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

Abstract

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

The application and practice of multimedia technology in the teaching of higher vocational logistics under the background of double carbon

“…With President Xi Jinping's commitment to the international community to reach carbon peaks and carbon neutrality at the 75th General Debate of the United Nations General Assembly, the teaching of practical courses for higher-level logistics majors has also ushered in a major challenge [1] . At present, the setting of practical aspects in the training program of higher vocational logistics majors requires high cost of site construction and later maintenance [2] , as well as the content of students' skills training is difficult to keep up with the development and changes of logistics industry because the teaching equipment is not updated for years, which leads to a serious disconnect between the skills possessed by students through practical training and the development of the industry [3] .…”

Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure

“…Copula functions retain variable correlation structures very well in data sampling [36], which is important in accurate event-based EV DR modelling. Previously copula functions have, for instance, been used to optimize and model charging schedules of EVs participating in implicit DR [37,38], to analyze the impact of EV charging on microgrids [39], to model EV charging in energy management algorithms [40], and to simulate EV charging events [36]. However, there exists no previous studies applying multivariate copulas with real EV charging event data in maximal FCR-D potential simulation.…”