• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2021, Vol. 42 ›› Issue (6): 76-85.doi: 10.12204/j.issn.1000-7229.2021.06.008

• Key Technologies of Electric Vehicle Participating in Power Grid Dispatching?Hosed by Associate Professor FU Zhixin? • Previous Articles     Next Articles

Demand Response Package Model of Electric Vehicle Charging Station Based on LSTM Neural Network and Optimal Operation of Distribution Network

XUE Mingfeng1, MAO Xiaobo1, PAN Yongtao1, YANG Yanhong2, ZHAO Zhenxing2, LI Yanjun2   

  1. 1. Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, Jiangsu Province, China
    2. Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2020-11-27 Online:2021-06-01 Published:2021-05-28
  • Contact: YANG Yanhong
  • Supported by:
    State Grid Jiangsu Electric Power Co., Ltd. Research Program(J2019078)


With the rapid growth of electric vehicles (EVs), the charging characteristics of large-scale EVs are randomness and spatiotemporal coupling, which poses a risk of exceeding the limit on the operating voltage of distribution network. Through the demand response (DR) based on price, it has become an important technical means to guide the orderly and reasonable charging of EVs in a large space-time range. In this paper, the DR characteristics of EV charging station based on data-driven and its participation in the operation optimization of distribution network are studied. Firstly, the charging model of single EV and the driving characteristics of EV considering the topological structure of traffic network are proposed, and the load simulation calculation method of regional EV charging station is established. On this basis, the electric power system based on LSTM deep neural network is proposed. The mapping model between charging cost and power response of EV charging station is obtained by encapsulating the DR model of EV charging station. Furthermore, a voltage operation optimization model of regional distribution network considering the DR of EV charging station is constructed, and the model is solved with particle swarm optimization algorithm. Finally, the comparison and analysis of the 33-node system with 3 charging stations verify the effectiveness of the proposed method of EV charging station DR and its participation in distribution network operation optimization. It provides reference for data-driven method to solve the problem of EV charging and demand response.

Key words: electric vehicle charging station, LSTM neural network, demand response, package model

CLC Number: