Optimization of Electric Vehicles Based on Frank-Copula-GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research

Improving the efficiency of renewable energy and electricity utilization is an urgent problem for China under objectives carbon peaking neutralization. This paper proposes optimization scheduling method electric vehicles (EV) combined with wind photovoltaic power based on Frank-Copula-GlueCVaR. First, a joint output model copula theory was built to describe correlation between output. Second, Frank-Copula-GlueCVaR index introduced in novel way. Operators can now predetermine future wind-photovoltaic range this according their risk preferences. Third, optimal aimed at reducing group charging cost EVs proposed, thereby encouraging EV owners participate demand response. Fourth, paper: application Variant Roth–Serve algorithm; regards as multi-intelligent group; finds Pareto strategy through continuous learning. Finally, case study results are shown effectively absorb more energy, reduce consumption group, suppress load fluctuation whole which has practical significance theoretical value.