combinations shown below in Table 9 were used in the GOM modeling (BC Hydro 2012b). 

Table 9. Water/Wind Year Combinations Used in the GOM Modeling

Water Year 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973

Wind Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2006

Source: BC Hydro. 2012b. BC Hydro Draft Integrated Resource Plan 2012: Appendix 6E Wind Integration Study 

Phase II. Draft. May 2012. Pg. 6E-22. www.bchydro.com/energy_in_bc/irp/document_centre/reports/

draft_irp.html#chapters. 

For the modeling of day-ahead impacts using GOM, however, wind data was limited by the 

availability of NWP forecasts. BC Hydro used NWP forecasts that corresponded to fiscal years 

2006, 2007, and 2008. These years of wind forecast data were paired with the fiscal years 1969, 

1970, and 1974 of water inflow data, representing normal, dry, and wet water conditions, 

respectively (BC Hydro 2012b). 

The wind integration cost was modeled as the sum of the variability cost, the incremental 

operating reserves cost, and the day-ahead wind opportunity cost. The variability impacts were 

estimated by the difference between wind as an ideal generator and wind profiles. Energy was 

added in blocks of light load hours and heavy load hours. The blocks of energy from the ideal 

generator were equivalent to the total energy generated during each block based on the wind 

profiles. For calculation purposes, variability costs were included with operating reserve costs, 

because variability makes up only a small portion of the wind integration cost. GOM was used to 

estimate incremental reserve and variability costs for each wind scenario and water-wind year, 

with the 10 nonsynchronous water-wind years studied. The incremental operating reserve 

requirements were determined using statistical analysis of 10 years of historic 1-minute load data 

and simulated 1-minute wind data, with reserve requirements set at three standard deviations. 

The requirements are determined for load only and wind only, with requirements for load net 

wind determined by combining these with the root-sum-squares method [Total = {(load value)2 + 

(wind value)2

}1/2]. 

More specifically, to calculate the regulating reserves, the 1-minute averages of load and wind 

are calculated separately, and the 1-hour centered rolling averages of load and wind are 
35 

calculated separately at each minute. Still executing wind and load calculations separately, the 

1-hour centered rolling average is then subtracted from the 1-minute averages for each minute. 

Three standard deviations are used to determine the regulating reserve requirements. Finally, the 

root-sum-squares method is used, combining load and wind regulating reserve requirements by 

adding the squared load value with the squared wind value, then taking the square root of the 

resulting value. Similarly, the load-following reserve requirement is calculated by taking, for 

each minute, the clock hour actual average subtracted from the 1-hour centered rolling average 

for wind and load separately, including a 10-minute ramp—plus or minus five minutes—at the 

top of each hour, and using three standard deviations. The separate wind and load calculations 

are then combined using the root-sum-squares method. Comparably, imbalance reserve 

requirements are determined, calculating wind and load separately, by taking the clock hour 

average subtracted from the forecasted hourly average for each hour and again using three 

standard deviations. In calculations for wind, the study used persistence for the hour-ahead wind 

forecasts. The wind and load values are then combined using the root-sum-squares method. 

Subtracting load requirements from the load net wind requirements results in the estimated 

incremental operating reserves for wind. After the incremental operating reserve requirements 

were estimated, the study used CAISO’s ancillary service market prices as a basis to determine 

the opportunity cost of holding these reserves (BC Hydro 2012b and Rucker 2010). 

In determining day-ahead opportunity costs, the study focuses on the day-ahead, but assumes 

200 MW from the real-time market could be used to assist with wind integration. Day-ahead 

wind opportunity costs are defined as the value of reduced trade opportunities in the 8-hour light 

load hour and 16-hour heavy load hour trading blocks to maintain flexibility in response to dayahead wind forecast errors. Three water/wind years were modeled, corresponding to normal, dry, 

and wet water conditions. The system flexibility required to deal with the day-ahead wind 

forecast error was defined at three standard deviations (99.7%).9 Day-ahead wind forecast error 

was determined using numeric weather prediction wind forecasts.

10 Two methods of obtaining 

flexibility were considered, using the GOM: through trade and generation schedule changes, and 

through a combination of spilling water, wind curtailment, and trade and generation schedule 

changes. Spilling water was, however, limited to one facility (the Seven Mile plant), given water 

spilling restrictions at other BC Hydro facilities due to water licenses, flood control, downstream 

user requirements, and the Columbia River Treaty. Imbalance and load-following reserves were 

also allowed to be used for system flexibility. The opportunity cost was valued, for power 

trading schedule impacts, at energy market prices less transmission costs; for water spilling, as 

the BC Hydro water and storage value; and for wind curtailment, as the BC Hydro water and 

storage value plus the value of the REC that could have been attained from the wind generation. 

The opportunity cost of using these methods to manage wind forecast uncertainty is determined 

on a daily basis, with the resulting most economical method selected daily. 

To determine the opportunity costs under an import scheduling framework11, the maximum 

potential wind generation swing is determined as the maximum hourly wind forecast output less

 

9 As explained earlier, wind power forecast errors may not be normally distributed (Hodge 2012). 10 NWP forecasts were determined in the May 2009 Wind Data Study: 

www.bchydro.com/etc/medialib/internet/documents/environment/winddata/pdf/wind_data_study_report_may1_200

9.Par.0001.File.bch_wind_data_study_may1_09.pdf 11 That is, the BC Hydro water and storage value is higher than the day-ahead market price. 
36 

any real-time market liquidity. The maximum hourly wind forecast output is the expected hourly 

wind forecast output plus the wind forecast error based on three standard deviations of 

confidence. Under an export scheduling framework,12 opportunity costs are calculated as the 

expected minimum wind generation plus any real-time market liquidity. The expected minimum 

wind generation is determined as the expected hourly wind forecast output minus the wind 

forecast error based on three standard deviations. The difference between this value and the 

average forecasted wind generation for the time period reflects the hydro reserves that would be 

needed to accommodate wind variability. The day-ahead opportunity cost is calculated by taking 

the difference between the market price net transmission costs and the BC Hydro water and 

storage value, and then multiplying it by the magnitude of the trading block of the missed 

import/export. The resulting value is then divided by wind power generation (BC Hydro 2012b 

and Rucker 2010). 

BPA

Background

BPA markets power from 31 federal hydropower plants and one nuclear plant that together total 

approximately 23,000 MW of nameplate capacity. The power plants and associated BPAoperated transmission system comprise the Federal Columbia River Power System (FCRPS). As 

of March 2012, BPA has more than 4,200 MW of wind power capacity connected to its 

balancing area, and predicts that amount could increase to almost 6,000 MW by 2013 (BPA 2012 

and BPA 2011d). 

BPA provides balancing services from 10 of the FCRPS hydro plants (the Big 10), which are 

controlled in real-time by BPA’s automatic generation control (AGC), allowing them to respond 

automatically to changes in the BPA system.13 The federal system dams are linked together in 

the Columbia River Basin. The availability of hydro system capacity is dependent on the amount 

of water currently in the Columbia River Basin, turbine availability (turbines need to be 

periodically taken out of service due to planned maintenance or experience unplanned outages), 

and operational objectives such as flood control; variable draft limits; water flow and operational 

limits related to fish, human safety, recreation, and Canadian Treaty operations.14 BPA maintains 

that the ability of the Big 10 projects to accommodate increasing grid variability from wind 

energy is reaching its limits during times of high water/high wind and/or low load as well as 

during periods of low water. 

BPA began examining the cost of integrating wind in 2007 during BPA’s 2008–2009 rate 

proceeding to establish transmission and ancillary and control area service rates. During the 

proceeding, BPA introduced the Wind Integration—Within-Hour Balancing Service rate to take 

effect for BPA’s 2009 rate year. The initial Wind Integration rate was the result of a settlement 

 

12 That is, the BC Hydro water and storage value is lower than the day-ahead market price. 13 The Big 10 hydro plants are Grand Coulee, Chief Joseph, Lower Granite, Little Goose, Lower Monumental, Ice 

Harbor, McNary, John Day, The Dalles, and Bonneville.

14 Variable Draft Limits are period-by-period draft limits at Grand Coulee and Hungry Horse from January to 

March 31. These are planned limits to Firm Energy Load Carrying Capability generation to protect the ability to 

refill Grand Coulee and Hungry Horse to their April 10 elevation objectives with an 85% and 75% confidence, 

respectively. Source: BPA, U.S. Bureau of Reclamation, U.S. Army Corps of Engineers, 2012 Water Management 

Plan, October 1, 2012.
37 

between BPA and interveners and reallocated certain costs of reserves from BPA’s power 

customers to wind generators. BPA recommended that $19,124,320 being collected as power 

rates to cover regulation and following service costs should be reallocated to wind generators. 

BPA estimated that wind capacity in 2009 would be approximately 28,124,000 kW-months; 

therefore, the Wind Integration rate required to collect the target amount of revenue was 

$0.68/kW-month applied to installed wind capacity (BPA 2009). 

During the 2010–2011 rate proceeding and again for the 2012–2013 proceeding, BPA forecasted 

the revenue requirement for balancing service and allocated the costs across the participants. 

Table 10 below shows the wind integration rates for the 2009 through 2011 and the 2012–2013 

rate, along with the monthly cost for a typical 100-MW wind facility. 

Table 10. BPA Wind Integration Rates

Year Rate Monthly Charge for a 100 MW Project

2009 $0.68 per kW-month $68,000 per month

2010–2011 $1.29 per kW-month $129,000 per month

2012–2013 Wind: $1.23 per kW-month

Solar: $0.21 per kW-month

$123,000 per month

$21,000 per month

Source: BPA: 

2009 Wind Integration Rate Case Final Proposal, Final Record of Decision, June 2008.

2010 Wholesale Power and Transmission Rate Adjustment Proceeding (BPA-10) Administrator’s Final Record 

of Decision, Appendix C, July 2009.

2012 Final Rate Proposal, Generation Inputs Study, July 2011.

For the 2012–2013 rate proceeding, BPA redefined the Wind Integration rate as the VERBS rate, 

and applied the rate to operating wind and solar plants. Additionally, BPA created a DERBS rate 

that applies to all non-Federal dispatchable energy resources (i.e., thermal generation) of 200 kW 

nameplate capacity and greater for deviations from scheduled generation amounts (BPA 2011b). 

Assumptions

Balancing Reserve Requirement Forecast 

BPA begins by forecasting the balancing reserve capacity quantity that will be required during 

the rate period. Balancing reserve capacity includes what is needed to provide the following, 

which are collectively referred to as balancing services:

• Regulating reserves—the capacity needed to provide for continuous balancing of 

generation and load; 

• Following reserves—the capacity required to balance variations within the hour of actual 

load and generation from the forecasted load and generation; 

• Imbalance reserves—reserves needed due to differences between the average scheduled 

energy during the hour and the average actual energy during the hour. 

Balancing services include both incremental (inc) and decremental (dec) generation for each 

category. BPA develops the forecast for cumulative inc and dec generation required to maintain 

load-resource balance for the required reserve time periods. 
38 

The balancing reserve capacity quantity forecast is based on 1-minute average data for a 

24-month period for: 

• Total wind, hydroelectric, federal thermal, and non-federal thermal generation;

• Total hydroelectric, federal thermal, and non-federal thermal schedules; and

• BPA balancing authority area load.

The historical data is used to develop forecasts for each of the above elements and is combined 

with a forecast of generation that is expected to come online during the rate case time period. For 

the 2012–2013 rate period, BPA used generation interconnection queue data to forecast 

generation capacity additions to the end of the rate period. Rather than instituting a hard cut-off 

regarding which projects in the queue would be included in the forecast, BPA evaluates certain 

factors to project the status of future projects during the rate period. These factors include where 

the project is in the interconnection study process; the status of the environmental review process 

and the projects permitting process; the grid upgrades that are required and how long it will take 

to complete them; information gained from discussions with the developer; and whether the 

developer has executed an interconnection agreement and committed to fund any necessary 

network upgrades. BPA then projects the amount of capacity that will be online between 2012 

and 2013 (see Table 11).

Table 11. Forecast of Average Generation Capacity for the 2012–2013 Rate Period

Installed wind capacity 4,693 MW

Installed solar capacity 21 MW

Non-AGC controlled hydroelectric capacity 2,604 MW

Non-federal thermal capacity 5,784 MW

Federal thermal capacity 1,276 MW

Source: BPA, 2012 Final Rate Proposal, Generation Inputs Study, July 2011.

The scheduling accuracy of wind generation is assumed to be equivalent to a 30-minute 

persistence measure. Therefore, the schedule for each wind facility for each hour is the 1-minute 

average of each wind facility’s actual generation 30 minutes prior to the hour.

BPA forecasts overall balancing reserve by calculating the inc and dec requirements for each of 

the three reserve components (regulation, following, and imbalance). Using a percentile 

distribution, BPA discards the upper and lower 0.25% of values, leaving 99.5% of values that are 

used to produce a forecast of balancing reserve capacity that is needed to meet balancing 

requirements 99.5% of the time. BPA considers this method to be generally consistent with using 

three standard deviations to calculate requirements (BPA 2011b). Following the estimation of the 

total balancing reserves, BPA uses an incremental standard deviation (ISD) methodology to 

allocate the reserves to load, hydro generation, federal thermal generation, non-federal thermal 

generation, and wind generation, based on how each contributes to the joint regulation, 

following, and imbalance requirements. The ISD estimates how much the total balancing reserve 

capacity deviation changes given a 1-MW change in the load and/or generation standard 

deviation, while recognizing diversity between load and generation error signals. Essentially, 

ISD applies the same ratio of each element’s contribution to the aggregate balancing reserve 
39 

capacity requirement for each 24-hour period, to the total joint balancing reserve capacity 

requirement that includes diversity benefits (BPA 2011b). 

The result of the modeling is as follows:

• Regulation inc and dec for load, hydro generation, federal thermal generation, nonfederal thermal generation, and wind generation. 

• Following inc and dec for load, hydro generation, federal thermal generation, non-federal 

thermal generation, and wind generation. 

• Imbalance inc and dec for load, hydro generation, federal thermal generation, non-federal 

thermal generation, and wind generation. 

