Abstract

For a long period, Namibia has been facing challenges of providing electricity for its rural households despite the country's abundant renewable energy resources. The primary barrier to electrification is the remote geographical location of many rural communities. To address this issue, the study proposes an optimization model for renewable energy systems to ensure cost-effective and reliable electrification of rural areas. Non-dominated Sorting Genetic Algorithm II, a multi-objective evolutionary algorithm, is employed to determine optimal microgrid configurations that simultaneously minimize five conflicting objectives (i.e., total life cycle cost, levelized cost of electricity, loss of power supply probability, total wasted renewable energy, and carbon dioxide emissions), representing economic, technical, and environmental goals. The Hybrid Renewable Energy System considered comprises of solar, wind, biomass, and fuel cell sources, supported by battery and supercapacitor storage. The model generates a Pareto front of non-dominated solutions, illustrating trade-offs between cost, reliability, and environmental impact. Optimal solutions are then selected using a weighted sum method by applying weights of 0.35 to both the normalized levelized cost of electricity and the loss of power supply probability, 0.2 to carbon dioxide emissions, and 0.1 to total wasted power. The results are validated by case studies of 3 villages: Oluundje, Ombudiya, and Onguati, yielding a levelized cost of electricity of 0.0042 USD/kWh, 0.0023 USD/kWh, and 0.0811 USD/kWh, and reliability levels of 99.28%. 79.82% and 94.20% respectively. These results demonstrate the robustness and adaptability of the proposed optimization model and serve as valuable guidance to policymakers, investors, and international partners in enhancing rural electrification in Namibia and other regions.