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

• Reliability-conscious GS–PSO dispatch co-optimizes cost, emissions and adequacy in hybrid microgrids. • Embedded Gauss–Seidel load flow inside PSO enforces nodal feasibility and accelerates convergence. • PV–wind–DG–BESS microgrid in Northern Cameroon cuts LOLE by 32.4% and EENS by 41.6% versus baseline. • Proposed framework lowers weekly operating cost by 18.5% while reducing CO₂ emissions by 26.2 kg. • Monte Carlo analysis quantifies reliability and cost sensitivity to MTTF, fuel price and PV efficiency. The 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 CO 2 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 .