The hydro and federal thermal generation requirements are assigned to load balancing; whereas

the non-federal thermal generation and wind generation requirements are assigned to thermal and 

wind generation customers to derive the following rate components: 

• Regulation Reserve Service = inc and dec regulation requirements for load, hydro 

generation, and federal thermal generation. 

• Load-Following Reserve Service = inc and dec following requirements for load, hydro 

generation, and federal thermal generation plus inc and dec imbalance requirements for 

load, hydro generation, and federal thermal generation.

• DERBS = inc and dec regulation requirements for non-federal thermal generation plus 

inc and dec following requirements for non-federal thermal generation plus inc and dec

imbalance requirements for non-federal thermal generation.

• VERBS = inc and dec regulation requirements for wind generation plus inc and dec

following requirements for wind generation plus inc and dec imbalance requirements for 

wind generation. Additionally, solar generation balancing requirements are incorporated 

into the wind requirement. For the 2012–2013 rate period, BPA did not have adequate 

amounts of solar generation in its BA to create a solar forecast. BPA used data from the 

University of Oregon Solar Radiation Monitoring Laboratory to assess the regionally 

within-hour variability of solar energy. BPA assumes that the use of balancing services 

for solar will be similar to that required for wind generation, but because solar facilities 

produce power only during daylight hours (i.e., about half of the time) BPA believes 

solar will require only about half as much balancing capacity service as wind. 

Additionally, BPA assumed a perfect schedule for solar generation and, therefore, did not 

include an imbalance component. 

Cost Allocation

The balancing reserve forecast estimates the total amount of balancing services required and how 

much each resource contributes to the total amount. BPA then determines the cost of providing 

the required amounts of balancing services and allocates these costs to the relevant resources 

according to their contribution under four rates: regulating reserve, load-following reserve, 

DERBS, and VERBS. BPA assigns cost responsibility for regulating reserves, DERBS, and 

VERBS (along with contingency reserves, both spinning and supplemental, collectively referred 

to as operating reserves), to BPA Transmission Services. For load-following reserves, BPA 
40 

assigns cost responsibility to BPA Power Services, with load-following reserves calculated 

separately as part of the power rates revenue requirement. 

For regulating reserve, DERBS, and VERBS, three categories of costs are estimated: embedded 

costs, direct assignment costs, and variable costs. 

Embedded Costs

Embedded costs consist of the revenue requirement associated with the Big 10 hydro projects 

that provide BPA balancing services. BPA estimates the capacity available from the Big 10 

projects by annually modeling the average of the top 120-hour sustained peaking capacity based 

on the 1958 water year, which represents average Columbia basin water conditions. This 

provides an annual average amount of sustained capacity available from the Big 10 for 

operational planning. The total estimated revenue requirement is divided by the average annual 

sustained capacity amount to estimate the embedded unit cost, which is then multiplied by the 

balancing reserve inc capacity quantity forecast for each resource, regulation, DERBS, and 

VERBS to derive the total embedded cost allocation for each. The embedded costs are only 

allocated based on forecast inc reserves. The embedded cost net revenue requirement of the Big 

10 includes project-specific power-related costs, associated fish mitigation costs, administrative 

and general expenses, and three revenue credits not related to hydro operations.15

Direct Assignment Costs

Direct costs are assigned only to the VERBS rate and relate to costs incurred by BPA for specific 

wind integration projects. For the 2012–2013 rate period, BPA is proposing to assign to VERBS 

a portion of the costs associated with the Wind Integration Team (WIT) and the Dec Acquisition 

Pilot project. The WIT is an internal cross-agency initiative that arose from the 2009 settlement 

agreement. WIT’s mission is to develop and implement methods to better integrate wind energy 

into the BPA grid. WIT funding is divided between Power Services and Transmission Services, 

with the Transmission Services portion directly assigned to the VERBS rate. The Dec

Acquisition Pilot seeks to incorporate BPA purchases of dec reserves from non-federal 

generation sources that can reduce the amount of dec reserves that need to be supplied by the 

FCRPS units. 

Variable Costs

Using the FCRPS system for balancing services requires BPA to hold a sufficient amount of 

machine capability in a state of readiness to meet balancing needs. This results in a loss of 

efficiency. BPA estimates the costs associated with efficiency losses under two categories as 

outlined in Table 12. Stand Ready Costs are costs associated with making a project capable of 

providing reserves and Deployment Costs are costs incurred when the system actually uses the 

reserve capability to respond when needed. 

 

15 Revenue credits include two non-operational components credited to Transmission Services from the Power 

Revenue Requirements Study. A third is a credit for costs associates with synchronous condensing, which is 

calculated individually and assigned to Transmission Services and so is credited to embedded costs to avoid doublecounting. Synchronous condensers are controls that regulate voltage by absorbing or supplying reactive power as 

needed. 
41 

Table 12. Variable Cost Categories

Stand Ready Costs

Energy shift

The cost differential incurred by BPA when energy 

needs to be shifted from periods of high load/higher 

prices to periods of low load/lower prices to meet 

balancing energy needs. 

Efficiency loss

Each FCRPS project has a “most efficient” generation 

set-point based on the amount of water flow per MW. 

BPA incurs an efficiency loss when a facility needs to 

be moved off its most efficient generation point to meet 

balancing reserve capacity requirements. 

Cycling losses

Costs associated with the additional synchronization 

and ramping of units needed for to maintain the 

required amount of balancing capacity. 

Spill losses

Costs associated with the need to spill energy when 

river flows are high but BPA must maintain enough 

headroom to provide adequate inc reserve capability.

Deployment Costs

Response 

losses

Efficiency losses (as described above) associated with 

deploying committed units to provide balancing energy.

Cycling losses Cycling losses (as described above) associated with 

dispatching units to provide balancing energy.

Spill losses

Spill losses that arise from BPA needing to reduce 

generation at a plant that would put it below water flow 

limits therefore, forcing the unit to spill water to maintain 

adequate downstream river flows. 

Source: BPA, 2012 Final Rate Proposal, Generation Inputs Study, July 2011.

BPA subdivides the total stand ready and deployment costs between regulation, variable 

generation balancing, dispatchable generation balancing, and a portion that gets assigned to load 

following. 

BPA’s 2012–2013 Rate Case

For the 2012–2013 rate case, BPA estimated the MW quantities associated with balancing 

services, and the annual revenue requirements. BPA conducted the modeling and cost estimation 

at a 99.5% level of service, adequate balancing capacity to provide balancing services for 99.5% 

of the required time. For the remaining time, BPA will curtail generation plants as required. BPA 

is forecasting a total balancing reserve capacity requirement of 791 MW inc and 1,012 MW of 

dec. Of the total reserve requirement, 333 MW of inc and 346 MW of dec was assigned to load 

following. The remaining balancing reserve requirement was allocated to regulation, VERBS, 

and DERBS. Table 13 provides BPA’s forecast revenue requirements for regulation, VERBS, 

and DERBS.
42 

Table 13. Revenue Forecast for BPA’s 2012–2013 Rate Case for Regulation, VERBS, and DERBS

2012–2013 Rate Years at a 99.5% Service 

Level

Category Quantity Annual Average 

Revenue ($)

Regulating 

Reserves

Embedded cost 60 MW 4,816,800

Variable cost 34 MW inc

35 MW dec 1,784,250

Total 6,601,050

VERBS

Embedded cost 470 MW 37,731,600

Direct assignment cost 8,214,701

Variable cost 469 MW inc

623 MW dec 9,801,896

Total 55,748,197

DERBS

Embedded cost 51 MW 4,094,280

Variable cost 51 MW inc

81 MW dec 1,659,163

Total 5,753,443

Total $68,102,690

Source: BPA, 2012 Final Rate Proposal, Generation Inputs Study, July 2011.

As noted earlier, the total cost of balancing services is allocated to different categories for each 

rate based on the balancing requirements for each service forecast in the balancing services 

capacity quantity forecast. BPA estimates the three different components for the VERBS 

individually. These components then account for: 

• VERBS regulation—accounts for the moment-to-moment variability attributed to 

variable generation (as opposed to load and thermal generation changes). 

• VERBS following—accounts for longer-duration within-hour variability attributed to 

variable generation.

• VERBS imbalance—accounts for the within-hour variability due to differences between 

the hourly scheduled amount and hourly average generation attributed to variable 

generation. 

Table 14 summarizes the estimated rates calculated and implemented by BPA for the 2012–2013 

rate period. VERBS charges to generators are based on the greater of the maximum 1-hour 

generation or the nameplate capacity of the wind or solar resource.
43 

Table 14. 2012–2013 Rates for Regulation, VERBS, and DERBS

Rate

Estimated 

Costs 

($000)

Average 

Annual 

Forecast 

(MW)

Rates

Regulation & Frequency Response 

Average annual costs 6,601

Balancing authority area load forecast 5,682

Rate (costs/load forecast) 0.13 mills/kWh

VERBS

Regulation average annual costs 4,335

Following average annual costs 20,610

Imbalance average annual costs 30,804

Total average annual costs 55,748

Average installed resources 4,693

Average customer supplied imbalance 1,393

Regulation rate (costs/installed resources) 0.08 $/kW-mo

Following rate (costs/installed resources) 0.37 $/kW-mo

Imbalance rate (costs/installed resources) 0.78 $/kW-mo

Total VERBS rate 1.23 $/kW-mo

DERBS

Annual average costs inc 4,576

Annual average costs dec 1,177

Total DERBS annual average costs 5,753

Hourly rate inc (Cost/Annual deviation) 14.50 mill/kW/hr

Hourly rate dec (Cost/Annual deviation) 3.60 mill/kW/hr

Source: BPA, 2012 Final Rate Proposal, Generation Inputs Study, July 2011.

The VERBS rate above is for the 99.5% service level and includes a reduction for BPA’s 

estimate of the amount of customer supplied generation imbalance service that will be in place 

during the rate period. BPA has instituted a pilot program, Customer-Supplied Generation 

Imbalance, where wind generators can opt to self-supply a portion of their balancing reserve 

capacity. BPA accounted for wind generation balancing self-supply by reducing the amount of 

balancing reserve requirement allocated to wind generation by the amount of self-supply that 

was contracted by wind generators for the rate period.16 As noted in Table 4, the average wind 

generation for the 2012–2013 rate period is forecast to be 4,693 MW. BPA forecasts the wind 

capacity that would self-supply balancing services at 1,393 MW; therefore, the imbalance 

component of the balancing reserve capacity requirement for variable generation was calculated 

using 3,300 MW of variable generation (wind capacity minus self-supply capacity) 

(BPA 2011b). 

As discussed earlier, the balancing services requirement for solar is estimated to be about half 

what is needed for wind generation and the assumption of a perfect schedule for solar results in 

no imbalance component. The solar VERBS rate then is half of the regulation and following 

components of the wind VERBS rate, which results in a solar VERBS rate for 2012–2013 of 

$0.21/kW-month. 

 

16 BPA also provides Provisional Balancing Service. This service is offered to customers that (1) elected to selfsupply but no longer are able to; or (2) had an estimated interconnection date beyond the rate period and, therefore, 

was not included in the study, but the project is completed earlier than estimated and goes into service during the 

rate period. Billing for provisional service is at the same VERBS rate.
44 

Idaho Wind Integration Charges

Background

The Public Utility Regulatory Policies Act of 1978 (PURPA) provides the Idaho Public Utilities 

Commission (IPUC) with the jurisdiction to require Idaho’s public utilities to offer power 

purchase contracts to QFs, including standard avoided cost purchase rates (published rates) for 

energy generated by QFs with a capacity of 10 MW or less. In February 2008, the IPUC issued 

three separate orders authorizing Idaho’s three major public electric utilities to subtract a wind 

integration discount from the published rate for wind-powered QFs: 

• Order No. 30488 for Idaho Power Company—Case No. IPC-E-07-03 

• Order No. 30497 for PacifiCorp—Case No. PAC-E-07-07 

• Order No. 30500 for Avista Corporation—Case No. AVU-E-07-02. 

In essence, these discounts established a wind integration charge for the utilities to impose on QF 

wind generators (i.e., 10 MW or less) in Idaho. At the time these discounts were established, the 

parties involved acknowledged that the science of wind integration cost modeling was in its 

infancy. As such, the utility-specific wind integration charges are based on wind integration cost 

estimates that were submitted by each utility to the IPUC, subsequent negotiations between the 

relevant parties, and the settlement agreements accepted by the IPUC (IPUC 2008d). 

The orders for Idaho Power17 and Avista established a tiered-discount rate for wind QF payments 

(expressed in terms of a percentage of the published QF rate) that increases as more wind is

added to the system, capped at a maximum of $6.50 per MWh. Table 15 summarizes the wind 

integration charges for Idaho Power and Avista, as established by the IPUC in February 2008. 

Table 15. Summary of Wind Integration Discounts for Idaho Power and Avista

Utility Company Wind Energy Penetration Level (MW)

Wind Integration Discount

(expressed as a percentage 

of the published QF rate)*

7% 8% 9%

Idaho Power Company <300 301–500 >501

Avista Corporation <199 200–299 >300

*The wind integration discounts for Idaho Power and Avista are capped at $6.50/MWh.

Note: The wind integration discount for PacifiCorp is a flat rate of $6.50/MWh.

Sources: Order No. 30488 (Idaho Power), Order No. 30500 (Avista), Order No. 31201 

(PacifiCorp).

 

17 Idaho Power provided limited comments to their draft profile. The company requested that we delete the 

background on PURPA implementation and wind projects in Idaho, but we felt it provided important context for the 

wind integration studies that Idaho Power and other utilities that serve load in Idaho have conducted.
45 

The February 2008 IPUC order for PacifiCorp established a flat wind integration charge of 

$5.10/MWh, as the IPUC reasoned that wind integration costs are lower in utility service areas 

with greater geographical diversity and larger control areas, which is the case with PacifiCorp 

(IPUC 2008d). In March 2010, the IPUC increased PacifiCorp’s wind integration charge to 

$6.50/MWh, but noted that there was still no consensus on the methodology used to calculate 

wind integration costs (IPUC 2010). 

The three IPUC orders also eliminated the “90/110 performance band” from any new Firm 

Energy Sales Agreement for future wind-powered QFs. The performance band provision meant 

that when output was less than 90% of projections or more than 110% of projections, utilities 

could pay developers the usually smaller market-based rate rather than the published rate under 

PURPA. The IPUC reasoned that the wind integration discount rates account for the variability 

of wind, thus diminishing the need for a performance band for wind. In addition to the new wind 

integration charges, new wind-powered QFs were subject to a mechanical availability guarantee 

and a wind forecasting charge. Furthermore, the orders allowed utilities to amend existing windpowered QF contracts to replace the performance band with a mechanical availability guarantee, 

should the wind-powered QFs also agree to fund their share of wind forecasting services and 

accept the wind integration charges. The mechanical availability guarantee requires windpowered QFs to demonstrate monthly that except for scheduled maintenance and force majeure

events, the QF is physically capable and available to generate at full output during 85% of the 

hours in the month. According to the orders, the cost of adding a wind-powered QF project to the 

wind forecasting service was attributed to the individual QF and shared equally between the 

utility and each wind-powered QF. The wind forecasting charges assigned to the wind-powered 

QF are subject to an annual cap set at 0.1% of the total energy payments the utility made to the 

QF (IPUC 2008a, 2008b, 2008c). 

In November 2010, Idaho Power, Avista, and PacifiCorp filed a joint petition asking the IPUC to 

investigate a number of issues related to small‐power projects that qualify for published rates 

under PURPA. The utilities requested that the eligibility cap on the size of projects that qualify 

for the published rate be reduced from 10 MW to 100 kW. The utilities contended that a rapidly 

expanding number of wind projects are having a profound price impact on customers and on 

transmission systems. The utilities argued that the small‐power projects PURPA was originally 

intended to encourage are now developed by sophisticated large‐scale wind companies that 

aggregate several projects within a mile apart from each other to fall under the 10 MW limit to 

qualify for the avoided‐cost rate. When combined, these projects can total up to 100 MW or 

150 MW interconnecting at one delivery point (IPUC 2011). 

In February 2011, the IPUC issued an order retroactively reducing the eligibility cap for wind 

and solar projects to qualify for published rates from 10 MW to 100 kW, effective 

December 14, 2010. The 10 MW limit remains for non‐wind and non‐solar renewable projects. 

The IPUC stated the smaller size limit for wind and solar projects is temporary until a number of 

issues can be resolved (IPUC 2011, p. 40). However, in June 2011, the IPUC issued another 

order leaving the eligibility cap under which wind and solar projects can qualify for commission 

published rates at 100 kW. IPUC staff and other parties attempted to establish criteria that would 

allow the IPUC more discretion in determining whether a QF was truly a small project as 

anticipated by PURPA or a larger project that had disaggregated. The IPUC declined to adopt the 

criteria, maintaining that the potential would still remain for such criteria to be circumvented. 
46 

The IPUC asserted that it is more appropriate to first establish the just and reasonable avoided‐

cost rates before implementing procedures for obtaining such a rate (IPUC 2011). 

In November 2011, the IPUC issued an order announcing the scheduling for a new docket, Case 

No. GNR‐E‐11‐03, to review the terms of PURPA power purchase agreements including, but not 

limited to, the surrogate avoided resource (SAR) and IRP methodologies for calculating avoided 

cost rates. Direct testimony was filed in January 2012 and a settlement conference was held at 

the end of February 2012 (IPUC 2011, p. 42). Interested parties submitted rebuttal testimony at 

the end of June and the IPUC held three days of hearings in August 2012. The main issues 

addressed during the hearings included curtailments, renewable energy certificates, 

determination of avoided-cost rates, contract length, and delay damages/security (IPUC 2012). 

In regard to the methodology used for determining avoided costs, Idaho Power proposed to 

replace the current SAR method with what it calls an “hourly incremental cost” methodology 

based on the highest-cost displaceable resource (e.g., a company-owned thermal plant or a longterm purchase contract). The hourly cost would be totaled each month to arrive at heavy-load and 

light-load pricing for each month of the contract term. The company stated that this more 

dynamic IRP method would more accurately reflect true avoided costs. Renewable developers, 

however, argued that Idaho Power is adopting a “short-run” avoided cost model and is arguing 

for shorter contract lengths to artificially deflate avoided cost rates. Renewable developers

contend the hourly method is too complex and needs hourly updating and could allow the 

utilities to manipulate the results. Further, renewable developers asserted that the proposed 

method would not take into account the value of market sales of QF power during times of 

surplus and wrongly excludes potential carbon costs (IPUC 2012). Finally, Idaho Power also 

proposed a new tariff, Schedule 74, into the IPUC’s PURPA case. This tariff would allow the 

utility to be relieved of its obligation to buy energy from QFs when the utility is operating only 

base-load resources during low-load hours (Fortnightly 2012). 

Separately, in June 2012, Idaho Wind Partners filed a petition with FERC, asking the 

Commission to give guidance to the IPUC on the proper interpretation of a FERC regulation 

under Section 304(f) (Petition for Declaratory Order, FERC Docket EL12-74). Known as the 

“light loading” rule, Section 304(f) grants utilities an exemption from buying QF power if it ends 

up costing the utility more than it saves. This could occur when QF power is delivered during 

light load off-peak hours, forcing base-load plants off-line. Idaho Wind Partners assert that 

Section 304(f) doesn’t apply if the QF has signed a contract with the utility that fixes the avoided 

cost rate in advance of power delivery, because the contract terms should already have taken 

such factors into account in setting the rate (Fortnightly 2012). In September 2012, FERC 

granted Idaho Wind’s petition for a declaratory order, finding that Idaho Power’s proposed 

Schedule 74 would be inconsistent with PURPA and FERC regulations (FERC 2012c). 

Finally, in November 2012, FERC issued an order stating that it will take the IPUC to federal 

court for rejecting unexecuted QF contracts between wind generator Murphy Flat and Idaho 

Power. According to the order, FERC will argue that the utility was bound by the agreements 

even though it did not sign them by IPUC deadline of December 14, 2010. FERC stated that the 

phrase “legally enforceable obligation” is broader than simply a contract between an electric 

utility and a QF, and that the phrase can be used to prevent an electric utility from avoiding its 
47 

PURPA obligations by refusing to sign a contract, or delaying the signing of a contract, so that a 

later and lower avoided cost is applicable (FERC 2012h). 

Meanwhile, in the state of Idaho, the IPUC has yet to issue a final order in its PURPA case. 

Consequently, the current utility-specific wind integration charges are based on wind integration 

cost estimates that were submitted by each utility to the IPUC, negotiations between the relevant 

parties, and the prior settlement agreements accepted by the IPUC. The subsequent sections of 

this chapter describe the methodologies used by each company to estimate the cost of integrating 

wind energy into their utility systems. Idaho Power published a wind integration study in 2007 

and is in the process of revisiting its analysis. Avista also published a wind integration study in 

2007. PacifiCorp included wind integration analyses and integration cost estimates in its IRPs in 

2003, 2004, 2007, and 2008; and published a wind integration study in 2010. PacifiCorp is also 

in the process of revising its wind integration study. 

Idaho Power Company

Idaho Power’s 2007 Wind Integration Study

In February 2007, Idaho Power submitted its wind integration study to the IPUC, Operational 

Impacts of Integrating Wind Generation into Idaho Power’s Existing Resource Portfolio; 

hereafter called the IPC Study. The IPC Study was prepared by EnerNex Corporation. The main 

objective was to estimate the quantifiable costs associated with integrating wind generation into 

Idaho Power’s system. In the IPC Study, an integration cost is defined as the economic impact of 

wind generation variability and uncertainty on the utility company charged with accepting and 

delivering that energy. 

Idaho Power asserted that the increased regulating reserves that must be maintained for wind 

generation have the effect of constraining hydro operations at Idaho Power’s Hells Canyon 

Complex, so the objective of the IPC study was to estimate the economic cost of this effect (i.e., 

the analysis attempted to quantify the opportunity cost of constraining hydro reserves to integrate 

wind into Idaho Power’s system). The IPC Study consisted of five main elements: gathering wind 

data and building wind generation profiles; gathering and analyzing current generation and load 

data without wind; analyzing combined wind and load data and determining operational changes; 

modeling operational changes to determine economic impacts; and evaluating the results.

The wind generation profiles were developed by WindLogics. The meteorological simulations 

used to produce wind speed data (which were converted to wind generation data) were based on 

the MM5 mesoscale model from the Pennsylvania State University / National Center for 

Atmospheric Research (IPC 2007a). To calculate hourly wind generation from the measured 

wind data, a turbine power curve was applied, in which wind speed was the independent variable 

and wind power was the dependent variable; the results were aggregated to hourly average 

values (IPC 2007a, Appendix A). 

Idaho Power used the Synexus Vista Decision Support System (Vista DSS) to estimate the 

variation in regulating reserve requirements between a “flat wind case” and a “variable wind 

case.” The daily cost estimate was calculated as the difference in the values of a case with a flat 

block of wind generation for a 24-hour period (e.g., a predictable and nonvariable resource) and 

another where the same amount of wind energy was delivered to the system, but exhibiting the 

variability and uncertainty of wind generation during that same 24-hour period. The wind 
48 

integration cost per MWh was defined as the difference between the dollar value of the total 

annual generation from the flat wind case run valued at market, and that of the normal wind 

generation run also valued at market, divided by total wind energy production. 

To account for seasonal variations in available hydroelectric resources, three different water 

condition years were used in the model: 1998 (good), 2000 (normal), and 2005 (poor). Four 

different levels of wind generation capacity were used: 300 MW, 600 MW, 900 MW, and 

1,200 MW (IPC 2007a). The two Vista DSS runs referenced above were used to evaluate each 

wind penetration level for each water condition. The average wind integration cost over all three 

water condition years was used to estimate the cost at each penetration level (300 MW, 600 MW, 

900 MW, and 1,200 MW). 

The IPC study included an initial wind integration cost estimate of $10.72/MWh, which was 

the midpoint between costs associated with the then currently contracted 384 MW of wind 

capacity and the additional cost that would be incurred at a 20% penetration level (by capacity) 

of 600 MW. After holding two public workshops, Idaho Power submitted a report addendum to 

the IPUC on October 31, 2007, with a revised wind integration cost of $7.92/MWh (IPUC 

2008a). There were six primary modeling adjustments that resulted in an overall decline in the 

estimated cost of integrating wind energy. These adjustments are described in the paragraphs

below. Note that because the 1,200-MW penetration level was shown to be beyond Idaho 

Power’s ability to integrate, the 1,200-MW penetration level was dropped from further 

consideration in the updated analysis. 

First, the Vista DSS model included a built-in arbitrage opportunity that allowed it to select the 

lower price between the two electricity markets, Mid-Columbia and Palo Verde. In practice, 

these opportunities do occur on occasion, but a review of the modeling results indicated that this 

feature was utilized far too frequently and it was preferential toward the flat wind case over the 

variable wind case, so the arbitrage opportunity was eliminated from the model. 

Furthermore, in the original study, regulating reserves were imposed by the Vista DSS model at a 

constant and bi-directional level. Model revisions, however, allowed for setting varying levels of 

regulation up and down on an hourly basis, with the ability to define dynamic reserves hourly. 

Public comments regarding the IPC Study suggested that the original reserve estimation 

methodology in the study double-counted the amount of necessary reserves. In the original study,

Idaho Power assumed that regulating reserves were necessary to cover variability in highresolution (i.e., minute-to-minute) load and wind data, along with instantaneous 10-minute load 

and wind data. These two sources of variability were combined through a root-mean-square 

operation, not a straight arithmetic addition. However, public comments suggested that the 

instantaneous 10-minute data could also include a portion of the variability present in the high 

resolution data, and consequently regulating reserves calculated from both time series may 

reflect double counting. As such, the updated model removed high-resolution load and wind 

data, and based estimates on the amount of reserve necessary to address variability in the 

instantaneous 10-minute time frame for load and wind. 

It was also suggested that the 24-hour flat wind case was biased because average wind 

generation during light-load hours in the synthetic wind time series exceeded average wind 
49 

generation during the heavy-load hours, so the value of the flat wind case was favorably biased 

prior to consideration of any effects related to wind integration. To remove this bias from the 

model, the flat wind case was broken up into two blocks for each 24-hour period (one for lightload hours and one for heavy-load hours). 

Additionally, 100 MW from the Elkhorn wind project in northeastern Oregon was included in 

the 300 MW scenario, and 100 MW from the Cotterel site in southern Idaho was removed to 

provide greater geographic diversification of the wind resource. 

Finally, the wind forecasting methodology was changed from a persistence forecast taken at 

65 minutes before the hour to a seasonal, autoregressive method. The updated wind forecasting 

process was simulated through the use of an autoregressive time-series model that expressed 

hourly average wind generation for an operating hour as a function of the six 10-minute readings 

occurring 65, 75, 85, 95, 105, and 115 minutes prior to the start of the operating hour (IPC 2007b). 

Determination of Current Integration Charge

After the submission of the report addendum, Idaho Power and the Renewable Coalition 

(Renewable Northwest Project and the Northwest Energy Coalition) agreed to a settlement

stipulation which resulted in the tiered integration charges presented in the beginning of this 

chapter (from Order No. 30488). The parties disagreed on the assumptions used in the modeling. 

Idaho Power stated that both the charges in the settlement agreement (which were capped at 

$6.50/MWh) and the amount from the revised estimate of $7.92/MWh were within reasonable 

ranges of the cost of integrating wind energy. The Renewable Coalition filed testimony in 

support of the settlement with an explanation of why the estimate of $7.92/MWh was too high. 

According to their testimony, Idaho Power’s conversion of wind speed data to wind generation 

data likely overestimated the variability that would be experienced by actual wind resources, 

leading to higher wind integration costs. The Renewable Coalition also disputed the assumption 

that the Hells Canyon hydro facility was the only resource used to cover the reserve capacity 

needed for wind variability (Dragoon 2007a). 

Idaho Power’s 2012 Wind Integration Study

Idaho Power is developing an updated wind integration study to determine how much wind 

generation can be integrated into Idaho Power’s electric system, and to refine the utility’s 

estimate of the cost of integrating wind energy into its system (IPC 2012a). 

