Optimal charging and discharging strategy for workplace electric vehicle charging stations with renewable energy and power network constraints

Jiawen Chen, Yuan Zou, Jun Zhang and Xudong Zhang

Energy, 2025, vol. 336, issue C

Abstract: Renewable energy offers a sustainable solution for charging electric vehicles (EVs), as it meets the increasing charging demand of EV users while mitigating carbon emissions. However, renewable generation is volatile and may not always meet EV charging demand. To overcome this limitation, our study proposes a hybrid approach that integrates both on-site renewable energy and the power grid. This approach ensures a more reliable supply for workplace EV charging stations. To optimize EV charging and discharging while maintaining power quality, we introduce a coordinated energy management strategy that involves both energy suppliers and distribution system operators. The optimization problem is formulated as a mixed-integer nonlinear programming (MINLP) model, which cannot be solved efficiently using traditional methods. Therefore, we employ deep reinforcement learning methods that excel at handling high-dimensional and nonlinear problems without requiring detailed system models. To assess the applicability of the proposed methods, real-world datasets are used. The results show that deep reinforcement learning methods effectively optimize charging and discharging patterns and demonstrate superior computational performance.

Keywords: Deep reinforcement learning; Electric vehicle; Optimal charging strategy; Power network constraint; Renewable energy (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:336:y:2025:i:c:s0360544225040642

DOI: 10.1016/j.energy.2025.138422

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