PRICES AND EMISSIONS IN A · EMF study, ˜Prices and Emissions in a Restructured Electricity...

EMF Report 17May 2001

Prices and Emissions in aRestructured Electricity Market

Energy Modeling ForumStanford University

Stanford, CA 94305-4026

Energy Modeling Forumii

Preface

The Energy Modeling Forum (EMF) was established in 1976 at Stanford University toprovide a structural framework within which energy experts, analysts, and policymakerscould meet to improve their understanding of critical energy problems. The seventeenthEMF study, �Prices and Emissions in a Restructured Electricity Market,� was con-ducted by a working group comprised of leading international energy analysts and deci-sionmakers from government, private companies, universities, and research and consult-ing organizations. The EMF 17 working group met three times between January 1999 toJune 2000 to discuss key issues and analyze the longer-run implications of restructuredelectricity markets.

This report summarizes the working group’s discussions of the modeling results on U.S.electricity markets. Although international electricity markets were featured prominentlyin the presentations and discussions, the comparison of model results discussed in thisreport focuses on the United States. Inquiries about the study should be directed to theEnergy Modeling Forum, 406 Terman Engineering Center, Stanford University, Stanford,California 94305, USA (telephone: (650) 723-0645; Fax: (650) 725-5362). Our web siteaddress is: http://www.stanford.edu/group/EMF.

We would like to acknowledge Edith Leni and Susan Sweeney for their assistance in theproduction of this report.

This volume reports the findings of the EMF working group. It does not necessarily rep-resent the views of Stanford University, members of the Senior Advisory Panel, or anyorganizations providing financial support.

Energy Modeling Forum iii

Contents

Working Group Members ................................................................................................... vAcknowledgements ............................................................................................................vi

EXECUTIVE SUMMARYIntroduction .......................................................................................................................viiThe Baseline Competition Case ........................................................................................viiAlternative Competition Cases.........................................................................................viii

REPORTIntroduction ......................................................................................................................... 1The EMF Study ................................................................................................................... 1The Baseline Competition Case .......................................................................................... 6 Capacity, Generation and Demand.................................................................................. 6 Electricity Prices ........................................................................................................... 12 Fuels Used for Electricity ............................................................................................. 13 Emissions ..................................................................................................................... 15 Interregional Electricity Trade ...................................................................................... 18Alternative Competition Cases.......................................................................................... 20 Capacity Additions........................................................................................................ 21 Electricity Prices ........................................................................................................... 22 Fuels .............................................................................................................................. 26 Emissions ..................................................................................................................... 29 Interregional Electricity Trade ..................................................................................... 31Conclusion ........................................................................................................................ 31

Energy Modeling Forumiv

List of Tables

1. U.S. Regions Reported to EMF Working Group .......................................................... 32. EMF 17 Modelers Submitting Results for Standardized Cases .................................... 43. EMF 17 Cases ............................................................................................................... 54. Detailed Assumptions for Baseline Competition Case ................................................. 75. Percent Change in Generation and Electricity Price in the High-Demand Cases ....... 24

List of Figures

1. Electric Generating Capacity (Percent) in 2000............................................................. 82. Cumulative Capacity Additions (Gigawatts) in Baseline Case, 2010............................ 93. Cumulative Capacity Additions (Gigawatts) for Renewables in Baseline Case, 2010... 94. Cumulative Capacity Retirements (Gigawatts) in Baseline Case, 2010 ....................... 105. Annual Growth in Baseline Case, 2000-2010 ............................................................. 116. Electricity Prices in Baseline Case, 2000-2010 ........................................................... 127. Regional Competitive Electricity Price in Baseline Case, 2010 ................................. 148. Fuel Consumption (Quadrillion BTUs) for Electricity in Baseline Case, 2010........... 149. Coal-Gas Price Ratio for Utilities in Baseline Case, 2010........................................... 1610. Sulfur Dioxide Emissions in Baseline Case, 2000-2010 ............................................ 1611. Nitrogen Oxide Emissions in Baseline Case, 2000-2010 ........................................... 1712. Carbon Emissions in Baseline Case, 2000-2010......................................................... 1813. Electricity Market Module Regions ............................................................................ 1914. Interregional Imports in Baseline Case, 2010 ............................................................ 1915. Percent Changes in Combined Cycle Cumulative Additions from Baseline Case .. 2016. Percent Changes in Coal Capacity Cumulative Additions from Baseline Case ...... 2117. Percent Changes in Wind Capacity Cumulative Additions from Baseline Case ..... 2218. Percent Change in Competitive Wholesale Electricity Price from Baseline Case ... 2319. Change in Fuel Prices for Generation in Low Gas Price from Baseline Case ............ 2520. Change in Fuel Consumption for Electricity Generation in NEMS............................ 2621. Change in Fuel Consumption for Electricity Generation in POEMS ......................... 2722. Change in Fuel Consumption for Electricity Generation in RFF................................ 2723. Change in Fuel Consumption for Electricity Generation in IPM................................ 2824. Change in Fuel Consumption for Electricity Generation in Energy2020 ................... 2825. Change from Baseline (%) in Nitrogen Oxide Emissions, 2010 ................................ 2926. Change from Baseline (%) in Carbon Emissions, 2010.............................................. 3027. Change in Interregional Imports, 2010 ....................................................................... 3228. Change in Interregional Imports in Expanded Transmission, 2010............................ 3229. Change in Interregional Imports in Low Transmission Fees, 2010 ............................ 3330. Change in Interregional Imports in High Transmission Capacity, 2010..................... 33

Energy Modeling Forum v

Working Group Members

Eirik AmundsenIgnacio Pérez ArriagaHiroshi AsanoRoss BaldickAbha BhargavaJacqueline BoucherJohn BowerDallas BurtrawHung-po ChaoJohn ContiDavid DeangeloMax DuckworthDenny EllermanRobert EntrikenRobert EynonDoug HaleUdi HelmanBen HobbsBruce HumphreyHillard HuntingtonHenry LeeElliot LiebermanWilliam MeroneyDale NesbittShmuel OrenKaren PalmerKen QuintyScott RogersGeoffrey RothwellAlex RudkevichHugh RudnickChris ShortYves SmeersPaul SotkieviczRussell TuckerBoddu VenkateshJohn WeyantFrances WoodAssef Zobian

University of Bergen, NorwayUniversidad Pontificia Comillas (Spain)Central Research Inst for Electric Power Industry, JapanUniversity of TexasCanadian Energy Research InstituteElectrabel, BelgiumLondon Business SchoolResources for the FutureElectric Power Research InstituteU.S. Department of EnergyPPL CorporationConstellation Power SourceMassachusetts Institute of TechnologyElectric Power Research InstituteU.S. Energy Information AdministrationU.S. Energy Information AdministrationU.S. Federal Energy Regulatory CommissionJohns Hopkins UniversityXenergyStanford UniversityHarvard UniversityU.S. Environmental Protection AgencyU.S. Federal Energy RegulatoryAltos ManagementUniversity of California at BerkeleyResources for the FuturePPL CorporationUniversity of TorontoStanford UniversityTabor, Caramanis & AssociatesUniversidad Catolica de ChileAustralian Bureau of Agricultural & Resource EconomicsUniversite Catholique de Louvain (Belgium)University of FloridaEdison Electric InstituteICF ConsultingStanford UniversityOnLocation, Inc.Tabor, Caramanis & Associate

Energy Modeling Forumvi

Acknowledgements

Financial support from a wide range of affiliated and sponsoring organizations allows theForum to conduct broad-based and non-partisan studies. During the 1999-2000 period,the Forum gratefully acknowledges the support for its various studies from the followingorganizations:

ARAMCOCentral Research Institute of ElectricPower Industry, JapanDaimler ChryslerDuke EnergyEdison Electric InstituteElectric Power Research InstituteEnvironment CanadaExxon MobilFord MotorGeneral MotorsKing Abdulaziz City for Science andTechnology

Mitsubishi CorporationNatural Resources CanadaNew Energy and Industrial TechnologyDevelopment Organization, JapanOntario Power GenerationPPL CorporationSandia National LaboratorySouthern Electric CompanyTokyo Electric PowerU.S. Department of EnergyU.S. Environmental Protection Agency

EXECUTIVE SUMMARY

Introduction

Over the last decade many countries and regionshave transformed their electricity sectors to makethem more competitive. Although the recentmodifications of the California market design castconsiderable uncertainty about how far this proc-ess will evolve, competitive forces are likely toplay a more influential role in the sale, transmis-sion and purchase of electricity than they did pre-viously.

This report summarizes the recent findings of theEnergy Modeling Forum’s working group onelectricity prices and emissions in a restructuredelectricity industry. As in previous EMF studies,the process focused partly on what could belearned from comparing the results of differentmodels.

Although the models were developed for differentreasons, they share some common traits that al-low their results to be compared. They projectregional electricity prices, generation, capacity,consumption, electricity exports and imports, andenvironmental emissions over at least the nextdecade and often until 2020. They each have im-portant links to the economy and policy and theyemphasize interregional competition betweenmultiple U.S. areas.

The working group considered the five competi-tion scenarios: baseline or reference, high de-mand, low natural gas prices, expanded transmis-sion, and a renewable portfolio standard (RPS).All of these cases assume that wholesale prices inall regional electricity markets are set equal to themarginal or incremental generation costs immedi-ately in the year 2000. The cases do not showhow restructuring would affect electricity deci-sions relative to a cost-based regulated environ-ment.

