A modified weighted average algorithm for optimal reactive power dispatch considering uncertain load and renewable power

Optimal reactive power dispatch (ORPD) is a crucial task in modern power systems, aimed at improving system performance by optimizing the flow of reactive power. In this paper, a modified weighted average algorithm (MWAA) is proposed to solve both the traditional ORPD problem and the stochastic ORPD (SORPD), applied to the 30-bus IEEE system, considering the presence of photovoltaics (PVs) and wind turbine units (WTs). The proposed MWAA incorporates three enhanced strategies including fitness distance balance (FDB) method, the Weibull flight method, and quasi-oppositional-based learning (QOBL). For the SORPD solution, the MWAA focuses on minimizing total expected power loss (TEPL) and total expected voltage deviation (TEVD). The inherent uncertainties in load demand and renewable power generation from PV and WT systems are jointly considered and modeled using normal/lognormal and Weibull probability-density function (PDF). The MWAA is tested on standard benchmark functions, and the CEC-2019 test suites results compared to recent methods regarding accuracy, convergence behavior, Friedman tests, and boxplots. The results confirm that MWAA is a robust and competitive optimization technique, effectively solving both ORPD and SORPD problems while demonstrating superior performance over other state-of-the-art methods.