Multi‐parameter optimization of performance and economic viability of Ferris wheel wind turbine for low wind speed regions in Africa

Previous studies on wind turbine and wind farm optimization for Levellized cost of energy (LCOE) and annual energy production (AEP) have focused on horizontal axis wind turbines (HAWT) and vertical axis wind turbines (VAWT). Regions with lower wind speed resources tend to have a higher levellized cost of energy and lower annual energy production. In this paper, the authors investigate the optimization of a novel, Ferris wheel wind turbine (FWT) for low wind speed regions of Africa. The research used an Excel‐based Multi‐Objective Optimization (EMOO) model. The EMOO program has both binary‐coded and real‐coded Elitist Non‐Dominated Sorting Genetic Algorithm (NSGA‐II). The optimization is conducted by studying the effect of varying the rim diameter, number of blades, and the rated wind speeds for an 800‐kW generator on the performance and economics in 21 African cities. The results show that, on average, the return‐on‐investment increases over the base design by up to 182%, and both the simple payback period (SPP) and the levellized cost of electricity decreased by 39% as the rim diameter increases combined with a 50% reduction in blade numbers. In addition, a 75% reduction in blade numbers caused a further 32% decrease on average for both the simple payback period and the levellized cost of electricity. In this paper, the authors investigate the optimization of a novel, Ferris wheel wind turbine (FWT) for low wind speed regions of Africa. The optimization is conducted by varying the rim diameter, the number of blades, and the rated wind speeds for this 800‐kW generator and studying the effects of these parameters on the performance and economics in 21 African cities. The results show that the return on investment (ROI) increases over the base design by up to 182% as the rim diameter increases, combined with a 50% reduction in blade numbers. While on average, both the simple payback period (SPP) and the levellized cost of electricity decreased by 39% for a 50% reduction in blade numbers.