Unit commitment in wind farms based on a glowworm metaphor algorithm

Abstract Mechanical health and operational efficiency of a wind turbine (WT) are important to the overall cost effectiveness in a wind farm. This paper presents a unit commitment (UC) model based on fatigue damage modeling of blades and uncertainty estimation of wind power forecasting (WPF). A novel glowworm metaphor algorithm (GMA) is developed to solve the proposed UC problem. During the pheromone updating of GMA, the luminescence carrying by glowworm reflects the net improvement by agent moving. This characteristic supports GMA to find the global optima for optimization of UC problem. The proposed UC objective is minimizing the mechanical damages of WTs in the whole wind farm. Uncertain interval of wind power generation is obtained as constraint function based on relevance vector machine (RVM). Data from a wind farm in China are used to validate the feasibility and effectiveness of the proposed method. Simulation results reveal the capabilities of GMA to efficiently get the better performance than benchmark methods, in terms of minimum mechanical damage, reliability and running efficiency. The benchmark methods are particle swarm optimization (PSO) and genetic algorithm (GA). The comparison between UC with and without consideration of WPF uncertainty exhibits the superiority of the incorporation of WPF uncertainty modeling.