研究目的
To optimize the charging schedules of EV loads to maximally match with stochastic wind power while minimizing the charging cost.
研究成果
The proposed method effectively matches EV charging load with uncertain wind power, improving wind power utilization and reducing the impact of wind power variation on the grid. The simulation-based policy improvement method, combined with EV aggregation, significantly reduces computational time while preserving optimality.
研究不足
The study assumes constant charging power for each EV and simplifies the relationship between driving distance and required charging energy. The computational efficiency of the proposed method may be challenged by very large-scale EV systems.
1：Experimental Design and Method Selection:
The study formulates the stochastic matching problem as a Markov decision process (MDP) to capture uncertainties in wind energy supply and EV charging demand.
2：Sample Selection and Data Sources:
Uses statistical analysis of wind speed data and EV parking events to generate sample paths for experiments.
3：List of Experimental Equipment and Materials:
Includes battery specifications of EVs and parameters of wind turbines.
4：Experimental Procedures and Operational Workflow:
Implements a simulation-based policy improvement method to obtain an improved charging policy from a base policy.
5：Data Analysis Methods:
Evaluates the performance of different charging policies through numerical experiments, focusing on matching degree and charging cost.
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