BMOTSM: design of a hybrid bioinspired model to determine optimal turbine sizing for capacity maximisation in environment-and-economy aware deployments

Abstract

Multiparametric optimisation determines optimal turbine size for eco-friendly wind farm repowering. This involves identifying turbine ratings, repowering locations, and wind zone analysis for maximum economic efficiency and low environmental impacts. Existing models that perform these tasks are either highly complex or cannot be scaled due to deployment-specific characteristics. Most of these models do not consider economic or environmental impacts when repowering wind farms. This text discusses the design of a novel hybrid bioinspired model to determine optimal turbine sizing in environment- and economy-aware deployments. The model combines GWO, PSO, and GA to optimise turbine ratings, economic impacts, and environmental impacts during the repowering process. GA model optimises new turbine locations, while PSO maximises turbine efficiency. Both these models are internally optimised via GWO due to economic and environmental effects. The GWO model continuously tunes GA and PSO to find the best multiobjective repowering solution. The integrated model was validated on real-time wind farms to evaluate power conversion efficiency, deployment cost, soil fragmentation percentage, and cost-to-power ratios. The proposed BMOTSM model achieved 6.5% higher conversion efficiency, 8.5% lower deployment cost, 15.4% lower soil fragmentation, and 3.5% lower cost-to-power ratio than state-of-the-art models, making it useful for a variety of real-time wind farm repowering scenarios.

Keywords

• ga
• pso
• gwo
• fragmentation
• power
• repowering