The Baseline Competition Case

In the EMF baseline competition case, gas-firedunits owned by electric-generating firms domi-nate the new capacity additions, providingroughly 84 to 98 percent of the cumulative addi-tions by 2010. Few new coal units are built dur-ing this period. Although generators expand ex-isting non-hydroelectric renewable capacity by33-48% in two models (NEMS and POEMS) overthe decade, total capacity for renewables remainsa relatively small share of the total.

The technology mix of additional capacity re-flects a number of conditions: the type of plantsthat retire, the relative growth in peak and non-peak demand, fuel prices, and assumptions abouttechnological progress in various types of units.One critical assumption for the reported simula-tions is that the natural gas price path remainswell below the April 2001 spiked level. Thecases implicitly assume that natural gas priceswill return to their 1999 levels and rise slightlyfaster than inflation during the next decade.

Another important caveat is that these models arestronger on economic than on technology factors.These outlooks incorporate thoughtful assump-tions about how technology may progress overtime, but this single path of technological oppor-tunities is maintained throughout the five cases.In the baseline case, new coal units remain rela-tively unattractive despite losing significant mar-ket share. If the coal-technology producing in-dustry should respond, the mix of capacity couldbe quite different.

Reflecting standardized economic and energyassumptions across the models, generation growsby 1.3 to 1.6 percent per year in each model,while the growth rate in demand generally rangesfrom 1.1 to 1.7 percent per year. The projectionsdiffer somewhat more in their estimates of peakand nonpeak electricity demands. A key differ-

Executive Summaryviii

ence concerns their treatment of retail pricing andthe ability to use time-of-day rates to shift loadsto less busy hours. We recommend strongly thatanalysts explain clearly how retail pricing hasbeen incorporated in any outlook that they pres-ent. Moreover, results will be sensitive to thecomposition of demand among different customertypes. Households, commercial firms, new-technology industry and more traditional firmsrespond differently to prices and economic activ-ity.

The U.S. average wholesale generation electricityprice in the near-term ranges between $25-$34per megawatt hour (MWH) in 1997 dollars. Theytend to fall slightly in real dollars over time to the$25-30 per MWH range in the baseline case, inwhich generators pay $2.93 to $3.26 per millionBtu for natural gas. One model demonstrates amore cyclical response, increasing in 2005 beforedropping in 2010. Electricity prices are projectedto vary considerably across the 13 regions. Ingeneral, the lowest prices are experienced in re-gions, which have existing low cost coal and nu-clear generation sources. Regions more reliant onoil and gas-fired generation and those with higherdelivered fuel costs have higher prices. Opportu-nities for trading can lead to higher or lowerprices than otherwise expected. The deliveredprices to consumers are based on their patterns ofconsumption and include transmission and deliv-ery costs.

Some older coal and nuclear plants are being re-placed by newer gas technologies in the baselinecase. However, by 2010, coal use still remains thedominant fuel, accounting for 19-22 quadrillionBTUs (quads) of a total nonrenewable fuel use of34-37 quads. Several models call for greater re-liance upon natural gas and nuclear than othermodels, resulting in their estimating smaller in-creases in carbon emissions.

Economic forces shift the U.S. power sector togreater use of natural gas rather than coal. As aresult, in the simulations, annual U.S. emissions

for sulfur dioxide, nitrogen oxides, and carbondioxide from the power sector generally do notkeep pace with electricity demand growth overthe next decade.

National sulfur dioxide emissions decline byanywhere between 1.0 to 2.5 million metric tonsover the 2000-2010 period. This trend reflectsthe national cap on emissions imposed by theClean Air Act and that generators are usingbanked allowances, or allowances earned prior to2000, during this period.

Nitrogen oxides emissions tend to remain quitesteady over the next decade. Most projections ofthe baseline competition case incorporate no newenvironmental policies that have not already beenapproved by policymakers.

Carbon dioxide emissions rise at slower rates(1.3% and 0.9% per annum over the 2000-2010decade) than electricity generation (1.6% per an-num) in 3 of the models. In the other two, emis-sions are relatively flat between 2000 and 2010.In one case, the slower retirement of nuclearplants contributes to the noticeably slower growthin carbon emissions, while in the other greaterretirements of coal capacity is the cause.

Alternative Competition Cases

The alternative cases also represent a workablycompetitive market. Each case shows how mar-ket conditions change with other assumptions forbaseline demand growth, natural gas prices,transmission fees and capacity, and a renewablesportfolio standard (RPS).

The cumulative additions for combined-cycleunits over the decade show their largest responseto high demand cases, followed by the low gasprice case. Changes for other scenarios areminimal.

Natural gas prices and electricity demands aretwo important influences on the future path forelectricity prices. Alternative conditions for elec-

Executive Summary ix

tricity transmission and the RPS generally havevery modest effects that remain below 5% of thebaseline values. These prices are the competitivewholesale levels before any adjustments forstranded costs are added.

The outlooks agree that lower natural gas priceswill reduce competitive electricity prices whilehigher electricity demand will increase electricityprices. However, the pattern across outlooks israther different. Some outlooks reveal rather largereductions in electricity prices when gas pricesfall; the declines in other outlooks are more mod-est.

Larger changes are seen for some regional elec-tricity prices. The largest gain relative to thebaseline (27%) occurs for the midwestern MAINregion in the NEMS model in the high demandcase. The largest reduction relative to the baseline(22%) occurs for New York in the RFF model inthe low gas price case. While the national priceschange by no more than 2% from their baselinelevels in the alternative transmission cases, re-gional prices can increase or decrease by as muchas 12 or 13% in these cases. In general, importingregions with higher prices in the baseline casewill experience price reductions with moretransmission access, while low cost exporting re-gions will see higher prices.

Higher demands tend to increase the power sec-tor’s carbon emissions more than nitrogen oxidesor sulfur dioxide emissions in these simulations.Annual carbon emissions for the nation grow by7-10 percent more than baseline levels by 2010when energy demands increase by 12 percent.Nitrogen oxides emissions for U.S. grow by ap-proximately 5 percent more by 2010, while U.S.sulfur dioxide emissions remain unchanged.

The lack of new nuclear plants plays an importantrole in this result. Lower natural gas prices andadditional incentives for renewable energy tech-nologies appear to decrease nitrogen oxides andcarbon emissions in this sector. National caps

tend to keep sulfur dioxide emissions close totheir baseline levels.

Sulfur dioxide emissions in 2010 remain rela-tively similar to baseline levels for most modelsand cases. By 2010, firms are assumed to haveused up their banked allowances and thus annualemissions will be close to the cap of 8.95 milliontons per year. Thus, sulfur dioxide emissions donot change much, although the alternative condi-tions can change the costs of SO2 allowances,which will influence generation costs.

Higher demand conditions increase nitrogen ox-ides emissions by approximately 5% by 2010 inthree of the five models, with the others havingno increase or a 20% increase. Unlike sulfur di-oxide, these emissions will be pushed higher byincreased electricity demand. The nitrogen oxidesemissions effect for higher demands is noticeablystronger than the effects in the expanded trans-mission case as well as in the low transmissionfee and higher transmission capacity cases. Onemodel anticipates sharp reductions in nitrogenoxides emissions by the end of the decade wheneither natural gas prices are lower or when a re-newable portfolio strategy (RPS) is implemented.

Carbon emissions in the power sector movestrongly with the higher demand conditions. Thistrend reflects the absence of growth in any newnuclear plants due to a combination of costs andpublic perceptions. Carbon emissions grow byabout 7% to 10% higher than baseline levels andhence increase somewhat less than total electric-ity demand. The reductions in nitrogen oxidesand carbon in the RPS case in one model stemfrom a decrease in coal capacity and thereforegeneration, as much as from an increase in re-newable generation. In comparison, anothermodel has a greater renewable response to theRPS but smaller gains in emission reductions.

Transmission policy can significantly influencethe amount of interregional electricity trade. Ex-panded transmission capacity and lower transmis-

Executive Summaryx

sion fees increase total interregional imports by23 percent above baseline levels in one modeland by 61 percent in another model. Lowertransmission fees have a particularly large effectin several models, underscoring the importance oftransmission pricing in determining the economicincentive for trade between regions.

Properly functioning prices are the cornerstone ofcompetition’s potential efficiency. Competition

does not guarantee a certain price, does not re-quire electric loads to be a given magnitude, anddoes not assure generators that their plants will beused when they want them to be. Participantswill need to protect themselves from these busi-ness risks. This study’s results show howchanges in electric load growth, natural gasprices, and transmission costs and expansionscould influence conditions in markets where effi-cient rules have been established.

Introduction

Over the last decade many countries and regionshave transformed their electricity sectors to makethem more competitive. Although the recentmodifications of the California market design castconsiderable uncertainty about how far this proc-ess will evolve1, competitive forces are likely toplay a more influential role in the sale, transmis-sion and purchase of electricity than they did pre-viously. These forces are changing the industryin fundamental ways. While they are decouplinggeneration, transmission, distribution, and retailsupply within the industry, they are also fosteringmuch greater interdependence among regions inproviding and using electricity. Moreover, thesechanges are occurring at a time when govern-ments are imposing tighter controls on environ-mental pollutants.

