Reinforcement learning for electric vehicle applications in power systems:A critical review
نویسندگان
چکیده
Electric vehicles (EVs) are playing an important role in power systems due to their significant mobility and flexibility features. Nowadays, the increasing penetration of renewable energy resources has been observed modern systems, which brings many benefits for improving climate change accelerating low-carbon transition. However, intermittent unstable nature sources introduces new challenges both planning operation systems. To address these issues, vehicle-to-grid (V2G) technology gradually recognized as a valid solution provide various ancillary service provisions Many studies have developed model-based optimization methods EV dispatch problems. Nevertheless, this type method cannot effectively handle highly dynamic stochastic environment complexity Reinforcement learning (RL), model-free online method, can capture uncertainties through numerous interactions with adapt state conditions real-time. As result, using advanced RL algorithms solve problems attracted surge attention recent years, leading outstanding research papers findings. This paper provides comprehensive review popular categorized by single-agent multi-agent RL, summarizes how be applied problems, including grid-to-vehicle (G2V), vehicle-to-home (V2H), V2G. Finally, key future directions discussed, involve five aspects: (a) data quality availability; (b) setup; (c) safety robustness; (d) training performance; (e) real-world deployment.
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ژورنال
عنوان ژورنال: Renewable & Sustainable Energy Reviews
سال: 2023
ISSN: ['1879-0690', '1364-0321']
DOI: https://doi.org/10.1016/j.rser.2022.113052