Idaho Power held public workshops on March 16, 2012 and April 6, 2012, to review some of the 

basic study methodology and to present preliminary results of the analysis. During the April 

workshop, Idaho Power presented updated estimates of wind integration costs at various levels of 

system integration (IPC 2012b): 

• $8.76/MWh at 800 MW of wind capacity 

• $13.30/MWh at 1,000 MW of wind capacity 

• $20.12/MWh at 1,200 MW of wind capacity 

The methodology of the 2012 study is similar to that of the original IPC Study. The test year has 

been identified as 2017, wind capacity is being studied at three potential levels (800, 1,000, and 
50 

1,200 MW), and three different water conditions are being modeled. The analysis is being 

conducted by 3TIER and Plexos Solutions, and the schedule is unknown. Consistent with 2007 

IPC Study, wind integration costs are estimated as the net-cost difference between the wind and 

non-wind cases divided by the total MWh of wind generation (IPC 2012a). 

Avista Corporation

Avista’s 2007 Wind Integration Study

In March 2007, Avista Corporation submitted its wind integration study to the IPUC, Final 

Report: Avista Corporation Wind Integration Study; hereinafter called the Avista Study. The 

Avista Study was also prepared by EnerNex Corporation. The Avista Study included the same 

definition of an integration cost that was used in the IPC Study, and the method used to calculate 

integration costs is similar to that of the IPC Study. 

The wind generation model was based on data from Oregon State University’s Energy Resources 

Research Laboratory. Historical data from five BPA anemometer sites as well as a wind plant in

Vansycle, Oregon, were used as the reference data points. Wind speed data at each location was 

converted into wind energy generation at 10-minute intervals using a power curve from a 

2.75 MW wind turbine model. The four wind generation scenarios used for the model were 

100 MW, 200 MW, 400 MW, and 600 MW. Three water level scenarios (low, average, and high) 

were also developed.

A proprietary dispatch model (LP model), driven by a linear programming engine, was used to 

optimize operations of Avista’s system. In the study, the LP model optimized a set of scenarios 

with wind energy, and another set without wind, where the wind energy is replaced by a nonvariable resource. The differences between the two analyses (with and without wind) were used 

to identify the incremental reserve obligations required with wind. In the Avista Study, reserves 

included regulation, load-following, and spinning and non-spinning reserves. The integration 

costs were estimated by comparing operation costs from the with-wind scenarios to those where 

the same amount of energy was delivered by a non-variable resource. To calculate an estimate of 

the wind integration cost per MWh, the difference between the runs was divided by the total 

wind energy produced during the year. This process was completed for each wind penetration 

level, wind source, and water year. 

Analogous to the IPC Study, the key wind integration cost driver was incremental reserves. The 

reserve obligations were estimated using historical utility data from 2002 through 2004. 

Specifically, regulation (up to 1 minute), load-following (1 minute to 1 hour), spinning and nonspinning operating reserves, and forecast errors are entered input in the Avista LP Model as 

constraints on system optimization. Incremental regulation and load-following reserves were 

calculated by identifying levels necessary to meet load variability alone, then performing the 

same analysis but netting wind generation against load when performing the calculations. 

Avista ran its LP Model under various levels of wind forecasting error, from 0%, or perfect 

foresight, to 30%. Wind forecasting error was a significant focus of the Avista Study. Two-hourahead wind forecasts were compared to wind generation levels. Forecast error was calculated at 

the 95% confidence interval and carried across all hours in the up and down directions. In the 

study, Avista assumed forecast error would be met with spinning reserves. 
51 

The results of Avista’s study were estimated wind integration costs expressed in terms of a 

percentage of market prices (because prices vary over time). Avista’s base case result was that 

the cost of integrating up to 400 MW of wind capacity was approximately 12% more than the 

cost of integrating other non-wind resources (Avista 2007a). 

Determination of Current Wind Integration Charge

Avista filed a petition with the IPUC requesting a wind integration discount of 12% of the 

published QF rate, applicable to wind-powered QFs. Avista stated that this figure represents the 

costs associated with the intra-hour variability of wind. When the wind-generated power is 

delivered on a firm hourly schedule, integration costs were approximately 6% in the Avista 

Study. Accordingly, if a wind-powered QF agreed to deliver on a firm hourly schedule, Avista 

proposed a wind integration discount of 6% of the published QF rate (Avista 2007b). 

The Renewable Coalition (Renewable Northwest Project and Northwest Energy Coalition) and 

other parties submitted petitions to intervene, disputing the methodology used in the Avista 

Study. After formal and informal discussions and negotiations between the parties, the 

Renewable Coalition filed a Motion for Approval of Settlement Stipulation before the IPUC. 

Avista reviewed the Motion and supported the Settlement Stipulation (Renewable Coalition 

2007). The settlement resulted in the tiered integration charges presented in the beginning of this 

chapter (from Order No. 30500). 

The parties disagreed on the assumptions used in the modeling. Avista advised that both the 

charges in the settlement agreement (which do not exceed 9%) and the amount from its study 

(12%) were within reasonable ranges of the cost of integrating wind energy. The Renewable 

Coalition filed testimony in support of the settlement with an explanation as to why they 

believed Avista’s cost estimate was too high (Renewable Coalition 2007). According to their 

testimony, the conversion from wind speed to wind generation in computer modeling is a 

complex and poorly understood issue. The Renewable Coalition stated that the resulting 

statistical characteristics of Avista’s wind generation data indicated that the process used to 

produce the wind generation data likely overestimated the variability that would be experienced 

by wind resources. This was a considerable issue because wind integration costs are mainly 

dependent on wind generation variability. Another concern was Avista’s method of computing 

reserve requirements. The Renewable Coalition stated that the total reserve requirements should 

be computed directly, or estimated as the square-root of the sum of the squares; whereas Avista 

maintained that its alternative approaches do not significantly change the result. Finally, the 

optimization algorithm used by Avista required that wind forecasts must be produced more than 

an hour in advance, and the Renewable Coalition stated that 20 to 30 minutes in advance should 

be sufficient (Dragoon 2007b). 

PacifiCorp

The wind integration charges that the IPUC established for PacifiCorp18 differ from the other two 

major utilities in Idaho. Instead of a tiered-discount rate, the IPUC authorized PacifiCorp to 

subtract a flat per-MWh charge from the published QF rate. Rather than submitting a wind 

integration study to the IPUC, PacifiCorp incorporated a wind integration analysis and integration 

cost estimate as part of its IRP. PacifiCorp’s wind integration cost estimates have changed over 

 

18 PacifiCorp did not respond to requests to review this section.
52 

time, based on varying model inputs and changing assumptions. Table 16 displays a chronological 

summary of PacifiCorp’s estimated cost of integrating wind energy into the system. 

Table 16. Chronological Summary of PacifiCorp Wind Integration Cost Estimate

Year and Source Cost Estimate 

(per MWh)

Wind Capacity Level 

(MW)

2003 IRP—Appendix L $5.50 1,000

2004 IRP—Appendix J $4.64 1,000

2007 IRP—Appendix J $5.10 2,000

2008 IRP—Appendix F $9.96 to $11.85* 2,734

2010 Wind Integration

Resource Study

$8.85

$9.70

1,372

1,833

2012 Wind Integration

Resource Study (Draft Version) $1.89 2,135

*This range is dependent upon the CO2 tax level scenario used in the modeling.

The subsequent sections describe the methodology used by PacifiCorp to estimate the cost of 

integrating wind resources into its system; starting with PacifiCorp’s 2003 IRP and concluding 

with PacifiCorp’s pending 2012 Wind Integration Resource Study. Note that PacifiCorp’s most 

recent published wind integration cost estimates are from the 2010 Wind Integration Resource 

Study (the 2012 estimates were under stakeholder review and subject to change at the time of the 

publication of this document). 

2003 IRP

In Appendix L of PacifiCorp’s 2003 IRP, the estimated cost of wind integration was based on 

two factors: imbalance costs and incremental operating reserve requirements. Imbalance costs 

were calculated as the difference in system costs between firm contract delivery at constant rates 

over time, and an equivalent amount of energy from simulated wind resources. Wind generation 

fluctuated hourly based on available historical wind data. Using a three-year average, PacifiCorp 

calculated imbalance costs to be about $3.00/MWh for integrating 1,000 MW of wind capacity. 

Incremental operating reserves were defined as the “resources that are available on short notice 

to provide additional power as needed” (PacifiCorp 2003). PacifiCorp assumed that intra-hour 

variability was insignificant, as experience to date had suggested that it resulted in no material 

cost issues. PacifiCorp did note, however, that this assumption was based only on observations 

of operations and it may change over time if the wind resource capacity was large enough or 

very centralized. 

PacifiCorp’s incremental reserve requirements were estimated by comparing loads with and 

without wind. This calculation was fairly straightforward. First, the standard deviation of hourly 

loads with wind for a year was calculated. Next, a new standard deviation was computed after 

subtracting out wind generation. The difference between the two standard deviations was taken 
53 

as an estimate of the increased need for operating reserves (PacifiCorp 2003). The resulting 

estimate was a cost of $2.50/MWh for incremental reserve requirements. Combining the 

estimated imbalance cost and the cost of incremental reserves resulted in a total wind integration 

cost estimate of $5.50/MWh for integrating 1,000 MW of wind capacity. 

2004 IRP

In Appendix J of the 2004 IRP, the imbalance cost estimate was not changed, but the cost of 

incremental reserve requirements were adjusted based on new and lower market prices, which 

was the only factor changed. The resulting estimate for integrating 1,000 MW of wind capacity 

was $4.64/MWh (PacifiCorp 2004). 

2007 IRP

The estimated wind integration cost included in Appendix J of PacifiCorp’s 2007 IRP was 

$5.10/MWh for integrating 2,000 MW of wind capacity. In the 2007 IRP, the same two cost 

components were used to estimate the cost of integrating wind energy: system balancing costs 

(i.e., imbalance costs) and incremental reserve requirements. System balancing costs were 

identified as the “difference in value between the energy delivered from wind resources 

compared to that delivered from less volatile resources.” To calculate system balancing costs, 

PacifiCorp used the same methodology from the previous IRPs, which resulted in an estimate of 

approximately $4.00/MWh for 2,000 MW of wind capacity. 

According to the 2007 IRP, incremental reserve requirements are based on the need for dynamic 

resources to be held in reserve, able to respond on a roughly 10-minute basis (PacifiCorp 2007c). 

For the 2007 analysis, PacifiCorp classified operating reserves into three categories based on 

purpose and characteristics: contingency reserves, regulating reserves, and load-following 

reserves. The need for contingency reserves, which are held for the purpose of responding to 

sudden equipment failures, was not expected to be affected by wind projects. Similarly, 

regulating reserves, which are held to respond to changes in system frequency over a period of a 

few seconds, did not appear to be to be significantly affected by wind energy. As such, 

incremental reserve requirements were based solely on load-following reserves, which were 

defined as “generation that can be brought on over a multiple-minute time period.” 

For incremental reserve requirements, the 2007 analysis was based on the hourly uncertainty in 

generation; whereas the 2003 and 2004 IRPs were based on the hourly variability of wind 

resources. The availability of hourly wind data from resources in PacifiCorp’s service territory

enabled the analysis to include proxy wind resources with realistic operating characteristics. 

PacifiCorp revised its methodology to estimate the load-following reserve requirement based on 

the uncertainty in load for the next hour. In the IRP modeling, estimates of next-hour loads are 

made, and the model moves to bring on or back off resources as necessary to accommodate the 

expected change in loads. Knowing that the actual load of the next hour will likely be different 

than the forecast and that there will be deviations within the hour, operators hold additional 

resources ready to respond should they underestimate the need for resources. In essence, this 

methodology entailed establishing the necessary level of reserves to ensure that the deviation 

between forecast load and actual load in a given hour can be met 95% of the time.
54 

The methodology was applied first to the system load with wind and then again to the system 

load net of wind generation. The difference between the two results represents the estimated 

incremental reserve requirement due to wind resources.. The resulting estimate was an 

incremental reserve requirement of 43 MW for 2,000 MW of wind capacity. The unit cost was 

calculated by dividing the total cost of additional reserves by the total wind energy, which 

resulted in $1.10/MWh of wind energy (PacifiCorp 2007c). System balancing costs combined 

with the cost of incremental reserve requirements resulted in the estimate of $5.10/MWh. 

2008 IRP

In Appendix F of its 2008 IRP, PacifiCorp further refined its methodology of estimating wind 

integration cost. The same two cost categories were used but referred to as inter-hour (system 

balancing) and intra-hour (incremental reserve requirements) costs. Existing wind plant production 

data from October 2008 through April 2009 were used in the calculations, but the data was scaled 

up to reflect planned wind capacity additions to PacifiCorp’s system. The intra-hour cost was 

calculated by estimating the MW quantity of reserves required as additional wind resources were 

added to the system. This was done by computing the deviation of hourly average wind generated 

energy from the historical hour-ahead wind generation forecast, and hourly wind generation. 

The inter-hour cost calculation was split into day-ahead and hour-ahead. PacifiCorp assumed 

forecast imbalances were addressed in the day-ahead market. Using the hourly differences 

between long-term expected wind generation (i.e., prior energy expectations) and historical wind 

generation forecasts for the day-ahead horizon, the day-ahead system balancing costs were 

estimated. A similar method was used to calculate hour-ahead variation, but using the variance 

between the day-ahead wind forecast and the hour-ahead wind forecast. The specific hourly 

variance was applied to the corresponding hourly real-time price. The size of the variance 

determined the imbalance cost attributable to wind, which was the product of the hourly price 

and the corresponding variance percentage. 

In its 2008 IRP, PacifiCorp estimated the overall wind integration cost to range from $9.96/MWh 

to $11.85/MWh, depending on which CO2 tax level was utilized. The lower-bound estimate 

($9.96/MWh) corresponded to an $8 CO2 tax scenario, whereas the upper-bound estimate 

($11.85/MWh) corresponded to a $45 CO2 tax scenario (PacifiCorp 2008). 

PacifiCorp’s 2010 Wind Integration Resource Study

On September 1, 2010, PacifiCorp published its 2010 Wind Integration Resource Study 

(PacifiCorp Study or 2010 analysis). The amount of operating reserves required for different 

levels of wind, and the estimated cost of holding those reserves on PacifiCorp’s system was 

calculated using the Planning and Risk (PaR) model (PacifiCorp 2010a). The PaR model was 

also used to estimate the wind integration costs associated with system balancing (PacifiCorp 

2010). Note that in the 2010 analysis, PacifiCorp assumed that there was no CO2 tax. 

