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Resource-driven Design and Optimization of Hybrid Renewable Energy Systems for Namibia’s off- Grid Communities

Submitted:

12 March 2026

Posted:

13 March 2026

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Abstract
Namibia’s rural communities continue to experience limited and unreliable electricity access despite the country’s exceptional solar, wind, and biomass renewable energy re-sources potential. Conventional grid extension remains financially and technically impractical for dispersed off-grid settlements, underscoring the need for cost-effective, re-renewable based alternatives. This paper presents a resource-driven design and multi objective optimization framework for Hybrid Renewable Energy Systems (HRESs) tailored to Namibia’s off-grid communities. The proposed model integrates solar PV, wind turbines, biomass generators, and hydrogen-based fuel cells with hybridized energy storage consisting of batteries, supercapacitors, and hydrogen tanks. Using the Non-dominated sorting Genetic Algorithm-II (NSGA-II), the system simultaneously minimizes Total Life Cycle Cost (TLCC), Levelized Cost of Electricity (LCOE), Loss of Power Supply Probability (LPSP), Carbon dioxide (CO₂) emissions, and Wasted Renewable Energy (WRE). The framework is applied to three rural villages, Oluundje, Ombudiya, and Onguati using high-resolution, site-specific renewable resource datasets and community-level load forecasts. Results demonstrate that resource-aligned configurations substantially improve system reliability (up to 99.28%), reduce LCOE (0.0023–0.0811 USD/kWh), and optimize dispatch behavior across seasonal variations. Storage hybridization further enhances stability by balancing transient and long-duration deficits. Com-pared to existing diesel mini-grids, the optimized HRESs achieve markedly superior techno-economic and environmental performance. The proposed framework offers a scalable, adaptable, and policy-ready tool for accelerating sustainable rural electrification in Namibia.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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