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

doi:10.12204/j.issn.1000-7229.2021.06.008

Abstract

Abstract:

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:

TM73

XUE Mingfeng, MAO Xiaobo, PAN Yongtao, YANG Yanhong, ZHAO Zhenxing, LI Yanjun. Demand Response Package Model of Electric Vehicle Charging Station Based on LSTM Neural Network and Optimal Operation of Distribution Network[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(6): 76-85.

Figures/Tables 14

Fig.1 Research framework of charging behavior encapsulation and distribution network optimization

Fig.2 Road network accross different functional areas

Fig.3 Relationship between price and charging load rate

Fig.4 Load curve of charging station

Fig.5 EV Charging price

Fig.6 Training model of LSTM package

Fig.7 Flow of charging station price optimization based on PSO

Fig.8 33-node system of distribution network

Fig.9 Active power output curve of three groups photovoltaic power supply

Fig.10 Training error of charging station LSTM package model

Fig.11 Optimization curve of node voltage evaluation function

Fig.12 Node voltage optimization of distribution network

Fig.13 Continuous optimization of node voltages in distribution network

Fig.14 Continuous optimization of price strategy for charging stations

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