Optimal Unit Commitment Using JAYA Optimization Technique

The UC problem exists as the main optimization challenge which power systems use to operate their networks. The process of the problem involves finding the best power generator timetable which satisfies electricity needs while achieving the lowest costs and maintaining system dependability and all operational restrictions. The UC problem creates its challenges through its mixed-integer nonlinear structure which requires multiple operational constraints to be solved including generator capacity restrictions and minimum operational times and ramp rate limits and spinning reserve requirements. The UC problem has been solved by researchers who used traditional optimization methods which include Dynamic Programming and Lagrangian Relaxation and Mixed Integer Programming but these methods encounter problems because they need more computation power and show slow progress and cannot manage extensive systems. Recent years have seen metaheuristic optimization algorithms become popular because they provide a fast solution for difficult nonlinear optimization problems. JAYA optimization algorithm stands out as an easy-to-use yet effective solution among these optimization techniques. The JAYA algorithm operates as a population-based optimization method which uses no parameters for its operations because it brings candidate solutions closer to the best solution while pushing them away from the worst solution at every iteration. JAYA operates differently from most metaheuristic algorithms because it needs no special control parameters which makes its implementation easier and decreases the time needed to adjust its parameters. The review paper studies how the JAYA optimization algorithm operates in solving the Optimum Unit Commitment (OUC) problem which exists in power system operations. The paper presents the mathematical structure of the UC problem which includes the cost reduction objective and all operational restrictions. The research paper assesses how recent studies have used the JAYA algorithm to improve generation scheduling for both thermal and renewable energy systems. The study compares the JAYA approach to Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution optimization methods to demonstrate its effectiveness. The study demonstrates that the JAYA algorithm achieves faster convergence while generating better solutions and requiring less processing power than most conventional and evolutionary optimization methods. The algorithm shows exceptional performance when it processes extensive power grid systems which include renewable energy sources like wind and solar energy. The JAYA optimization algorithm functions as an effective and advanced solution for contemporary unit commitment challenges which arise in smart grid systems.