New structures for organizing the industry haveintroduced novel ways of operating in electricitymarkets. They have also created alternative waysof thinking about and planning for successfulbusiness strategies. Companies can no longerignore the potential competition from suppliers orthe potential opportunity of servicing customersin other regions. In addition, governments creat-ing market rules in one region need to be awarehow their plans work with or against those rulesadopted in other regions. They should developflexible rules that produce meaningful incentiveswithout trying to dictate the outcomes of marketprocesses.

1 Poorly designed rules have contributed significantly toCalifornia’s power crisis. Implementation problems in-clude siting delays for new plants, no long-term contracts,and fixed and subsidized retail prices that do not reflectmarket conditions.

Although modeling competitive electricity mar-kets is in its early stages, these frameworks arealready demonstrating their value in terms ofhelping decision makers to anticipate and plan forwidespread structural changes. Uncertainty abouthow restructuring will unfold and how marketparticipants will respond to more liberalized con-ditions makes any single forecast of the industry’sfuture highly suspect. But each projection con-tains some important elements about how com-petition might operate. While participants cannotpredict prices and other market outcomes, theycan learn to protect themselves from unexpectedswings. This perspective should prepare partici-pants to respond to unexpected developmentsmore quickly and efficiently than otherwise,much like a person driving a car in a city neigh-borhood who expects the unlikely event that achild will run into the street before his vehicle.

The EMF Study

This report summarizes the recent findings of theEnergy Modeling Forum’s working group onelectricity prices and emissions in a restructuredelectricity industry. As in previous EMF studies2,the process focused partly on what could belearned from comparing the results of different 2 The EMF 17 working group continued the previous workinitiated by the EMF 15 group on a competitive electricityindustry. That particular study foresaw the problem ofmarket power, especially when market rules preventedlong-term contracts and did not allow the demand side torespond to price. Please see Energy Modeling Forum, ACompetitive Electricity Industry, Stanford University, Stan-ford, CA, 1998. The EMF process and some earlier EMFresults are described in H.G. Huntington, J.P. Weyant, andJ.L. Sweeney. “Modeling for Insights, not Numbers: TheExperiences of the Energy Modeling Forum,” OMEGA:The International Journal of Management Science, Volume10, No. 5, 1982, pp. 449-462.

PRICES AND EMISSIONS IN ARESTRUCTURED ELECTRICITY MARKET

Energy Modeling Forum2

models. The critical elements that were analyzedinclude electricity prices, generation, capacity,interregional trade, and environmental emissionsin North American electricity markets.3 The studydrew its members from leading advisors, electric-ity modelers, and electricity experts from gov-ernment, companies, universities, and researchorganizations. Over the period from September1998 through June 2000, the group met threetimes to discuss the key issues driving the elec-tricity restructuring topic and how modeling re-sults could help to develop a more comprehensiveunderstanding of the new conditions. Participa-tion by corporate and government advisors helpedto define scenarios that would be useful for un-derstanding the interactions among businessstrategies and public policy.

Although the models were developed for differentreasons, they share some common traits that al-low their results to be compared. They projectregional electricity prices, generation, capacity,consumption, electricity exports and imports, andenvironmental emissions over at least the nextdecade and often until 2020. They each have im-portant links to the economy and policy and theyemphasize interregional competition betweenmultiple U.S. areas. For this study, these regionalU.S. results have been aggregated to the 13 re-gions linked to NERC regions that are listed inTable 1.4

Participating modelers are identified along withtheir frameworks and organization in Table 2.While EIA’s NEMS and CERI’s Energy 2020models are used primarily for developing industry 3 The group also discussed extensively emerging marketdesign issues such as strategic behavior under different con-straints, organization of the transmission system operator,and the advantages and disadvantages of considering powertransmission flows rather than nodes in managing conges-tion. The group’s discussions of these issues are not cov-ered in this report.4 Some variance in reporting of results occurs in CERI’s2020, which reports results for 7 regions in Canada plus anaggregate for US. Although Energy 2020 models the USby each of the 50 States, for the purpose of this exercise,US is aggregated.

outlooks and special evaluations, RFF’s Haikumodel was developed as a small, tractableframework for conducting risk assessment andunderstanding fundamental market uncertainties.MarketPoint is used mainly for such tasks asvaluing electricity assets and evaluating other keybusiness strategies. POEMS is based in part uponNEMS but has been restructured to use for elec-tricity policy analysis and business applications.IPM is used both for evaluating policy analysisand for understanding business strategies.

The working group considered the five competi-tion scenarios: baseline or reference, high de-mand, low natural gas prices, expanded transmis-sion, and a renewable portfolio standard (RPS).These cases are listed in Table 3. The baselinecase adopted the economic and energy assump-tions from the reference case of the U.S. EnergyInformation Administration’s Annual EnergyOutlook (AEO).5 The high demand case exam-ined power market conditions if electricity con-sumption grew by 1 percent per year more rapidlythan in the baseline competition case, for a totalof 2.4 to 2.8 percent per year. The low gas pricecase kept the natural gas price paid by electricutilities in all future years at its projected 2000inflation-adjusted (or real) level in the baselinecase. The high transmission case allowed bothexpanded physical volume and lower transmis-sion charges between regions of the UnitedStates. The renewables portfolio standard (RPS)assumed that the industry must meet a targetshare of 7.5 percent for renewables excluding hy-droelectric power. As explained later, alternativeversions of both the high demand and transmis-sion cases were simulated in order to understandbetter the results obtained in these cases.

All of these cases assume that prices in every re-gional electricity market are immediately set by 5 The Reference case for Energy 2020 was not completelyaligned with the assumptions of AEO99 as provided in Ta-ble 4. Specifically, assumptions on electricity demand, heatrate improvements, transmission and distribution COS re-ductions, and reserve margins are not aligned with theAEO.

Prices and Emissions in a Restructured Electricity Market

3

Table 1: U.S. Regions Reported to EMF Working Group

NERC Subregion Subregion Name Geographic Area

ECAR East Central MI, IN, OH, WV; part of KY, VA, PA

ERCOT Elec Reliability Counc of Texas Most of TX

MAAC Mid-Atlantic MD, DC, DE, NJ; most of PA

MAIN Mid-America Most of IL, WI; part of MO

MAPP Mid-Continent MN, IA, NE, SD, ND; part of WI, IL

NE New England VT, NH, ME, MA, CT, RI

NY New York NY

FRCC Florida Most of FL

STV Southeast (ex. Florida) TN, AL, GA, SC, NC; part of VA, MS, KY, FL

SPP Southwest KS, MO, OK, AR, LA; part of MS, TX

NWP Northwest WA, OR, ID, UT, MT, part of WY, NV

RA Rocky Mtn & Ariz-NM AZ, NM, CO, part of WY

CNV California & So. Nevada CA, part of NV


Table 2: EMF 17 Modelers Submitting Results for Standardized Cases

ID in Charts Model Name EMF Modeler OrganizationNEMS National Energy Modeling System Robert Eynon

Laura MartinU.S. Energy Information Administration

POEMS Policy Office Electricity ModelingSystem

John ContiFrances Wood

US Department of Energy, Policy OfficeOnLocation Inc.

RFF Haiku Dallas BurtrawKaren PalmerRanjit BharvirkarAnthony Paul

Resources for the Future

IPM IPM Elliot LiebermanBoddu Venkatesh

U.S. Environmental Protection AgencyICF Consulting, Inc.

E2020* Energy 2020 Abha BhargavaChristopher Joy

Canadian Energy Research Institute

** MarketPoint Dale NesbittTed Forsman

Altos Management Partners

Notes:* In this study, CERI reported results from Energy 2020 for seven Canadian regions and the United States as a whole.** MarketPoint results are not compared graphically with the others. This model reported detailed regional results that were not easily aggregated to the core 13regions shown in Table 2. However, discussion of their results helped to identify the basic principles of competitive markets exhibited by other models.


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Table 3: EMF 17 Cases

Case* Market Assumptions NotesBase Competition 1999 AEO** Demands and Fuel Prices

See Table 4 for other assumptions.Fully integrated so that demands respond to price.

Low Gas Prices AEO Demands and Fuel Prices + Hold de-livered gas prices constant at projected AEOprice in 2000 for all classes of customers.

Fully integrated.

High Electricity Demands AEO Demands and Fuel Prices + Increasebase electricity growth by 1% per year.

One case allows fuel prices to change incorporatingthe effects of higher natural gas prices, while theother has fixed (at baseline) natural gas prices.Electricity demands are unresponsive to electricityprice changes.

High Transmission Increase transmission capability by 50%.Reduce transmission hurdle rate to$0.10/mWh.Reduce transmission fees by 50%.

Two additional cases separate the change in trans-mission capacity from the change in the transmis-sion fees and hurdle rate.

Renewable Portfolio Stan-dard

Impose an RPS goal of 7.5% non-hydropower renewables (excluding MSW)as a percent of sales by 2010. Assume theRPS requirement expires after 2015. Thecost of the credit was capped at $15/mWh.

