Deep reinforcement learning for energy trading and load scheduling in residential peer-to-peer energy trading market

Wang, J; Li, L; Zhang, J

Deep reinforcement learning for energy trading and load scheduling in residential peer-to-peer energy trading market

Wang, J Li, L

Zhang, J

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: International Journal of Electrical Power and Energy Systems, 2023, 147
Issue Date:: 2023-05-01

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Field	Value	Language
dc.contributor.author	Wang, J
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.contributor.author	Zhang, J
dc.date.accessioned	2024-02-01T05:09:35Z
dc.date.available	2024-02-01T05:09:35Z
dc.date.issued	2023-05-01
dc.identifier.citation	International Journal of Electrical Power and Energy Systems, 2023, 147
dc.identifier.issn	0142-0615
dc.identifier.issn	1879-3517
dc.identifier.uri	http://hdl.handle.net/10453/175230
dc.description.abstract	The popularization of solar generation enables residential households to supply their loads and trade the surplus energy through residential peer-to-peer (P2P) energy trading market. In a residential community with multi-households, households are able to determine demand-side management (DSM) and energy trading strategies to reduce electricity cost. However, residential users often decide market participation biddings according to their experience and guesses on other households’ biddings, and it is difficult to know how far these biddings are from the best possible market biddings achievable by the P2P market trading mechanism. Targeting the maximum cost savings achievable from this P2P market, this paper focuses on how to obtain such a maximum saving under optimal energy trading and DSM strategies using a multi-agent deep reinforcement learning approach to obtain the optimal solution. In this regard, the energy trading and DSM problem for residential households is formulated as a partially observable Markov decision process (POMDP). Then, a reinforcement learning algorithm with decentralized training and execution approach is adopted in the proposed POMDP problem to fit the policy function and value function, which are trained against each other to learn the optimal strategies by trial-and-error. The simulation result validates that by using the proposed multi-agent reinforcement learning method to optimize the P2P energy trading and DSM strategies, the average reward for households can be significantly improved by 8.52% and 14.03%, respectively, compared with other two state-of-the-art deep reinforcement learning methods.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	International Journal of Electrical Power and Energy Systems
dc.relation.isbasedon	10.1016/j.ijepes.2022.108885
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4601 Applied computing
dc.title	Deep reinforcement learning for energy trading and load scheduling in residential peer-to-peer energy trading market
dc.type	Journal Article
utslib.citation.volume	147
utslib.for	0906 Electrical and Electronic 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	embargoed	*
utslib.copyright.embargo	2025-05-01T00:00:00+1000Z
dc.date.updated	2024-02-01T05:09:34Z
pubs.publication-status	Published
pubs.volume	147

Abstract:

The popularization of solar generation enables residential households to supply their loads and trade the surplus energy through residential peer-to-peer (P2P) energy trading market. In a residential community with multi-households, households are able to determine demand-side management (DSM) and energy trading strategies to reduce electricity cost. However, residential users often decide market participation biddings according to their experience and guesses on other households’ biddings, and it is difficult to know how far these biddings are from the best possible market biddings achievable by the P2P market trading mechanism. Targeting the maximum cost savings achievable from this P2P market, this paper focuses on how to obtain such a maximum saving under optimal energy trading and DSM strategies using a multi-agent deep reinforcement learning approach to obtain the optimal solution. In this regard, the energy trading and DSM problem for residential households is formulated as a partially observable Markov decision process (POMDP). Then, a reinforcement learning algorithm with decentralized training and execution approach is adopted in the proposed POMDP problem to fit the policy function and value function, which are trained against each other to learn the optimal strategies by trial-and-error. The simulation result validates that by using the proposed multi-agent reinforcement learning method to optimize the P2P energy trading and DSM strategies, the average reward for households can be significantly improved by 8.52% and 14.03%, respectively, compared with other two state-of-the-art deep reinforcement learning methods.

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http://hdl.handle.net/10453/175230