Ten-minute interval load and wind data was used to estimate the amount of operating reserve, 

both up and down, needed to manage fluctuations in load and fluctuations in wind within 

PacifiCorp’s balancing authority. The operating reserves were limited to spinning reserves and 

non-spinning reserves, which are needed for regulation, load-following, and contingency 

reserves. In the PacifiCorp study, regulation service refers to the operating reserve required to 

manage the variability of load and wind generation in 10-minute periods, and load-following 
55 

service represents the operating reserve required to manage the variability as measured in hourly 

periods (PacifiCorp 2010b). Note that in PacifiCorp’s study, contingency reserves were assumed 

to remain unchanged by wind generation. As used below, operating reserves encompass both 

regulation and load following. 

To estimate the incremental operating reserves required for wind, PacifiCorp used existing 

operating reserve requirements for load and production data from 2007–2009, subdivided into 

regulation and load following. The 2007–2009 load data was the baseline case (zero wind 

generation) in each scenario, whereas coincident wind data, as observed (plus estimated wind 

data by the Brattle Group), was added in increasing levels of wind penetration capacity to gauge 

the change in operating reserves demand. The wind data set selected for the 2010 analysis 

contained gaps so PacifiCorp utilized the Brattle Group to simulate missing wind data. The 

methodology was based on using available wind data to estimate the missing wind data. The 

statistical relationships between pairs of sites were studied and those relationships were used to 

derive or estimate the wind output for periods that historical data were incomplete or missing

(PacifiCorp 2010a). 

In the PaR Model, the hourly wind forecast is done by persistence; applying the instantaneous 

sample of the wind generation output 20 minutes past the current hour to the next hour as a 

forecast and balancing the system to that point (PacifiCorp 2010b). 

Two scenarios of 1,372 MW and 1,833 MW of wind were simulated in the PaR model to 

estimate the incremental operating reserve demand. The first simulation was based on a nonvariable/flat resource and the second was based on wind profiles. The differences in system cost 

between the two simulations were divided by the total volume of wind generation in each 

scenario to derive the estimated operating reserve costs, i.e., regulation and load following 

(PacifiCorp 2010b). 

Another set of PaR simulations was used to estimate the system balancing costs associated with 

wind integration. Two more PaR runs were used to simulate PacifiCorp’s system operations. The 

first run determined the unit commitment of PacifiCorp’s generation given the day-ahead 

forecast of wind and load, whereas the second simulation used that unit commitment, but 

dispatched units based on wind and load. The change in system costs between this second 

simulation and the original wind simulation (from the above paragraph) was used to isolate the 

wind integration cost due to system balancing (PacifiCorp 2010a). 

In its 2010 analysis, PacifiCorp estimated that the cost of integrating 1,372 MW of wind capacity 

is $8.85/MWh and $9.70/MWh for 1,833 MW of wind capacity (PacifiCorp 2010b). Note that in 

the 2010 analysis, the vast majority of the wind integration costs were associated with 

incremental operating reserves (i.e., about 90%). PacifiCorp included the results of the 2010 

analysis in its 2011 IRP. 

PacifiCorp’s 2012 Wind Integration Resource Study

PacifiCorp is in the process of completing an updated wind integration study. PacifiCorp is 

planning to publish the 2012 Wind Integration Resource Study (2012 analysis) by late 2012, and 

include the results in the Company’s 2013 IRP. PacifiCorp’s proposed methodology is largely 

similar to the methodology used in its 2010 study—wind integration costs are to be estimated as
56 

the sum of the operating reserve costs and system balancing costs. PacifiCorp will use data from 

operations between 2007 and 2011; including 10-minute system load data, 10-minute average 

wind production data, and day-ahead load and wind forecast data (PacifiCorp 2012a). 

Although the study methodology is similar to the previous analysis, the integration cost estimates 

included in the draft 2012 analysis are considerably lower than those in the 2010 analysis due to 

updated input assumptions. According to the draft version, the 2012 analysis reflects a 

significantly depressed commodity price environment than the 2010 analysis, which is the primary 

reason for the cost differential. The effect of changing power and natural gas prices on the cost of 

wind integration is significant, even if the volume of wind being integrated does not change. In the 

2012 analysis, the value of reserves is considered the opportunity cost of a lost sale at a given 

generation station. This opportunity cost is the foregone margin (which is equal to the lost revenue 

from the wholesale sale) less the variable cost to run the generation plant at a higher level, which is 

primarily the cost of fuel. The PaR model showed that this sale would have been made had the unit 

not been backed down to provide the required reserves (PacifiCorp 1012b). 

In addition, PacifiCorp included day-ahead load forecast error in the system balancing costs in 

the 2010 analysis, which should not have been attributed to wind resources according to the draft 

version of the 2012 analysis (PacifiCorp 2012b). In the draft 2012 analysis, PacifiCorp estimated

that the cost of integrating 2,135 MW of wind capacity is $1.89/MWh—$0.36 attributable to 

system balancing, and $1.52 for incremental operating reserves (PacifiCorp 2012b). Note that 

these results were under stakeholder review at the time of publication of this document, and are 

subject to change. 

Determination of PacifiCorp’s Wind Integration Charge in Idaho

The establishment of PacifiCorp’s wind integration charge dates back to IPUC Case No. PAC-E07-07 in 2007, in which PacifiCorp originally requested a wind integration charge of 

$5.04/MWh. This cost is equivalent to the estimate from the 2004 IRP, $4.64/MWh, adjusted for 

inflation to 2007 dollars (PacifiCorp 2007a). In October 2007, PacifiCorp and the Renewable 

Coalition filed a stipulated settlement asking the IPUC to approve a wind integration discount of 

$5.10/MWh, citing the updated estimate in PacifiCorp’s 2007 IRP (PacifiCorp 2007a). In 

February 2008, the IPUC agreed that the estimate of $5.10/MWh was within a reasonable range, 

thus approving the settlement agreement and issuing Order No. 30497. 

In September 2009, PacifiCorp requested the IPUC increase the applicable wind integration 

discount to $9.96/MWh, citing the findings from a wind integration analysis in its 2008 IRP. The 

Renewable Northwest Project submitted comments to the IPUC in response to PacifiCorp’s 

petition, arguing that the estimate from PacifiCorp’s 2008 IRP was flawed. According to the 

Renewable Northwest Project, the most fundamental shortcoming was that the variability and 

uncertainty introduced by wind was considered separately from the variability and uncertainty 

attributed to load. Because the variability of load and the variability of wind are not correlated, 

the net variability of load and wind is less than the variability of load and wind individually. 

PacifiCorp did not respond to the Renewable Northwest Project’s comments. 

In March 2010, the IPUC stated that they continue to find that the costs of wind integration are 

real and greater than zero. The IPUC also acknowledged that there was not a consensus 

methodology for calculating wind integration costs. However, the IPUC found it reasonable to 
57 

increase the integration charge from $5.10/MWh to $6.50/MWh (Order No. 31201), which was 

the maximum wind integration rate authorized for the other two major utilities in Idaho. The 

IPUC stated that the PacifiCorp case was not the appropriate forum to select a methodology for 

estimating wind integration discounts; the IRP process, however, was a more appropriate forum 

open to all stakeholders (IPUC 2010). 

NorthWestern Energy – Montana

Background

NorthWestern Energy has applied to FERC to charge its retail supply customers to reflect the 

regulation demands of wind generation (FERC 2012e). NorthWestern’s wind integration cost 

estimates are based on the Dave Gates Generating Station (DGGS) at Mill Creek, a 150-MW

natural gas-fired generating unit in Montana (NorthWestern Energy 2011a and NorthWestern 

Energy 2011b). The plant is used to provide regulation service for NorthWestern Energy, and is 

therefore the basis for its regulation cost calculations. 

NorthWestern contends that 60 MW is an appropriate amount of regulation capacity used by its 

traditional load, and 105 MW is necessary for regulation services for its total load. Therefore, 

NorthWestern proposed to allocate 60/105ths of the revenue requirement of DGGS to its 

wholesale and bundled retail customers under its Montana OATT Schedule 3—Regulation and 

Frequency Response Service; and 45/105ths solely to its retail supply customers to reflect the 

regulation demands of wind generation. Note that these allocations are based on a 12 coincident 

peak load. In addition, NorthWestern submitted two studies to FERC in support of its proposal: 

(1) the NorthWestern Energy Montana Wind Integration Study, which was part of 

NorthWestern’s 2011 Electricity Supply Resource Procurement Plan; and (2) a study completed 

NorthWestern witnesses, Dr. Richard Tabors. Both studies utilize historical data from 

NorthWestern’s balancing authority area to calculate regulation capacity needs (FERC 2012e). 

The parties involved in the NorthWestern’s rate case reached an impasse and were unable to 

come to a settlement agreement. There was disagreement among the interveners regarding both 

the numerator and the denominator proposed by NorthWestern. The Montana Large Customer 

Group (LCG) argued that the numerator should actually be 19 MW. Furthermore, LCG argued 

that the denominator should be based on the nameplate capacity of the DGGS, 150 MW, rather 

than NorthWestern’s proposal of 105 MW. The Administrative Law Judge assigned to the case

issued an Initial Decision in September 2012, finding portions of NorthWestern’s application 

unjust and unreasonable, siding with the LCG for both the numerator and the denominator 

(FERC 2012e). As such, the proceeding will go through FERC’s hearing process, including 

additional rounds of briefs before FERC issues a final order. The two studies NorthWestern 

submitted to FERC, and a study from LCG, are summarized below. 

NorthWestern’s Electricity Supply Resource Procurement Plan

For its 2011 Electricity Supply Resource Procurement Plan, NorthWestern Energy defined a 

wind integration price adder based on the DGGS natural gas power plant. As stated above, the 

plant is used to provide regulation service for NorthWestern Energy, and is therefore the basis 

for estimating regulation costs. The wind integration price adder represents how much net 

incremental regulation is going to be needed due to the variability of wind. The wind integration 
58 

price adder is added to the cost of wind when evaluating supply options in NorthWestern 

Energy’s procurement plan. To determine the wind integration price adder, a no-wind scenario 

with 60 MW of total regulation (42 MW allocated to energy supply) was analyzed and compared 

to a scenario including existing wind (141 MW), and an additional 25 MW of regulation 

(NorthWestern Energy 2011b). 

The wind integration price adder is comprised of the costs of operating the Mill Creek facility 

above 60 MW, which has historically been the amount of regulation NorthWestern Energy has 

needed. These costs are adjusted for revenue credits from energy sales as a byproduct of 

regulation and distributed across wind projects in NorthWestern’s electric supply portfolio. 

NorthWestern Energy determined that 25 MW of additional regulation will be needed in 2012 to 

accommodate the 141 MW of existing wind capacity by applying an 18% wind integration factor 

to the total wind capacity. NorthWestern Energy does not specify how they determined this 

value. The wind integration factor is the ratio of regulation per megawatt of wind capacity 

needed to meet reliability requirements. 80% of the costs of the Mill Creek facility are assigned

to energy supply, and, of that, about 14% ($5.575 million) is assigned to wind. The integration 

cost was determined to be $11.28/MWh, which is wind’s share of the costs of the Mill Creek 

facility, divided by the expected total wind generation (given a 40% capacity factor) 

(NorthWestern Energy 2011b). 

Determination of Regulation Service Requirements19

In December 2011, NorthWestern submitted rebuttal testimony to FERC that included the study 

performed by Dr. Tabors (Tabors Study) analyzing the regulation service needs of 

NorthWestern. Using a six-step methodology, Dr. Tabors estimated NorthWestern’s regulating 

capacity requirements for its traditional load: 

• First, Dr. Tabors used 2009 1-minute ACE20 data as a baseline for his analysis.

• Second, he subtracted on a minute-by-minute basis an estimate of the wind forecast 

uncertainty. 

• Third, Dr. Tabors subtracted the amount of regulation that was actually procured from 

third party suppliers by NorthWestern on a minute-by-minute basis.

• Fourth, he averaged the 1-minute data into 10-minute blocks. 

• Fifth, he aggregated the 10-minute blocks into calendar months to identify the maximum 

variation both up and down that is required. 

• Finally, Dr. Tabors subtracted L1021 values from both the up and down variability to 

arrive at regulation up and regulation down quantities (FERC 2012e). 

 

19 The determination of regulation service requirements is discussed in the body of this report only and is not 

included in the summary tables in the beginning of this report. 

20 ACE is defined as the instantaneous difference between a balancing authority’s net actual and scheduled 

interchange, taking into account the effects of frequency bias and correcting for meter error (NERC 2012). 21 L10 is a statistically derived value derived from NERC standards that reflects the maximum 10-minute deviation 

from ACE that is allowable. It is not necessary to perfectly drive ACE to zero, but rather ACE should be within the 

L10 value from zero. 
59 

Note that Dr. Tabors corrected an error in his analysis discovered by FERC staff. Initially, Dr. 

Tabors incorrectly associated a positive open loop ACE value with a need for regulation up 

capacity, as well as a negative open loop ACE value with a need for regulation down capacity.

Using this methodology, Dr. Tabors analyzed NorthWestern’s total capacity requirements for a 

range of NERC’s CPS2 compliance targets. Ranging from the minimum CPS2 compliance level 

of 90% up to 98%, Dr. Tabors argued that NorthWestern would need between 52 MW and 

101 MW of regulation capacity. Specifically, Dr. Tabors concluded that at a 92% compliance 

level NorthWestern would require 59 MW of regulation. For a CPS2 compliance level of 94%, 

Dr. Tabors explained that NorthWestern would require 67 MW of regulation capacity. Finally, to 

meet a 95% CPS2 compliance level, Dr. Tabors stated that NorthWestern requires 73 MW of 

regulation capacity (FERC 2012e). 

LCG argued that 19 MW was a more appropriate numerator with a CPS2 compliance target of 

95%. LCG witness James Dauphinais submitted a separate study to support this estimate, and 

LCG offered three critiques of Dr. Tabors’ study. First, LCG argued that Dr. Tabors failed to 

eliminate regulation down capacity from the calculation of NorthWestern’s non-wind integration 

capacity need, in accordance with FERC precedent. Second, LCG contended that Dr. Tabors 

failed to apply regulation limits to 1-minute open loop ACE values. Finally, LCG argued that Dr. 

