Towards microgrid resilience enhancement via mobile power sources and repair crews: a multi-agent reinforcement learning approach
File(s)
Accepted version
Author(s)
Wang, Yi
Qiu, Dawei
Teng, Fei
Strbac, Goran
Type
Journal Article
Abstract
Mobile power sources (MPSs) have been gradually deployed in microgrids as critical resources to coordinate with repair crews (RCs) towards resilience enhancement owing to their flexibility and mobility in handling the complex coupled power-transport systems. However, previous work solves the coordinated dispatch problem of MPSs and RCs in a centralized manner with the assumption that the communication network is still fully functioning after the event. However, there is growing evidence that certain extreme events will damage or degrade communication infrastructure, which makes centralized decision making impractical. To fill this gap, this paper formulates the resilience-driven dispatch problem of MPSs and RCs in a decentralized framework. To solve this problem, a hierarchical multi-agent reinforcement learning method featuring a two-level framework is proposed, where the high-level action is used to switch decision-making between power and transport networks, and the low-level action constructed via a hybrid policy is used to compute continuous scheduling and discrete routing decisions in power and transport networks, respectively. The proposed method also uses an embedded function encapsulating system dynamics to enhance learning stability and scalability. Case studies based on IEEE 33-bus and 69-bus power networks are conducted to validate the effectiveness of the proposed method in load restoration.
Date Issued
2024-01-01
Date Acceptance
2023-01-25
Citation
IEEE Transactions on Power Systems, 2024, 39 (1), pp.1329-1345
ISSN
0885-8950
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1329
End Page
1345
Journal / Book Title
IEEE Transactions on Power Systems
Volume
39
Issue
1
Copyright Statement
Copyright © 2025 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Innovate UK
Innovate UK
Engineering & Physical Science Research Council (EPSRC)
Economic & Social Research Council (ESRC)
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Innovate UK
Engineering & Physical Science Research Council (E
Identifier
http://dx.doi.org/10.1109/tpwrs.2023.3240479
Grant Number
EP/L001039/1
EP/R030235/1
104224 / 16369
104249
EP/R045518/1
ES/T000112/1
EGR1224-119
EP/T021780/1
105843
EP/W034204/1
Subjects
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
Indexes
Uncertainty
Reactive power
Maintenance engineering
Load modeling
Resilience
Routing
Mobile power sources
repair crews
microgrid resilience
power-transport network
hierarchical multi-agent reinforcement learning
STABILITY ASSESSMENT
NEURAL-NETWORK
SMALL-SIGNAL
SYSTEM
PENETRATION
MODEL
0906 Electrical and Electronic Engineering
Energy
Publication Status
Published
Date Publish Online
2023-01-30