Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/128246
Título: | Power flow traceable P2P electricity market segmentation and cost allocation | Autores/as: | Lou, Chengwei Yang, Jin Vega Fuentes, Eduardo Zhou, Yue Min, Liang Yu, James Meena, Nand Kishor |
Clasificación UNESCO: | 3306 Ingeniería y tecnología eléctricas 332201 Distribución de la energía |
Palabras clave: | Distributed energy resources Dynamic power flow tracing Loss allocation P2P electricity market Feed-In Tariff, et al. |
Fecha de publicación: | 2024 | Publicación seriada: | Energy | Resumen: | 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 | Fuente: | Energy [ISSN 0360-5442], v. 290 (Marzo 2024) |
Colección: | Artículos |
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