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

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 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.