A ring crossover genetic algorithm for the unit commitment problem

The unit commitment problem (UCP) is a nonlinear, mixed-integer, constraint optimization problem and is considered a complex problem in electrical power systems. It is the combination of two interlinked subproblems, namely the generator scheduling problem and the generation allocation problem. In large systems, the UCP turns out to be increasingly complicated due to the large number of possible ON and OFF combinations of units in the power system over a scheduling time horizon. Due to the insufficiency of conventional approaches in handling large systems, numerous metaheuristic techniques are being developed for solving this problem. The genetic algorithm (GA) is one of these metaheuristic methods. In this study, an attempt is made to solve the unit commitment problem using the GA with ring crossover and swap mutation operators. One half of the initial population is intelligently generated by focusing on load curve to obtain better convergence. Lambda iteration is used to solve the generation allocation subproblem. Tests are carried out on systems with up to 100 generators over a time horizon of 24 h. Test outcomes demonstrate the proficiency of the presented scheme when compared with previously used techniques.