Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/128246
Title: Power flow traceable P2P electricity market segmentation and cost allocation
Authors: Lou, Chengwei
Yang, Jin
Vega Fuentes, Eduardo 
Zhou, Yue
Min, Liang
Yu, James
Meena, Nand Kishor
UNESCO Clasification: 3306 Ingeniería y tecnología eléctricas
332201 Distribución de la energía
Keywords: Distributed energy resources
Dynamic power flow tracing
Loss allocation
P2P electricity market
Feed-In Tariff, et al
Issue Date: 2024
Journal: Energy 
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.
URI: http://hdl.handle.net/10553/128246
ISSN: 0360-5442
DOI: 10.1016/j.energy.2023.130120
Source: Energy [ISSN 0360-5442], v. 290 (Marzo 2024)
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