Orderly charging strategy for electric vehicles based on multi-level adjustability - Teng, Changlong; Ji, Zhenya; Yan, Peng; Wang, Zheng; Ye, Xianglei (International Journal of Renewable Energy Development, 2024)
The development of electric vehicles (EVs) is one of the essential ways to reduce environmental pollution. With the rapid growth in EVs, an orderly charging strategy based on multi-level adjustable charging power is proposed to address the problem of increasing peak-to-valley difference due to disorderly charging in different scenarios. Based on the information of multi-level adjustable charging power, information about staying in the residential area, and charging demands of EVs, this research designs a centralized charging mode with complete information under the centralized scenario and a decentralized charging mode with incomplete information under the decentralized scenario. This research takes the minimization of peak-to-valley difference in the residential area as the objective function and considers that the charging pile can have the function of multi-level adjustable charging power to support these two scenarios. Two charging modes of the charging pile are designed, and orderly charging model of EVs in the residential area is constructed. EVs can select charging time and charging power by using Bluetooth or code scanning in the charging pile. This research aims to design two orderly charging modes to effectively implement peak shaving and valley filling while ensuring the charging demand of EVs. This research uses the CPLEX solver in MATLAB to solve the objective. The simulation results show that EVs can reasonably select the multi-level adjustable charging power under different scenarios and provide a reference for engineering related to orderly charging. Strategy 4, proposed in this research, has the lowest peak-to-valley difference of the four strategies. The peak-to-valley difference is only 87 kW under the centralized scenario, and the peak-to-valley difference is 282 kW under the decentralized scenario.