Wind farm layout optimization for maximizing output power by varying turbine hub height using genetic algorithm
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Date
2023
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Publisher
UMT Lahore
Abstract
The current focus in the energy community is optimizing wind farm designs, with ongoing research enhancing power generation and minimizing wake impact. Factors like base location, rotor diameter and maintaining a consistent total number of wind turbines remain constant. Using genuine wind speed data from the Jhampir region in Pakistan's Sindh province (considering Hydro Power China power plant as a case study), this study applies the Jensen wake model to compute wakes of downwind turbines. It introduces a novel approach, employing a genetic algorithm for wind farm design optimization, considering ideal hub parameters and a simulation model for the wake effect. Simulating the wake impact using the Jensen technique and local wind and geographical data, the research reveals that, despite maintaining the same number of wind turbines, a wind farm's power production increases considerably with varying wind turbine hub heights. Various cost models are considered, indicating that wind turbines with diverse hub heights can lower the electricity cost per unit in a wind farm. The study concludes by exploring wind farm located in Jhampir region (Hydro Power China), highlighting the benefits of operating turbines with varying hub heights in realistic scenarios for maximizing energy yield.