Research On Optimization Of Charging And Discharging Scheduling Strategy Considering Electric Vehicle Connected To Microgrid

In the context of global energy shortage and worsening environmental pollution,electric vehicles have been strongly supported by governments around the world because of their excellent low-carbon and environmental protection characteristics.However,with the increasing penetration rate of electric vehicles,the access of large-scale electric vehicles also poses a great threat to the safe and stable operation of the power grid.In this paper,a microgrid model architecture coupled with multi-energy and multi-energy storage structure of converged electric vehicles is constructed on the background of industrial zone,so as to realize coordinated optimization of electric vehicles and microgrids and reduce operating costs.The main research work of this paper is summarized as follows:Firstly,in view of the negative impact of random charging and discharging behavior of electric vehicle users on the power grid,a concept of charging and discharging pressure was proposed based on the time anxiety of users,electricity price preference and the state of charge of electric vehicles,so as to restrict the charging and discharging behavior of electric vehicle owners.In addition,in order to deal with the uncertainty of renewable energy generation and the volatility of industrial load,a robust optimization method was used to convert the uncertainty problem into a deterministic problem,and a robust mixed integer programming problem was constructed to solve it.The influence of charging and discharging pressure on the energy scheduling of microgrid under different scenarios was analyzed.Secondly,in order to make full use of the flexibility potential of electric vehicles,a demand response program based on price incentives is proposed,and subsidies are given to electric vehicles participating in the demand response to improve user participation.Integrate two-way energy trading for renewables,electric vehicles and storage systems and different demand response strategies into a microgrid system,with operators publishing information to control the energy diversion between renewables and distributed storage devices,and setting priorities and penalty costs for different types of power connected to the grid.The uncertainty of real-time market price was processed by robust optimization,and the data of wind turbine and photovoltaic power generation were obtained by Latin hypercube sampling and short and long time memory neural network respectively.The effectiveness of the proposed demand response strategy was analyzed by comparing different scenarios.Finally,A two-layer optimal energy management and pricing model based on Stackelberg game was proposed to solve the energy pricing and scheduling problems faced by operators during large-scale EV grid-connection.The upper level model aims to maximize the benefits of microgrid operators and determine the selling price to end consumers,while taking into account the randomness of renewable energy generation and the uncertainty of real-time market prices.According to the pricing of the upper level,the lower level model optimizes the purchasing mode of the consumer,takes minimizing the electricity cost of the end consumer as the objective function,and introduces the load-demand response plan based on price to reduce the operating cost.In order to reduce the computational complexity,the strong duality theory is used to convert the nonlinear bilayer model into mixed integer linear programming.The simulation results verify the effectiveness of the proposed strategy.