Review of Power Generation Optimization Algorithms: Challenges and its Applications

Optimizing the power system is challenging to address because power systems are enormous and complicated and can be impacted by several unanticipated occurrences. As a result, approaches to addressing these challenges should be an active main focus of this study. Energy users can optimize the power system to help the system run efficiently, cut down on peak load use, and minimize control adjustments. The new approaches and management techniques to ensure flexibility have emerged from the increasing trend of renewable energy integration with power generation uncertainty and availability. This paper contains a research study of different optimization algorithms. The pros and cons of recently used optimization techniques in power systems are briefly discussed in this paper. The algorithms that are discussed in this article are Power optimization algorithm (PSO), Genetic Algorithm (GA), Tabo Search Algorithm (TSA), Artificial Neural Network (ANN), Fuzzy Logic, Level Shifting Phase Disposition (LSPD), Simulated Annealing (SA) and Random Search Algorithm (RS). A thorough analysis of power optimization techniques, the difficulties with traditional approaches, and finally, the most modern optimization algorithms are covered. Overall, this review will strengthen the initiatives toward developing reliable and sustainable power systems using practical power optimization algorithms in future applications.