基于MDP及激励需求响应的电动汽车有序充电控制
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TM714

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国家自然科学基金(61572311,61872230)


Research on coordinated charging control for electric vehicles based on MDP and incentive demand response
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    摘要:

    电动汽车充电行为的不确定性及随机性使充电负荷短时间内大量接入电网从而导致较大的负荷波动,同时,电动汽车的无序充电行为,不能在分时电价的条件下保证充电用户的利益。为缓解这些问题带来的负面影响, 首先,基于强化学习中马尔科夫决策过程(MDP)分析电动汽车的充电行为;然后,构造激励函数引导电动汽车根据电网供电裕度进行充电选择,得出同时满足负荷波动最小和用户花费最小的有序充电策略;最后,通过蒙特卡洛方法模拟电动汽车充电情况。有序充电仿真结果表明,该策略能有效地改善负荷叠加曲线,起到削峰填谷作用,并减少用户充电花费。

    Abstract:

    The uncertainty and randomness of the charging behavior of electric vehicles make a large number of charging loads connect to the grid in a short period of time, which will lead to large load fluctuations. At the same time, the disorderly charging behavior of electric vehicles can not guarantee the interests of charging users under the condition of time-of-use electricity prices. In order to alleviate the negative impact of these problems, the charging behavior of electric vehicles is firstly analyzed based on the Markov Decision Process (MDP) in the reinforcement learning. Secondly, an incentive function is constructed to guide the electric vehicle to make charging choice according to the power supply margin of the grid. Then an orderly charging strategy that meets the minimum load fluctuation and the minimum user cost at the same time is produced. Finally, the Monte Carlo method is utilized to simulate the charging status of electric vehicles. Results of orderly charging simulation show that the strategy can effectively improve the load superposition curve, play the role of peak shaving and valley filling and reduce the user charging cost.

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廖鑫,李婧,徐佳,等.基于MDP及激励需求响应的电动汽车有序充电控制[J].电力科学与技术学报,2021,36(5):79-86.
Liao Xin, Li Jing, Xu Jia, et al. Research on coordinated charging control for electric vehicles based on MDP and incentive demand response[J]. Journal of Electric Power Science and Technology,2021,36(5):79-86.

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  • 在线发布日期: 2021-11-16
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