Optimal Decision-making Algorithm for Electric Vehicle Aggregator Based on Hybrid Action Reinforcement Learning
Electric vehicles(EV),when managed centrally by aggregators,can be utilized as flexible and adjustable resources to participate in energy market arbitrage and provide ancillary services to the grid.To optimize this potential,this study introduces an advanced decision-making algorithm for EV aggregators based on hybrid action reinforcement learning.The algorithm uses continuous actions to optimize market bidding decisions and discrete actions to manage the dynamic switching between different power disaggregation strategies,realizing a joint optimization of market bidding and power disaggregation.In addition,the study presents an EV aggregator flexibility modelling method that considers the value of unit flexibility,aiming to maximize the total daily flexibility value while ensuring that the charging demand of each vehicle is met.Simulation results show that dynamic policy switching effectively leverages the strengths of both priority decomposition and proportional decomposition strategies,helping to reduce battery degradation and maintain the flexibility of two-way battery regulation.The proposed algorithm enhances the operational economy of EV charging stations,outperforming algorithms that focus solely on optimizing the bidding decision.
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