A decentralized optimization framework for peer-to-peer trading in multivector energy systems
The transition towards sustainable and decentralized energy systems is driving the need for advanced market mechanisms capable of coordinating multiple energy vectors at community scale. This paper presents a novel multivectorial decentralized peer-to-peer (P2P) energy trading framework capable of j...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/450325 |
| Acceso en línea: | https://hdl.handle.net/2117/450325 https://dx.doi.org/10.1016/j.seta.2025.104784 |
| Access Level: | acceso abierto |
| Palabra clave: | Multivectorial energy systems Peer-to-peer energy trading Decentralized optimization Distributed energy resources (DER) Àrees temàtiques de la UPC::Energies::Gestió de l'energia Àrees temàtiques de la UPC::Energies::Tecnologia energètica::Emmagatzematge i transport de l'energia Àrees temàtiques de la UPC::Energies::Eficiència energètica |
| Sumario: | The transition towards sustainable and decentralized energy systems is driving the need for advanced market mechanisms capable of coordinating multiple energy vectors at community scale. This paper presents a novel multivectorial decentralized peer-to-peer (P2P) energy trading framework capable of jointly managing consumption, generation, storage, and cross-vector transformations. A fully decentralized optimization model is formulated in which each peer autonomously optimizes its local multi-energy system and participates in an asynchronous negotiation protocol without any central coordinator. Three decentralized trading strategies are implemented, prioritizing distance, price, or a combination of both when selecting trading partners. For benchmarking performance purpose, a centralized global optimization model is developed with two objective functions: minimization of total system cost and minimization of total exchanged energy. Both centralized and decentralized models share the same mathematical constraints, and are driven by 24-hour forecasts of demand, generation, and P2P prices obtained from supervised LSTM-based time series models. Uncertainty in PV generation and demand forecast is quantified through Monte Carlo simulations. The framework is applied to a real-world case study involving 12 interconnected buildings, exchanging five energy vectors: electricity, heat, cold, gas, and biomass. Results show that the decentralized optimization matches the centralized benchmark in terms of energy efficiency and external economic dependence within 0.7%–7%, while exhibiting superior scalability as the number of peers increases. The findings validate decentralized multivectorial P2P energy systems as a viable and scalable alternative for future community-scale energy markets. |
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