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

Tao QIAN; Mengji REN; Chengcheng SHAO; Dandan ZHU; Qian ZHOU; Xiuli WANG

doi:10.7500/AEPS20220221002

您当前的位置：

首页 >

文章列表页 >

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

Special Topic on Key Technologies of Urban Power Grid and Transportation Integration for New Power Systems | 更新时间：2025-06-16

- Deep-learning-based Electric Vehicle Charging Load Scenario Generation Considering Travel Mode
- Automation of Electric Power Systems Vol. 46, Issue 12, Pages: 67-75(2022)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
  
  State Grid Corporation of China(5400-202099508A-0-0-00)
- DOI：10.7500/AEPS20220221002
  CLC：
- Received：21 February 2022，
  
  Revised：2022-04-14，
  
  Published Online：18 May 2022，
  
  Published：25 June 2022
- 稿件说明：
移动端阅览
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.

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.
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.

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。

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.

关键词

Keywords

references

VAGROPOULOS S I ， BALASKAS G A ， BAKIRTZIS A G . An investigation of plug-in electric vehicle charging impact on power systems scheduling and energy costs ［J］. IEEE Transactions on Power Systems ， 2017 ， 32 （ 3 ）： 1902 - 1912 .

高赐威，张亮 . 电动汽车充电对电网影响的综述［J］. 电网技术， 2011 ， 35 （ 2 ）： 127 - 131 .

GAO Ciwei ， ZHANG Liang . A survey of influence of electrics vehicle charging on power grid ［J］. Power System Technology ， 2011 ， 35 （ 2 ）： 127 - 131 .

邵成成，李徐亮，钱涛，等 . 基于交通均衡的电动汽车快速充电负荷模拟［J］. 中国电机工程学报， 2021 ， 41 （ 4 ）： 1368 - 1376 .

SHAO Chengcheng ， LI Xuliang ， QIAN Tao ， et al . Simulation of EV fast charging load based on traffic equilibrium ［J］. Proceedings of the CSEE ， 2021 ， 41 （ 4 ）： 1368 - 1376 .

张洪财，胡泽春，宋永华，等 . 考虑时空分布的电动汽车充电负荷预测方法［J］. 电力系统自动化， 2014 ， 38 （ 1 ）： 13 - 20 .

ZHANG Hongcai ， HU Zechun ， SONG Yonghua ， et al . A prediction method for electric vehicle charging load considering spatial and temporal distribution ［J］. Automation of Electric Power Systems ， 2014 ， 38 （ 1 ）： 13 - 20 .

王毅，谷亿，丁壮，等 . 基于模糊熵和集成学习的电动汽车充电需求预测［J］. 电力系统自动化， 2020 ， 44 （ 3 ）： 114 - 121 .

WANG Yi ， GU Yi ， DING Zhuang ， et al . Charging demand forecasting of electric vehicle based on empirical mode decomposition-fuzzy entropy and ensemble learning ［J］. Automation of Electric Power Systems ， 2020 ， 44 （ 3 ）： 114 - 121 .

SHAO C C ， WANG X F ， WANG X L ， et al . Cooperative dispatch of wind generation and electric vehicles with battery storage capacity constraints in SCUC ［J］. IEEE Transactions on Smart Grid ， 2014 ， 5 （ 5 ）： 2219 - 2226 .

HE F ， YIN Y F ， LAWPHONGPANICH S . Network equilibrium models with battery electric vehicles ［J］. Transportation Research Part B： Methodological ， 2014 ， 67 ： 306 - 319 .

WANG B ， ZHAO D B ， DEHGHANIAN P ， et al . Aggregated electric vehicle load modeling in large-scale electric power systems ［J］. IEEE Transactions on Industry Applications ， 2020 ， 56 （ 5 ）： 5796 - 5810 .

HE F ， YIN Y F ， WANG J H ， et al . Sustainability SI： optimal prices of electricity at public charging stations for plug-in electric vehicles ［J］. Networks and Spatial Economics ， 2016 ， 16 （ 1 ）： 131 - 154 .

黄南天，杨学航，蔡国伟，等 . 采用非平衡小样本数据的风机主轴承故障深度对抗诊断［J］. 中国电机工程学报， 2020 ， 40 （ 2 ）： 563 - 574 .

HUANG Nantian ， YANG Xuehang ， CAI Guowei ， et al . A deep adversarial diagnosis method for wind turbine main bearing fault with imbalanced small sample scenarios ［J］. Proceedings of the CSEE ， 2020 ， 40 （ 2 ）： 563 - 574 .

