基于多维状态空间MCMC充电负荷预测的充电站规划
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上海电力大学

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国家自然科学基金项目(面上项目,重点项目,重大项目);上海市科委项目


Charging station planning for electric vehicle based on charging load forcast by Markov Chain Monte Carlo forecasting method in multi-dimensional state space
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Shanghai University of Electric Power

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    摘要:

    电动汽车充电站的规划布局与电动汽车充电负荷的出行特性密切相关,因而合理预测充电负荷需求才能得到有效的充电站规划结果。为此,本文首先定义了多个维度下的电动汽车充电负荷状态空间,在此基础上建立充电负荷状态转移概率矩阵,进而提出一种基于电动汽车多维状态空间的马尔科夫链蒙特卡洛(Markov Chain Monte Carlo, MCMC)负荷预测模型,结合车辆实际出行实时样本数据得到充电负荷时空预测分布。其次,建立考虑企业建站经济效益及用户满意度的双层规划模型,通过变权重粒子群算法进行求解,得到充电站的最优站址和规模。最后通过算例仿真,验证了所提方法的合理性和有效性。

    Abstract:

    The location and capacity planning of electric vehicle charging stations are closely related to the travel characteristics of electric vehicle loads. Therefore, only when the charging load demand is reasonably predicted can an effective charging station planning result be obtained. To this end, this paper defined the state space of electric vehicle charging load in multiple dimensions firstly, on this basis, the probability matrix of state transfer of charging load is established. Furthermore, a Markov chain Monte Carlo (MCMC) load forecasting model based on the multi-dimensional state space of electric vehicles is proposed, the spatial-temporal prediction distribution of charging load is obtained by combining the real-time sample data of vehicle travel. Then, a two-level programming model considering the economic benefits and user satisfaction of enterprise station construction is established. With using variable weight particle swarm optimization, the optimal site and scale of charging station can be obtained. Finally, the simulation results demonstrate the rationality and effectiveness of the model and method.

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  • 收稿日期:2020-09-03
  • 最后修改日期:2020-11-17
  • 录用日期:2020-11-23
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