Senthil Krishnamurthy, Oludamilare Bode Adewuyi, Sunday Adeleke Salimon
The growing complexity of modern power systems, driven by the integration of renewable energy sources, distributed generation, and smart grid technologies, poses significant challenges to ensuring efficient and reliable operation. Traditional optimization methods often fall short in addressing the dynamic, nonlinear, and uncertain nature of present-day power system variables. This review article explores the application of Artificial Intelligence (AI)-based optimization techniques as promising solutions to enhance power system performance, stability, and flexibility. The study begins with an overview of traditional power system optimization problems and solution approaches, including economic load dispatch, optimal power flow, unit commitment, demand response, fault diagnosis, and the integration of renewable energy. It examines the classical optimization methods and their limitations, as well as the intricacies of metaheuristic techniques. The core of this review paper focuses on the application of contemporary artificial intelligence techniques, highlighting their strengths, use cases, comparative effectiveness, and discussing the implementation challenges that arise. By analyzing recent literature, tools, and real-world applications, the review identifies critical trends and challenges and provides recommendations for future work. This review task highlights the potential of AI to support resilient, adaptive, and sustainable power systems in the era of energy transition. • AI-based techniques enhance power system performance and reliable operations. • Traditional methods often struggle with nonlinear and uncertain system dynamics. • Review covers several areas and horizons of power systems planning and operations. • Relevant optimization methods are analyzed, emphasizing strengths and limits. • The study highlights AI’s essential roles in creating resilient, adaptive power systems.