Peer-to-Peer Energy Trading for Residential Prosumers With Photovoltaic and Battery Storage Systems

Wang, J; Zhang, J; Li, L; Lin, Y

Peer-to-Peer Energy Trading for Residential Prosumers With Photovoltaic and Battery Storage Systems

Wang, J Zhang, J Li, L

Lin, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Systems Journal, 2023, 17, (1), pp. 154-163
Issue Date:: 2023-03-01

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Field	Value	Language
dc.contributor.author	Wang, J
dc.contributor.author	Zhang, J
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.contributor.author	Lin, Y https://orcid.org/0000-0003-2394-3055
dc.date.accessioned	2023-05-08T00:43:09Z
dc.date.available	2023-05-08T00:43:09Z
dc.date.issued	2023-03-01
dc.identifier.citation	IEEE Systems Journal, 2023, 17, (1), pp. 154-163
dc.identifier.issn	1932-8184
dc.identifier.issn	1937-9234
dc.identifier.uri	http://hdl.handle.net/10453/170274
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. Facing the increasing complexity of the market structure and decision-making strategies, this article proposes a P2P energy trading model for residential households, and the objective is to help the centralized market coordinator optimize the benefit of participants under such a P2P market. To this end, a new mathematical model, including the rules for buying and selling energy, is presented. In this model, a supply function bidding mechanism is formulated to match the power supply imbalance and calculate the market-clearing price. An optimization problem is formulated to identify the optimal strategies for energy buying and selling, which consists of two parts: the first part is to maximize the social welfare; the second part is to minimize the unfair benefit distribution that participants can gain through P2P energy trading. The case study based on the real data for four different household categories has revealed that households can achieve 26.38% net cost reduction, and the proposed fair benefit distribution function also can fairly allocate the benefit by enforcing households' benefit variance indexes at a low level.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Systems Journal
dc.relation.isbasedon	10.1109/JSYST.2022.3190976
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Operations Research
dc.title	Peer-to-Peer Energy Trading for Residential Prosumers With Photovoltaic and Battery Storage Systems
dc.type	Journal Article
utslib.citation.volume	17
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	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2024-07-31T00:00:00+1000Z
dc.date.updated	2023-05-08T00:42:46Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	1

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. Facing the increasing complexity of the market structure and decision-making strategies, this article proposes a P2P energy trading model for residential households, and the objective is to help the centralized market coordinator optimize the benefit of participants under such a P2P market. To this end, a new mathematical model, including the rules for buying and selling energy, is presented. In this model, a supply function bidding mechanism is formulated to match the power supply imbalance and calculate the market-clearing price. An optimization problem is formulated to identify the optimal strategies for energy buying and selling, which consists of two parts: the first part is to maximize the social welfare; the second part is to minimize the unfair benefit distribution that participants can gain through P2P energy trading. The case study based on the real data for four different household categories has revealed that households can achieve 26.38% net cost reduction, and the proposed fair benefit distribution function also can fairly allocate the benefit by enforcing households' benefit variance indexes at a low level.

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http://hdl.handle.net/10453/170274