Energy Sharing Method For Multiple PV Prosumers Based On Game Theory

With the further development and application of distributed PV(photovoltaic)power generation system,more and more PV prosumers are emerging in the power system.For this kind of power user with variable characters,the conventional single-object optimization method has been difficult to achieve great optimization results.Taking full advantage of the input-output characteristics of prosumers,clustering and networked optimization of energy management have gradually been proved to be an effective method.Therefore,it is very necessary to study the multi-objects energy management optimization method for PV prosumers.Firstly,a hour-ahead energy management method for the PVCSs(PV-assisted Charging Stations)networks is proposed considering their economic profits,the local consumption level of the PV energy and the impacts on the utility grid.In this paper,multiple separate PVCSs in a distribution network are seen as a PVCS cluster and it is qualified to directly trade with the power plant based on a quadratic pricing model,which is called direct power supply for large consumers.In order to ensure the quality of service,a two-level FCR(Feasible Charging Region)model is proposed considering the charging stations level and the electric vehicles level.Besides,the off-line optimization based on ELM(Extreme Learning Machine)is utilized to assist the real-time decision making.The simulation results verify that the proposed method is effective to increase the economic profits of the network,improve the local consumption level of the PV energy and reduce the negative impacts on the utility grid.Secondly,a new concept which is called ESP(Energy Sharing Provider)is inrtoduced in this paper and then an energy sharing method for multiple industrial PV prosumers is proposed based on the Stackelberg game theory.In the day-ahead stage,the ESP needs to make decisions on ES scheduling using the stochastic optimization method to reduce the negative impacts which are caused by the uncertain factors.In the real-time stage,a real-time demand response model is built based on the Stackelberg game and the best internal prices of the network and the optimal energy consumption of the prosumers are obtained by finding the equilibrium of the game.The simulation results show that the proposed method can increase the economic benefits of both the ESP and the prosumers.Besides,the netload profile of the network is improved and the local consumption level of PV energy is also enhanced.