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Enhanced PSO-Based Optimisation With Probabilistic Analysis for Standalone DC Microgrid Design

aut.relation.articlenumber118847
aut.relation.endpage118847
aut.relation.journalJournal of Energy Storage
aut.relation.startpage118847
aut.relation.volume140
dc.contributor.authorJayasinghe, H
dc.contributor.authorGunawardane, K
dc.contributor.authorHossain, MA
dc.contributor.authorZamora, R
dc.contributor.authorPreece, MA
dc.date.accessioned2025-11-17T19:19:32Z
dc.date.available2025-11-17T19:19:32Z
dc.date.issued2025-10-27
dc.description.abstractOffshore industries face significant challenges in integrating renewable energy sources (RES) to achieve a sustainable and reliable energy supply, due to the intermittency and unpredictable offshore weather conditions, which hinder the reliability of standalone microgrids. To address this issue, this study explores the integration of a hydrogen gas energy storage station within a standalone DC microgrid, evaluating its potential to enhance stability and reduce emissions in offshore maritime operations. The research investigates the effectiveness of hybrid energy storage systems (HESS) in mitigating RES intermittency, incorporating solar PV, wind, and wave energy as primary generation sources. Using an enhanced particle swarm optimisation (PSO) method, the study compares various energy storage configurations, with results indicating that a battery-supercapacitor HESS achieves the lowest levelised cost of electricity (LCOE), which is 19.63 US Cents /kWh, making it the most cost-effective solution. A probabilistic model is further developed to validate the microgrid's resilience under real-world conditions, bridging the gap between theoretical design and practical implementation. Additionally, the study assesses the feasibility of integrating wave energy, concluding that current market dynamics render it financially unviable for offshore microgrid applications. The proposed enhanced PSO algorithm demonstrates superior performance compared to commonly used heuristic optimisation methods such as Genetic Algorithm (GA), standard PSO, and Ant Colony Optimisation (ACO). This improvement is attributed to the integration of quadratic interpolation and extended local search mechanisms. Additionally, the study introduces an energy storage system (ESS) degradation algorithm that outperforms the traditional Rainflow counting method in both accuracy and computational efficiency, particularly in modelling partial charge–discharge cycles. Overall, this work provides critical insights into optimising standalone microgrids for offshore industries, alongside technical performance and economic viability.
dc.identifier.citationJournal of Energy Storage, ISSN: 2352-152X (Print); 2352-152X (Online), Elsevier BV, 140, 118847-118847. doi: 10.1016/j.est.2025.118847
dc.identifier.doi10.1016/j.est.2025.118847
dc.identifier.issn2352-152X
dc.identifier.issn2352-152X
dc.identifier.urihttp://hdl.handle.net/10292/20130
dc.languageen
dc.publisherElsevier BV
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S2352152X25035601?via%3Dihub
dc.rights© 2025 The Authors. Published by Elsevier Ltd. Creative Commons. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.
dc.rights.accessrightsOpenAccess
dc.subject40 Engineering
dc.subject4015 Maritime Engineering
dc.subject4008 Electrical Engineering
dc.subject4009 Electronics, Sensors and Digital Hardware
dc.subject7 Affordable and Clean Energy
dc.subject13 Climate Action
dc.subjectStandalone microgrids
dc.subjectRenewable energy sources
dc.subjectHybrid energy storage systems
dc.subjectProbabilistic study
dc.titleEnhanced PSO-Based Optimisation With Probabilistic Analysis for Standalone DC Microgrid Design
dc.typeJournal Article
pubs.elements-id746180

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