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基于深度学习考虑出行模式的电动汽车充电负荷场景生成
作者:
作者单位:

1.西安交通大学电气工程学院,陕西省西安市 710049;2.国网江苏省电力有限公司电力科学研究院,江苏省南京市 211103

摘要:

随着电动汽车的快速普及,交通网与电网的耦合进一步加深,交通网出行模式将对电动汽车充电负荷产生显著影响。传统的充电负荷模拟方法依赖于对交通路网和电动汽车个体建模并有较强的假设。文中提出了一种基于数据驱动的卷积自编码器和条件对抗生成网络的电动汽车充电负荷场景生成方法。该方法首先采用基于无监督学习的卷积自编码器对交通网出行预测数据降维并自适应地抽取出特征信息。其次,设计了一种适用于日前交通网充电负荷场景生成的条件生成对抗网络,并利用卷积自编码器抽取出的特征信息,隐式地学习得到不同交通网出行模式对应的电动汽车充电负荷条件概率分布,从而实现日前的电动汽车充电负荷场景生成,为电网运行与充电站运营提供了支撑。最后,以实际城市路网为例验证了所提出充电负荷场景生成方法的有效性。

关键词:

基金项目:

国家自然科学基金委员会-国家电网公司智能电网联合基金资助项目(U1766205);国家电网公司科技项目(5400-202099508A-0-0-00)。

通信作者:

作者简介:

钱涛(1995—),男,博士研究生,主要研究方向:电网交通网联合优化运行、深度学习与强化学习在电力交通系统中的应用。E-mail:taylorqian@stu.xjtu.edu.cn
任孟极(1998—),男,硕士研究生,主要研究方向:深度学习在电力系统可靠性评估中的应用。E-mail:renmengji@ stu.xjtu.edu.cn
邵成成(1989—),男,通信作者,博士,副教授,博士生导师,主要研究方向:电力能源系统规划与运行。E-mail:ccshao3@xjtu.edu.cn


Deep-learning-based Electric Vehicle Charging Load Scenario Generation Considering Travel Mode
Author:
Affiliation:

1.School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China;2.Electric Power Research Institute of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211103, China

Abstract:

With the rapid popularization of electric vehicles, the coupling between the traffic network and power grid is further deepened, and the travel mode of the traffic network will have a significant impact on the charging load of electric vehicles. The traditional charging load simulation method relies on the individual modeling of the traffic network and electric vehicles, and has strong assumptions. A method of electric vehicle charging load scenario generation based on data-driven convolutional autoencoder and conditional generative adversarial network (CGAN) is proposed. Firstly, the convolutional autoencoder based on unsupervised learning is used to reduce the dimension of travel prediction data in traffic network and adaptively extract the feature information. Secondly, a CGAN suitable for the generation of the day-ahead current traffic network charging load scenario is designed, and the feature information extracted from the convolutional autoencoder is used to implicitly learn the conditional probability distribution of electric vehicle charging load corresponding to different travel modes of traffic network. Thus, the generation of the day-ahead electric vehicle charging load scenario is realized, which provides the support for the operation of the power grid and charging station. Finally, taking an actual city traffic network as an example, the necessity of the proposed convolutional autoencoder and the effectiveness of the CGAN are verified.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid (No. U1766205) and State Grid Corporation of China (No. 5400-202099508A-0-0-00).
引用本文
[1]钱涛,任孟极,邵成成,等.基于深度学习考虑出行模式的电动汽车充电负荷场景生成[J].电力系统自动化,2022,46(12):67-75. DOI:10.7500/AEPS20220221002.
QIAN Tao, REN Mengji, SHAO Chengcheng, et al. Deep-learning-based Electric Vehicle Charging Load Scenario Generation Considering Travel Mode[J]. Automation of Electric Power Systems, 2022, 46(12):67-75. DOI:10.7500/AEPS20220221002.
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  • 收稿日期:2022-02-21
  • 最后修改日期:2022-04-14
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  • 在线发布日期: 2022-06-27
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