研究目的
Investigating how 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 EV aggregation can effectively reduce computing time and the SBPI method can achieve a good enough solution.
研究不足
The study assumes the charging power for each EV is the same and set to be constant, the required charging energy for each parking event is proportional to the consuming energy during driving, and the consuming energy during driving is proportional to the driving distance. 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 the uncertainties in the supply and demand side. The simulation-based policy improvement (SBPI) method is developed to obtain an improved charging policy from the base policy.
2：Sample Selection and Data Sources:
The study uses data from the National Renewable Energy Laboratory National Wind Technology Center for wind speed and vehicle data from Winnipeg for driving patterns.
3：List of Experimental Equipment and Materials:
The battery specification of the EVs is acquired from the BYD e6 and the parameter of the wind turbine comes from Vestas.
4：Experimental Procedures and Operational Workflow:
The study discretizes a day into 24 stages, each stage of which is 1 h. The MDP is used to formulate this problem.
5：Data Analysis Methods:
The study uses Monte Carlo simulations to estimate the Q-factor for each action.
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