Tabors erred by allocating the entire amount of diversity benefits between wind schedule 

deviations and non-wind schedule deviations to NorthWestern’s wind integration regulation 

capacity need, when a cost-causation approach would produce more appropriate results (FERC 

2012e). Mr. Dauphinais initially performed the seven steps to determine NorthWestern’s 

regulation service requirement: 

• First, for each 1-minute interval, he subtracted the non-wind balancing authority 

generation amount from the balancing authority load amount to get a net balancing 

authority load amount. 

• Second, he converted these 1-minute instantaneous values to 10-minute average values. 

• Third, he dropped the first and sixth 10-minute intervals for each hour to eliminate 

ramping periods between hourly schedule amounts. 

• Fourth, for each hour, he calculated from the remaining 10-minute interval data the 

difference between the maximum 10-minute balancing authority net load amount for that 

hour and the minimum 10-minute balancing authority net load amount for that hour to get 

an hourly gross regulation service capacity amount for that hour. 

• Fifth, for each month, he then sorted, from highest to lowest, the hourly gross regulation 

service capacity amounts.

• Sixth, he determined for each month the gross hourly regulation service capacity amount 

that would be necessary to cover 90% of the hours for that month. 

• Lastly, he subtracted NorthWestern’s L10 value of approximately 24 MW from the 

monthly 90th percentile gross regulation service capacity amounts (FERC 2012e). 

Using this methodology, Mr. Dauphinais determined that NorthWestern’s regulation capacity 

requirement should be 19 MW. Note that Mr. Dauphinais made the same correction noted above 

for the Tabors study, and made an additional modification based on feedback from a BPA 
60 

witness. BPA argued (and LCG agreed) that Dr. Tabors incorrectly assigned all the diversity 

benefits to wind integration capacity need, which would necessarily result in an overstatement of 

the Schedule 3 rate. Instead, BPA advocated for an approach that allocates the benefit provided 

by diversity to NorthWestern’s Schedule 3 customers (FERC 2012e). 

PGE

Background

PGE22 incorporates a wind integration cost into its resource plan modeling, as part of comparing 

wind resources with other demand and supply resource options. In 2007, PGE undertook Phase I 

of a wind integration study to estimate the wind forecasting costs associated with a PGE “selfintegration” of wind generation (i.e., costs based on operations and constraints of PGE’s owned 

generation and power purchases). In November 2010, the Oregon PUC directed PGE to assess

wind integration costs in their IRP. As a result, in 2011, PGE reported that the wind integration 

cost would be decreased from $13.50/MWh in the 2009 IRP to $9.15/MWh (in 2014 dollars) for 

the next IRP (PGE 2011a; and Oregon Public Utilities Commission 2011). 

Assumptions

The wind integration cost estimate of $9.15/MWh reflects PGE’s estimated cost of using its own 

generating resources to integrate 450 MW of existing wind resources and 400 MW of new wind

resources into PGE by 2014. Using the NREL Western Wind and Solar Integration Study 

(WWSIS) Resource Database, PGE developed wind power capacity factors and shapes. The 

database provided simulated wind power output at 10-minute intervals, and day ahead forecasts 

at one hour intervals. The 450 MW of existing wind are produced by Biglow Canyon Wind 

Farm, which was assumed to be self-integrated. The 400 MW of new wind generation were 

assumed to be located in the Columbia River Gorge. 

PGE currently has an operational schedule that includes one-, two-, and three-day ahead 

forecasts for load and generation resources. Load and resource forecasts generated on Monday, 

Tuesday, and Wednesday each week provide one-day ahead forecasts; those generated on 

Thursday provide a day-ahead forecast for Friday and a two-day ahead forecast for Saturday; 

whereas forecasts generated on Friday yield a two-day ahead forecast for Sunday and a three-day 

ahead forecast for Monday. The WWSIS wind data provides a day-ahead forecast for every day. 

PGE supplemented the WWSIS data with historical hourly forecast data and corresponding 

generation data from 2007 to 2010 for the Biglow Canyon wind plant. The difference in timing 

between PGE’s load and forecast schedules are meant that the forecast errors in the WWSIS data 

were not comparable to PGE’s wind forecast errors for the two- and three-day ahead forecasts as 

shown in Figure 2. 

 

22 PGE did not respond to requests to review this section.
61 

Source: PGE and EnerNex, 2011. PGE Wind Integration Study Phase II. September 30, 2011, pp. 14. 

www.uwig.org/PGE_Study/PGE_Phase%202_Wind_Integration_Report_9-30-11.pdf. 

Figure 2. Mean absolute error for PGE wind forecasts of one, two, and three days ahead 

PGE adjusted the forecast error in the NREL database forecasts without increasing the actual 

energy forecasted. Day ahead forecasts were not altered, but PGE increased the wind forecast 

error for two-day ahead forecasts by 14.1%, and by 24.1% for three-day ahead forecasts. In other 

words, the day-ahead forecasts were modified such that the forecast energy from the WWSIS

data would not change, but the forecast error would increase to approximate the same increase in 

error as observed in PGE’s historical (wind forecast and generation) data. 

PGE is a “net short” utility, meaning total load in PGE exceeds the capacity of PGE’s generation 

capacity. PGE relies upon market transactions to make up the difference. As a result, PGE states 

that only a limited number of PGE’s generating plants can provide reserves, and several thermal 

units were not used as part of PGE’s modeling. Additionally, scheduling in the Pacific Northwest 

is mostly done on an hourly basis. Changes in load and variable generation in real-time must 

therefore be compensated for with pre-scheduled generation, specifically set aside to manage 

generation changes within the hour. The model PGE used accounts for regulation, loadfollowing, and contingency reserves (PGE and EnerNex 2011a). 

The model estimates reserves for the hour-ahead uncertainty of wind; within-hour load following 

for wind; and generation resource requirements for within-hour regulation for wind. The study 

subdivided integration costs into day-ahead uncertainty (for day-ahead wind forecast error); 

hour-ahead uncertainty (for hour-ahead wind forecast errors); intra-hour load following; and 

within-hour changes of wind generation from wind schedules (regulation). 
62 

Modeling

To create a long-term forecast of wholesale electricity prices, PGE used the AURORAxmp 

Electric Market Model, which simulates electricity markets sorted by NERC area. It was also 

used to forecast 2014 hourly electric prices for the Pacific Northwest. 

The primary model used by PGE to determine the wind integration estimate of $9.15/MWh, 

however, was an internally developed constrained optimization model, designed to minimize 

system operating costs under a given set of constraints that include plant dispatch requirements 

and system requirements (contingency reserves—spinning and non-spinning, regulation, load 

following, etc.) . The constrained optimization model is a mixed integer programming model that 

derives the estimated incremental operating costs of integrating wind. It was created using 

GAMS, Gurobi, and Microsoft Excel. The model optimizes dispatch and operations for one 

year—in this case, 2014. The model was run in three stages that corresponded to day-ahead, 

hour-ahead, and intra-hour, with commitments made in prior stages carrying through to the next 

stages as constraints. The total system operating costs at the third stage were used in determining 

wind integration costs. 

The cost of wind integration was defined as the savings in system operating costs that would 

result if wind placed no incremental requirements on system operations. To that end, wind 

integration costs were derived by running the model with and without incremental reserve 

requirements for wind and dividing the resulting value by total wind generation. 

Initially, the model produced an integration cost estimate of $11.04/MWh. This value was 

reduced to the $9.15/MWh value that will be used in PGE’s next IRP when PGE assumed that it

would deploy new flexible thermal resources—specifically, two 100-MW GE LMS 100 simple 

cycle combustion turbines (PGE and EnerNex 2011a). 

PSCo – Wind

Background

Xcel Energy, doing business as PSCo, estimates wind integration and cycling costs when 

evaluating wind resources as part of PSCo’s Electric Resource Plan and in comparing bids from 

wind generators in wind-only or all-source utility RFPs. PSCo also incorporates a solar 

integration cost, which is discussed in the next section. Cost values vary based on natural gas 

prices and the amount of existing wind capacity. PSCo has conducted three wind integration 

studies, with the most recent completed in August 2011. Xcel’s prior studies examined 10%, 

15%, and 20% wind penetration levels. However, Xcel asserts that previous integration studies 

based on percent wind penetration levels are no longer comparable due to growth in PSCo’s peak 

load, which is the denominator in wind penetration percentage calculations. For this reason, 

PSCo opted to begin performing studies using installed nameplate wind capacity as their basis

(Xcel Energy, Inc 2011a; and Xcel Energy, Inc. and EnerNex Corporation 2011). 

Xcel’s 2011 study estimated the average wind integration cost (excluding coal cycling) to be 

$3.68/MWh at 2 GW of wind, and $4.09/MWh at 3 GW of wind, given a base case gas price of 

$5.06/MMBtu and with an on/off peak proxy. Incremental integration costs (excluding coal 

cycling) are estimated to be $4.32/MWh for 200 MW of wind added to 2 GW of wind and a gas 

price of $5.06/MMBtu. The average coal cycling and curtailment costs are estimated to be
63 

$0.77/MWh with wind curtailment and $0.83/MWh with coal deep cycling (both at 2 GW of wind) 

and $1.03/MWh with wind curtailment and $1.08/MWh with coal deep cycling (both at 3 GW of 

wind) (Xcel Energy, Inc. and EnerNex Corporation 2011; and Xcel Energy, Inc. 2011b). 

Assumptions and Modeling: Wind Integration Charge

Wind integration costs were defined as including regulation, system operations (i.e., suboptimal 

unit commitment), and gas storage. PSCo uses incremental wind integration costs as an adder to 

wind generation costs in electric resource planning and in resource selection, whereas average 

wind integration costs are used as an input into the incremental wind integration cost calculations

(Xcel Energy, Inc. and EnerNex Corporation 2011). 

For wind integration costs without coal cycling, PSCo examined scenarios with 2 GW and 3 GW 

of wind by 2018, with multiple sensitivities ranging from varying gas costs, geographic diversity 

of wind projects, incremental pumped hydro storage, wind forecasting, demand response, and 

carbon prices. PSCo did not determine wind integration costs stemming from wind curtailment, 

trading inefficiencies because of wind’s uncertainty, or higher operations and maintenance costs 

incurred by thermal units that are providing more flexibility to accommodate wind (Xcel Energy, 

Inc. and EnerNex Corporation 2011). 

To estimate the wind integration costs (excluding coal cycling), PSCo used the Couger unit 

commitment and dispatch model to estimate the nominal values of integrating 2 GW and 3 GW 

of wind into PSCo. The Couger model creates an optimal plan for day-ahead unit commitment, 

and can dispatch committed generation at least-cost. In this case, the model was used to produce 

commitment and dispatch plans for every hour of the study year (2018). Incremental wind 

integration costs are determined as the sum of the incremental costs for regulation, system 

operations, and gas storage, divided by the incremental wind generation. Average wind 

integration costs are calculated by determining the total annual integration costs divided by total 

annual wind energy for each scenario (Xcel Energy, Inc., and EnerNex Corporation 2011). 

To determine average regulation related to of wind integration, PSCo used a comparative 

statistical analysis of net load and load, which derived the required regulation capacities at 

varying levels of wind. This was then multiplied by the regulation cost in PSCo’s transmission 

tariff ($6.740/kW-month, $80.88/kW-year). The cost was then divided by the predicted annual 

wind production for each scenario (Xcel Energy, Inc. and EnerNex Corporation 2011).

The average system operations costs were determined through a five-step process of Couger 

model runs. The first step involves projecting the day-ahead unit commitment plan every hour 

for 2018, whereas the second step simulates serving daily load with actual load data and 

produces an hourly system operating cost. Two proxies of hourly wind shapes were used to 

represent wind generation for both of these steps: a flat block that distributes wind power evenly 

over each hour of the 24 hour period, and an on/off peak proxy that distributes wind power over 

an on-peak block and an off-peak block. The third step has a new unit commitment with a dayahead wind forecast, and the fourth step does economic dispatch with hourly wind profiles. The 

fifth and final step was to calculate the average system operations wind integration costs, which 

is the difference between the system production costs in the second step (load dispatch) and the 

fourth step (load dispatch with wind), then dividing by annual wind energy production (Xcel 

Energy, Inc. and EnerNex Corporation 2011). 
64 

The third part of the average wind integration cost, the average gas storage component, was 

determined based on projections of gas consumption developed from modeling in Couger. The 

largest over- and under-nominations of natural gas volumes were selected for a gas day and 

annually for a gas day. The largest of these values for a gas day was used to set the demand 

charge, whereas total annual amounts were used to set the commodity charge. The demand and 

commodity charges were totaled, and the value that required the greatest storage system demand 

was used in determining the average gas storage cost component of the average wind integration 

cost (Xcel Energy, Inc. and EnerNex Corporation 2011). 

