Exploring Evolutionary Algorithms for Optimal Power Flow: A Comprehensive Review and Analysis

Abstract

It has been more than five decades since optimum power flow (OPF) emerged as one of the most famous and frequently used nonlinear optimization problems in power systems. Despite its long-standing existence, the OPF problem continues to be widely researched due to its critical role in electrical network planning and operations. The general formulation of OPF is complex, representing a large-scale optimization model with nonlinear and nonconvex characteristics, incorporating both discrete and continuous control variables. The inclusion of control factors such as transformer taps and shunt capacitors, and the integration of renewable energy sources like wind power further complicates the system’s design and solution. To address these challenges, a variety of classical, evolutionary, and improved optimization techniques have been developed. These techniques not only provide new solution pathways but also enhance the quality of existing solutions, contributing to reductions in computational cost and operational efficiency. Multi-objective approaches are frequently employed in modern OPF problems to balance trade-offs between competing objectives like cost minimization, loss reduction, and environmental impact. This article presents an in-depth review of various OPF problems and the wide array of algorithms, both traditional and evolutionary, applied to solve these problems, paying special attention to wind power integration and multi-objective optimization strategies.