Multi-Scale Numerical Simulation and Optimization Strategies for Wind Farm Layouts in High-Altitude Regions

This study explores the optimization of wind farm layouts in high-altitude regions using a multi-scale numerical simulation approach integrated with advanced optimization strategies. Data were collected from various wind farms in the Tibetan Plateau and the Himalayan region, including wind speed, direction, air density, temperature, and terrain elevation over a five-year period. The research methodology comprised data preprocessing, wind flow modeling via Computational Fluid Dynamics (CFD) and the turbulence model, wind turbine performance modeling based on the Betz limit and Jensen wake model, and optimization using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The simulated results were validated against actual data through Root Mean Square Error (RMSE) and sensitivity analysis. The findings reveal substantial enhancements in wind farm performance, with optimized layouts significantly increasing total power output and reducing turbine interference. Specifically, the GA-optimized layout achieved a total power output of 102 MW and an efficiency of 82%, while the PSO-optimized layout attained 101.5 MW and 81.5% efficiency, compared to the initial layout’s 95 MW and 75% efficiency. This research highlights the potential of multi-scale simulations and optimization techniques to improve wind farm efficiency in challenging high-altitude environments.