As discussed earlier, PSCo uses the incremental wind integration cost, not the average, for 

resource planning and selection. To determine the integration cost for any incremental generation 

above 2 GW of wind, the difference between the total average integration costs for 2 GW of 

wind and for the new level of wind must be determined, and then that value must be divided by 

the incremental annual wind generation. Table 17 shows an example of this calculation; in this 

case, for adding a 200 MW wind project to 2 GW of wind (Xcel Energy, Inc. and EnerNex 

Corporation 2011). 
65 

Table 17. Example Total Incremental Wind Integration Cost Calculation

Step Value and (Calculation) Result

2,000 MW Calculation

a Total Actual Annual Wind Energy Assumption (MWh) 6,000,000

b Average Regulation Wind Integration Cost ($/MWh) 0.14

c Regulation Wind Integration Cost ($) (a*b) 840,000

d Average System Operations Wind Integration Cost ($/MWh) 3.40

e System Operations Wind Integration Cost (($) (a*d) 20,400,000

f Average Gas Storage Wind Integration Cost ($/MWh) 0.14

g Gas Storage Wind Integration Cost ($) (a*f) 840,000

h Total Wind Integration Cost ($) (c + e + g) 22,080,000

2,200 MW Calculation 

i Capacity Addition Between 2,000 and 3,000 MW 1000

j Capacity Factor of Added Wind Assumption 0.5

k Amount of Added Wind Capacity Assumption (MW) 200

l Hours in a Year 8,760

m Total Actual Annual Wind Energy (MWh) (a + (j*k*l)) 6,876,000

n Average Regulation Wind Integration Cost at 3,000 MW ($/MWh) 0.21

o Average Regulation Wind Integration Cost ($/MWh) (b + ((k/i)*(n-b))) 0.15

p Regulation Wind Integration Cost ($/MWh) (m*o) 1,058,904

q Average System Operations Wind Integration Cost at 3,000 MW ($/MWh) 3.71

r Average System Operations Wind Integration Cost ($/MWh) (d + ((k/i)*(qd))) 3.46

s System Operations Wind Integration Cost ($) (m*r) 23,804,712

t Average Gas Storage Wind Integration Cost at 3,000 MW ($/MWh) 0.17

u Average Gas Storage Wind Integration Cost ($/MWh) (f+((k/i)*(t-f))) 0.15

v Gas Storage Wind Integration Cost MW ($) (m*u) 1,003,896

w Total Wind Integration Cost ($) (p+s+v) 25,867,512

Total Incremental Wind Integration Cost Calculation

x Total Incremental Wind Integration Cost ($/MWh) ((w-h)/(m-a)) 4.32

Source: Xcel Energy, Inc. and EnerNex Corporation 2011

Assumptions and Modeling: Coal Cycling Costs

The coal cycling costs that PSCo factors in when evaluating wind resources as part of PSCo’s 

IRP, and in comparing bids from wind generators in wind-only or all-source utility RFPs, are 

determined separately from the wind integration costs. Xcel defines cycling costs as having both 

a plant cycling component and a wind curtailment component. As with wind integration costs, 

these values vary to reflect the amount of existing wind capacity, and Xcel’s current assumptions 

with respect to load forecasts, fuel, and curtailment costs. Xcel plans to update the model and 

resulting values as needed to reflect any changes. PSCo evaluated two coal plant cycling 

protocol scenarios with 2 GW and 3 GW of wind by 2020. One scenario, considered the wind 

curtailment scenario, had coal plants cycle down to the economic minimum, or “shallow cycle,” 

to allow for wind. Wind was then curtailed as needed. In the second scenario, “deep cycle,” coal 

plants were cycled to their lower emergency minimum levels, with any excess wind beyond that 
66 

curtailed as needed. The average costs are estimated to be $0.77/MWh with wind curtailment and 

$0.83/MWh with coal deep cycling (both at 2 GW of wind) and $1.03/MWh with wind 

curtailment and $1.08/MWh with coal deep cycling (both at 3 GW of wind). Incremental coal 

cycling costs are higher than average costs, with an additional 1 GW of wind capacity above the 

2 GW level estimated at $2.18/MWh for the curtailment scenario, and $2.22/MWh for the deep 

cycle scenario (Xcel Energy, Inc. 2011a and Xcel Energy, Inc. 2011b). 

To calculate these costs, PSCo only considered load-following cycles, excluding on/off cycling 

and AGC cycles for frequency regulation. PSCo also assumed perfect foresight of load and wind, 

and did not quantify the potential costs relating to wind curtailment during wind ramping events. 

To determine coal plant cycles attributable to wind, PSCo developed a spreadsheet model that 

used load forecasts before and after a user-specified level of wind generation. The cost per coal 

unit cycle was derived from previous work analyzing cycling costs at the Pawnee coal plant, 

done in 2008 by Aptech Engineering, which was used to ascertain costs at other PSCo units. 

Existing forecasts of coal prices, REC prices and CO2 emission costs were used to determine 

wind curtailment costs. Generating resources were selected to meet the load forecast, the number 

of coal cycles by unit was estimated, and then the wind curtailment costs were added. The model 

was run twice, with and without wind, with the cost difference representing cycling and wind 

curtailment costs (Xcel Energy, Inc. 2011b). 

PSCo – Solar

Background

In approving PSCo’s 2007 Resource Plan in Docket No. 07A-447E, the Colorado Public Utilities 

Commission directed PSCo to examine solar integration costs. PSCo will add incremental solar 

integration costs to bid prices from competitive suppliers for solar capacity, and to solar costs as 

part of evaluating demand and generating resources in PSCo’s electric resource planning. 

Average solar integration costs ranged from $1.25/MWh to $6.06/MWh, however the actual 

solar integration costs that PSCo will impose will vary depending on natural gas prices and the 

amount of existing solar capacity (Xcel Energy, Inc. and EnerNex Corporation 2009). 

Assumptions and Modeling

The solar integration charges are estimated as determined in PSCo’s most recent solar integration 

cost study performed in 2009 that focused on system dispatch inefficiencies from solar forecast 

errors. PSCo did not estimate solar integration costs from added regulation capacity, impacts on 

gas supply nominations, impacts on operations and maintenance on existing conventional 

generation units, transmission expansion costs or energy trading inefficiencies with higher levels 

of solar capacity. Six scenarios were developed ranging from 200 to 800 MW of solar, with at 

least 200 MW coming from a solar thermal parabolic trough plant with four hours of thermal 

energy storage (Xcel Energy, Inc. and EnerNex Corporation 2009). 

The solar integration cost is determined by running the Couger unit commitment and dispatch 

model twice: once for day-ahead forecasted load and actual load, and then with day-ahead solar 

forecasts and solar generation. The difference in costs between the two model runs, divided by 

solar generation, is considered the solar integration cost. Average solar integration costs ranged 

from $1.25/MWh to $6.06/MWh, with integration costs roughly increasing at $1/MWh with each 
67 

100 MW of additional solar capacity. Solar day-ahead forecasts were compiled averaging solar 

insolation for that hour in a month (i.e., solar forecast for any day in May at 3 p.m. is the average 

insolation for 3 p.m. for every day in May) (Xcel Energy, Inc. and EnerNex Corporation 2009). 

PSE 

Background

PSE first petitioned FERC in June 2010 to institute a new ancillary service, termed Schedule 12, 

in PSE’s OATT (PSE 2010a). PSE said the new ancillary service was intended to ensure there is 

enough capacity to follow and balance the within hour variability of wind generation, with costs 

being assigned to wind generators in PSE’s balancing authority. 

The first phase of PSE’s Wild Horse wind facility went into service in 2006. PSE began supplying 

following capacity for the 229 MW project in late 2006 in addition to what PSE provided for load 

and other generating resources. Over the next few years, PSE received interconnection requests 

from third-parties for wind facilities that were to be located within its balancing authority area. 

Based on their experience providing balancing service for the Wild Horse wind facility, PSE 

expected their costs of providing balancing service to third-parties to exceed its existing rates 

under their OATT and undertook an effort to file for a new rate schedule to be added to their 

OATT. In spring 2008, PSE said demand for flexibility surpassed available supply, and the 

company had to purchase operating reserves in the short-term wholesale market. PSE noted

demand was due to high water flows at several of PSE’s hydro plants; environmental restrictions 

on some hydro plants that limited their production and operational flexibility; and limited flexible 

capacity from PSE’s combined-cycle and combustion turbine plants (PSE 2010b). 

In June 2010, PSE proposed a Schedule 12 rate of $2.70/kW-month to be levied upon wind 

generators, defined as the Following Capacity Fixed Charge (FCFC). The company determined 

this charge by setting the incremental monthly cost per kW of capacity based on a proxy natural 

gas peaking plant, defined as the Following Capacity Fixed Charge (FCAC), that it considered 

representative of the incremental market price of capacity needed to follow the intra-hour 

variability of wind generation. PSE assigned wind an 18.1% Following Cost Allocation Factor 

(FCAF). PSE proposed to charge a wind facility based on their installed nameplate capacity. For 

example, a 100-MW facility would pay $270,000 per month for Schedule 12 service. 

In August 2010, FERC dismissed Puget’s application without prejudice to Puget filing a new 

proposal. FERC agreed with PSE that “changing system conditions, such as an increasing 

amount of wind generation ..., present unique challenges that may require novel 

solutions”(FERC 2010b). FERC explained, however, that proposals to FERC must address the 

problems they are intended to resolve, but PSE’s proposal “... was not related to the actual, 

demonstrable costs incurred in providing service” (FERC 2010b). FERC noted that PSE 

supported its proposal for a proxy rate based on FERC’s previous allowance of proxy rates in 

imposing generator imbalance charges under Order 890. However, FERC said that “while it will 

allow for the recovery of legitimate and verifiable opportunity costs,” FERC will approve such 

proposals only if the recovery of opportunity costs will not contribute to the over-recovery of 

costs and that PSE has not shown that determining its opportunity costs through the use of a 

proxy generating unit will not result in the over-recovery of PSE’s costs. 
68 

Following FERC’s rejection of the proposed FCFC, PSE initiated discussions with FERC staff 

regarding a wind integration rate. In June 2011, PSE filed a new OATT amendment requesting 

authority to implement a wind integration rate for regulation and frequency response services for 

exporting wind generators (PSE 2011b). PSE proposed to update Schedule 3—Regulation and 

Frequency Response Service, and Schedule 13—Regulation and Frequency Response Service for 

Generators Selling Outside of Control Area. In the filing, PSE reiterated its need for additional 

regulation capacity related to variable generation in its balancing authority. PSE stated that it was 

currently providing regulation service to its own 273-MW Wild Horse facility (the 44-MW 

Phase 2 went into service in 2009) and Invenergy’s 96-MW Vantage Wind facility, which went 

into service in late 2010 and exports all of its output to points outside of the PSE balancing 

authority area. Additionally, PSE noted that as of June 2011, another 377 MW of wind projects 

were in the PSE generation interconnection queue. 

PSE’s June 2011 proposal consisted of updating its cost of capacity for providing regulation 

service from the current value of $5.50/kW-month set in 1998, to $12.39/kW-month, based on a 

revised cost of service study. PSE also sought to update its Schedule 13 charges by creating a new 

category for VERs. The variable energy resource obligation for purchasing regulation reserves was 

proposed at 16.77% of a resources point-to-point transmission service schedule for export out of 

the PSE balancing authority. The variable energy resource charge would then be $2.08/kW-month 

(16.77% of $12.39) for all exported energy from a wind facility within the PSE footprint. 

Assumptions

PSE’s wind integration rate focuses purely on providing within-hour regulation service for the 

wind generators in its balancing authority. PSE’s study used the 2010 4-second interval 

generation data for the full year for the Wild Horse facility and for from October to December 

for the Vantage Wind plant went into service October 4, 2010. The 4-second data was 

aggregated up to 10-minute interval data. In sum, the wind generation data set consisted of 

10-minute generation data for the 273 MW Wild Horse plant from January 1, 2010 through 

October 3, 2010, and then the combined 369 MW of Wild Horse and Vantage Wind from 

October 4, 2010 through December 31, 2010. 

PSE used 4-second 2010 load data and 4-second dispatchable generation plant data, both 

aggregated to 10-minutes. For the dispatchable generation, PSE chose six plants—the Colstrip 

coal facility units 1&2, the Goldendale and Sumas combined-cycle natural gas plants, and the 

Upper and Lower Baker hydroelectric units. For the forecast data sets, PSE created 60-minute 

before-the-hour persistence forecasts for wind generation, load, and the dispatchable generation 

units. Therefore the specific data sets used are:

• Hour-ahead forecasted 2010 load

• Hour-ahead forecasted 2010 wind generation 

• Hour-ahead forecasted 2010 dispatchable generation from six plants 

• 10-minute actual 2010 loads

• 10-minute actual 2010 wind generation 

• 10-minute actual 2010 generation for the six representative dispatchable generation plants 
69 

• 2010 total monthly energy production for all dispatchable plants in the PSE 

balancing authority. 

Determination of Regulation Reserve Charges

To calculate the variable energy resource rate, the following steps were performed:

1. Forecasted load was compared to actual load. PSE used a 95% confidence interval by 

taking the standard deviation of forecasted load versus actual load multiplied by two, 

resulting in a base within-the-hour regulation requirement of 71 MW. 

2. The same computation was applied to forecasted wind versus actual wind resulting in a 

base within-the-hour regulation requirement of 77.8 MW. 

3. The same computation for the representative dispatchable generation resulted in the 

following base within-the-hour regulation requirements—8.53 MW for coal plants, 

7.53 MW for natural gas plants, and 1.99 MW for hydro plants. 

4. Using the 10-minute deviations derived in steps 1 to 3, PSE determined the covariance 

pairs between each element. Then the square root of the sum of the five adjusted 

variances times two yielded the overall regulation requirement for PSE of 106.27 MW 

(Table 18). 

Table 18. PSE Variable Energy Resource Rate Adjusted Variance Calculations

Individual 

Std Dev 

(MW)

Covariance Covariance Covariance Covariance Covariance 

with PSE with Wind with Coal with Hydro with CCCT /

Load Plants Plants Plants CT/Misc. 

(MW) (MW) (MW) (MW) (MW)

Adjusted 

Variance 

PSE BAA Load 

Wind Plants 

Coal-Fired Steam Plants 

Hydro Plants 

CCCT/CT/Misc. Plants

35.50

38.90

4.27

0.99

3.77

8.36 1.29 (0.99) (2.57) 

8.36 (0.27) 0.25 1.27 

1.29 (0.27) 0.09 0.58 

(0.99) 0.25 0.09 0.08 

(2.57) 1.27 0.58 0.08

1,266.4

1,522.8

19.9

0.4

13.5

Total Adjusted Variance 

Total 2*Standard Deviation

2,823.1 

106.27

Source: PSE FERC filing, Prepared Direct Testimony of Lloyd C. Reed on Behalf of Puget Sound Energy, Inc., 

Docket No. ER11-3735, June 6, 2011.

