Optimal scheduling for maintenance period of generating units using a hybrid scatter-genetic algorithm

Finding an optimal maintenance schedule for generating units and securing the adequate amount of generation availability as a preventive measure to keep the power system reliable is becoming of great importance under the tight operating reserve margin in electricity markets. Therefore most recent research on maintenance scheduling concentrates on how to economically and reliably determine the optimal set of maintenance period for each generating unit while satisfying a variety of constraints given by a system operator. This study presents a methodology for optimal maintenance scheduling of generating units using a hybrid algorithm that combines a scatter search and a genetic algorithm. To verify the effectiveness of the proposed algorithm, a sample test system of 21 units is selected and the results are compared with those of the recent meta-heuristic algorithms, including genetic algorithms, discrete particle swarm optimisation and modified discrete particle swarm optimisation. This study does a comparison between the proposed method and other conventional ones to determine the maintenance schedule for each generating unit. The proposed method shows a higher performance in the objective function evaluation than the conventional ones, and consequently yields a more optimal solution of the maintenance scheduling problem. The proposed method has also been explored on the IEEE reliability test system, and the results show that the proposed algorithm can provide a robust and consistent performance.