Enhanced PSO-based optimisation with probabilistic analysis for standalone DC microgrid design

Jayasinghe, H; Gunawardane, K; Hossain, MA; Zamora, R; Preece, MA

Enhanced PSO-based optimisation with probabilistic analysis for standalone DC microgrid design

Jayasinghe, H

Gunawardane, K

Hossain, MA Zamora, R Preece, MA

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Journal of Energy Storage, 2025, 140
Issue Date:: 2025-12-30

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Field	Value	Language
dc.contributor.author	Jayasinghe, H https://orcid.org/0009-0002-1638-7433
dc.contributor.author	Gunawardane, K https://orcid.org/0000-0001-5254-0162
dc.contributor.author	Hossain, MA
dc.contributor.author	Zamora, R
dc.contributor.author	Preece, MA
dc.date.accessioned	2026-02-10T03:37:19Z
dc.date.available	2026-02-10T03:37:19Z
dc.date.issued	2025-12-30
dc.identifier.citation	Journal of Energy Storage, 2025, 140
dc.identifier.issn	2352-152X
dc.identifier.issn	2352-152X
dc.identifier.uri	http://hdl.handle.net/10453/193138
dc.description.abstract	Offshore 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.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Journal of Energy Storage
dc.relation.isbasedon	10.1016/j.est.2025.118847
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	40 Engineering
dc.title	Enhanced PSO-based optimisation with probabilistic analysis for standalone DC microgrid design
dc.type	Journal Article
utslib.citation.volume	140
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/Faculty of Science/Science Related HDR Students
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-02-10T03:37:17Z
pubs.publication-status	Published
pubs.volume	140

Abstract:

Offshore 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.

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http://hdl.handle.net/10453/193138