A leader-driven Wild Horse Optimizer for solving ORPD with integrated stochastic renewable sources

Abstract

Optimal Reactive Power Dispatch (ORPD) has emerged as a vital requirement for the safe, efficient, and economical operation of power networks. This study presents a leader-based enhancement to the original Wild Horse Optimizer (WHO), resulting in a more powerful algorithm referred to as LWHO. The performance of the LWHO algorithm is rigorously evaluated using 23 mathematical benchmark functions, encompassing unimodal, multimodal, and composite optimization problems.

Furthermore, both single-objective and multi-objective deterministic/stochastic ORPD formulations are examined on two standard test systems: the IEEE 30-bus and IEEE 57-bus networks. To effectively model uncertainty, a scenario-based approach is utilized, incorporating variations in load demand and RES output. Simulation results confirm that the proposed LWHO algorithm delivers highly accurate and robust solutions for ORPD under uncertainty. Statistical validation using the Wilcoxon rank-sum test confirms the significant superiority of the proposed LWHO compared to the original WHO in five out of eight single-objective cases (p < 0.05). This method offers a practical and efficient strategy for addressing the complexities introduced by RES integration, ultimately contributing to enhanced energy efficiency and more resilient power system operations.