Hybrid Optimization Techniques for Large-Scale Power Systems

Abstract

ABSTRACT Large-scale power systems are becoming more complex due to integration of renewable energy sources, distributed generation, smart grids and dynamic load behavior. Classical optimization techniques are often not sufficient to handle non-linearity, uncertainty, multi-objective constraints and huge dimensionality involved in modern power network problems. In recent years, hybrid optimization techniques which combine two or more algorithms such as evolutionary algorithms, swarm intelligence, mathematical programming, and artificial intelligence methods have gained significant attention. These hybrid approaches improve convergence speed, solution quality and robustness for solving power flow, economic dispatch, unit commitment, optimal power flow and renewable integration problems. This review paper presents comprehensive discussion on hybrid optimization methods applied in large scale power systems. Different combinations of algorithms, their advantages, limitations, and application areas are discussed. Comparative analysis is also provided to show effectiveness of hybrid techniques over traditional methods. The paper also highlights future directions in this area for smart grid and renewable dominated systems.

KEYWORDS: Hybrid optimization, large-scale power systems, optimal power flow, economic dispatch, unit commitment, evolutionary algorithms, swarm intelligence, smart grid.