Wind energy is paramount to the European Union’s decarbonization and electrification goals. As wind farms expand with larger turbines and more powerful generators, conventional ‘greedy’ control strategies become insufficient. Coordinated control approaches are increasingly needed to optimize not only power output but also structural loads, supporting longer asset lifetimes and enhanced profitability. Despite recent progress, the effective implementation of multi-objective wind farm control strategies—especially those involving yaw-based wake steering—remains limited and fragmented. This study addresses this gap through a structured review of recent developments that consider both power maximization and fatigue load mitigation. Key concepts are introduced to support interdisciplinary understanding. A comparative analysis of recent studies is conducted, highlighting optimization strategies, modelling approaches, and fidelity levels. The review identifies a shift towards surrogate-based optimization frameworks that balance computational cost and physical realism. The reported benefits include power gains of up to 12.5% and blade root fatigue load reductions exceeding 30% under specific scenarios. However, challenges in model validation, generalizability, and real-world deployment remain. AI emerges as a key enabler in strategy optimization and fatigue damage prediction. The findings underscore the need for integrated approaches that combine physics-based models, AI techniques, and instrumentation to fully leverage the potential of wind farm control.

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.

As wind farms mature, operational strategies that jointly consider energy production, structural fatigue, and market conditions become increasingly relevant for lifetime extension. This paper presents a cost-function framework that integrates power production, fatigue damage, and electricity price to support market-aware wind turbine operation. The methodology follows a two-level structure. Stationary Pareto fronts are first constructed offline using surrogate models trained on aeroelastic simulations of a two-turbine wind farm configuration, capturing the fundamental power-fatigue trade-offs under fixed inflow conditions. Electricity price is then introduced at the decision layer to select operating points on these Pareto fronts without altering the underlying physical optimization problem. Results show that yaw-based Pareto fronts define a continuous set of physically efficient operating strategies, while market-aware selection enables navigation along these fronts as a function of electricity price. An illustrative one-day example demonstrates how the framework can be applied sequentially under time-varying inflow and price signals. The proposed approach provides a methodological pathway toward future quantitative assessment of lifetime extension strategies, including cumulative fatigue accumulation and economic performance metrics such as Levelized Cost of Energy.