Research On The Impact Of Electric Vehicles On The Power Grid In V2G Mode

With the continuous development of distributed energy storage in power grid,the consistent promotion of energy structures,and the comprehensive strengthening of carbon peak and carbon neutrality,EVs(Electric Vehicles)are gradually receiving widespread attention at home and abroad.On the one hand,as a new type of transportation,electric vehicles have incomparable advantages in alleviating energy crisis and reducing environmental pollution.However,the charging behavior of EVs is random in time and space.With the continuous increase of EVs ownership,the disorderly access of a large number of EVs to the power grid will undoubtedly increase the pressure and loss of power grid,reduce the voltage level of the power grid,and bring adverse effects to distribution network.On the other hand,with the development of Vehicle-to-Grid(V2G)technology,EVs can be connected to the power grid for charging in the load valley,and the energy can be fed back to power grid through V2 G technology in the load peak,which is an important means to improve the economy and stability of power grid operation.Therefore,there are great theoretical significance and potential for application to study the impact of EVs on power grid in V2 G mode.Firstly,the main concepts of V2 G technology are explained,the different charging modes and different user behavior characteristics of EVs are analyzed in detail.On this basis,the load distribution of EVs after disorderly access to the grid is simulated by the Monte Carlo method according to the daily load curve of the residential neighborhood of Future Ark in Guiyang City,and then the charging load model of EVs is established.The simulation results show that the charging load of EVs will increase the load variance,peak-to-valley ratio,and peak value of power grid under the disorderly charging condition,which will aggravate the fluctuation of the grid load.Secondly,in the context of Time-of-Use price(TOU price),a single-objective optimal scheduling model is established to reduce the daily load variance of power grid by combining the characteristics of the grid side and the user side and taking into account the charging and discharging power,the EVs power battery capacity and other constraints.Further,the proposed optimal scheduling model is solved by the Particle Swarm Optimization(PSO)algorithm.The simulation results show that a reasonable time-of-use price can not only economically motivate customers to change their habitual electricity consumption behavior,alleviate the tension in peak periods and exploit the demand in low periods,thus playing the role of peak reduction and valley filling,but also optimize the charging and discharging power of EVs to reduce the daily load variance of power grid and motivate customers to participate in V2 G behavior.Finally,considering that the randomness of EVs access time and the initial state is unsuitable for the global algorithm.From real-time scheduling optimization,the paper establishes a real-time scheduling model of EVs charging and discharging based on V2 G mode with the goal of reducing EVs charging cost and power grid loss cost from local scheduling optimization.The calculation is carried out in the distribution network of Group H and Group E around the Future Ark charging station in Guiyang,and the scheduling model is solved by real-time optimal scheduling of EVs randomly connected to power grid using a convex optimization algorithm.The results show that the model can effectively reduce the charging cost and network loss cost under the premise that the charging task of EVs is completed on time.It is further demonstrated that different EVs penetration rates and V2 G participation rates can both reduce the cost and optimize the load profile.