Optimal Scheduling of Electric Vehicles for Peak Load Regulation: A Multi-Time Scale Approach with Comprehensive Evaluation and Feedback

Fei Xue, Wei Xiong, Jiahao Chen, Yonghai Yi, Zehui Liu, Jun Zeng and Junfeng Liu ()
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Fei Xue: Jiangmen Heshan Power Supply Bureau China Southern Power Grid Co., Ltd., Heshan, Jiangmen 529000, China
Wei Xiong: Jiangmen Heshan Power Supply Bureau China Southern Power Grid Co., Ltd., Heshan, Jiangmen 529000, China
Jiahao Chen: School of Electric Power, South China University of Technology, Guangzhou 510006, China
Yonghai Yi: Jiangmen Heshan Power Supply Bureau China Southern Power Grid Co., Ltd., Heshan, Jiangmen 529000, China
Zehui Liu: Jiangmen Heshan Power Supply Bureau China Southern Power Grid Co., Ltd., Heshan, Jiangmen 529000, China
Jun Zeng: School of Electric Power, South China University of Technology, Guangzhou 510006, China
Junfeng Liu: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510006, China

Energies, 2025, vol. 18, issue 7, 1-23

Abstract: With the increasing prevalence of electric vehicles (EVs), optimizing their scheduling for grid peak-shaving has become a focal point of research. This study develops a multi-time-scale optimization model for EV clusters to participate in peak shaving, integrating a comprehensive evaluation and feedback mechanism. The innovation of this paper lies in the addition of an evaluation and feedback loop to the multi-time-scale scheduling optimization method for EVs participating in peak shaving, which fully utilizes the scheduling potential of EV clusters and mitigates the impact of uncertainties associated with EV clusters. The multi-time-scale approach mitigates response errors stemming from EV uncertainties. A feedback loop enables the grid to adaptively adjust scheduling commands to match real-time conditions. Simulations on the IEEE 33-node system demonstrate that the proposed model effectively optimizes EV load profiles, reducing the peak-to-valley difference rate from 41.74% to 35.19%. It also enhances response accuracy to peak-shaving instructions and upgrades the peak-shaving evaluation from a C rating to a B rating, ultimately increasing the revenue for aggregators participating in peak shaving.

Keywords: electric vehicles; multi-time scale; peak shaving; evaluation; feedback (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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