Time-Segmented Multi-Level Reconfiguration in Distribution Network: A Novel Cloud-Edge Collaboration Framework

被引：14

作者：

Gao, Hongjun ^{[1
]}

Ma, Wang ^{[1
]}

He, Shuaijia ^{[1
]}

Wang, Lingfeng ^{[2
]}

Liu, Junyong ^{[1
]}

机构：

[1] Sichuan Univ, Coll Elect Engn, Chengdu 610065, Peoples R China

[2] Univ Wisconsin, Dept Elect Engn & Comp Sci, Milwaukee, WI 53211 USA

来源：

IEEE TRANSACTIONS ON SMART GRID | 2022年 / 13卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Substations; Transformers; Switches; Cloud computing; Collaboration; Distribution networks; Load shedding; Cloud-edge collaboration; distribution network; multi-level reconfiguration;

D O I：

10.1109/TSG.2022.3156433

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Dynamic reconfiguration is an effective method to improve the unbalanced distribution of load demand. The traditional global dynamic reconfiguration based on the centralized cloud service system may lead to large-scale power flow regulation and increase the computational burden in large-scale distribution networks. In this letter, a time-segmented multi-level method preferring local reconfiguration is proposed to minimize the photovoltaic power generation curtailment and load shedding. Furthermore, a novel cloud-edge collaboration framework is devised to alleviate the huge pressure bought by excessive concentration of computational tasks. Numerical results on a 445-node system are presented to demonstrate the effectiveness of the proposed method.

引用

页码：3319 / 3322

页数：4

共 29 条

[1] Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture
Siyuan Jiang
Hongjun Gao
Xiaohui Wang
Junyong Liu
Kunyu Zuo
[J]. Global Energy Interconnection, 2023, 6 (01) : 1 - 14
[2] Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network: a cloud-edge collaboration architecture
Jiang, Siyuan
Gao, Hongjun
Wang, Xiaohui
Liu, Junyong
Zuo, Kunyu
[J]. GLOBAL ENERGY INTERCONNECTION-CHINA, 2023, 6 (01): : 1 - 14
[3] Cloud-Edge Collaboration-Based Distribution Network Reconfiguration for Voltage Preventive Control
Yue, Dong
He, Ziwei
Dou, Chunxia
[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2023, 19 (12) : 11542 - 11552
[4] A Cloud-Edge Collaboration Solution for Distribution Network Reconfiguration Using Multi-Agent Deep Reinforcement Learning
Gao, Hongjun
Wang, Renjun
He, Shuaijia
Wang, Lingfeng
Liu, Junyong
Chen, Zhe
[J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2024, 39 (02) : 3867 - 3879
[5] Edge-Cloud-Coordinate Framework for Distribution Network Reconfiguration
Li, Chenchen
Chen, Liudong
Liu, Nian
[J]. 2021 IEEE IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (IEEE I&CPS ASIA 2021), 2021, : 225 - 228
[6] A heterogeneous network structure publishing security framework based on cloud-edge collaboration
Qu, Lianwei
Wang, Yong
Yang, Jing
Zhao, Meng
[J]. COMPUTER NETWORKS, 2023, 234
[7] Cloud-Edge Collaboration Feature Extraction Framework in Satellite Multi-access Edge Computing
He, Chao
Zheng, Mingwen
[J]. PROCEEDINGS OF 2021 IEEE 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2021), 2021, : 61 - 64
[8] Network reconfiguration and multi-level reactive power coordination optimization in active distribution network
Ye, Xueshun
He, Kaiyuan
Liu, Keyan
[J]. Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2019, 47 (13): : 115 - 123
[9] Double-layer zonal voltage regulation strategy for distribution network based on cloud-edge collaboration
Yan, Renwu
Liu, Yunzhang
Bao, Jinsheng
Chen, Xiangwei
[J]. International Journal of Cloud Computing, 2024, 13 (05) : 425 - 462
[10] Distributed V2G Dispatching via LSTM Network within Cloud-Edge Collaboration Framework
Shang, Yitong
Li, Zekai
Shao, Ziyun
Jian, Linni
[J]. 2021 IEEE IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (IEEE I&CPS ASIA 2021), 2021, : 1531 - 1538

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