An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging - Yuxi Liu; Jie Zhu; Yuanrui Sang; Mostafa Sahraei-Ardakani; Tianjun Jing; Yongning Zhao; Yingying Zheng (Frontiers in Energy Research, 2023)
High penetration of electric vehicles (EVs) in an uncontrolled manner could have disruptive impacts on the power grid, however, such impacts could be mitigated through an EV demand response program. The successful implementation of an efficient, effective, and aggregated demand response from EV charging depends on the incentive pricing mechanism and the load shifting protocols. In this study, a genetic algorithm-based multi-objective optimization model is developed to generate hourly dynamic Time-of-Use electricity tariffs and facilitate the decision making in load scheduling. As an illustrative example, a case study was carried out to examine the effect of applying demand response for EVs in Beijing, China. With the assumptions made, the maximum peak load can be reduced by 9.8% and the maximum customer savings for the EVs owners can reach 11.85%, compared to the business-as-usual case.