Technical assessment of large-scale integration of Solar electrification in energy systems in kenya

Abstract

Kenya has witnessed a significant increase in electricity demand, reaching 1.5 GW in 2022 compared to a production of 12.65 TWh. This growth is primarily driven by population expansion and industrialization. However, continued reliance on fossil fuels remains environmentally unsustainable. To address this, the Kenyan government has set a target of achieving 100% renewable energy integration by 2030, with a strong emphasis on solar and wind energy. With its abundant solar resources, Kenya has the potential to generate more solar power than its total electricity demand. This thesis investigates the feasibility and impact of large-scale integration of solar power systems into Kenya’s energy mix. EnergyPLAN tool was employed to simulate hourly energy production and demand, enabling a comprehensive assessment of the technical, economic, and environmental implications. Cross-sectoral analysis was conducted to evaluate interdependencies and sectoral dynamics. A novel Whale Optimization Algorithm (WOA) based Maximum Power Point Tracking (MPPT) algorithm was developed in MATLAB and benchmarked against conventional methods, including Incremental Conductance, Fuzzy Logic, and Particle Swarm Optimization (PSO). Simulation results showed a 32% increase in solar power capacity—from 212.5 MW (6.8% of total generation) to 4,601 MW—at an annual cost of KSh 145.5 billion, compared to KSh 186.9 billion under the baseline scenario. With further solar power integration, optimal generation reached 10.01 TWh (39.56% of total), while renewable electricity output increased from 11.90 TWh to 19.76 TWh. CO₂ emissions dropped significantly from 0.134 Mt to 0.021 Mt, and total annual production costs decreased to KSh 134.3 billion. These findings demonstrate that optimized solar power integration offers substantial benefits in cost savings, emissions reduction, energy security, and system reliability. Sectoral Innovation System (SIS) analysis revealed that global cost declines primarily drive solar power adoption, with minimal local adaptation needed. The proposed WOA-based MPPT algorithm achieved a tracking efficiency of 99.95% with a steady-state error of 0.04%, outperforming PSO (99.7% efficiency, 0.2% error). Although PSO successfully tracked the global maximum power point, its dynamic response was inferior to that of the developed WOA-based MPPT system.