Grey Wolf Optimizer algorithm-based unit commitment problem

It is known that the electricity generator uses massive quantities of energy per year. Studies on the unit commitment problems have now been a very critical activity in a power plant. However, the current optimal solution of unit commitment problem obtained from the existing methods is very easy to collapse into a local minimum, resulting in bad results. To solve the unit commitment problem, a new evolutionary algorithm, the Grey Wolf Optimizer (GWO) algorithm is presented. This integer coding algorithm has been developed to minimize the total energy scheduling cost over the entire scheduling range while satisfying all constraints. In addition, the shortest up / downtimes are directly encoded without using a penalty function method. Considering the one-day and seven-day scheduling cycles, the algorithm has been applied to 10 generators, up to 100 generators. The most important advantage of this approach is its fast convergence rate. The simulation results of this technique are compared to the results of bacterial foraging algorithm, particle swarm optimization, and genetic algorithm and Lagrangian relaxation. The comparison outcomes prove the effectiveness of the proposed technique.