Advancements in Wind Farm Control: Modelling and Multi-Objective Optimization Through Yaw-Based Wake Steering

Tiago R. Lucas Frutuoso (), Rui Castro, Ricardo B. Santos Pereira and Alexandra Moutinho ()
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Tiago R. Lucas Frutuoso: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1000-029 Lisbon, Portugal
Rui Castro: INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Rua Alves Redol, 9, 1000-029 Lisbon, Portugal
Ricardo B. Santos Pereira: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1000-029 Lisbon, Portugal
Alexandra Moutinho: IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1, 1000-029 Lisbon, Portugal

Energies, 2025, vol. 18, issue 9, 1-29

Abstract: 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.

Keywords: wind energy; wind farm control; wake steering; multi-objective; optimization; closed-loop control; artificial intelligence (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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