5. To account for system diversity and allocate a portion of the diversity benefits to wind, 

PSE calculated a system benefit ratio at a 95% confidence interval of 0.637. The wind 

regulation requirement was adjusted to account for the diversity benefit, which yielded a 

total wind regulation requirement of 49.72 MW. This requirement was divided by the 

total MW of installed wind in the PSE balancing authority, which resulted in an intrahour regulation allocation for wind of 16.77% (PSE 2011b). The same method resulted in 

a 1.21% purchase obligation for load and a 0.38% purchase obligation for dispatchable 

generation. However, the Schedule 3 and 13 purchase obligations are currently set at 

2% and PSE did not request authority to change the regulation allocation for load and 

dispatchable generators at the time of June 2011 filing (FERC 2011). 
70 

Charges under Schedule 13 are determined via (1) reserved transmission, (2) purchase 

obligation, and (3) capacity rate. For exporting generators, the reserved transmission is their 

point-to-point firm transmission reservation capacity for exports outside the balancing authority. 

The purchase obligation as determined by the calculations outlined above were 16.77% for wind 

and 2% for dispatchable generators. 

As noted earlier, the capacity rate was set at $5.50/kW-month and has not been changed since 

1998. In the June 2011 FERC filing, PSE requested authority to change the capacity rate to 

$12.39/kW-month, based on a revised cost of service study. The cost of service study was 

conducted using the pool of resources used by PSE to provide balancing services in its balancing 

authority. This pool consists of PSE’s share of Colstrip coal units 1 to 4; PSE’s share of MidColumbia hydro; the Upper and Lower Baker hydro plants; and the Encogen, Goldendale, Mint 

Farm, and Sumas combined-cycle natural gas plants. PSE excluded its peaking units and run-ofriver hydro units as PSE argued that the peaking plants are used for spinning reserves and the 

run-of-river hydro units do not have adequate dispatch flexibility (PSE 2011b). 

The study estimated the weighted average cost of capacity of the pool using the net capability of 

each plant. The annual revenue requirement of each plant was divided by the net capability of 

each plant to get an annual capacity rate for each, which was divided by 12 to derive a $/kWmonth value for each plant. The weighted average of the individual capacity rates yielded an 

overall capacity rate of $12.39/kW-month for balancing services (PSE 2011b). 

PSE notes in its filing that the capacity available from its hydro facilities used to provide 

regulation service has been reduced in recent years. PSE’s share of Mid-Columbia resources 

dropped to 720 MW in July 2012 and increased restrictions on the Baker river plants limit the 

ramping capability of the hydro plants. PSE is increasingly relying on its thermal units to provide 

regulation service (PSE 2011b). 

Status of PSE’s Integration Rate

On October 20, 2011, FERC issued an order accepting, but suspending, PSE’s proposed tariff 

revisions and ordering settlement proceedings (FERC 2011). FERC suspended the proposed 

changes for five months to become effective January 5, 2012, but subject to refund, pending the 

outcome of settlement proceedings. At issue is the validity of the data sets that PSE used with 

respect to developing the 60-minute persistence forecast and how diversity benefits are 

determined. FERC also ordered PSE to revise the purchase obligation for load and dispatchable 

generation in the same way it is proposing to create the variable energy resource purchase 

obligation. Also to be examined is the cost of service methodology PSE used to derive the 

regulation capacity costs, particularly with respect to PSE’s revenue requirement calculation and 

the return on equity. 

On September 14, 2012, PSE submitted a Stipulation and Offer of Settlement (Settlement) for 

approval by FERC. The Settlement resolves the issues referenced in the above paragraph, and all 

parties in the proceeding either support or do not oppose the Settlement (FERC 2012d).

According to the Settlement, Schedule 3 will be revised to reflect a 1.21% purchase obligation; 

consistent with the directive included in the October 2011 FERC order. In addition, the capacity 

charge included in Schedule 3 is proposed to be set at $10.50/kW-month, rather than $12.39/kWmonth (PSE 2012a). 
71 

The Settlement includes more comprehensive revisions for Schedule 13. The Schedule 13 

generator regulation charge for VERs exporting energy outside the PSE balancing authority will 

be set at a “Base Rate” of $1.55/kW-month. PSE indicates that this charge for regulation service 

is a black box, negotiated value. It is a composite charge that ultimately derives from a 

volumetric purchase obligation for regulation service and a fixed capacity charge. However, for 

the limited purpose of self-supplying regulation and frequency response service, a variable 

energy resource’s volumetric purchase obligation is 15% of the generator’s point-to-point 

transmission service reservation. The Schedule 13 regulation charge for dispatchable generators 

delivering outside of the PSE balancing authority is proposed to be set at $0.105/kW-month. PSE 

states that this charge is also a black box, negotiated charge that ultimately derives from an 

unspecified volumetric component and capacity rate (PSE 2012a). 

The variable energy resource generator regulation charge is defined as a “Base Rate” because 

generators are eligible for specific discounts based upon certain scheduling practices. According 

to the revised Schedule 13 included in the Settlement, transmission customers purchasing service 

under PSE’s Schedule 13 must submit, either manually or through a mutually agreeable 

automated process, transmission schedules derived from a T-30 persistence forecast for each 

hourly scheduling interval.23 According to the proposal, a generator may receive a discount of 

30% off the Base Rate charge in exchange for a commitment to submit a T-30 persistence 

schedule for each and every 30-minute scheduling interval. Furthermore, PSE will offer a 

discount of 50% off the Base Rate charge in exchange for a commitment to submit a T-25 

persistence schedule for each and every 15-minute scheduling interval (PSE 2012a).24

On September 20, 2012, FERC authorized PSE to institute the proposed rates for Schedule 3 and 

Schedule 13, effective September 1, 2012, and remaining in effect pending FERC’s approval of 

the Settlement (FERC 2012f). Shortly thereafter, in October 2012, FERC issued an order 

terminating the settlement judge procedures, noting that the Settlement was uncontested by all 

parties involved in the proceeding (FERC 2012g). As of the end of November 2012, FERC has 

not issued a final order regarding the Settlement. 

Westar Energy

Background

Westar Energy25 submitted its first FERC filing in June 2009, requesting authority to implement 

a new regulation charge for variable generation (Westar 2009). Westar is a utility in Kansas and

is also a member of the Southwest Power Pool (SPP). The Westar balancing authority includes 

seven transmission owners and four different wholesale suppliers. Westar noted in the filing that 

there are several existing and proposed merchant generation projects that are/will be connected 

to the Westar grid or to a transmission operator in its balancing authority. Several of these 

generators requested to be a part of the Westar balancing authority, most recently wind facilities 

that connect to transmission operators other than Westar. As part of SPP, some ancillary services 

are recovered by SPP through SPP’s OATT, but regulation service for grid operation within 

 

23 A T-30 persistence forecast consists of the average actual output of the generation facility between T-31 to T-30 

minutes preceding the scheduling interval.

24 T-25 persistence schedule consists of the average output of the generation facility between T-26 to T-25 minutes 

preceding the scheduling interval.

25 Westar did not respond to requests to review this section.
72 

Westar’s balancing authority is largely Westar’s responsibility, as is recovering the costs for it. 

Westar’s OATT at the time recovered balancing authority ancillary service costs from merchant 

generators that serve load within Westar, but not from independent generators that export energy 

out of the Westar balancing authority. 

Westar’s filing sought authority to create a new agreement and a new Schedule 3A-Generator 

Regulation and Frequency Response Service for generators exporting energy out of the Westar 

balancing authority or into SPP’s energy imbalance service (EIS) market. Generation that 

participates in the EIS is under functional control of SPP and provides imbalance service to the 

SPP region as a whole, i.e., it is not contracted to serve load in the Westar balancing authority. 

Westar’s existing Schedule 3–Regulation and Frequency Response Service–for generation 

serving load within Westar remains unchanged. In its first filing, Westar proposed to impose a 

regulation purchase obligation of 1.35% on dispatchable generation exporting energy out of 

Westar’s balancing authority, the same value as for load-serving generation under Schedule 3. 

For variable generation, Westar’s charge was 7.8% of nameplate capacity (Westar 2009). FERC 

disagreed with the methodology Westar used to derive the 7.8% charge as it did not account for 

any reduction in regulation from decreased variability because of the geographic diversification 

of wind projects. FERC ordered Westar to revise its calculations. In January 2010, Westar 

submitted a revised methodology approved by FERC on March 18, 2010 (FERC 2010a). 

Westar’s current Schedule 3A purchase obligation, as set in March 2012, for variable generation 

is 3.47% of the amount of variable generating capacity within the Westar balancing area, 

multiplied by the current regulation rate as follows: 

1. For Yearly delivery, $53,358.74/MW 

2. For Monthly delivery, $ 4,446.56/MW 

3. For Weekly delivery, $ 1,026.13/MW 

4. For Daily delivery, $ 205.23/MW 

5. For Hourly delivery, $ 12.83/MW 

The above rates are the same as charged under Schedule 3 for within the Westar balancing 

authority load-serving generation and have not changed since the original Schedule 3A filing. 

The amount of generation inside Westar’s balancing authority is equal to nameplate capacity, 

minus the amount of the generation capacity that the customer is self-supplying regulation for, 

minus the amount of generating capacity that is supplying power to load inside the Westar 

balancing authority. The charge is applicable to wind generation and other non-dispatchable 

resources that export power outside of Westar’s balancing authority or to the SPP EIS market. As 

per FERC order, Westar submits an annual update and revised percentage of regulation purchase 

obligation requirements every March. Westar’s regulation rate for variable generators is in effect 

only until SPP launches its new market design in March 2014, with day-ahead and ancillary 

service markets at locational marginal pricing. 

Assumptions and Methodology

In June 2009, when Westar submitted its original filing, there was 382 MW of wind in the Westar 

balancing authority, 297 MW of which was Westar-owned or purchased by Westar under contract, 
73 

and 85 MW of which was third-party wind generation, which was selling its output outside 

Westar’s balancing authority or into the SPP EIS market. Westar noted that at the time, it was 

anticipating installing another 500 MW of wind over the next several years from a Westar RFP for 

wind. In addition, the SPP generation interconnection queue contained another 2,500 MW of wind 

projects seeking to interconnect in Westar’s balancing authority (Westar 2009). 

Westar’s proposed Schedule 3A regulation charge was based on the following data and 

methodology: 

• Westar used 10-minute interval data from three different wind facilities based in west, 

central and eastern Kansas.

• Westar calculated 10-minute changes in output by comparing each 10-minute interval to 

the one before. 

• The regulation percentage is calculated as the standard deviation multiplied by two, 

divided by the total nameplate capacity of the wind facilities. 

This methodology did not account for any diversity benefits from load and dispatchable 

generation and was deemed inadequate by FERC. Westar then submitted revised methodology 

that utilized a portfolio approach encompassing wind, load, generation providing regulation 

service within Westar, and generation in Westar controlled by SPP. Westar’s revised 

methodology resulted in a wind regulation percentage of 4.04% for wind selling into the Westar 

balancing authority and 4.05% for exporting wind. FERC accepted this methodology and 

directed Westar to submit a compliance filing with the revised methodology for calculating an 

updated charge and to update the rate annually with new data. 

Westar’s latest update was submitted in March 2012 (Westar 2012a). Westar’s methodology for 

calculating the regulation percentage obligation is as follows: 

• The data set consists of 10-minute interval meter observations from Westar’s Energy 

Management System for: 

o Westar Wind – three wind facilities either owned or contracted to Westar to serve 

load in Westar 

o Other Wind – two wind projects that export their energy or sell it to the SPP EIS 

market

o Frequency – consists of deviations of generators that are providing regulation 

service, from dispatch instruction 

o Load – deviations of actual load from the load forecast 

o RBASE Generation – Generation in Westar’s balancing authority controlled by 

SPP. The deviations are equal to the difference between observed values and 

dispatch instructions.26

• Wind deviations are equal to the difference between the output at the beginning of each 

10-minute interval to the output at the end of each interval. 

 

26 Westar does not provide the time period for the data that was used, simply states it is ‘during the study period’.
74 

• Westar then calculates the correlation coefficients of each data set creating a correlation 

matrix.

• Westar then calculates the individual standard deviations and the variances, which are 

used to calculate covariances, the adjusted standard deviations, and the adjusted 

regulation requirement of each element in the portfolio at a 95% confidence interval (two 

standard deviations).

• The regulation percentage purchase obligation then becomes the regulation requirement 

divided by the nameplate capacity of each element. 

This methodology is illustrated below in Figure 3. 

As noted before, the reserve costs (1-5) are the same as was approved by FERC for Schedule 3 

and represent the fixed costs associated with the generation resources that Westar is most likely 

to use for regulation service. These costs were developed as part of a settlement in FERC Docket 

No. ER05-925 (Westar 2009). 

Source: Westar Energy, FERC filing, Westar Energy, Inc., Docket No. ER09-1273-000 

Informational Filing, OATT, Schedule 3A, March 30, 2012.

Figure 3. Westar wind regulation rate calculation methodology
75 

WACM

Background

In 2006, FERC approved Rate Schedule L-AS3 that implemented a rate for regulation and 

frequency response service for WACM (FERC 2006). WACM27 was concerned that it would 

become the default Regulation Service provider for new variable generation plants, and would 

need additional regulation (WAPA 2006).

WACM’s regulation and frequency service rates are assessed on both load and variable 

generation and are as follows (WAPA, Rocky Mountain Region 2011): 

• Hourly: $0.000458/kW-hour 

• Daily: $0.011/kW-day 

• Weekly: $0.076/kW-week

• Monthly: $0.331/kW-month

The rates are effective from October 1, 2011, through September 30, 2016. 

Assumptions and Determination of Charge

WACM does have some resources to provide Regulation Services, including Federal 

hydroelectric resources and non-federal thermal generation resources. The thermal resources, 

however, are not under WACM’s control and are limited in their availability to provide 

regulation, as they generally operate at full capacity and are slow to respond to regulation 

requirements (WAPA 2006). 

To derive their regulation and frequency service rates, WACM estimates the amount of 

regulation that is required by adding the installed capacity of variable generation to a rolling 

12-month average of the system peak. The rate is calculated by dividing this value by the total 

annual revenue requirement. The revenue requirement for regulation is based on the annual costs 

of plants providing regulation and the costs of buying regulation on the wholesale market 

(WAPA 2006 and DOE Deputy Secretary 2006). 

As of 2011, WACM no longer provides regulation and frequency response to variable generation 

being exported out of WACM. Any variable generation being exported out of WACM must be 

dynamically scheduled, self-supply regulation, or receive regulation service from a third party

(WAPA, Rocky Mountain Region 2011).

 

27 WAPA did not respond to requests to review this section.
76 

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