Research On Market Decision Optimization Of Cooperative Operation Of Photovoltaic And Electric Storage For Multi-service Under Multiple Time-scales

Recent past years have witnessed a continuous and rapid growth of new energy,typically photovoltaic,driven by the global goal of carbon neutrality.The development impetus of distributed photovoltaic(DPV)is particularly prominent,which has become the main driving force for the photovoltaic industry.It brings about a tough problem of consumption that large-scale DPV power generates online.The original administrative consumption methods are no longer suitable for the current context.How to promote the rational benefits and healthy development of large-scale DPVs through market-oriented methods is a key issue and a widespread concern in the power industry.This paper studies the market decision optimization of the cooperative operation of DPV and electric storage(ES)for multi-service under multiple time-scales.It selects the aggregated entity of large-scale DPV and ES as the research object to effectively alleviate the endogenous disadvantage of randomness of DPV.And it sets the research background in the mature spot market to fit the market-oriented trading environment in the future.Based on these,the main work of this paper is summaried as follows:Firstly,based on the framework of a mature spot market,this paper excavates the potential of improving the total benefits of electric energy and regulation markets when considering the coupling relationship between them.Then establishes a multi-service model for the aggregated operation of DPV and ES,taking the regulation power as a handle.The model connects the two markets’ revenue functions through real-time regulation power to fully release the regulation options,and achieve the profit-oriented self-optimization of regulation strategy.Secondly,this paper proposes a multi-stage stochastic optimization model for market decision of the aggregated entity providing multi-service under multiple time-scales,considering the total process of market participation.It divides the market participation process into three stages,namely day-ahead bidding stage,real-time decision stage,and real-time regulation stage.The model is solved by stochastic dual dynamic programming(SDDP)algorithm,which preserves the progressive relationships and internal connections between different stages,and searches the optimal solution through continuous attempts and modifications.Next,based on the background of the three-stage market decision model above,this paper presents a profit allocation model for numerous stakeholders involved in the DPV and ES aggregated entity.Referring to Nash bargaining game theory,the model describes the utility functions,negotiation power,and negotiation initial points of stakeholders of different types to form a fair profit allocation scheme for the DPV and ES alliance.Finally,this paper conducts a case study with a DPV and ES alliance composed of five stakeholders.The results can fully prove the effectiveness of the models established in this paper in enhancing the benefits of the aggregated entity and promoting the cooperation of DPV and ES.They can provide a decision-making reference for large-scale DPV and ES to cooperate in the power market in the future,and support for the goal of carbon neutrality.