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A Multiagent Q-learning-based Restoration Algorithm for Resilient Distribution System Operation

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Date Issued:
2017
Abstract/Description:
Natural disasters, human errors, and technical issues have caused disastrous blackouts to power systems and resulted in enormous economic losses. Moreover, distributed energy resources have been integrated into distribution systems, which bring extra uncertainty and challenges to system restoration. Therefore, the restoration of power distribution systems requires more efficient and effective methods to provide resilient operation.In the literature, using Q-learning and multiagent system (MAS) to restore power systems has the limitation in real system application, without considering power system operation constraints. In order to adapt to system condition changes quickly, a restoration algorithm using Q-learning and MAS, together with the combination method and battery algorithm is proposed in this study. The developed algorithm considers voltage and current constraints while finding system switching configuration to maximize the load pick-up after faults happen to the given system. The algorithm consists of three parts. First, it finds switching configurations using Q-learning. Second, the combination algorithm works as a back-up plan in case of the solution from Q-learning violates system constraints. Third, the battery algorithm is applied to determine the charging or discharging schedule of battery systems. The obtained switching configuration provides restoration solutions without violating system constraints. Furthermore, the algorithm can adjust switching configurations after the restoration. For example, when renewable output changes, the algorithm provides an adjusted solution to avoid violating system constraints.The proposed algorithm has been tested in the modified IEEE 9-bus system using the real-time digital simulator. Simulation results demonstrate that the algorithm offers an efficient and effective restoration strategy for resilient distribution system operation.
Title: A Multiagent Q-learning-based Restoration Algorithm for Resilient Distribution System Operation.
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Name(s): Hong, Jungseok, Author
Sun, Wei, Committee Chair
Zhou, Qun, Committee Member
Zheng, Qipeng, Committee Member
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2017
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Natural disasters, human errors, and technical issues have caused disastrous blackouts to power systems and resulted in enormous economic losses. Moreover, distributed energy resources have been integrated into distribution systems, which bring extra uncertainty and challenges to system restoration. Therefore, the restoration of power distribution systems requires more efficient and effective methods to provide resilient operation.In the literature, using Q-learning and multiagent system (MAS) to restore power systems has the limitation in real system application, without considering power system operation constraints. In order to adapt to system condition changes quickly, a restoration algorithm using Q-learning and MAS, together with the combination method and battery algorithm is proposed in this study. The developed algorithm considers voltage and current constraints while finding system switching configuration to maximize the load pick-up after faults happen to the given system. The algorithm consists of three parts. First, it finds switching configurations using Q-learning. Second, the combination algorithm works as a back-up plan in case of the solution from Q-learning violates system constraints. Third, the battery algorithm is applied to determine the charging or discharging schedule of battery systems. The obtained switching configuration provides restoration solutions without violating system constraints. Furthermore, the algorithm can adjust switching configurations after the restoration. For example, when renewable output changes, the algorithm provides an adjusted solution to avoid violating system constraints.The proposed algorithm has been tested in the modified IEEE 9-bus system using the real-time digital simulator. Simulation results demonstrate that the algorithm offers an efficient and effective restoration strategy for resilient distribution system operation.
Identifier: CFE0006746 (IID), ucf:51856 (fedora)
Note(s): 2017-08-01
M.S.E.E.
Engineering and Computer Science, Electrical Engineering and Computer Engineering
Masters
This record was generated from author submitted information.
Subject(s): Power System Restoration -- Q-learning -- Multiagent System -- MAS -- Distribution Automation -- Distribution Restoration
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0006746
Restrictions on Access: public 2017-08-15
Host Institution: UCF

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