* All cases assume immediate deregulation in all states today.** Although most models standardized on the 1999 Annual Energy Outlook, the NEMS system used the 2000 version.


incremental or marginal costs in the year 20006.This assumption contrasts with the AEO refer-ence case, which assumed a mixture of deregula-tion and regulation in the various states. As a re-sult, the EMF scenarios show how changing con-ditions influence electricity markets in a deregu-lated environment. The cases do not show howderegulation would affect electricity decisionsrelative to a regulated environment. The groupdid not simulate a regulated case because partici-pants had differing opinions of what continuedregulation would mean, which regions would beaffected, and the degree of market deregulation ina business-as-usual scenario.

A related, important issue concerns the form ofthe deregulation itself. The group asked the mod-elers to consider a wholesale electricity marketthat was workably competitive. Each region hadsufficient generators or access to interregionaltrade that muted the problems of market powerbeing exercised on a consistent basis. This per-spective also required that transmission systemswithin a region were sufficiently efficient in re-ducing congestion to allow plants to be dis-patched on the basis of least cost.7 Finally, allmodels, except IPM, allowed demand to respondto prices, with some, but not all, frameworks al-lowing load shifting in response to time-of-dayprices. As a result, the cases are optimistic on theregulatory front by assuming that each govern-ment steers its way towards a reasonably com-petitive market design.

The group did not ask to have different marketdesigns or market imperfections simulated withthese models because the systems represented inthe current study do not contain the necessary in-stitutional constraints or detailed transmissionnetworks that allow one to consider such issues.The principal advantage of the current models lies 6 As a result, the year 2000 values are hypothetical and donot reflect actual markets outcomes.7 POEMS performs the dispatch and trade at the subre-gional level, with representation of transmission capabilityamong these subregions.

in their detailed representation of interregionalcompetition within the electricity industry, withappropriate links to other energy markets and theeconomy.

The Baseline Competition Case

The EMF baseline competition case combines thereference case economic and energy price as-sumptions from the AEO forecast8 with the as-sumption that all U.S. regional markets are im-mediately competitive with generation prices be-ing set at the incremental production cost of thelast unit generating in any period. Models dif-fered in how much fixed operations and mainte-nance (O&M) costs they incorporated in marketprices. Modelers were asked to implement thekey assumptions for competition shown in Table4, unless they had strong reasons for overridingthese specifications.

The group was primarily interested in howchanges in the economic and energy assumptionsinfluence the competitive electricity market andemissions results. However, it is easier to under-stand this discussion by first emphasizing a fewkey characteristics of the baseline case.

Capacity, Generation, and Demand

According to the NEMS model, the U.S. electric-ity industry has about 777 gigawatts9 of name-plate capacity in 2000. Coal accounts for 39% oftotal capacity (Figure 1). Gas accounts for 16%,either as combined-cycle units10 or combustionturbines11. Older gas and oil units (“other fossilfuel”) amount to 18%, nuclear plants account for

8 NEMS used AEO 2000 assumptions while the other mod-els used AEO 1999 assumptions.9 Each gigawatt is enough power to meet the demands of amillion homes in California.10 Combined cycle units use a steam turbine to produceelectricity from waste heat that exits from a combustionturbine fired by natural gas. This process improves theunit’s efficiency.11 Gas turbine plants burn natural gas to produce hot gasesthat turn the turbine.


7

Table 4. Detailed Assumptions for Baseline Competition CaseCategory Input Specification

Electricity Markets Competitive wholesaleCompetitive retail beginning 2000 for all states

Market Structure Perfect competition

Electricity Demand AEO 1999 (or 2000) Reference Case

Fuel Prices AEO 1999 (or 2000) Reference Case

Cost of Capital Life time for new plant is 20 years (17 for wind & solar).Debt/equity ratio for new builds is 60/40. The debt interest rate

is 5.5% real and equity is 15% real.

Renewables Extended wind tax credits to 2005

Generation Pricing Marginal cost pricing as defined by each modeler

Ancillary services Defined by each modeler

Transmission- Hurdle rate for trading $1/MWh (1997$)

- Organization Postage Stamp (zonal)- Wheeling Fees $3/Mwh (1997$) average between neighboring

NERC regions

O&M and G&A Costs Savings relative to Cost-of-Service (COS):- Generation

- O&M 1.8% per year decline, 2000 to 2010- G&A 5% per year decline, 2000 to 2010

Transmission Cost Reductions 0.75% per year decline, 2000 to 2010Distribution Cost Reductions 1.5% per year decline, 2000 to 2010

Heat rates 0.4% per year improvement for fossil plants, 2000 to 2010

Reserve Margins Goal of 13-15% (see regional table), or endogenously derived

Stranded Cost Recovery-Generation Assets 10 year recovery, 10% discount, 100% Recovery

Transitional Charges- Regulatory Assets Recovery of existing regulatory assets

- Decommissioning costs Recovery of required costs

Externality Costs None


1wc

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3% and hydroelectric plants account for 10%,ith the remaining 4% allocated to the “other”

ategory.

umulative capacity additions of all types overhe next ten years (until the year 2010) rangerom 124 to 204 gigawatts, or 16% to 26% of to-al capacity in 2000 in the four main U.S. modelsisted at the top of Table 2. Cumulative capacityetirements of all types over this decade varyrom 48 to 69 gigawatts, or 6.2% to 8.8% of totalapacity in 2000 in these projections.

as-fired units owned by electric-generatingirms dominate the new additions, providingoughly 84 to 98 percent of the cumulative addi-ions by 2010. Figure 2 shows that electric gen-rators choose almost exclusively from com-ined-cycle gas units and combustion gas tur-ines rather than from coal-fired and renewablenits. POEMS, RFF and E2020 indicate a prefer-nce for combined-cycle plants, while NEMS andPM tend to select a more even balance betweenhe two types of gas technologies although with a

slight preference for combustion turbines. Com-bined-cycle plants have greater efficiencies andtend to be operated over more hours than com-bustion turbines, which are used primarily formeeting peak demands. The strong shift towardsnatural gas may not be as pronounced if gasprices are sustained at the current high prices.

The technology mix of additional capacity is inpart linked with the type of plants that retire, aswell as the relative growth in peak and nonpeakdemand. Demand patterns shifting more towardspeak consumption will favor the addition of com-bustion turbines, if higher electricity prices do notreduce consumption. Patterns shifting more to-wards baseload use will favor the addition ofcombined-cycle and coal plants.

Fuel prices will also be important. The prefer-ence for gas-fired units in the baseline case mayreflect the relatively low gas prices assumed bythe AEO99 at the time. If today’s higher gasprices prevail for the next decade, the growth innew gas-fired units would be less.

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Figure 1. Electric Generating Capacity (Percent) in 2000 (NEMS Projection)

Combined Cycle5%

Coal39%

Combustion Turbines

11%

Hydro10%

Nuclear13%

Other Fossil18%

Other 4%

Prices and Emissions in a Restructured Electricity Market 9

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Figure 2. Cumulative Capacity Additions (GigaWatts) in Baseline Case, 2010

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Figure 3. Cumulative Capacity Additions (GigaWatts) in Baseline Case, 2010

0

0.5

1

1.5

2

2.5

Geothermal MSW/Refuse Biomass/Wood Solar Wind

NEMS

POEMS��RFF


Finally, most outlooks must adopt a set of as-sumptions about technological progress that oftenremain relatively fixed across scenarios. Chang-ing these assumptions could produce differentoutlooks. For example, the exclusive choice forcombined-cycle plants, especially in E2020, isdriven mostly by relatively lower costs for thistype of technology. In addition, progress in coal-fired technologies will undoubtedly continue, es-pecially if they are losing market share rapidly.Thus, coal-fired units could be more attractiveeconomically than assumed in these outlooks.

Although generators expand existing non-hydroelectric renewable capacity by 33-48% intwo models (NEMS and POEMS) over the dec-ade, total capacity for renewables remains a rela-tively small share of the total.12 The scale for re-newable capacity additions (Figure 3) is less than2 percent of the scale for the more traditionalsources (Figure 2). All of the projections showmore wind-turbine additions than other renewablesources. NEMS also shows some building of

12 The IPM results are not shown in this graph because theyare represented in aggregate and not by technology.

municipal system waste and refuse (MSW/refuse)units as well.

Retirements over this decade (Figure 4) are con-centrated in the older oil and gas units (“otherfossil fuel”) and in nuclear plants. Most modelsretire plants on economic grounds, although someconsider scheduled life extensions. In addition toage and licensing agreements, retirements may bemore frequent if the model assumes that there islimited rehabilitation of older units. RFF andPOEMS project greater retirements of oil and gasunits, and these are also the models that projectgreater combined cycle capacity additions. Thelow nuclear retirements in RFF will have impor-tant implications for environmental emissions, ascontinued operation of nuclear plants will help tolessen emissions of carbon dioxide, nitrogen ox-ides, and sulfur dioxide.

