| Electric vehicles can effectively mitigate the global greenhouse phenomenon and reduce the fossil energy crisis by replacing fuel vehicles for travel.However,a large number of electric vehicles connected to the distribution network for charging for a short period of time is undoubtedly a major test of whether the power system can operate normally and stably under a high-load environment.When the number of electric vehicles gradually increases,their overall charging load will increase accordingly,and the charging time coincides with the daily electricity intensive time for residents of the power system,which is bound to break the peak load and increase the peak-valley gap.In addition,the peak load period is also the peak electricity price period,and the overlap of time periods leads to high charging costs for users,which increases the cost of using electric vehicles and is not conducive to the promotion of new energy vehicles.Therefore,it is important to alleviate the current situation of disorderly charging of electric vehicles and guide the orderly charging of electric vehicles to improve the safety of grid operation and reduce the charging cost of users.To address the above problems,this dissertation proposes to do resource integration of EV demand response load with EV load aggregator and combine it with multi-time dynamic tariff to regulate load shifting.First,we combine EV travel data to fit the mathematical probability distribution satisfied by the travel law,and then use Monte Carlo random sampling method to generate the EV charging situation in the disorder state;the EV load aggregator combines the short-term base load forecast to dynamically adjust the electricity price division interval and optimize the price segmentation that better fits the base load curve;set the optimization target and use the adaptive genetic algorithm with elite operator to The optimization objective is set,and an adaptive genetic algorithm with elite operators is used to solve for the vehicle charging state.By setting up a local grid virtual scenario,the proposed orderly charging strategy is applied to this virtual scenario,and the simulation results are compared with the disorderly charging situation to analyze the load peak-to-valley difference,operational stability and charging economy.The simulation results show that when the EV charging behavior is in a disorderly state,the amount of load brought will increase with the increase of EV penetration factor,and the method adopted in this dissertation can effectively reduce the total load peak-to-valley difference and user charging cost,and make the load curve of the power system smoother,thus enhancing the stability of power system operation.An IEEE 33-node distribution network model is built,and the simulation result data of disordered charging and ordered charging are imported into the model for calculation,and the effects of both on the network loss and voltage offset indexes of the distribution network are analyzed.The simulation results show that the proposed multi-period dynamic tariff strategy plays a role in reducing the network loss rate and mitigating the degree of voltage excursion. |
