Operation Optimization Of Electric Vehicle Aggregator Considering The Participants' Behaviors

To cope with the problem of global warming,countries all over the world are promoting large-scale application of electric vehicles(EVs)to push the low-carbon transition of energy use in society.The disorderly charge of large-scale EVs will increase the burden of the grid and affect the safe and stable operation of the power system,especially during the peak load period.However,EV is a kind of controllable demand-side energy storage resource.In the smart grid(SG)environment,the orderly charge/discharge of EVs can realize load transfer,load adjustment,and provide reserve service,which is beneficial to improve the operation stability and economy of the power system.The question is the owners of most EVs are individual users,and there are many participants related in the EV industry with uncertain behaviors.Therefore,the implementation of EV aggregator's operation optimization considering users' behaviors can reduce the occurrence of disorderly charge,and promote users to participate in the management of orderly charge/discharge,eventually realize more EVs access to the grid and ensure the operation security and economy of the grid.The operation optimization of EV aggregator mainly includes the prediction of state parameters(the number of EV,access-to-grid time,off-grid time,battery power and user's charge demand,etc.)when the EVs access to grid,and the optimization of charge/discharge strategies in the power market environment.Current researches in the area of the EVs' state parameters prediction mainly focus on the modeling of EV users' behaviors.Generally,probabilistic models,fuzzy inference,game equilibrium models or computer agents are used.But,the above methods are difficult to accurately describe the behavior of EV users when EV has not been widely popularized,thereby affecting the estimation accuracy of EVs' state parameters.Current researches in the area of the optimization of EVs' charge/discharge strategies generally aim at the operation economy of the power system or revenue maximization of a single market participant,and lack a market contract mechanism that takes multiple participants' willingness into account.These make the optimized strategies difficult to be practical and affect the realization of the given optimization goal.Therefore,in order to predict the EVs' state parameters and the market price information more accurately,and then propose reasonable charge/discharge strategies,a few of main achievements in this paper are as follows:1.In view of the problems that the classical experimental economics(EE)method is difficult to be applied,and the multi-agents are difficult to reflect the subjective intentions and game behaviors of the special populations,an EE-based hybrid simulation to quantitatively analyze participants' decision-making behaviors is proposed in this paper.The potential number of EV in the future under different economic and technological conditions is quantitatively evaluated by studying customers' willingness to buy EVs.Multi-agents are built to match the probabilistic distributions of multi-responder behaviors based on multi-layer correlation information from a limited number of questionnaires.Then,multi-agents and human participants are put into a unified simulation environment,one can use the former,whose colony has the same multidimensional statistical distributions as the studied human colony,to replace the majority of real participants,and use the latter to represent the behaviors of the rest humans.This hybrid simulation is therefore capable of analyzing participants' decision-making problem quantitatively.The authenticity of both the multi-agent and the algorithm are validated by error analysis.With the aid of multi-agent,the effects of minority participants with specific preferences on the simulation results are also discussed.2.To solve the problem that it is difficult to predict the travel preference of users who have purchased EV in different travel scenarios.A method based on multi-agent with a comprehensive causal/statistical/behavioral model for analyzing EV users' travel willingness is proposed.Firstly,the causal relationship is considered in questionnaire designing and data extracting,then behavioral statistic model is introduced in the EE-based simulation,which enable the integration of causal/statistical/behavioral models into the same multi-agent container which can reflect EV users' travel willingness statistically.The generated multi-agents are used to replace human participants in the EE-based simulation,in order to evaluate EV users' travel demand in different scenarios.The multi-agents are used to filter the travel data of oil-powered vehicle users,and find the actual travel situations of EV users.3.In power market environment EVs will affect the spot price and thus affect the orderly charge/discharge strategies of EVs,this paper builds a spot market model which allows EVs participate in bidding,then the bidding game behaviors are analyzed.Firstly,a short-term bidding model for the spot market that can accommodate EVs is built.Secondly,the dynamic timing is designed and participant's response behavior to the dynamic environment is edited.Then,human experimenters participate in the spot market bidding,and the decision characteristics of different power suppliers are analyzed.Lastly,the predicted spot price is obtained for the optimization of EV charge/discharge strategy.4.In current research the influence of market time-scale and participants' willingness are ignored in the assessment of EV's reserve capability.A method of EV's reserve capability assessment considering power and duration is proposed in this paper.Firstly,a charge/discharge contract principle is designed to balance the needs between the system operator and the EV user.Then,an algorithm to calculate an EV's short-term reserve capability is proposed.Based on this method,case studies are done aiming to give the primary assessment on capability to supply reserve capacity/energy instantly but with short duration from a typical individual EV or EV clusters under different charge/discharge strategies.Furthermore,parameter influences e.g.the price and design features of a reserve market on EV's reserve capability are also analyzed.5.Current researches of the optimization of EVs' charge/discharge strategies only aim at economic operation of the power system or revenue maximization of the single market participant.Under the background of EV provide reserve service and considering multiple market participants' interests,an EV cluster charge/discharge strategy optimization algorithm is proposed.The relevant factors affecting EV aggregator's profits are first analyzed,then the charge/discharge contract mechanism for EV users,and the corresponding market mechanism which allow EVs participate in market competition are discussed.Considering the reserve capability of EV under the background of limited transformer capacity,an improved distributed optimization algorithm based on “efficiency capacity ratio(ECR)” is proposed.The simulation analysis shows that the model proposed in this paper improves the availability of charge/discharge strategy optimization for large-scale EV cluster.