Research Article
Optimization of Orderly Charging Strategy of Electric Vehicles Based on GWO Algorithm
@INPROCEEDINGS{10.1007/978-3-031-96146-5_29, author={Shaokui Yan and Haili Ding and Fei Peng and Yuanfeng Zhou and Ting Hu}, title={Optimization of Orderly Charging Strategy of Electric Vehicles Based on GWO Algorithm}, proceedings={Smart Grid and Innovative Frontiers in Telecommunications. 8th EAI International Conference, EAI SmartGIFT 2024a, Santa Clara, United States, March 23-24, 2024, Proceedings}, proceedings_a={SMARTGIFT}, year={2026}, month={9}, keywords={Electric Vehicles Dynamic Pricing Mechanism GWO Algorithm Optimization Strategy Orderly Charging}, doi={10.1007/978-3-031-96146-5_29} }- Shaokui Yan
Haili Ding
Fei Peng
Yuanfeng Zhou
Ting Hu
Year: 2026
Optimization of Orderly Charging Strategy of Electric Vehicles Based on GWO Algorithm
SMARTGIFT
Springer
DOI: 10.1007/978-3-031-96146-5_29
Abstract
Recently, the rapid increase in electric vehicles (EVs) has significantly raised their integration into the power grid. This overlap between EV charging times and daily routines has created ’peaks upon peaks,’ exerting substantial pressure on distribution networks. Therefore, shifting EV charging loads to off-peak periods is essential. The existing time-of-use (TOU) static pricing is no longer suitable for current demands, leading to the introduction of a dynamic pricing mechanism. EV charging demand is highly adjustable yet unpredictable, necessitating the proposal of a compliance incentive coefficient. Based on this, a dual-objective function is established, aiming to minimize charging costs and the grid load peak-valley difference, thereby enhancing the predictability and controllability of the charging load. The existing Particle Swarm Optimization (PSO) algorithm faces issues with global search capability and convergence speed. To address these shortcomings, the Grey Wolf Optimization (GWO) algorithm is proposed. This algorithm can achieve an orderly charging strategy for EVs under dual constraints, effectively reducing user charging costs and minimizing the grid’s peak-valley difference, successfully achieving the goal of peak shaving and valley filling.