A Novel Hybrid ZOA-AOA Approach for Optimal Reactive Power Dispatch in Power Systems With Renewable Energy Integration and Uncertainty Modeling

Abstract

This paper proposes a novel hybrid ZOA-AOA optimization framework to effectively balance exploration and exploitation for solving the ORPD problem under renewable uncertainty. Unlike conventional hybrid approaches, the proposed method incorporates an adaptive role assignment where ZOA handles exploration and AOA handles exploitation. This design enhances convergence performance and solution quality. Solar and wind energy uncertainties were modeled using lognormal and Weibull probability density functions (PDFs) across different seasonal scenarios, capturing temporal variability patterns that significantly impact reactive power requirements. The methodology was implemented and tested on the actual JMB 25-bus, IEEE 118-bus, and IEEE 300-bus systems with various renewable DG penetration levels. Comprehensive comparative simulations demonstrate that ZOA-AOA consistently outperforms other metaheuristic algorithms and standalone ZOA variants, achieving active power loss reduction of up to 37.16% in the JMB 25-bus system. The hybrid approach maintained stable voltage profiles with zero under-voltage buses in most scenarios while requiring minimal reactive power compensation. In the IEEE 118-bus system, ZOA-AOA achieved a 12.5% reduction from the baseline, while in the 300-bus system, it maintained 17–18% lower losses and exhibited fast convergence. Specifically, the algorithm recorded processing times of 71.8 s, 150.7 s, and 122.8 s for the JMB 25-bus, IEEE 118-bus, and IEEE 300-bus systems, respectively. Statistical analyses over 33 independent runs confirm that the hybrid ZOA-AOA approach achieves significant improvements, including a 15.7% reduction in power loss, a 31.3% enhancement in voltage profile, and a 16.8% improvement in voltage stability, while effectively managing renewable uncertainties. The proposed method provides an effective approach to addressing constraints, nonlinear behavior, and inherent uncertainties in ORPD challenges involving DG, thereby enhancing the efficiency and reliability of modern power system operations.