Optimal Coordination for Multiple Network-Constrained VPPs via Multi-Agent Deep Reinforcement Learning

Liu, X; Li, S; Zhu, J

Optimal Coordination for Multiple Network-Constrained VPPs via Multi-Agent Deep Reinforcement Learning

Liu, X Li, S Zhu, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Smart Grid, 2022, PP, (99), pp. 1-1
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Liu, X
dc.contributor.author	Li, S
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.date.accessioned	2023-04-17T03:02:19Z
dc.date.available	2023-04-17T03:02:19Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Smart Grid, 2022, PP, (99), pp. 1-1
dc.identifier.issn	1949-3053
dc.identifier.issn	1949-3061
dc.identifier.uri	http://hdl.handle.net/10453/169848
dc.description.abstract	This paper proposes a multi-agent deep reinforcement learning method to coordinate multiple microgrids owned virtual power plants (VPPs) connected in the active distribution network. A communication-based independent twin delayed deep deterministic policy gradient method is proposed to implement a partially observable Markov game for coordinating multiple VPPs (agents). The decentralized training process can protect the privacy of the agent-based entity participating in the framework, and each entity can share equal rights with simultaneous decision-making. Internal markets are proposed in our framework that the price contains the information of energy trading and network information. The price quota curve based external market is proposed for further exploiting distributed energy resource operation potential to enable VPPs with market power to be price-makers. The proposed method has been verified in a modified IEEE test system to prove its effectiveness. The results confirm that the proposed framework can simultaneously improve the operation cost and maintain network operational safety.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Smart Grid
dc.relation.isbasedon	10.1109/TSG.2022.3225814
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0915 Interdisciplinary Engineering
dc.title	Optimal Coordination for Multiple Network-Constrained VPPs via Multi-Agent Deep Reinforcement Learning
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-17T03:02:18Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

This paper proposes a multi-agent deep reinforcement learning method to coordinate multiple microgrids owned virtual power plants (VPPs) connected in the active distribution network. A communication-based independent twin delayed deep deterministic policy gradient method is proposed to implement a partially observable Markov game for coordinating multiple VPPs (agents). The decentralized training process can protect the privacy of the agent-based entity participating in the framework, and each entity can share equal rights with simultaneous decision-making. Internal markets are proposed in our framework that the price contains the information of energy trading and network information. The price quota curve based external market is proposed for further exploiting distributed energy resource operation potential to enable VPPs with market power to be price-makers. The proposed method has been verified in a modified IEEE test system to prove its effectiveness. The results confirm that the proposed framework can simultaneously improve the operation cost and maintain network operational safety.

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http://hdl.handle.net/10453/169848