Figure 5 shows that the models portray reasona-bly similar growth rates in U.S. electricity con-sumption and generation over the 2000-2010 pe-riod. Reflecting the attempt to standardize as-sumptions across the models, generation growsby 1.6 percent per year in three of the models,

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Figure 4. Cumulative Capacity Retirements (GigaWatts) in Baseline Case, 2000-2010

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Combined Cycle Coal CombustionTurbines

Nuclear Other Fossil Total

NEMS

POEMS��RFF

IPM��e2020


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hile the growth rate in demand generally rangesrom 1.5 to 1.7 percent per year. However, bothPM and E2020 indicate slower demand growthate at 1.4 and 1.1 percent per year13. The lowerrowth in demand in E2020 results from slightlyigher prices than in the other models and from aelatively high demand response to price.

oreover, the composition of demand betweeneak and nonpeak times is also extremely impor-ant. The POEMS peak demand14 grows moreapidly (1.8% per annum) than total demand.his peak-load growth requires greater capacityxpansion than in the other projections. On thether hand, capacity tends to be used more inten-ively over time in NEMS, as demand in the non-eak hours grows more rapidly than peak de-and. Demand during peak hours in this model

rows more slowly at 1.4% per annum.

3 Demand and generation may increase at slightly differentates due to changes in losses, imports and cogeneratedlectricity sold to the grid.4 Peak demand is measured here as the sum of the peaks inach region. Since peaks in each region occur at differentours and on different days, their sum will not be equal tohe national peak at a certain hour and day.

The growth in peak demand in POEMS resultsfrom the growth of the individual demand end-uses (e.g., lighting, air conditioning, etc.). Thepeak demand will increase more rapidly than av-erage sales if the demands in those end-useswhich contribute the most to peak are increasingmore rapidly than those that have a flatter loadshape. The residential and commercial loads areincreasing somewhat more than industry (espe-cially, heavy industry which tends to have threeshifts working throughout the day and thereforeflatter loads). The POEMS results in this study,like the IPM results, do not incorporate loadshifting in response to time-of-day pricing.

The RFF simulations also do not incorporatetime-of-use retail rates for electricity. Each classof consumers faces the annual average electricityprice for that class in each time block of the year.The RFF model allows 12 load blocks with thepeak block including 1% of the hours in each sea-son (roughly 22 hours for summer and winter and44 hours for spring/fall). Models with greater de-tails on electricity loads than RFF may showgreater peaks and larger peak demands.

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Figure 5. Annual Growth (percent) in Baseline Case, 2000-2010

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

1.8%

2.0%

Capacity Generation Demand Peak Demand

NEMS

POEMS��RFF

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he NEMS results for this study assumed fullompetition, including time-of-use retail pricingor all sectors. Some of the sectors respond torices by shifting their demand from peak-timeeriods to off-peak hours. The end-use sectorshat were assumed to be able to shift load in-luded water heating and clothes drying in theesidential sector, space heating/cooling and wa-er heating in the commercial sector, and shiftork in the industrial sector. The assumed elas-

icity in this model was -0.15, based on the short-un elasticity also used within the NEMS demandodels. In addition, load shifting in the commer-

ial sector requires that thermal storage use be-omes more widespread.

oad shifting to nonpeak hours reduces not onlyhe peak load but also the amount of new capacityeeded. It may also potentially influence the fuelix of electricity generation and emissions, e.g.,

way from peaking gas-fired units and towardsaseload coal-fired units, although such a trend isot directly observable below because other fac-ors influence this decision, too.

Electricity Prices

Figure 6 compares the U.S. average wholesalegeneration electricity price, which is earned by agenerator that operates throughout the year with-out any downtime.15 Generators are paying $2.93to $3.26 per million Btu for natural gas in 2010 inthis case. All prices are in 1997 dollars. Elec-tricity prices rise from $26 per megawatt hour(MWH) to $30 per MWH in POEMS, remainrelatively steady at $29 per MWH in NEMS andat $25 per MWH in IPM, and fall from $31 to$25 per MWH in the first five years before lev-

15 Modelers reported both “wholesale” and “delivered toconsumers” competitive generation prices. The wholesaleprices (reported above) are paid to generators averaged overall hours in the year, in other words paid to a generatorwhich runs all the time ("time weighted average"). Theprices to the consumer include losses and are averaged overthe amounts purchased in each time period ("quantityweighted average"). These two effects lead to higher pricesfor the “delivered” than for the “wholesale” concept. Inparticular, customers buy more power at peak when pricesare higher, which raises the average over the one that istime-weighted.

Figure 6. Electricity Prices in Baseline Case, 2000-2010

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2000 2005 2010

19

97

$ p

er

mW

h

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IPM

POEMS

e2020

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e2020

Competitive Wholesale Price

Delivered Residential Price


13

eling off in RFF.16 The prices in E2020 are morecyclical, increasing from about $34 in 2000 to 36in 2005 before dropping to about $31 in 2010.The change in E2020’s price appears to corre-spond directly with the tightening of the market.The reserve margins in E2020 are determinedendogenously and not set to those specified inAEO99. The reserve margins decrease from 25%in 2000 to 14% in 2003 and then increase to 20%in 2010. There appears to be a 1-2 year laggedprice response.

The delivered prices to consumers, also shown inFigure 6, are based on their patterns of consump-tion and include transmission and delivery costs.IPM results are not shown because the modeldoes not project consumer prices. In addition,consumer prices may include transitional costsfor stranded capacity where the book value of thegeneration assets is higher or lower than the mar-ket competitive prices. Projected delivered pricesincrease less quickly or decrease more quicklycompared to the wholesale prices for at least tworeasons. The cost of distribution was assumed todecline annually in this scenario. Moreover, thestranded cost charges decline over time in infla-tion-adjusted terms as well.

In NEMS and POEMS, a total value of strandedcosts is determined from the net present value ofnet cash flows and the net book value. In POEMSpositive and negative stranded costs are netted atthe company level, while in NEMS they are net-ted across all plants in a region. This amount isrecovered over 10 years in flat nominal dollarseach year, which means it declines in inflation-adjusted dollars. Therefore, this component ofrates declines over time. RFF calculates strandedcost on a NERC region/subregion basis in theversion of the model used for this study. Underthis approach, profits earned by some utilities in

16 $30 per megawatt-hour would equal 3 cents per kilowatt-hour. These are wholesale electricity prices that excludetransmission and distribution costs.

the region are netted against the stranded costs ofothers. The net result is always no stranded costsfor the region as a whole and thus, there is nostranded cost recovery reflected in retail electric-ity prices in the RFF results. 17

Electricity prices are projected to vary considera-bly across the 13 regions. The wide range of re-gional wholesale electricity prices is displayed inFigure 7. In general, the lowest prices are experi-enced in regions, such as ECAR, which have ex-isting low cost coal and nuclear generationsources. Regions more reliant on oil and gas-firedgeneration and those with higher delivered fuelcosts have higher prices. Opportunities for trad-ing can lead to higher or lower prices than other-wise expected. For example, the Northwest re-gion has considerable hydroelectric resources,which without trade would lead to low electricityprices. In all the models18 except RFF, the NWPprices do not appear to be significantly lower thanother regional prices, because other regions setthe marginal prices. In POEMS and NEMS deliv-ered prices in NWP are reduced by a credit ordiscount to account for low cost Federal prefer-ence power.

Fuels Used for Electricity

The information on additions and retirements in-dicates that some older coal and nuclear plantsare being replaced by newer gas technologies.However, by 2010, coal use still remains thedominant fuel, accounting for 19-22 quadrillionBTUs (quads) of a total nonrenewable fuel use of34-37 quads (Figure 8). NEMS and POEMShave reasonably similar utility fuel consumptionpatterns. RFF and E2020 call for greater relianceupon natural gas and RFF calls for more nuclear. 17 In subsequent versions of the model, RFF has incorpo-rated stranded costs on a utility by utility basis.18 The regional definition of NWP and RA are slightly dif-ferent in IPM than in the other models. What has been la-beled here as NWP is a subset of the full NWP used by oth-ers, and the remaining part is included with RA.


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Figure 7. Regional Com petitive W holesale E lectric ity Price in Baseline Case, 2010

0

5

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35

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45

ECAR

ERCOT

MAAC

MAIN

MAPP NY NE FL

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Figure 8. Fuel Consumption (Quadrillion BTUs) for Electricity in Baseline, 2010

22.3 21.8 20.122.2

18.8

7.4 7.48.1 5.5

8.6

6.66.3 7.4

6.2 5.8

0

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NEMS POEMS RFF IPM E2020

Nuclear

Oil

Gas��Coal


15

IPM registers lower total fuel use, primarily in itslower use of natural gas.

A number of factors contribute to these fuel usepatterns. The pattern of end-use loads or de-mands has an important role. If peak demandgrows rapidly and is not strongly responsive torising prices during the peak period, generatorswill be encouraged to build natural gas plantswith low capital costs that can be operated overfewer hours to meet the higher demand. Coalplants with higher capital costs and lower oper-ating costs might be built to meet demands oflonger duration or base loads.

The dispatch of existing and new units is deter-mined by relative operating costs and by the op-portunities to trade among regions. Operatingcosts are affected by heat rates, which measureshow much energy is used to generate a kilowatthour of electricity. Assumptions about improve-ments in existing plant heat rates and the heatrates of new plants might therefore affect the dis-patch choice. Industry restructuring may intro-duce pricing reforms that will change the loadpattern and will bring forth competitive pressuresthat will encourage cost-cutting operations andpurchasing new units with improved heat rates.These heat rates directly affect the amount of fuelrequired for generation. Although these factorsare difficult to disentangle, they will stronglyshape the industry’s response to future conditions.

