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Existing microgrid modeling often ignores the electrical mismatch in the modules and obstructions in the vicinity, creating unrealistic estimates of energy production. To fill this gap, this study combines weather data from Meteonorm 8.1 with PVsyst for accurate 3D spatial shading analysis and Hybrid Optimization Model for Electric Renewables Pro for life-cycle cost optimization of an off-grid solar-wind-battery system in Dibo, Ethiopia. The system was designed to supply a primary electrical load of 2714.32 kWh/day together with a deferrable load of 38.8 kWh/day. The annual solar yield penalty and total system loss for explicit 3D shading constraints are 3.63% and 15.63%, respectively. With baseline unshaded conditions, the system’s Net Present Cost (NPC) is $740 112, its Levelized Cost of Energy (LCOE) is $0.0332/kWh, and the mix of renewables provided by the system is 67.9% solar PV and 32.1% wind energy. With a realistic 3D spatial layout and near-shading losses, the system dynamically scales up its component size to reliably deliver the community demand of 1 004 133 kWh/yr. As a result, the overall generation mix is slightly modified to 65.4% solar PV and 34.6% wind, and the overall NPC of the system rises to $747 512, with the LCOE at $0.0335/kWh, while the unmet load is very small at 0.0734%. The structural economic robustness is verified by sensitivity analyses in the face of market changes. This mechanism is foundational in the pipeline of renewable energy projects in sub-Saharan Africa, where explicit terrain and module designs ensure component sizing, ensuring a solid route to rural electrification.
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Geremew et al. (2026) studied this question.
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