Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/wiley/online-topology-based-voltage-regulation-a-computational-performance-pEtMWLHYK2
References (26)
-
Hanchen Xu, A. Domínguez-García, P. Sauer (2018)
Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement LearningIEEE Transactions on Power Systems, 35
-
O. Alimi, K. Ouahada, A. Abu-Mahfouz (2020)
A Review of Machine Learning Approaches to Power System Security and StabilityIEEE Access, 8
-
Sheng-Kuan Wang, Jiajun Duan, Di Shi, Chunlei Xu, Haifeng Li, R. Diao, Zhiwei Wang (2020)
A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using Deep Reinforcement LearningIEEE Transactions on Power Systems, 35
-
Qiuling Yang, Gang Wang, A. Sadeghi, G. Giannakis, Jian Sun (2019)
Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement LearningIEEE Transactions on Smart Grid, 11
-
M. Muhammad, H. Mokhlis, A. Amin, K. Naidu, J. Franco, L. Wang, M. Othman (2019)
Enhancement of simultaneous network reconfiguration and DG sizing via Hamming dataset approach and firefly algorithmIET Generation, Transmission & Distribution
-
Chunyu Chen, Mingjian Cui, F. Li, Shengfei Yin, Xinan Wang (2021)
Model-Free Emergency Frequency Control Based on Reinforcement LearningIEEE Transactions on Industrial Informatics, 17
-
Wei Wang, N. Yu, Yuanqi Gao, Jie Shi (2020)
Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution SystemsIEEE Transactions on Smart Grid, 11
-
Qiangqiang Xie, Hongxun Hui, Yi Ding, Chengjin Ye, Zhenzhi Lin, Peng Wang, Yonghua Song, L. Ji, Rong-Tai Chen (2020)
Use of demand response for voltage regulation in power distribution systems with flexible resourcesIet Generation Transmission & Distribution, 14
-
Brett Robbins, Hao Zhu, A. Domínguez-García (2016)
Optimal Tap Setting of Voltage Regulation Transformers in Unbalanced Distribution SystemsIEEE Transactions on Power Systems, 31
-
Yujian Ye, D. Qiu, Jing Li, G. Strbac (2019)
Multi-Period and Multi-Spatial Equilibrium Analysis in Imperfect Electricity Markets: A Novel Multi-Agent Deep Reinforcement Learning ApproachIEEE Access, 7
-
Xianzhuo Sun, J. Qiu (2021)
Two-Stage Volt/Var Control in Active Distribution Networks With Multi-Agent Deep Reinforcement Learning MethodIEEE Transactions on Smart Grid, 12
-
Ying Zhang, Xinan Wang, Jianhui Wang, Y. Zhang (2020)
Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution SystemsIEEE Transactions on Smart Grid, 12
-
Xiangqi Zhu, Jiyu Wang, N. Lu, N. Samaan, Renke Huang, Xinda Ke (2019)
A Hierarchical VLSM-Based Demand Response Strategy for Coordinative Voltage Control Between Transmission and Distribution SystemsIEEE Transactions on Smart Grid, 10
-
Yan Du, F. Li (2020)
Intelligent Multi-Microgrid Energy Management Based on Deep Neural Network and Model-Free Reinforcement LearningIEEE Transactions on Smart Grid, 11
-
Lizon Maharjan, Mark Ditsworth, Manish Niraula, Carlos Narvaez, B. Fahimi (2019)
Machine learning based energy management system for grid disaster mitigationIET Smart Grid
-
Tian Zhuang, Wenchuan Wu, Boming Zhang, A. Bose (2016)
Mixed-integer second-order cone programing model for VAR optimisation and network reconfiguration in active distribution networksIet Generation Transmission & Distribution, 10
-
Debmalya Mandal, Sourav Medya, Brian Uzzi, C. Aggarwal (2021)
MetaLearning with Graph Neural NetworksACM SIGKDD Explorations Newsletter, 23
-
P. Gangwar, S. Singh, S. Chakrabarti (2019)
Multi‐objective planning model for multi‐phase distribution system under uncertainty considering reconfigurationIET Renewable Power Generation
-
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip Yu (2019)
A Comprehensive Survey on Graph Neural NetworksIEEE Transactions on Neural Networks and Learning Systems, 32
-
P. Gangwar, Aasim Mallick, S. Chakrabarti, S. Singh (2020)
Short-Term Forecasting-Based Network Reconfiguration for Unbalanced Distribution Systems With Distributed GeneratorsIEEE Transactions on Industrial Informatics, 16
-
Di Cao, Weihao Hu, Junbo Zhao, Qi Huang, Zhe Chen, F. Blaabjerg (2020)
A Multi-Agent Deep Reinforcement Learning Based Voltage Regulation Using Coordinated PV InvertersIEEE Transactions on Power Systems, 35
-
H. Hasselt, A. Guez, David Silver (2015)
Deep Reinforcement Learning with Double Q-Learning -
Thomas Kipf, M. Welling (2016)
Semi-Supervised Classification with Graph Convolutional NetworksArXiv, abs/1609.02907
-
Sachin Sharma, K. Niazi, K. Verma, Tanuj Rawat (2020)
Impact of battery energy storage, controllable load and network reconfiguration on contemporary distribution network under uncertain environmentIet Generation Transmission & Distribution, 14
-
Yuanqi Gao, Wei Wang, N. Yu (2020)
Consensus Multi-Agent Reinforcement Learning for Volt-VAR Control in Power Distribution NetworksIEEE Transactions on Smart Grid, 12
-
Di Cao, Junbo Zhao, Weihao Hu, F. Ding, Qi Huang, Zhe Chen (2021)
Attention Enabled Multi-Agent DRL for Decentralized Volt-VAR Control of Active Distribution System Using PV Inverters and SVCsIEEE Transactions on Sustainable Energy, 12
- Publisher
- Wiley
- Copyright
- © 2022 The Institution of Engineering and Technology.
- eISSN
- 1751-8695
- DOI
- 10.1049/gtd2.12433
- Publisher site
- See Article on Publisher Site
Abstract
The increasing use of distributed generation (DG) in power systems can result in frequent online voltage problems. In scenarios in which substantial DG prediction errors occur because of high DG accommodation levels, traditional technical solutions cannot meet the online voltage regulation requirements. Hence, new resources for online voltage regulation are needed. Here, flexible network reconfiguration is proposed to coordinate with the existing resources for severe online voltage deviations. For online topology‐based voltage regulation (OTVR), the authors develop a deep reinforcement learning (DRL) algorithm based on the following specially designed modelling to enhance the computational performance. The mechanism of action incorporates the concepts of local research, branch exchange, and action separation, and it effectively simplifies the action dimension and action space. In addition, for the graph data in OTVR, a graph convolution network (GCN) is applied to obtain better feature extraction. Case studies performed on IEEE 14‐bus, 33‐bus, 141‐bus systems and a practical system verify that our proposed algorithm can obtain close to optimal solutions in 2 s which can meet the needs of online voltage regulation. Moreover, we verify that the developed OTVR effectively increases DG penetration and decreases the need for investment in additional regulating devices.
Journal
"IET Generation, Transmission & Distribution" – Wiley
Published: Dec 1, 2022