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.
