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Abstract Wind energy plays a vital role in the European Union’s decarbonization and electrification efforts. The present study addresses wind farm control strategies exploring the design space of optimal solutions and understanding how these solutions vary when different objectives and meteorological scenarios are considered. The goal is to bridge the gap in existing literature by avoiding the simplification of the multi-objective optimization problem into single-objective using the weights method. This method was not chosen due to its various limitations, particularly its inability to deal with non-convex Pareto fronts. Instead, in this work, a multiobjective optimization method is adopted that overcomes these constraints and offers a set of solutions (the Pareto front) where the decision maker selects the most suitable solution. The methodology involves four main steps: flow calculation using FLORIS, aerodynamic load calculation with CCBlade, conversion of loads into damage equivalent moments (DEM), and multi-objective optimization using a direct multi-search (DMS) algorithm. By exploring various objectives, such as power and fatigue loads at different components, and different wind turbines, this study aims to explore the design space and quantify the potential improvement in terms of the defined cost-functions. The study utilizes data from Tocha Wind Farm in Portugal, that comprises five VESTAS wind turbines. Strain gauges, accelerometers, and meteorological measurements are used for data collection. Simulations under ‘greedy control’, where a turbine is affected by a wake, are compared with campaign data, considering a fixed wind speed and varying turbulence intensity. The optimization reveals Pareto-optimal strategies and the respective optimal yaw angles. Considering a wind velocity of 8 m.s −1 and a wind direction of 308°, power may improve up to 4.6%, and the blade root DEM and tower base DEM up to 5.7% and 84%, respectively. The study highlights the impact of considering multiple objectives and supports the selection of wind farm control strategies to effectively manage critical structural components, thereby extending the lifespan of the wind farm.
Frutuoso et al. (Sat,) studied this question.