研究目的
To optimize the charging schedules of EV loads to satisfy two objectives, i.e., maximally matching with stochastic wind power while minimizing the charging cost.
研究成果
The study demonstrates that the wind power fluctuation can be counteracted by the EV charging load to reduce the impact of wind power variation to the grid. The proposed method saves tremendous computing time and improves the optimization efficiency.
研究不足
The study assumes the charging power for each EV is the same and set to be constant, and the required charging energy for each parking event is proportional to the consuming energy during driving. These assumptions may not hold in all real-world scenarios.
1：Experimental Design and Method Selection:
The study uses a Markov decision process (MDP) model to capture uncertainties in supply and demand.
2：Sample Selection and Data Sources:
Wind speed data from the National Renewable Energy Laboratory National Wind Technology Center and vehicle data from Winnipeg are used.
3：List of Experimental Equipment and Materials:
Battery specification of the EVs from BYD e6 and parameter of the wind turbine from Vestas.
4：Experimental Procedures and Operational Workflow:
The study discretizes a day into 24 stages, each stage of which is 1 h, and uses the rollout method with common random number (CRN) and EV aggregation for simulation.
5：Data Analysis Methods:
The performance of the method is evaluated by the average matching degree and the total charging cost during the entire scheduling time.
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