Research On Optimal Operation Of Electric Vehicle Charging Station Based On Demand Response

With the continuous reduction of fossil energy and the increasing degree of environmental pollution,the energy-saving model featuring electric vehicles and economical and non-polluting characteristics has entered the research field of scholars from all over the world,helping people to travel by using electric vehicles instead of fuel vehicles.Energy consumption can be greatly reduced.As a centralized charging site for electric vehicles,based on full consideration of the user's charging requirements and the impact on the safe operation of the charging station,has developed a reasonable electric vehicle orderly charging strategy and an effective electricity price response mechanism to realize the behavior of electric vehicles.Orderly guidance can effectively reduce the peak-to-valley difference of the grid load,and reduce the user's charging cost.While guiding the electric vehicle to be orderly charged,the user can also assist the power grid to ensure safe and stable operation of the power system.Therefore,it is of great significance to study the optimal operation strategy of charging stations.Electric vehicle charging load forecasting is the basis for studying orderly charging.Firstly,based on the factors affecting the charging load of electric vehicles,the user's travel habits are analyzed.According to the probability distribution of the user's daily driving time and return time,the Monte Carlo algorithm is used to simulate the travel data and the return data.Finally,the load model of the disorderly charging of electric vehicles is established,which provides a theoretical basis for studying the orderly charging of electric vehicles.Based on the analysis of the user's electric vehicle charging habits and the consumer's electricity psychology,the user demand elasticity matrix-electric price response model is adopted.By analyzing the influencing factors between the user's electricity demand and the electricity price,a demand response based electricity price response model.The user's participation in demand response to the best responsiveness concept is used to analyze the relationship between the participation of the user's demand response and the power system load peak-to-valley difference and the charging cost of the participating demand-responsive users,to solve the user's best participation and further establish A more reasonable demand response optimization model.On the basis of satisfying the charging demand of electric vehicle users,the cell charging station is taken as the research object,and an optimization model aiming at minimizing the peak-to-valley difference of the grid load is established.The genetic algorithm is used to solve the optimization model,and the load situation of the user in each period of demand response is obtained.The optimized peak-to-valley price and corresponding user responsiveness are established through the demand response model.The optimization model established in this paper can not only effectively reduce the load peak-to-valley difference of the power grid,but also reduce the charging cost of users participating in orderly charging,and further improve the enthusiasm of users to participate in orderly charging.According to the charging demand of electric vehicles and the state of charge of the battery,the electric vehicle is regarded as distributed energy storage,and the schedulable capacity of the electric vehicle can participate in the frequency modulation of the power system is estimated,and the electric vehicle based on single area is involved in the frequency modulation of the electric system.The mathematical model,through the analysis of the simulation results of the example,confirms the effectiveness and timeliness of the electric vehicle participating in the power system frequency modulation.While ensuring the electric vehicle charging demand,it can meet the system's frequency modulation requirements and reduce the system's frequency modulation cost.