Moreover, the share of operations and mainte-nance (O&M) costs assumed to be included in thebid prices vary among the models. When amodel assumes that more O&M costs are passedthrough to the bid price, units with higher O&Mcosts will be dispatched less frequently than thosewith lower O&M costs. This effect could influ-ence fuel use in the power sector.

Although the RFF coal prices remain lower rela-tive to gas prices than for the other projections,

they indicate more natural gas use for electricpower. RFF has slightly more optimistic as-sumptions about technological change in com-bined-cycle units than those found in NEMS andPOEMS. As a result, more new plants are builtand these units are fired by natural gas. The RFFmodel can not directly simulate reductions incosts of building new plants as a result of priorlearning as is done in NEMS and POEMS. In-stead, the model tries to incorporate this effect byassuming lower costs initially that, over severalyears, will approximate the effect of learning oncosts found in other models.

These developments, the past and future im-provements of gas turbine technologies, andhigher cost of capital in competitive markets ex-pand the use of gas in combined-cycle units overcoal to meet baseload demand, even though fuelprices paid by generators tend to move slightly infavor of coal over time. While inflation-adjusted(or real) coal prices remain relatively stable overthe decade, natural gas prices tend to rise. In2000, coal prices delivered to generators in mostmodels are approximately 40-45% of the compa-rable gas price (Figure 9). By 2010, they declineto about 30-35% of the gas price.

Emissions

This shift in the U.S. power sector to natural gasrather than coal causes annual U.S. emissions forsulfur dioxide, nitrogen oxides, and carbon diox-ide from the power sector to grow more slowlythan electricity demand over the next decade.

Figure 10 reveals that the national sulfur dioxideannual emissions decline by anywhere between1.0 to 2.5 million metric tons over the 2000-2010period. This trend reflects the national cap onemissions imposed by the Clean Air Act and thatgenerators are using banked allowances, or al-lowances earned prior to 2000, during this period.The similarity across different projections reflects


Figure 10. Sulfur Dioxide Emissions in Baseline Case, 2000-2010

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16

2000 2005 2010

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Figure 9. Coal-Gas Price Ratio for Utilities in Baseline Case, 2000-2010

0.000

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2000 2005 2010

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e2020


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he national caps that have already been imposedn these emissions in the baseline conditions. Noational cap is specified in E2020 and thereforehe base level emissions are higher across the en-ire time period. However, similar amounts ofeductions over time are achieved.

itrogen oxides emissions in Figure 11 tend toemain quite steady over the next decade, exceptor the increase between 2000 and 2005 in NEMSnd Energy 2020. The RFF emissions remainore than 1 million metric tons (or approxi-ately 25%) above the POEMS emissions

hroughout the decade. The NEMS emissionsemain between the RFF and POEMS emissionaths. These three projections of the baselineompetition case incorporate no new environ-ental policies that have not already been ap-

roved by policymakers.19 At the end of the dec-

9 Hence, the projections, except those of IPM, ignored theestrictions on summer-peak NOx emissions in 19 eastern

ade, nitrogen oxides emissions increase by 0.6million metric tons (or 11.8%) above 2000 levelsin NEMS and decrease by 0.2 million metric tons(3.8%) in RFF. However, the lower IPM trendincorporates the summer peak NOx restrictionsand reveals a decline of about 0.6 million metrictons (13.3%) of NOx emissions over the decade.

Carbon dioxide emissions in Figure 12 rise inNEMS and POEMS but at slower rates (1.3% and0.9% per annum over the 2000-2010 decade) thanelectricity generation (1.6% per annum). Emis-sions grow by 95 million metric tons in NEMSand by 65 million metric tons in POEMS andIPM over the decade. The RFF emissions arerelatively stable throughout the ten years. The

states that were imposed by the Ozone Transport Rule(OTR) established by the Clean Air Act Amendments of1990 and by State Implementation Plans (SIP) Call policies,which were being challenged in court at the time of thisstudy..

Figure 11. Nitrogen Oxide Emissions in Baseline Case, 2000-2010

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lower retirement of nuclear plants in this projec-ion contributes to this noticeably slower growthn carbon emissions. The initial drop in RFFmissions from 2000 to 2005 is due to a shiftrom oil to gas generation and to more efficientnits, as a large share of oil and gas steam plantsetire. The E2020 carbon emissions are relativelytable over the forecast period, decreasing by 5illion metric tons in 2010 relative to 2000. This

s largely due to a shift towards gas-generatedower and slower electricity demand growth thanhe other models.

nterregional Electricity Trade

he projections expect similar volumes of totallectricity trade among the 13 different regionsor which all models report results. These regionsre based upon those used by the North Americanlectric Reliability Council (NERC) shown in

Figure 13. There is no trend towards increasingelectricity trade over time in these baseline pro-jections. It should be emphasized that these tradeestimates ignore any imports and exports betweenpower control areas or between companies withinthese large regions. They are also annual aver-ages that do not reflect seasonal conditions.

By 2010, NEMS projects 259 billion kWh of im-ports into these NERC-related regions from oneof the other areas, POEMS projects 209 billionkWh, RFF expects 238 billion kWh, and IPM an-ticipates 171 billion kWh. As a percent of totalU.S. generation, these estimates range from 4.1%to 6.2%. However, Figure 14 shows that the re-gional patterns for interregional imports do varyamong projections. For example, relative to theother projections, RFF calls for more imports intothe midwestern states represented by ECAR andthe eastern MAAC region and fewer imports intoIllinois and Wisconsin represented by the MAINregion and into California and Nevada within the

Figure 12. Carbon Emissions in Baseline Case, 2000-2010

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Figure 13. Electricity Market Module Regions

Source: U.S. EnergyInformation Administration

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Figure 14. Interregional Im ports in Baseline Case, 2010

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WSCC region. IPM projects considerably lesstrade in the Eastern Interconnection regions.20

These four U.S. outlooks anticipate that electric-ity imports from our North American neighbors,Canada and Mexico, will range from 29 to 44billion kWh in 2010. The lower end of these es-

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Alternative Competition Cases

The alternative cases show how competitive mar-ket conditions change with other assumptions forbaseline demand growth, natural gas prices,transmission fees and capacity, and a renewablesportfolio standard (RPS). Often, changes in as-

imates are only slightly higher than the 25 billionWh that the Canadian Energy Research InstituteCERI) is projecting for Canadian exports to thenited States. The latter estimate, of course, ex-

ludes the Mexican exports included in the U.S.rojections. The similarity between estimatesrom some U.S. models and a Canadian modelrobably reflects that these models are projectingnternational electricity trade on the basis of cur-ent permits.

0 The IPM results for the Western regions are not quiteomparable with the other models due to a different re-ional definition (see footnote 18).

sumed conditions will lead to several effects thatcan be difficult to discern. Therefore, to help un-derstand these cases, the group also requested thatthe modelers run additional cases for the ex-panded transmission case and for the higher de-mand case. The two additional transmissioncases considered the effects of lower transmissionfees and higher transmission capacity separately.The additional high electricity demand case keptnatural gas prices at their baseline levels, ratherthan allowing them to rise with the additionalload growth. All models except IPM reported theadditional transmission and high demand cases.IPM also did not report the RPS case.

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Figure 15. Percent Changes in Combined Cycle Cumulative Additions from Baseline Case, 2000-2010

-20%

0%

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160%

ExpandedTransmission

High Capacity Low Fees High Demand High DemandFixed

Low Gas RPS

NEMS

POEMS��

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apacity Additions

he cumulative additions for combined-cyclenits (Figure 15) over the decade remain within0 percent or less of the baseline competitionase for all cases except the high demand casesnd the RFF and IPM lower natural gas pricease.21 Combined-cycle plants dominate new ca-acity additions under the baseline conditions.heir prospects do not change much for any of

he models in any of the expanded transmissionases.

atural gas price changes are important in shift-ng the mix or quantity of capacity additions inFF and IPM. In IPM the mix of additions shifts

rom 84 percent gas technologies to 93 percentnd the mix between the combined cycles and

1 The RFF RPS case is also almost 12% above the baselineompetition case.

turbines shifts to a greater reliance on combinedcycles. In RFF, additions are dominated by com-bined cycles in both cases (over 80% of total), butlow gas prices create greater capital stock turn-over, and total additions by 2010 increase 41 per-cent relative to the Base case. For both NEMSand POEMS, low gas prices increase retirementsand additions only modestly and the share ofcombined cycle additions changes little.

However, total demand is the dominating factorin NEMS for cumulative combined-cycle addi-tions. For NEMS the very significant proportionalincrease in additions due to high growth is pri-marily the result of relatively low additions in thebaseline case, and to a lesser extent, due to a shiftin the mix of additions to greater use of combinedcycles and fewer combustion turbines.

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Figure 16. Percent Change in Coal Capacity Cumulative Additions from Baseline Case, 2000-2010

-100%

-50%

0%

50%

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150%

200%

250%

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400%



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2020 demonstrates smaller response for capacitydditions, and much of it is in combined cyclelants. High demand generates the largest re-ponse at approximately 7 percent. Changes forther scenarios are minimal.

erhaps reflecting the models’ assumptions thatoal technology does not progress significantly,e cumulative additions for coal capacity overe decade are relatively small in the baseline

ase. With higher demand conditions, these addi-ons over the decade expand by at least 25% inach model (with the exception of E2020) and byore than 350% in the NEMS projections (Figure

6). Higher demands expand total capacity addi-ons, including coal capacity. Because gas pricescrease in integrated high demand case, the coal

hare while still small increases slightly. Loweratural gas prices almost eliminate any new coalapacity in NEMS and RFF, and strongly reduce in POEMS and IPM.

