Research on Optimal Scheduling Method for Electric Vehicles Participating in Load Aggregators Based on Demand-Side Response

As 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, leading to the peak load being breached and the peak-valley gap increasing. Therefore, how to guide the orderly charging of electric vehicles becomes an urgent problem to be solved. This paper proposes to integrate electric vehicles (EV) with load aggregators for demand response. Firstly, by analysing EV travel data, establish a probability distribution model of EV travel and adopt Monte Carlo random sampling method to generate EV charging in a disorderly state. Secondly, combining with the short-term base load prediction model of EV load aggregators, dynamically adjust the tariff segmentation interval to optimise a price segment that better fits the basic load forecasting models. Finally, by setting multiple optimisation objectives, an adaptive genetic algorithm with elite operators is used to solve for the vehicle charging state. The simulation analysis shows that the proposed multi-period dynamic tariff strategy can reduce the difference between peak and valley loads and mitigate the overall charging cost by simulation analysis.