Power flow traceable P2P electricity market segmentation and cost allocation

Lou, Chengwei, Yang, Jin, Vega Fuentes, Eduardo, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Min, Liang, Yu, James and Meena, Nand Kishor 2024. Power flow traceable P2P electricity market segmentation and cost allocation. Energy 290 , 130120. 10.1016/j.energy.2023.130120

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Abstract

This study explores peer-to-peer (P2P) electricity trading, emphasizing not just the export and consumption, but also the feasible physical supply of electricity and the use of distribution network assets. Building on a transaction-oriented dynamic power flow tracing model, a novel P2P market architecture is proposed. This architecture integrates the electricity market with the power network, considering technical constraints, network losses, and asset usage. The network is segmented into potential markets using second-order cone programming (SOCP), with an optimization problem introduced for loss-allocation. This problem merges network physical analysis and variable outputs from distributed energy resources (DERs). A graph-based P2P electricity trading model is designed to determine optimal transaction cost allocation and maximize benefits for both DERs and consumers. A case study on a modified IEEE 33-node test feeder substantiates the benefits of this market structure, demonstrating increased revenues for DERs and reduced bills for consumers compared to traditional feed-in-tariffs.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Start Date: 2023-12-26
Publisher:	Elsevier
ISSN:	0360-5442
Date of First Compliant Deposit:	2 January 2024
Date of Acceptance:	22 December 2023
Last Modified:	06 Feb 2024 16:21
URI:	https://orca.cardiff.ac.uk/id/eprint/165157

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