Reliability-conscious power flow optimization in hybrid renewable microgrids: a case study in Sub-Saharan Africa using Gauss-Seidel and metaheuristic techniques

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dc.authorid0000-0002-4049-0716
dc.authorid0000-0001-9444-753X
dc.authorid0000-0002-4353-1261
dc.authorid0000-0001-6944-4775
dc.contributor.authorMbasso, Wulfran Fendzi
dc.contributor.authorHarrison, Ambe
dc.contributor.authorJangir, Pradeep
dc.contributor.authorDagal, Idriss
dc.contributor.authorKhishe, Mohammad
dc.contributor.authorSmerat, Aseel
dc.contributor.authorChebaane, Saleh
dc.date.accessioned2026-01-31T15:08:18Z
dc.date.available2026-01-31T15:08:18Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractThe operation of hybrid renewable microgrids is vital for alleviating energy poverty in Sub-Saharan Africa, particularly in areas with weak grid integration and intermittent renewable resources. This study proposes a reliability-conscious power-flow and dispatch framework-Gauss-Seidel-Particle Swarm Optimization (GS-PSO)-that jointly optimizes dependability, cost, and environmental impact. Unlike conventional Monte Carlo-Newton-Raphson approaches, the method couples a fast-converging Gauss-Seidel solver with the global search capability of PSO to reduce Expected Energy Not Supplied, Loss Of Load Expectation, and operating costs while improving supply continuity. A realistic case study in Northern Cameroon integrating solar, wind, diesel, and battery systems demonstrates a 32.4 % reduction in Loss Of Load Expectation and 41.6 % reduction in Expected Energy Not Supplied, with availability increasing by 21.7 %. Total operational expenses decline by 18.5 %, and CO2 emissions drop by 26.2 kg relative to the Monte Carlo-Newton Raphson baseline. The GS-PSO framework also shortens computational time by 43 %, supporting edge or real-time energy management in resource-constrained settings. These results confirm the framework's effectiveness for sustainability and resilience in line with United Nations Sustainable Development Goal 7.
dc.description.sponsorshipNorthern Border University, Saudi Arabia [NBU-CRP-2025-2225]
dc.description.sponsorshipThe authors extend their appreciation to Northern Border University, Saudi Arabia, for supporting this work through project number (NBU-CRP-2025-2225) .
dc.identifier.doi10.1016/j.ijepes.2025.111350
dc.identifier.issn0142-0615
dc.identifier.issn1879-3517
dc.identifier.scopus2-s2.0-105023294976
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org./10.1016/j.ijepes.2025.111350
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10651
dc.identifier.volume173
dc.identifier.wosWOS:001632726900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofInternational Journal of Electrical Power & Energy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260128
dc.subjectHybrid Renewable Microgrids
dc.subjectGauss-Seidel Power Flow
dc.subjectMetaheuristic Optimization
dc.subjectReliability Assessment
dc.subjectSub-Saharan Africa
dc.subjectEnergy Access and Resilience
dc.titleReliability-conscious power flow optimization in hybrid renewable microgrids: a case study in Sub-Saharan Africa using Gauss-Seidel and metaheuristic techniques
dc.typeArticle

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