Optimal Wind Farm Layout Design Using the Tornado Optimizer with Coriolis Force

Wind farm layout optimization (WFLO) is essential for reducing wake losses and maximizing energy output. However, the conventional micro-siting approaches usually apply rigid rectangular constraints and static grid constraints, which restrict the flexibility of spatial arrangement and confine the optimizers to sub-optimal local minima. This study overcomes this limitation by proposing a dynamic spatial allocation framework where wind farm dimensions (X, Y) are considered as continuous decision variables along with turbine placement coordinates. To manage the generated non-convex search space, a new tornado optimizer with Coriolis force (TOC), a metaheuristic inspired by the atmospheric fluid dynamics, is introduced. The optimization framework combines the Jensen kinematic wake model, the power curve of the AV 928 turbine and a normalized economic cost objective function. The comparative simulation of gray wolf optimizer (GWO), light spectrum optimizer (LSO) and Runge–Kutta algorithms show that TOC has better performance, deploying 53 wind turbines in a dynamic footprint to produce a total power output of 124.16 MW with an optimal normalized cost fitness value of 0.0002856. The resulting lateral staggering strategy greatly reduces the wake interactions and offers an efficient and scalable approach for complex micro-siting engineering problems.