Managing Solar Photovoltaic Integration In The Western .

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Managing Solar Photovoltaic Integrationin the Western United StatesResource Adequacy ConsiderationsGord Stephen, Elaine Hale, and Brady Cowiestoll

Managing Solar PhotovoltaicIntegration in the Western UnitedStates: Resource AdequacyConsiderationsGord Stephen, Elaine Hale, and Brady CowiestollNational Renewable Energy LaboratorySuggested CitationStephen, Gord, Elaine Hale, and Brady Cowiestoll. 2020. Managing Solar PhotovoltaicIntegration in the Western United States: Resource Adequacy Considerations. Golden,CO: National Renewable Energy Laboratory. sti/72472.pdf.

NOTICEThis work was authored by the National Renewable Energy Laboratory, operated by Alliance for SustainableEnergy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Researchcommissioned by the Western Interstate Energy Board (WIEB). Funding provided by the DOE Office of EnergyEfficiency and Renewable Energy Solar Energy Technologies Office. The views expressed herein do notnecessarily represent the views of the DOE or the U.S. Government.This report is available at no cost from the National RenewableEnergy Laboratory (NREL) at www.nrel.gov/publications.U.S. Department of Energy (DOE) reports produced after 1991and a growing number of pre-1991 documents are availablefree via www.OSTI.gov.Cover Photo by Dennis Schroeder, NREL 30542NREL prints on paper that contains recycled content.

PrefaceThis report is one in a series examining potential challenges related to planning future power systems with higher solar photovoltaic (PV) penetrations. In recent years, numerous renewable integration studies have examined power system operations with various wind and solar penetrationsand have found it feasible to balance supply and demand. There are also examples of power systems currently operating with significant penetrations of wind or solar power in the literature.This series of reports focuses on solar PV generation specifically and delves deeper into potentialintegration issues that may not be so challenging at moderate penetrations but could be of moreimport at higher PV penetrations.The series uses the western U.S. power system for these investigations because it is a region theauthors and their colleagues have already extensively studied. We are therefore well-suited to analyze even higher PV penetrations and then examine the results in multiple models to determinewhether our current approaches are missing key details that only emerge at higher PV penetrations. We also examine three regions in the western United States with significantly differentexisting power systems and connections to neighboring regions; this provides a more balancedpicture as to how high PV penetration systems might emerge in different contexts and what theresulting issues, if any, might be.The four publications in this series are listed and described in Table A.Table A. Reports in the Managing Solar PhotovoltaicIntegration in the Western United States SeriesTitleManaging Solar Photovoltaic Integration in theWestern United States: Power System FlexibilityRequirements and SupplyManaging Solar Photovoltaic Integration in theWestern United States: Resource Adequacy ConsiderationsDescriptionAssessment of net load ramping needs and what resources are available to provide upward and downwardramping at different timescalesProbabilistic resource adequacy assessment ofhigh PV penetration scenarios and comparison toplanning reserve margin approaches using capacitycredit approximation methodsAn exploration of how two renewable portfoliostandard design elements can influence the interactionof behind-the-meter PV and total renewable generationBehind-the-meter Solar Accounting in RenewablePortfolio StandardsResource Planning Model (RPM) inputs, scenarioframework, and results for RPM-AZ, RPM-CO, andRPM-OR; two of the papers in the series use thesescenarios as their starting point for analysisThis report is listed in bold type.Managing Solar Photovoltaic Integration in theWestern United States Appendix: Reference and HighSolar Photovoltaic Scenarios for Three RegionsThis report series was commissioned by the Western Interstate Energy Board (WIEB) as partof the Enhanced Distributed Solar Photovoltaic Deployment via Barrier Mitigation or Removalin the Western Interconnection project funded by the U.S. Department of Energy’s Office ofiii

Energy Efficiency and Renewable Energy Solar Energy Technologies Office (SETO).1 For moreinformation, including links to other reports, see stributed-solar-photovoltaic/.1 Anadditional work was published as a journal article: Kenyon, Rick Wallace, Matthew Bossart, MarijaMarković, Kate Doubleday, Reiko Matsuda-Dunn, Stefania Mitova, Simon A. Julien, Elaine T. Hale, and BriMathias Hodge. 2020. “Stability and Control of Power Systems with High Penetrations of Inverter-Based Resources:An Accessible Review of Current Knowledge and Open Questions.” Solar Energy, Special Issue on Grid Integration,210: 149–68. https://doi.org/10.1016/j.solener.2020.05.053.iv

