计及耦合因素的电动汽车充电负荷时空分布预测
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TM715

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国家自然科学基金资助项目(52107108)


Prediction of temporal and spatial distribution of electric vehicle charging load considering coupling factors
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Project supported by the National Natural Science Foundation of China (52107108)

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    摘要:

    实现电动汽车与电网互利共赢的前提之一是如何有效预测电动汽车的充电负荷,而电动汽车时空转移的随机性和转移过程中各因素的耦合性增加了充电负荷预测的难度,文中提出一种计及动态转移规划和耦合因素的电动汽车充电负荷时空分布预测方法。首先,基于出行链技术建立含多类型电动汽车的单体出行数学模型;在此基础上,考虑交通流量、行驶路况和温度,构建电动汽车的单位里程能耗数学模型。然后,基于马尔可夫决策过程理论,考虑剩余行程和路网拥堵信息,动态更新路网信息和随机规划电动汽车时空转移路径。最后,基于算例,对比分析电动汽车及其充电负荷在不同策略、职能区域和出行日情况下的时空分布。结果表明:文中所提方法能够全面反映电动汽车车主的出行决策,且预测结果能真实反应电动汽车类型和职能区域不同导致的充电负荷在幅值和分布上的差异。

    Abstract:

    One of the components to realize the mutual benefit and win-win between electric vehicle (EV) and power grid is to effectively predict the charging load of EVs while the difficulty of charging load prediction is increased because of the randomness of temporal and spatial transfer of EV and a variety of coupling factors in the transfer process. In this paper,a method for predicting the spatial and temporal distribution of EV charging load considering dynamic transfer planning and coupling factors is proposed. Firstly,an individual travel mathematical model with multiple types of EVs is established based on travel chain technology. On this basis,considering the traffic flux,road conditions and temperature,the mathematical model of energy consumption per mileage of EV is constructed. Secondly,based on Markov decision process theory,considering the residual path and road network congestion information,the road network information is dynamically updated and the temporal and spatial transfer path of EVs is randomly planned. Finally,based on an example,the temporal and spatial distribution of EV and its charging load are compared and analyzed under different strategies,functional areas and travel days. The results show that the proposed method can fully reflect the travel decision of EV owners,and the prediction results can truly reflect the differences in the amplitude and distribution of charging load due to EV types and functional areas.

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程杉,赵子凯,陈诺,于子豪.计及耦合因素的电动汽车充电负荷时空分布预测[J].电力工程技术,2022,41(3):194-201,208

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历史
  • 收稿日期:2021-08-28
  • 最后修改日期:2021-11-24
  • 录用日期:2021-11-29
  • 在线发布日期: 2022-05-24
  • 出版日期: 2022-05-28