Research On Optimal Operation Of Electric Vehicle Battery Swap Charging System With Photovoltaic

With the rapid development of the smart grid technology,electric vehicles(EVs)and their corresponding battery charging/ swapping station have been applied widely.Meanwhile,since countries around the world have made great efforts to use renewable energy,more and more solar photovoltaic(PV)applications are integrated into the power system.Obviously,the power system with PV could effectively decrease the consumption of traditional nonrenewable energy,while the scale of EVs will significantly reduce the carbon emission.It is noted that the high uncertainty of the solar PV could not be ignored.Hence,the integration of PV into the power system will bring great difficulties to the scheduling,and related studies of the uncertainty of PV for the operation of power systems should be conducted and have certain practical significance.In additions,owing to the scale of EVs,the system will face the large charging demand.However,the traditional charging scheme of EVs may lead to the excessive charging time,the stochastic charging behavior and the increase of peak-valley difference of load fluctuation,which could cause potential safety hazard.Therefore,in order to overcome these disadvantages,a suitable charging scheduling strategy for EVs should be found.In addition,for providing charging services,the EV charging station will interact with the power grid,which means that the research of their interaction and the optimization problem of their profits are worth investigating.Therefore,this paper mainly discusses the solar PV and EV charging.In order to investigate the uncertainty representation of PV,the Dirichlet process mixture model(DPMM)is adopted.And a multi-objective optimization scheduling model of the centralized battery swap charging system(CBSCS)with PV is proposed to study the quantitative relationship between the total operational cost and the load fluctuation.Meanwhile,this paper presents a supply function equilibrium(SFE)game problem,which is based on the interaction between CBSCS and the power grid.Furthermore,the worst-case conditional value-at-risk(WCVa R)is introduced to conduct a risk analysis.The main research work of this paper is as follows:(1)In order to deal with the uncertainty of PV,firstly,the feedforward neural network(FNN)is adopted and trained based on the historical weather information.Therefore,the forecast value of PV power output is obtained.On the basis,considering that the distributions of forecast errors are various under different levels of forecast value,the DPMM is adopted to fit the joint distribution between the actual value and the forecast value of PV power output.Then,the conditional distribution of forecast error is obtained and the uncertainty representation of PV is realized.(2)In addition,the Refined Stratified Sampling(RSS)method and the scenario reduction approach are used to obtain PV power samples.Subsequently,a multi-objective optimization scheduling model of CBSCS with PV is proposed,and a non-dominated sorting genetic algorithm III(NSGA-III)is adopted and modified to solve the problem.Lastly,a fuzzy decision method with equal weights is introduced to derive the decision solution.(3)What is more,the operational model based on the interaction between CBSCS and power grid is presented.Meanwhile,the WCVa R is adopted to further propose a SFE game problem.Finally,the simulation studies verify the necessarity and effectiveness of considering WCVa R.The result could provide some reference for decision makers to formulate the scheduling plan.