Bi-layer Coordinated Control Strategy of Distribution Network Considering Participation of Electric Vehicles and Microgrid

被引:0
作者
Fan, Peixiao [1 ,2 ]
Yang, Jun [1 ,2 ]
Wen, Yuxin [3 ]
Ke, Song [1 ,2 ]
Liu, Xuecheng [1 ,2 ]
Ding, Leyan [1 ,2 ]
机构
[1] Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, Wuhan
[2] School of Electrical Engineering and Automation, Wuhan University, Wuhan
[3] Electric Power Research Institute of China Southern Power Grid Company Limited, Guangzhou
来源
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 19期
基金
中国国家自然科学基金;
关键词
deep reinforcement learning; distribution network; electric vehicle; microgrid; vehicle-to-grid; voltage control;
D O I
10.7500/AEPS20240203001
中图分类号
学科分类号
摘要
The large-scale construction of microgrids with large-scale distributed power sources and the application of the vehicle-to-grid (V2G) technology lead to the instability of the operation voltage of the distribution network and the demand loss of electric vehicle (EV) users, and also bring a new means for the regulation of the power system. Therefore, this paper proposes a bi-layer active power-reactive power coordinated control strategy based on enhanced evolutionary-deep reinforcement learning (EDRL) for distribution networks and microgrids with V2G. Firstly, considering the influence of V2G process on the demand of EV users, a bi-layer coordinated control model including distribution networks and microgrids with V2G is constructed based on the travel chain. Secondly, the enhanced EDRL algorithm is constructed to further enhance the convergence ability of the agent. Then, defining the operation information of the distribution network as the state set, the power regulation signal of each unit as the action set, and the comprehensive cost such as voltage deviation, network loss and user demand loss as the reward function index, the structure design of the bi-layer coordinated control is completed. The case results show that, the proposed strategy can reduce the voltage deviation and network loss of the distribution network on the premise of meeting the charging demand of EV users. © 2024 Automation of Electric Power Systems Press. All rights reserved.
引用
收藏
页码:60 / 68
页数:8
相关论文
共 29 条
[1]  
WEI Hongyi, ZHUO Zhenyu, ZHANG Ning, Et al., Transition path optimization and influencing factor analysis of carbon emission peak and carbon neutrality for power system of China [J], Automation of Electric Power Systems, 46, 19, pp. 1-12, (2022)
[2]  
ZHANG Yongjun, YI Yingqi, LI Licheng, Et al., Prospect of new low-voltage distribution system technology driven by carbon emission peak and carbon neutrality targets[J], Automation of Electric Power Systems, 46, 22, pp. 1-12, (2022)
[3]  
WANG Weijie, HUANG Haiyu, XU Yuantu, Et al., Strategy research on electric vehicles participating in active distribution network voltage regulation[J], Guangdong Electric Power, 36, 10, pp. 93-104, (2023)
[4]  
TERZIJA V., A distributed P and Q provision-based voltage regulation scheme by incentivized EV fleet charging for resistive distribution networks[J], IEEE Transactions on Transportation Electrification, 7, 4, pp. 2376-2389, (2021)
[5]  
HU Junru, DOU Xiaobo, LI Chen, Et al., Distributed cooperative reactive power optimization strategy for medium-and low-voltage distribution network[J], Automation of Electric Power Systems, 45, 22, pp. 47-54, (2021)
[6]  
LIU Yanghua, YANG Yuerong, LIN Shunjiang, A multi-object convex optimization method for the coordinated allocation of energy storage and reactive power compensation devices in distribution network integrated with photovoltaics[J], Journal of Electric Power Science and Technology, 38, 5, pp. 22-33, (2023)
[7]  
FAN Junjie, QI Lei, SUN Xiaofeng, Et al., Active adaption control of inverter for distribution network with reactive power compensation capacitor [J], Automation of Electric Power Systems, 47, 9, pp. 175-183, (2023)
[8]  
NADEEM KHAN M F,, ALI SAJJAD I, Et al., Optimal control of active distribution network using deep reinforcement learning, 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), pp. 1-6
[9]  
HE Songtao, SHAO Zhenguo, ZHENG Wendi, Et al., Bi-level uncertain reactive power planning of distribution network considering SVG dynamic voltage regulation strategy[J], Power System Technology, 47, 12, pp. 5158-5170, (2023)
[10]  
KANG Tianyuan, LIU Keyan, LI Zhao, Hierarchical voltage coordination control strategy of middle-and low-voltage level power distribution network based on photovoltaic inverter adjustments[J], High Voltage Engineering, 50, 3, pp. 1225-1234, (2024)