eng Science and Education Publishing American Journal of Energy Research 2328-7330 2025-07-28 13 2 72 79 10.12691/ajer-13-2-3 AJER20251323 article Omboto J.K ombotojane04@gmail.com 1 Kamau J.N 1 Saoke C.O 1 Wekesa D.W 2 Jomo Kenyatta University of Agriculture and Technology MultiMedia University of Kenya Wind farm design increasingly demands careful balancing of energy yield, wake effects, and economic costs to meet renewable energy targets affordably. This study develops a simulation-based optimization framework tailored to the Ngong Hills wind farm in Kenya. Using a Python-based genetic algorithm paired with the Jensen wake model, we evaluate four layout scenarios: homogeneous layouts of Vestas V52 (850?kW) and NREL 5?MW turbines, as well as mixed configurations combining these or mid-sized Vestas V66 (1.75?MW) and V90 (3.0?MW) turbines, across 800,000?m² site with realistic wind speed and direction distributions. The genetic algorithm optimized turbine placements while enforcing minimum rotor-diameter-based spacing and varying hub heights in the same wind farm to mitigate wake interactions. Results showed that homogeneous V52 and NREL layouts achieved comparable LCOE of $52/MWh, but required trade-offs between the number of turbines and spacing. A hybrid layout mixing V52 and NREL turbines achieved the lowest LCOE of $37/MWh and reduced wake losses by 18.2%, highlighting the value of integrating heterogeneous turbines with staggered hub heights. In contrast, the V66+V90 layout, despite its higher AEP potential, suffered from increased wake interactions and capital costs, leading to an LCOE of $58/MWh. These findings show that a comprehensive economic evaluation, beyond assessing power output alone, proved essential to identifying truly optimal layouts. The mixed V52?+?NREL configuration exemplifies how combining high-capacity turbines with densely packed smaller units can mitigate wake effects and minimize LCOE, outperforming homogeneous arrangements. These insights support a strategic layout and turbine design philosophy that considers site-specific wind characteristics, varied turbine types, and cost metrics to create more cost-effective and sustainable wind farms. https://pubs.sciepub.com/ajer/13/2/3/ajer-13-2-3.pdf Wind farm layout optimization Genetic algorithm Jensen wake model Heterogeneous turbine configurations Annual Energy Production Levelized Cost of Energy