Large-scale wind farms operate under highly unsteady atmospheric inflows, where transient turbulence, dynamic wake interactions, and inflow-wake coupling reduce energy production and exacerbate turbine loads. Over the past five years, advances in high-fidelity computational fluid dynamics (CFDs), large eddy simulation (LES), machine learning (ML)-based wake modeling, and multi-objective optimization have reshaped wind farm layout optimization under dynamic inflow conditions. This review synthesizes recent progress in five key areas: dynamic inflow and high-fidelity wake modeling (including LES-driven transient wake evolution and turbulence-resolved inflow generation), data-driven wake prediction, multi-objective layout optimization (considering the annual energy production (AEP), fatigue load constraints, and the levelized cost of energy (LCOE)), blockage modeling for complex terrain and yaw misalignment, and real-time optimization addressing inflow, turbine performance, and modeling uncertainties. Coupling transient wake models with surrogate-assisted multi-objective optimization enables a computationally efficient and physically consistent layout design. Key open challenges (dynamic wake controllability, real-time optimization under uncertainty, and integration with next-generation farm-level control systems) and future directions for enhancing large-scale wind farm resilience and cost-competitiveness are also identified. However, despite significant progress, existing models still face fundamental limitations, such as oversimplified treatment of complex turbulence structures, poor generalization under extreme or atypical conditions, and inadequate capture of long-timescale dynamic responses, which constrain their reliability in practical optimization settings.

A common retrofitting method for wind farms is the replacement of low-power turbines with high-power ones. The determination of the optimal replacement sequence for the purpose of maximizing revenue is a significant challenge. This paper employs a combination of FLORIS and a sequencing algorithm to simulate the power output resulting from the replacement of 1.5 MW small turbines with 5 MW large turbines. This study demonstrates that the optimal strategy for maximizing the overall power output is to replace the turbines in the first column. When the turbines situated in the first column have already undergone replacement or are unable to be replaced due to the characteristics of the terrain, it would be prudent to prioritize those in the final column. In the case of staggered arrangements, priority should be given to diagonal points that do not have turbines situated behind them. In the case of replacing the same number of large wind turbines, the preferred replacement option has a minimal impact on the power output of the existing small wind turbines, with an estimated reduction of 0.67%. This effectively enhances the economic efficiency of wind farm renovation.

Considering the global shift towards clean energy, advancing wind power generation technology is crucial. In complex terrain, turbines face significant challenges, such as increased fatigue loads from turbulent airflow, which reduce efficiency and structural integrity. Yaw optimization has emerged as a key solution to enhance performance in these environments. By dynamically adjusting the nacelle orientation, it improves wind capture, mitigates load fluctuations, and alleviates stress on turbine components. This not only boosts energy output but also extends equipment lifespan and reduces operational costs, supporting the economic feasibility of wind projects in complex terrain. This paper reviews current research and development trends in yaw optimization for wind farms in such settings, focusing on adaptive control strategies and the balance between load management and power efficiency. It examines the impact of different yaw optimization approaches on both individual turbines and overall wind farm performance. In conclusion, tailored yaw optimization strategies are proposed to maximize wind resource utilization in complex terrain, providing a reference for more resilient and efficient wind energy systems.