Proposed Ant Colony Optimization Approach for Wind Farm Layout Optimization Problem

Harnessing wind energy with wind turbines presents a viable and sustainable approach to electrical power generation. Nonetheless, maximizing energy capture through wind farm layout optimization (WFLO) remains a critical and complex challenge. WFLO strategically the optimal positioning of the wind turbines within a designated wind farm area to mitigate wake effects and maximize overall power output. Wake interactions, in which upstream turbines decrease the wind speed available to downstream turbines, can significantly reduce wind farm power output. This paper introduces a proposed ant colony optimization (ACO) algorithm to address WFLO, employing a continuous-based wind farm model that enables flexible turbine placement across the farm. The proposed ACO algorithm is evaluated on a 2 km × 2 km square wind farm at constant wind speed and variable wind direction scenarios. Compared to prior grid-based WFLO studies, computational analyses show that the proposed ACO algorithm could increase total power output by 2.64-6.24% and wind farm efficiency by 2.67-6.32%, demonstrating its potential as an effective method for WFLO. This study contributes to SDG 7 by optimizing WFLO to improve turbine placement, enhance energy capture efficiency, reduce fossil fuel dependence, and promote global sustainable energy access.