Multi-agent systems for optimising smart energy clusters: A case study on cost and emission reduction in industrial seaport facilities

Amin, Amin ORCID: https://orcid.org/0000-0002-6891-5640, Ghoroghi, Ali, Petri, Ioan ORCID: https://orcid.org/0000-0002-1625-8247, Hodorog, Andrei ORCID: https://orcid.org/0000-0002-4701-5643, Genest, Thomas and Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400 2025. Multi-agent systems for optimising smart energy clusters: A case study on cost and emission reduction in industrial seaport facilities. Energy Reports 14 , pp. 5061-5071. 10.1016/j.egyr.2025.11.003

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Abstract

This study investigates the use of multi-agent systems (MAS) to optimise energy flow in industrial seaport clusters, with an emphasis on reducing operational costs, minimising carbon emissions, and increasing renewable energy utilisation. The proposed framework combines decentralised control via MAS, dynamic load scheduling, and smart grid interaction across three operational strategies: individual building-level optimisation, cluster-level coordination, and peer-to-peer energy trading. Simulation results from a UK-based pilot site indicate that cluster-level optimisation achieves an average 60% cost saving and 30% emission reduction. The peer-to-peer strategy enables up to 90% renewable self-consumption, reducing grid dependence by 30–35%. In contrast, the individual strategy remains sensitive to demand fluctuations, with grid dependency rising to 70% under high-load conditions due to limited energy sharing and battery saturation. These findings demonstrate the scalability and adaptability of MAS-based energy frameworks in industrial contexts. By critically evaluating performance under variable operational scenarios, this study offers practical insights for sustainable energy management and industrial decarbonisation pathways.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by/4.0/, Start Date: 2025-11-06
Publisher:	Elsevier
ISSN:	2352-4847
Date of First Compliant Deposit:	3 December 2025
Date of Acceptance:	5 November 2025
Last Modified:	03 Dec 2025 11:00
URI:	https://orca.cardiff.ac.uk/id/eprint/182859

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