Research On Operation Optimization And Frequency Regulation Auxiliary Service Bidding Strategy Of Electric Vehicle Aggregator

With the rapid development of the electric vehicle(Electric Vehicle,EV)industry,the number of EVs has also increased steadily.However,when a large number of electric vehicles are connected to the power grid for disorderly charging,the safe and stable operation of the power grid may be affected.At the same time,electric vehicle aggregators,as emerging commercial institutions,are the "bridge" between electric vehicles and the power grid.How to balance the relationship between profit requirements and power grid demand has become an urgent problem to be solved.Therefore,it is particularly important to reasonably analyze the impact of EV disorderly charging on the power grid,and on this basis,to formulate a reasonable EV charging strategy to reduce the impact of EV charging on the power grid.At the same time,the auxiliary frequency regulation service is a service provided to ensure the normal frequency of the power grid.For aggregators,how to formulate an optimal bidding strategy according to the market demand for frequency regulation ancillary services to maximize their own profits has become a key research direction.In view of this,the research contents of this paper are summarized as follows:Firstly,according to the influencing factors of EV charging load distribution,a time characteristic variable model that affects EV charging load distribution is established;based on EV usage,EVs are classified and corresponding models are established;At the same time,the specific application steps of Monte Carlo method in the study of EV disordered charging are analyzed.an example is constructed based on the Monte Carlo method to verify the impact of EV disorderly charging on the power grid.The results show that large-scale disordered charging of private cars or taxis will adversely affect the operation of the power grid.Then,in view of the problem that EV charging modes are not classified in previous studies,a multi-objective optimal charging method for electric vehicles based on reservation and real-time charging modes is proposed.According to whether the car owner has an appointment and whether he is willing to wait,the EV charging mode is classified,and on this basis,the corresponding EV charging model is established;considering the different types of EV charging modes,different types of selling electricity prices are set;According to the profit requirements of the aggregator and the demand of the power grid,the optimal charging model of EV reservation and real-time charging mode is established with the objective function of maximizing the profit of the aggregator and minimizing the load variance,and using the particle swarm algorithm to optimize the solution.Taking the EV optimized charging in a certain area as an example,the results show that the optimized charging strategy can reduce the load variance and increase the aggregator’s income.Finally,based on the analysis of the interaction between the aggregator and the grid trading center,a multi-level EV charging and discharging model is established,the relationship between the subsidized electricity price and the user response rate is determined by Stevens’ law,and the EV charging and discharging response capacity is evaluated by the Monte Carlo method.Considering the risk of EV interruption and exit,aiming at maximizing the profit of aggregators,an aggregator bidding decision model considering the risk of interruption and exit is established based on Conditional Value at Risk(CVaR),and the toolbox of YALMIP and CPLEX in MATLAB was used to realize optimization solution.A numerical example is used to verify the effectiveness of the proposed bidding decision model.