Wind capacity additions over the decade increasedramatically from 2.3 gigawatts in the baselinecompetition case to 8.0 gigawatts in the RPS casein POEMS. They also increase strongly in RFFas well, from 0.1 gigawatts in the baseline com-petition case to 4.6 gigawatts in the RPS case.However, Figure 17 indicates that wind capacityadditions in RFF do not change across the othercases, nor do they change in any case in NEMS.As a result, only POEMS reveals any fluctuationsin wind capacity additions across all cases.22 Thisfigure indicates that expanded transmission fa-cilities (especially, higher capacity) and lowernatural gas prices (in addition to the RPS condi-tions described above) significantly reduce cu-mulative wind capacity additions in POEMS.

Electricity Prices

Figure 18 reveals that natural gas prices andelectricity demands are two important influenceson the future path for electricity prices. The only 22 IPM did not report wind capacity separately.

Figure 17. Percent Change in W ind Capacity Cumulative Additions from Baseline Case, 2000-2010

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

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High Capacity Low Fees High Dem and High Dem andFixed

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ther significant change at the national level oc-urs with the RPS case in the RFF model. Other-ise, the alternative conditions for electricityansmission and the RPS generally have veryodest effects that remain below 5% of the base-ne values.

arger changes are seen for some regional elec-icity prices. The largest gain relative to theaseline (27%) occurs for the midwestern MAINegion in the NEMS model in the high demandase. The largest reduction relative to the baseline22%) occurs for New York in the RFF model ine low gas price case. While the national prices

hange by no more than 2% from their baselinevels in the alternative transmission cases, re-ional prices can increase or decrease by as muchs 12 or 13% in these cases.

general, importing regions with higher prices the baseline case will experience price reduc-ons with more transmission access, while lowost exporting regions will see higher prices. Asiscussed in the section on the baseline case,

these prices are the competitive wholesale levelsbefore any adjustments for stranded costs areadded.

The outlooks agree that lower natural gas priceswill reduce competitive electricity prices whilehigher electricity demand will increase electricityprices. However, the pattern across outlooks israther different. RFF and IPM reveal rather largereductions in electricity prices when gas pricesfall; the declines in NEMS, POEMS and E2020are more modest. By contrast, the NEMS andPOEMS electricity price increases are greaterwhen electricity demands grow more vigorously,while the RFF and IPM price increases are moremodest. The increase in E2020 electricity pricesis much larger than other models, because totalavailable capacity is fixed, resulting in higherprices and declining reserve margins.

Several factors could be contributing to the higherelectricity prices in the high electricity demandcase. First, the industry could be pushed out fur-ther along its supply stack and forced to use more

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Figure 18. Percent Change in Competitive Wholesale Electricity Price from Baseline Case, 2010

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%

30%

35%


High Capacity Low Fees High Demand

NEMS

POEMS��

RFF

IPM��e2020

High Demand Fixed

Low Gas

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Table 5. Percent Change in Generation and Electricity Price in the High-Demand Cases (from the

xpensive units to meet the higher demand. Sec-nd, the addition of new capacity will createigher demand for fuel by generators and this in-reased consumption could bid fuel prices higher.oth limits on the gas resource base and gasipeline bottlenecks could induce these higherrices. In using more natural gas, the power sec-or must shift gas away from other end-use sec-ors as well as encourage more natural gas pro-uction. These two factors will both lead toigher competitive electricity prices than underhe baseline conditions.

everal other factors are also changing the aver-ge electricity price between these two cases.ven if the demand curve is shifted outward by

he same proportion in all hours and days, pricesould move upward more quickly during peakimes and loads could be shifted away from theseigh-price periods. As a result, electricity useight grow more rapidly in nonpeak than in peak

imes, thereby decreasing the average price asonsumption was shifted between periods. An-ther complication lies in the effect of higher de-and on changing retirements and additions, both

f which will influence the shape of the supplytack between the two cases.

hen demands are increased and natural gasrices are fixed at their baseline values, the re-ults show what happens to electricity priceshen the increased fuel costs are ignored.

POEMS shows a 7.5% increase and IPM a 2.4%increase in wholesale competitive electricityprices, which is consistent with the view thatcosts will rise as the industry moves further outon its supply stack. NEMS reveals a decrease ofalmost 5% by the end of the decade, which couldbe due to shifts in the supply stack attributable toretirements and additions. There is virtually nochange in RFF’s electricity price, and E2020 didnot report results for this case. The POEMS in-crease is less than the 10.5% increase in totalelectricity demand in that model.

These results demonstrate that natural gas condi-tions can have a significant effect on electricityprices. In an effort to achieve greater consistencyin the demand shock, the modelers in this studydid not allow these higher electricity prices to re-duce the use of power. In actual electricity mar-kets and when these models are usually simu-lated, however, the higher power prices wouldoffset some, but not all, of the initial growth inelectricity consumption. Under these conditions,both electricity consumption and gas use by thepower sector would be lower, as would the priceof natural gas and electricity.

Given the important qualifications on which fac-tors are changed in this case, the high-demandcases also reveal some information about howmuch electricity prices will respond relative toelectricity generation when the desire for elec-

Baseline Case), 2010

High Demand with Fixed Price High Demand with Integrated Price

Generation Price Generation Price

NEMS 12.54 -4.53 12.54 13.04POEMS 10.41 7.83 10.45 17.05RFF 10.71 -0.82 12.82 7.70IPM 10.33 2.44E2020 13.21 33.81


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ricity expands. Table 5 shows that total genera-ion and the competitive wholesale electricityrice each rise by about 12.5-13% above baselineevels in NEMS in the high-demand case withntegrated fuel prices (the last set of columns).hus, the inferred supply response or elasticity isear unity in this model.23 Prices increase theost in E2020; generation increases are not that

igh, indicating a much lower supply elasticity.imilarly, prices increase more than generation inOEMS, inferring a lower supply elasticity, al-

hough the gap between the change in price andeneration is much smaller than in E2020. Pricesncrease less than generation in RFF, inferring aigher supply elasticity.

3 The price elasticity of electricity supply is defined as theercentage change in electricity quantity supplied dividedy the percentage change in price, holding other factorsonstant. Clearly, these other factors that are held constantill certainly influence the measured response. Thus, the

eader is discouraged from computing implicit elasticitiesrom the numbers in Table 5 as being a model’s elasticity.

The first set of columns in Table 5 shows thesame computations when higher demands are al-lowed but natural gas prices are kept at theirbaseline levels. The results show a much lowerincrease in electricity prices when natural gasprices do not increase. As a result, the inferredelasticity is much greater. In fact, there is a de-cline in the NEMS competitive wholesale elec-tricity price in this case, while the RFF price re-mains virtually unchanged. This result probablyreflects the influence of retirements and additionson the supply stack in the two cases.

Competitive wholesale electricity prices fall be-low baseline levels in the lower gas price case asshown in Figure 18. Figure 19 shows that natu-ral gas prices in RFF fall sharply to more than15% below baseline values in 2005 and almostreach 20% below baseline by 2010 because theyrose more quickly in their baseline case. (Thefigure shows decreases as negative values thatincrease as you move upward in the chart.) The

Figure 19. Percent Change in Fuel Prices for Generation in Low Gas Price Case from Baseline Case, 2000-2010

-25%

-20%

-15%

-10%

-5%

0%

5%

2000 2005 2010

NEMS Coal

NEMS Gas

POEMS Coal

POEMS Gas

RFF Coal

RFF Gas

IPM Coal

IPM Gas

e2020 Coal

e2020 Gas


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Fu

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tural gas prices relative to the baseline case fallre slowly in the other models, although the

anges in gas price trends move closer to eacher by the end of the decade. Moreover, theF coal prices also decline more than 5% below baseline by 2010. The downward pressure onl prices paid by the power sector contributes to relatively strong decline in competitive powerces in that model shown in Figure 18.

els

anges in electricity demands and natural gasces dominate the response of combined-cycleacity additions in the alternative competitiones. This same pattern is observed for the fuelsd by the power sector in these cases. In gen-l the percent change in gas consumption will

greater than a percent change in coal, because base coal consumption is substantially larger.

Figure 20, NEMS shows natural gas use in thewer sector increasing by 45% above the base-e when demands are increased and natural gasces are fixed at the baseline levels. This in-

crement declines to 30% above the baseline levelswhen natural gas prices are allowed to increase aswell. The higher gas prices discourage gas use inthe power sector.24 In this model, lower gasprices cause 10% more gas consumption by thepower sector relative to the baseline case.

In Figure 21, POEMS displays a similar pattern,although the estimates vary somewhat. The highdemand case with fixed prices results in a 36%increase in gas consumption in the power sectorabove the baseline when fuel prices are fixed anda 31% increase in the integrated high demandcase with rising gas prices. Gas use for genera-tion increases by 15% above the baseline whengas prices fall below the baseline.

