Decentralized Collaborative Pricing and Shunting for Multiple EV Charging Stations Based on Multi-Agent Reinforcement Learning
CoRR(2024)
摘要
The extraordinary electric vehicle (EV) popularization in the recent years
has facilitated research studies in alleviating EV energy charging demand.
Previous studies primarily focused on the optimizations over charging stations
(CS) profit and EV users cost savings through charge/discharge scheduling
events. In this work, the random behaviors of EVs are considered, with EV users
preferences over multi-CS characteristics modelled to imitate the potential CS
selection disequilibrium. A price scheduling strategy under decentralized
collaborative framework is proposed to achieve EV shunting in a multi-CS
environment, while minimizing the charging cost through multi agent
reinforcement learning. The proposed problem is formulated as a Markov Decision
Process (MDP) with uncertain transition probability.
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