李康平，张展耀，王飞，等 . 基于GAN场景模拟与条件风险价值的独立型微网容量随机优化配置模型［J］. 电网技术， 2019 ， 43 （ 5 ）： 1717 - 1725 .

LI Kangping ， ZHANG Zhanyao ， WANG Fei ， et al . Stochastic optimization model of capacity configuration for stand-alone microgrid based on scenario simulation using GAN and conditional value at risk ［J］. Power System Technology ， 2019 ， 43 （ 5 ）： 1717 - 1725 .

孙秋野，刘月，胡旌伟，等 . 基于GAN的非侵入式自能源建模［J］. 中国电机工程学报， 2020 ， 40 （ 21 ）： 6784 - 6794 .

SUN Qiuye ， LIU Yue ， HU Jingwei ， et al . Non-intrusive we-energy modeling based on GAN technology ［J］. Proceedings of the CSEE ， 2020 ， 40 （ 21 ）： 6784 - 6794 .

董骁翀，孙英云，蒲天骄 . 基于条件生成对抗网络的可再生能源日前场景生成方法［J］. 中国电机工程学报， 2020 ， 40 （ 17 ）： 5527 - 5536 .

DONG Xiaochong ， SUN Yingyun ， PU Tianjiao . Day-ahead scenario generation of renewable energy based on conditional GAN ［J］. Proceedings of the CSEE ， 2020 ， 40 （ 17 ）： 5527 - 5536 .

REN C ， XU Y . A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data ［J］. IEEE Transactions on Power Systems ， 2019 ， 34 （ 6 ）： 5044 - 5052 .

MIRZA M ， OSINDER S . Conditional generative adversarial nets ［EB/OL］. ［ 2022-02-01 ］. https：//arxiv.org/abs/1411.1784 https://arxiv.org/abs/1411.1784 ．

LIM J H ， YE J C . Geometric GAN ［EB/OL］. ［ 2022-02-01 ］. https：//arxiv.org/abs/1705.02894 https://arxiv.org/abs/1705.02894 .

WEI W ， WU L ， WANG J H ， et al . Network equilibrium of coupled transportation and power distribution systems ［J］. IEEE Transactions on Smart Grid ， 2018 ， 9 （ 6 ）： 6764 - 6779 .

杨天宇，郭庆来，盛裕杰，等 . 系统互联视角下的城域电力-交通融合网络协同［J］. 电力系统自动化， 2020 ， 44 （ 11 ）： 1 - 9 .

YANG Tianyu ， GUO Qinglai ， SHENG Yujie ， et al . Coordination of urban integrated electric power and traffic network from perspective of system interconnection ［J］. Automation of Electric Power Systems ， 2020 ， 44 （ 11 ）： 1 - 9 .

李徐亮 . 西安算例原始数据［EB/OL］.［ 2022-02-01 ］. https：//share.wei yun.com/5l4O45k https://share.weiyun.com/5l4O45k .

LI Xuliang . Raw data of Xi'an example raw data ［EB/OL］. ［ 2022-02-01 ］. https：//share.wei yun. com/5l4O45k https://share.weiyun.com/5l4O45k .

盖亚开放数据集［EB/OL］.［ 2022-02-01 ］. https：//outreach.didichuxing.com/app-vue/dataList https://outreach.didichuxing.com/app-vue/dataList .

GAIA open dataset ［EB/OL］. ［ 2022-02-01 ］. https：//outreach.didichuxing.com/app-vue/dataList https://outreach.didichuxing.com/app-vue/dataList .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Scenario Generation Method for Electric Ship Charging Loads Based on Improved Robust Auxiliary Classifier Generation Adversarial Network

Aggregated and Schedulable Capacity Modeling of Large-scale Electric Vehicles for Multi-scenario Auxiliary Services

Related Author

LIAO Fei

YANG Jun

LIN Yi

XUE Jingwei

WU Shaojiang

ZHU Rui

WANG Yangyang

MAO Meiqin

Related Institution

Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network

School of Electrical Engineering and Automation, Wuhan University

Economic and Technology Research Institute of State Grid Fujian Electric Power Co., Ltd.

Fujian Shuikou Power Generation Group Co., Ltd.

Research Center for Photovoltaic Systems Engineering of Ministry of Education, Hefei University of Technology

⁰