Incentive-compatibility auction and renewable energy pricing strategy based on incomplete Information game of price cost declaration

ABSTRACTThe construction and development of the Electricity Spot Market (ESM) are beneficial to the fair distribution of power resources and the improvement of power system regulation ability. The original ESM was not designed to include renewable energy. Since there is almost no marginal cost for renewable energy, the renewable energy can not copy the thermal power unit price model after participating in ESM. The prevalent Locational Marginal Price (LMP) mechanism within the extant ESM structure encounters limitations wherein optimal benefits accrue to buyers and sellers only under specific conditions. This paper thus introduces an innovative electricity pricing model and trading settlement strategy tailored to the characteristics of wind turbine-generated power. To begin, we devise a novel electricity pricing model grounded in the construction expenses and predictive precision intrinsic to renewable energy sources. Subsequently, in synergy with the ESM framework, we formulate a Bayesian game model to compute benefits contingent upon bid success probabilities. A pricing strategy based on Vickrey – Clarke – Groves (VCG) is selected, which pays the other generating strategy as an incremental cost by removing itself. Empirical evaluations affirm the efficacy and rationality of the proposed approach. The outcomes of our study not only validate its applicability but also lay the groundwork for a fresh theoretical paradigm underpinning energy trading modalities in future ESM contexts.KEYWORDS: Electricity Spot Market (ESM)Renewable EnergyVickrey–Clarke–Groves (VCG)Bayesian gameLocational Marginal Price (LMP) Nomenclature APE=Annual generating capacity of the unitf=Revenue functionfP−i,tg,C−ig=dispatching cost of the power grid after excluding unit i in the linefPi,tg,Cig=dispatching cost incurred by unit i after participating in the power gridI=Total investment of the wind farmlam12=Shadow price of the lower limit of unit powerlam21=Shadow price of the upper limit of the line powerlam22=Shadow price of the lower limit of the line powerlam3=Shadow price of Power Balancelam41=Shadow price of positive spinning reservelam42=Shadow price of negative spinning reservelam11=Shadow price of the upper limit of unit powern=Anticipated service life of the wind turbineQiVCG=Payment allocated to unit iv=Comprehensive annual interest rate of capital costΘk=Comprehensive set of all participant typesθk=Employed to denote the type of participant kCg=Fixed costs incurred from the maintenance of renewable energy generation unitsCpu=Penalty factor for wind and power abandonmentCi,ts=Settlement electricity priceCi,tWb=Quotation function of wind energy generator i at time tCi,tWC=Reported cost function of wind energy generator i at time tCigPi,tg=Reported cost function of unit iCiLMP=Unit return of the LMP strategyD=Index of load setd=Index of loadDtfo=Percentage of prediction error at time tFco=Expenses related to cost recoveryFfe=Costs incurred on the transaction day due to forecast errorsi=Index of generatork=Index of participantL=Index of line setl=Index of lineMRi,t=Revenue per unit of generator set i at time tNid=Down ramp rates of unit iNiu=Up ramp rates of unit iPF=Count of power-generating entities, inclusive of wind power units.p=Conditional probability distributionPd=Cost at each momentPi,t=Generated power by generator set i at time tPi,tfcst=Power generation predicted by generator set i at the declared time tPi,tG=Output of the conventional thermal power generator set i at time tPi,tW=Output of wind turbine generator set i at time tPl,t=Power flow in line lR=Rotational standby demandSk=Aggregate of all strategies constitutes the participant’s strategy spaceT=Index of time sett=Index of timeui,t=On-off status of unit i during time period tuk=Benefit accrued by a participant under strategy skδi,t=The generation profitθ−k=Ensemble of all participant types except for participant kCtfe=Cost function attributed to prediction errorsFi,t=Quoted price set by power generationMi,tl=Marginal cost functionMi,tq=Aggregate cost functionPW,avg=Average historical wind power valuPW,er=Average value of historical wind power prediction errorsPW,fore=Forecasted value of historical wind energyPW,real=Actual value of historical wind energyPd,t=Load at node d at time tPiG,max, PiG,min=Upper and lower limit of unit power generationPlmax, Plmin=Upper and lower constraints of the transmission lineRmax, Rmin=Positive and negative spinning reserve constraints respectivelys−k=Collection of strategies encompassing all participants with the exception of participant ksk=Strategy for participant kUi,t=Profit function for participants in the gameξi,t=Revenue from power generationai=Coefficients of power generation costbi=Coefficients of power generation costci=Coefficients of power generation costMRi,tG=Cost reported by thermal power generation units to ESM, which fluctuates with the amount of electricity generatedMi,ta=Average cost functionESM=Electricity Spot MarketLMP=Locational Marginal PricePSO=Particle Swarm OptimizationSCED=Security Constrained Economic DispatchSCUC=Security Constrained Unit CommitmentVCG=Vickrey–Clarke–GrovesDisclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported in part by the National Natural Science Foundation of China (Grant Nos. U22A2055, 62173081) and in part by the LiaoNing Revitalization Talents Program (Grant No. XLYC2002032).