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The Effect of Capacity Payments on Peaking Generator Availability in PJM

This study examines the effect of capacity payments on the availability and switching behavior of peaking power plants in the PJM electricity market. The research investigates how changes in gas costs, competition, and environmental regulation influence decisions such as mothballing, startups, and retirements. The findings show that capacity payments incentivize peakers to be more available, resulting in less switching activity. The analysis also highlights the importance of capacity payments for the adequacy and reliability of the electricity system.

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The Effect of Capacity Payments on Peaking Generator Availability in PJM

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  1. The Effect of Capacity Payments on Peaking Generator Availability in PJM Isaac Newton Institute Workshop Electricity systems of the future: incentives, regulation and analysis for efficient investment March 2019 Stein-Erik Fleten, Benjamin Fram, Carl J Ullrich Magne E. Ledsaak, Sigurd B. Mehl, Ola E. Røstum

  2. Motivation • Peakingpower plants = cornerstones • Low or highrenewableshare • Missingmoney problem -> capacityremuneration (Joskow 2008) • How much to pay for availability? • If youpay more, howmuch more availablewillthe generators be?

  3. Research question • Weobserve, per year, peakerdecisionson • Mothballing / shutdown • Startups • Retirement • How aretheseaffected by changes in gas costs, competition and environmentalregulation? • How do PJM capacitypaymentsaffecttacticalswitching for peaking plants?

  4. Summary • Less switchingactivityaftercapacitypaymentsintroduced 2007 • Counterfactual shows a clearconnectionbtwcapacitypayments and switchings • Currentpaymentlevels in PJM incentthepeakers to be available

  5. Literature • Energy onlymarkets • Oren (2005), Hogan et al (2005), workshop speakers • Capacitypaymentsunnecessary, bidsshould be higherthan SRMC during scarcity • Cramton and Stoft (2005): Imperfectionsarepervasive • Capacitypaymentsneeded for adequacy and reliability

  6. Background: real options • Profitability in $/unit capacity • Brennan and Schwartz (1985) Profit indicator (x)

  7. Status changes • Shutdown • Startup • Retirement 7

  8. Structuralestimation problem

  9. Structuralestimation problem • Maximize log likelihood • Likelihoodofobserving plant status given state variables: profitability in $/kW, levelofcompetition, changes in gas prices, environmentalregulation and plant status last year • Subject to • Decision makers behaveaccording to our real optionsswitchingspecification • Forming expectationsaccording to howtheprofitabilityindicator have been ”transitioning” in thepast (k-meansclustering) • Output • Value functions: value for different profitabilitylevels given OP or SB state • Switching and maintenancecost parameters

  10. Currentyearprofitfunction • Parameters to be estimated: MOP = maint. cost in OP state MSB = maint. cost in OP state KSD() = shutdowncost = g0 + gTX KSU() = start up cost = l0 + lTX KRE() = abandonment cost = h0 + hTX

  11. Sample period 2001-2016 • EIA 860 (data source) format changes in 2001 • Focus on peaking plants (CTs) • Natural gas, #2 oil (DFO) and kerosene • Final sample: • 859 unique generators in 252 power plants Photo: calpine.com

  12. Status code of generator • From EIA 860 • OP – operating • SB – on standby (mothballed/shutdown) • RE – retired

  13. From EIA860 “Layout” file “Cold Standby (Reserve): deactivated (mothballed), in long-term storage and cannot be made available for service in a short period of time, usually requires three to six months to reactivate.”

  14. Electricity Prices ($/MWh) • Average daily peak price • Hours Ending 07:00 - 22:00

  15. Capacitypayments: ReliabilityPricing Model (RPM) • Auctions 3 yr in advance, 1 yr duration • Assumepeakers in sample clearthemarket

  16. Fuel Prices ($/MMBtu)

  17. Spark spread ($/MWh) and profit indicator Pi ($/kW) • PEn = day nelec price (per PJM zone) • HRp = heat rate for plant p • PFj,n = day n fuel price for fuel j • VOMp = variable O&M costs for plant p • PptC = capacity payments • Profit indicator Pi is pre-calculated as SPRDpjn = PEn – HRpPFjn – VOMp + PptC Pi

  18. Exogenousstate variables • Profit indicator ($/kW), per plant and year • Intensityofcompetition, per plant and year • Changes in natural gas prices, per year • Stricterenvironmentalregulation, per year

  19. Intensityofcompetition • How inefficient is this generator compared to nearby (same zone) generators from competingfirms • HR = heat rate = inverse ofefficiency • A = setof generators in thestateof plant p (HRA is avg) • A high C meansyourcompetitorsarestrong (relativelyefficient)

  20. Environmentalregulation • 2003: NOxbudget trading program (NBP) • From ca 2011: Clean Air InterstateRule (CAIR), later replaced by Cross-State Air PollutionRule (CSAPR) • Incentiviseslower NOx and SOxemisions • Hits the most polluting units hardest (coal) • Gas fired plants and renewablesdisplacingcoal

  21. Descriptivestatistics for state variables Profitabilityindicator =SPRD Strengthofcompetition Changes in gas prices Env. regulation

  22. Average yearly payments from energy and capacity markets for all switching decisions. Profitabilities in [$/kW - year ]. 4 N obs 15

  23. Observedswitchingbehavior and distributionoftheprofitabilityindicator

  24. Results PJM 2001-2007 PJM+NEISO+NYISO

  25. How is switchingbehavioraffected by state variables?

  26. Counterfactualanalysis • Increase in theshareofavailable plants

  27. Counterfactualanalysis • The higherthecapacitypayment, the more attractive it is to keep generators available

  28. Counterfactualanalysis • Few plants in the SB state

  29. Predictednumberofswitches from OP state • You know that it would be untrueYou know that I would be a liarIf I was to say to youGirl, we couldn't get much higher • The Doors, “Light my Fire”

  30. Conclusions • Natural gas pricechanges, levelofcompetition and environmentalregulationchangethebarriers to entry and exit in expectedways • Regulators should understand coststructure • Less switchingactivityaftercapacitypaymentsintroduced 2007 • Less mothballing, less startups • Peakersdispatched more often • Shale gas, EPA regulations • Counterfactualanalysisindicatesquantitativelytheeffectofcapacitypaymentsonavailability • Currentpaymentlevels in PJM incentthepeakers to be available

  31. To do • Robustnesswrt sample periodwindow • Include data onentry and replacement

  32. Thankyou for listening… • Comments and questions ? • stein-erik.fleten@ntnu.no • benjamin.fram@nhh.no • ullriccj@jmu.edu

  33. K-meansclustering • Plant managers form expectationsonstatedynamics • Weassignobservations to 30 clusters and counttransitions to form probabilitymatrix • Here 5 clusters for illustration

  34. Gas turbine plant retirements PJM

  35. Regressionsplit by fuel

  36. Regressionsplit by age

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