AcknowledgmentsThis work was supported by the U.S. Department of Energy (DOE) Office of Energy Efficiencyand Renewable Energy (EERE) Solar Energy Technologies Office (SETO), as part of a widerproject led by the Western Interstate Energy Board (WIEB). The authors would specifically liketo thank Michele Boyd (DOE) for supporting this and other SEEDS2-SES2 projects; and MauryGalbraith, David Manning, Richard McAllister, and Holly Taylor (WIEB) for partnering with uson the Enhanced Distributed Solar Photovoltaic Deployment via Barrier Mitigation or Removalin the Western Interconnection project. We would also like to thank Lori Bird and David Hurlbutof the National Renewable Energy Laboratory (NREL) for their project management support andleadership.The idea for this series of reports was developed with input from several of our colleaguesat NREL and the project’s technical advisory committee. Lori Bird, Kara Clark, MichaelCoddington, Paul Denholm, Barry Mather, Michael Milligan, Bryan Palmintier, and Mark Ruth(NREL) provided input to an initial screening analysis of PV reliability barriers. The barriersscreening analysis was then reviewed with the committee, as was a research plan developed inresponse to the screening results. We would like to thank committee members for their participation in those processes as well as for the review and guidance they provided throughout theexecution of the research. The results and findings in this report and the broader project do notnecessarily reflect their opinions or the opinions of their institutions. The committee is composedof the following individuals: Jim Baak, Vote SolarGuru Belavadi, Arizona Corporation CommissionKen Bolton, Western Electricity Coordinating CouncilEnoch Davies, Western Electricity Coordinating CouncilTom Flynn, California Energy CommissionJennifer Gardner, Western Resource AdvocatesDaniel Haughton, Arizona Public Service Electric CompanyCarl Linvill, Regulatory Assistance ProjectToby Little, Arizona Corporation CommissionClyde Loutan, California Independent System OperatorLouise Nutter, Federal Energy Regulatory CommissionVijay Satyal, Western Electricity Coordinating CouncilCourtney Smith, California Energy CommissionFinally, the authors would like to acknowledge Bethany Frew, David Hurlbut, Jaquelin Cochran,Dan Bilello, Kristin Ardani, Doug Arent, Mark O’Malley, and Gian Porro (NREL) for providing2 SEEDS2-SESis the Solar Energy Evolution and Diffusion Studies 2 – State Energy Strategies program. Forinformation about the program, see gies-seeds2-ses.v

feedback and review on drafts of this paper, as well as Sheri Anstedt and Devonie McCamey foreditorial and publication support.vi

Acronym RASPRMPVReEDSRMRGRPMRPSSETOSRPSRSGTEPArizona Public Service CompanyAnnual Technology Baselinebalancing authorityCalifornia/MexicoU.S. Department of Energydistributed photovoltaicsequivalent firm capacityU.S. Energy Information Administrationequivalent load-carrying capabilityEnergy Efficiency and Renewable Energyexpected unserved energyInstitute of Electrical and Electronics Engineersincremental net load duration curveloss-of-load expectationloss-of-load probabilitynormalized expected unserved energyNorth American Electric Reliability CorporationNevada Power CompanyNational Renewable Energy LaboratoryPacifiCorp WestPortland General Electric CompanyPublic Service Company of ColoradoNorthwest Power Pool CanadaNorthwest Power Pool United Statesparts per million (energy fraction)Probabilistic Resource Adequacy Suiteplanning reserve marginphotovoltaicRenewable Energy Deployment SystemRocky Mountain Reserve GroupResource Planning ModelRenewable Portfolio StandardSolar Energy Technologies OfficeSalt River ProjectSouthwest Reserve Sharing GroupTucson Electric Power companyvii

UPVWACMWALCWIWIEButility-scale photovoltaicsWestern Area Power Administration, Colorado MissouriWestern Area Power Administration, Lower Colorado RegionWestern InterconnectionWestern Interstate Energy Boardviii