The RFF results in Figure 22 show a substantiallylarger adjustment in coal and gas generation inthe low gas price case than in the other cases.The power sector’s gas use in 2010 expands by

24 For reasons given in the previous section, if the modelershad allowed electricity consumption to decline with higherelectricity prices, natural gas use would have declined morebut electricity prices would not have risen as much.

Figure 20. Percent Change in Fuel Consumption for Electricity Generation in NEMS from the Baseline Case, 2010

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

Expan

ded

Trans

miss

ion

High C

apac

ity

Low F

ees

High D

eman

d

High D

eman

d Fixe

d

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Coal

Gas


Figure 21. Percent Change in Fuel Consumption for Electricity Generation in POEMS from the Baseline Case, 2010

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

Expan

ded

Trans

miss

ion

High C

apac

ity

Low F

ees

High

Deman

d

High

Deman

d Fixe

d

Low G

asRPS

Coal

Gas

Figure 22. Percent Change in Fuel Consumption for Electricity Generation in RFF from the Baseline Case, 2010

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

ande

d Tra

nsm

ission

High C

apac

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ees

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eman

d

Coal

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Demand Fixed


Figure 23. Percent Change in Fuel Consumption for Electricity Generation in IPM from the Baseline Case, 2010

-10%

0%

10%

20%

30%

40%

50%

60%

Expanded Transmission High Demand Fixed

Coal

Gas

Low Gas

Figure 24. Percent Change in Fuel Consumption for Electricity Generation in ENERGY2020 from the Baseline Case, 2010

-10%

0%

10%

20%

30%

40%

50%

60%


High Capacity Low Fees High Demand

Coal

Gas

Low Gas


29

58% above the baseline while its coal use con-tracts by 22%. This gas expansion is strongerthan that observed for the high-demand case.

The expansion in natural gas generation in theIPM results in Figure 23 is stronger for the highdemand case with fixed prices than for the lowernatural gas price case. However, the model’s re-sponse to gas prices is in between the NEMS andPOEMS results.

In Figure 24 showing E2020 results, high demandgenerates a 41% increase for gas use in the elec-tricity sector, which is similar to the other mod-els. However, the model’s response to low gasprice is minimal at 3.7%.

In all but RFF, the increased transmission caseslead to a modest shift from gas to coal generation.As new gas construction changes the existing re-gional endowment of capacity, this shift towardscoal becomes less pronounced and the changesappear less in 2010 than in 2000 and 2005.

Emissions

Higher demands tend to increase the power sec-tor’s carbon emissions more than nitrogen oxidesor sulfur dioxide emissions in these simulations.Annual carbon emissions for the nation grow by7-10 percent more than baseline levels by 2010when energy demands increase by 12 percent.Nitrogen oxides emissions for U.S. grow by ap-proximately 5 percent more by 2010, while U.S.sulfur dioxide emissions remain unchanged.

No new nuclear plants play an important role inthis result. Lower natural gas prices and addi-tional incentives for renewable energy technolo-gies appear to decrease nitrogen oxides and car-bon emissions in this sector. National caps tendto keep sulfur dioxide emissions close to theirbaseline levels in all scenarios.

Sulfur dioxide emissions in 2010 remain rela-tively similar to baseline levels for most modelsand cases. By 2010, firms do not change their

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Figure 25. Change from Baseline (%) in Nitrogen Oxide Emissions, 2010

-25%

-20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

25%



NEMS

POEMS��

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missions levels much from the baseline, al-hough the alternative conditions can change theosts of SO2 allowances, which will influenceeneration costs.

igure 25 reveals that the higher demand condi-ions increase nitrogen oxides emissions by ap-roximately 5% by 2010 in three of the fourodels. Unlike sulfur dioxide, these emissionsill be pushed higher by increased electricityemand. The nitrogen oxides emissions effect forigher demands is noticeably stronger than theffects in the expanded transmission case as wells in the low transmission fee and higher trans-ission capacity cases. RFF anticipates sharp

eductions in nitrogen oxides emissions by thend of the decade when either natural gas pricesre lower or when a renewable portfolio strategyRPS) is implemented.

arbon emissions in the power sector move withhe higher demand conditions as shown in Figure6. This trend reflects the absence of growth in

any new nuclear plants due to a combination ofcosts and public perceptions. Carbon emissionsgrow by about 7% to 10% higher than baselinelevels and hence increase somewhat less than to-tal electricity demand.25 Electricity demand is13.7% higher than 2010 baseline levels in E2020,12.5% higher in NEMS, 10.5% higher in PO-EMS, and 10.3% higher in RFF. The latter model(RFF) shows stronger reductions in carbon emis-sions with lower natural gas prices or with theRPS than do the other models. The reductions innitrogen oxides and carbon in the RPS case inRFF stem from a decrease in coal capacity andtherefore generation, as much as from an increasein renewable generation. In comparison, POEMShas a greater renewable response to the RPS butsmaller gains in emission reductions.

25 Constraints on carbon emissions could be imposed bypricing carbon through taxes or allowances, as has beenanalyzed by the Energy Modeling Forum Working Group16. See Weyant, John P. (1999), editor, The Costs of theKyoto Protocol: A Multi-Model Evaluation, Energy Jour-nal, special issue.

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Figure 26. C hange from B aseline (%) in C arbon Em issions, 2010

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6%

8%

10%

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ExpandedTransm ission

High Capacity Low Fees High Dem and H igh Dem andFixed

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31

Interregional Electricity Trade

Transmission policy can significantly influenceelectricity markets that are undergoing restruc-turing. The expanded transmission case estab-lishes both higher transmission capacity andlower transmission fees. These conditions en-courage more aggregate trading between all re-gions in the various projections shown in Figure27. Total interregional imports increase by 23percent above baseline levels in RFF and by 61percent in NEMS. The increase is greatest (85%)for IPM, although that model had a lower base-line level of interregional imports.

The chart also shows that lower transmission feesdominate the effects of the expanded transmissioncase in NEMS and POEMS. These results under-score the importance of transmission pricing indetermining the economic incentive for tradebetween regions, and that fees can be more con-straining on trade than physical limits. In theweaker total effect in RFF, higher transmissioncapacity appears to be more important than lowerfees, at least at an aggregate level.

These results have noticeable regional differencesas well. No one regional pattern flows throughall the outlooks. Figure 28 shows how the ex-panded transmission case affects imports intoeach major region by 2010. For example, Cali-fornia imports over 15 million kWh more than inthe baseline case in NEMS and more than 35million additional kWh in POEMS. Most of thesmaller NEMS effect is due to lower transmissionfees (Figure 29), while most of the larger POEMSeffect is attributable to higher transmission ca-pacity (Figure 30).

Conclusion

Properly functioning prices are the cornerstone ofcompetition’s potential efficiency. Competitiondoes not guarantee a certain price, does not re-

quire electric loads to be a given magnitude, anddoes not assure generators that their plants will beused when they want them to be. Participantswill need to protect themselves from these busi-ness risks. This study’s results show howchanges in electric load growth, natural gasprices, and transmission costs and expansionscould influence conditions in markets where effi-cient rules have been established.

Electricity demand and natural gas prices will beimportant drivers for the power sector over thisnext decade as the industry undergoes further re-structuring. For one of the scenarios in this study,total annual electricity demand was increased by1% per year above the baseline levels. The pro-jected response was a 10-14% increase in totalelectricity consumption over the decade and anincrease in the competitive delivered price ofelectricity of between 7.7 and 17% above base-line levels after 10 years for most models exceptE2020, which has an increase of 33%. Theseadjustments incorporated higher natural gasprices that were necessary to keep gas flowinginto the power sector to support this expansion.The cases assume that there are no severe limita-tions on large-scale natural gas pipeline expan-sion. Ignoring these higher gas prices, the com-petitive delivered electricity price would be nomore than 7.8% higher than baseline in any of theprojections. Thus, higher natural gas prices maycontribute to higher electricity prices over thenext decade if expanded electricity demand ormore stringent environmental policy encouragemore natural gas generation.

Lower natural gas prices with the same electricdemand growth will reduce electricity prices andwill help to stimulate natural gas use that mayprovide environmental benefits in terms of re-duced carbon and sulfur dioxide but also costs interms of. nitrogen oxides Thus, uncertainty aboutthe cost of finding additional natural gas supply


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Figure 27. Change in Interregional Im ports, 2010

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Figure 28. Change in Interregional Imports in Expanded Transmission (w rt Baseline), 2010

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Figure 29. Change in Interregional Im ports in Low Transm ission Fees w rt Baseline, 2010

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Figure 30. Change in Interregional Im ports in High Transm ission Capacity w rt Baseline , 2010

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will be an important unknown for understandingthe electricity industry’s future.

National sulfur emissions within the power sectorare expected to decline over the next decade asutilities use banked allowances or cleaner coalunder the national cap for these emissions. Thisprogram was implemented in two phases. Un-used emission allowances in the first phase (whenthe cap was less stringent) could be saved or“banked” for use in the second phase. In contrast,the NOx cap is implemented in one phase, which

reduces the incentive to bank. Instead, firmsmust comply with the NOx cap immediately andare unlikely to accumulate banked allowances forthe future. In summary, banking will play a moreminor role for this emission than it has in the SO2program.

As a result, both nitrogen oxides and carbonemissions in the power sector are expected togrow but by less than electric loads or demand.Moreover, faster economic growth will meanhigher emissions for these two pollutants.

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