Executive SummaryThis study examines the impact of reserve margin-based reliability assessment, as commonlyused in capacity expansion models, on planning resource-adequate power systems under highpenetrations of solar photovoltaics (PV). As a generation resource, PV is operationally differentfrom the conventional dispatchable resources for which most capacity expansion models weredesigned. The question this study attempts to answer is whether large amounts of PV on a system(in this case, the Western Interconnection of North America) would bias the results of conventional reserve margin-based capacity expansion modeling towards an over- or under-provisioningof resource adequacy.This analysis used NREL’s Resource Planning Model (RPM) for capacity expansion modelingand NREL’s Probabilistic Resource Adequacy Suite (PRAS) for resource adequacy assessment.RPM uses a reserve margin requirement to enforce resource adequacy. PRAS, a collection oftools for studying the resource adequacy of power systems and the adequacy contributions ofindividual resources on a probabilistic basis, was used to compute multiple resource adequacymetrics across a number of simulated scenarios and system representations with differing regional detail. In all cases, including high PV penetrations (up to 33% annual generation fromPV, interconnection-wide), RPM was able to produce resource-adequate systems as measured bynormalized expected unserved energy and loss-of-load expectation results from PRAS.The accuracy of reserve margin approaches depends heavily on the underlying assumptionsinforming the capacity credit assigned to variable and energy-limited resources, particularly whensuch resources are abundant in the modeled system. RPM’s standard methodology for estimatingvariable and flexible resources’ capacity contributions, which is based on the top 100 hours ofnet load, did not appear to systematically undervalue or overvalue variable generation relativeto a more rigorous equivalent firm capacity assessment using PRAS, although both over- andundervaluations were observed in specific scenarios. In the worst cases, the top 100 hour methodunderestimated the equivalent firm capacity of PV by two percentage points, and overestimatedthe equivalent firm capacity of PV by five percentage points. Calculating capacity contributionsbased on the top 10 hours of net load systematically underestimated equivalent firm capacities atmore modest PV penetrations, but was often a better approximation of equivalent firm capacitythan the current 100-hour approach in scenarios with higher PV penetrations.ix

Table of Contents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 Scenario Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2.1 Regional Planning Model (RPM) Background . . . . . . . . . . . . . . . . . . . .2.2 High PV Scenario Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2243 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.1 System-Level Adequacy Assessment . . . . . . . . . . . . . . . . . . . . . . . . .3.2 Adequacy Contributions of Individual Resources . . . . . . . . . . . . . . . . . .6694 System Resource Adequacy Assessment . . . . . . .4.1 System Representation . . . . . . . . . . . . . .4.2 Copper Plate Analysis . . . . . . . . . . . . . . .4.3 Transmission-Constrained Analysis . . . . . . . .4.4 Regional Expected Unserved Energy Occurrences.11111213155 Variable Generation Contributions to Resource Adequacy5.1 Distributed and Utility-scale Solar PV . . . . . . . . .5.2 Wind . . . . . . . . . . . . . . . . . . . . . . . . . . .5.3 Method Comparison . . . . . . . . . . . . . . . . . . .17171919.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21x

List of FiguresFigure 1.RPM focus models studied in this report: RPM-AZ, RPM-OR, and RPM-CO .3Figure 2.RPM’s planning reserve constraints are defined over NERC subregions . . . . .3Figure 3.Hierarchical relationship between the five study scenarios. The Referencescenario uses mid-line assumptions. The study scenarios are formed by varying renewableenergy goals (RPS), technology costs, and fuel prices. . . . . . . . . . . . . . . . . . . 4Figure 4.Western Interconnection resource adequacy (log scale) without transmissionconsidered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Figure 5.Copper plate metrics versus planning reserve margin . . . . . . . . . . . . . . 13Figure 6.Western Interconnection resource adequacy (log scale) with transmission considered. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Figure 7.Transmission-constrained probabilistic resource adequacy metrics versusplanning reserve margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Figure 8.Percent of BAs with expected dropped load as a function of load scaling . . . . 16Figure 9.Capacity value results for the Western Interconnection as a function of scenario solar penetration, grouped by focus region case and resource type, after 30%load increase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18List of TablesTable 1.PV Penetration (%) in 2035 for All Scenarios and Focus Models . . . . . . . . .xi5

1IntroductionResource adequacy, that is, ensuring a sufficiently low risk of available generation supply fallingshort of demand, is a key concern for all power system planners, operators, and load-servingentities. The North American Electric Reliability Corporation (NERC) produces three annualreports on this topic to document the current resource adequacy status of NERC-jurisdictionpower systems: a summer assessment, a winter assessment, and an overall long-term reliabilityreport.3 In this way, all stakeholders can be aware of how well poised our power systems are toprovide affordable electricity at peak times, both now and into the future.The NERC reliability assessments primarily report resource adequacy in terms of planning reserve margins. A planning reserve margin is firm capacity over and above the peak load forecast,typically expressed as a percentage of the peak load forecast. Traditionally, the supply shortfallrisk mitigated by this extra capacity has been driven by random outages of dispatchable generators and transmission, as well as peak load forecast error. As such, NERC recommends marginsof 10% for hydro-dominated systems and 15% for thermal-dominated systems (NERC 2017).While planning reserve margins are straightforward to compute and understand for systems dominated by fully dispatchable generators, with increasing penetrations of variable resources the keyresource adequacy risks shift to phenomena not easily expressed as an extra quantity of genericcapacity. These risks include correlated lulls in variable-generation output measured against (alsocorrelated) time-varying load, as well as increasing risk related to outages of transmission linksfrom renewable resources to load centers. It is therefore difficult to fold variable generation intoplanning reserve margin frameworks. However, doing so is still attractive because of the relativesimplicity of those frameworks as compared to fully accounting for reliable operations at hourlyor finer resolution. The translation is often made by expressing variable-generation resources’contributions to meeting peak load as a capacity credit, that is, as a fraction of nameplate capacitythat can be considered firm in the sense of contributing generation at times that help the systemserve more load (Ensslin et al. 2008; Madaeni, Denholm, and Sioshansi 2012; Zhou, Cole, andFrew 2018).This study assesses the ability of a capacity expansion model that uses such a planning reservemargin methodology to ensure resource adequacy under high penetrations of distributed andutility-scale solar photovoltaics (PV). Specifically, this report summarizes how the ResourcePlanning Model (RPM) was used to generate high-penetration PV systems for three regionsin the Western United States (Section 2), describes methods for evaluating resource adequacyand capacity credit (Section 3.1 and Section 3.2), and then applies probabilistic methods toevaluate the overall resource adequacy of those scenarios (Section 4) as well as the contributionof variable-generation resources to meeting peak load (Section 5). The report concludes bysummarizing findings related to planning for resource adequacy in the case of systems with highpenetrations of solar PV (Section 6).3 NERCis responsible for reliable operations of the power systems in the contiguous United States, Canada, anda small part of Mexico. The reliability reports are available at .1

2Scenario Framework2.1 Regional Planning Model (RPM) BackgroundRPM is a regional capacity expansion model that projects least-cost capacity and transmissionexpansion through 2035 every 5 years. It represents a single interconnection; this study focuseson the Western Interconnection, which includes 36 model balancing authorities (BAs) as theprimary regions in RPM. Embedded within this structure, the model has a “focus region,” withinwhich generation units, transmission lines, and loads are represented at a high level of detail,and the optimization is carried out nodally. Outside of the focus region, load and generatorsare aggregated and transmission is modeled zonally. The underlying data used to construct thismodel comes from Lew et al. (2013). Announced retirements, generators under construction, fuelcosts, and technology costs are exogenous to the model and updated regularly (EIA 2018; Hale,Stoll, and Mai 2016; NREL 2018; Ventyx 2010). This analysis studies three focus models definedby different groups of BAs (Figure 1): Nevada Power Company (NEVP), Western Area Power Administration, Lower ColoradoRegion (WALC), Salt River Project (SRP), Arizona Public Service Company (APS), andTuscon Electric Power Company (TEP) define the focus region for RPM-AZ. Portland General Electric Company (PGN) and PacificCorp West (PACW) define the focusregion for RPM-OR Public Service Company of Colorado (PSC) and Western Area Power Administra

Photovoltaic Integration in the Western United States Appendix: Reference and High Solar Photovoltaic Scenarios for Three Regions Resource Planning Model (RPM) inputs, scenario framework, and results for RPM-AZ, RPM-CO, and RPM-OR; two of the papers in the series use these scenarios as their starting point for analysis This report is listed in .

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