Multi-objective optimization of generation maintenance scheduling

For the generation maintenance scheduling (GMS) problem, a producer hopes to maximize its profit while ISO is to guarantee the system reliability. Thus, the GMS is a multi-objective optimization problem. In the GMS, there are large numbers of both continuous and integer variables, which complicates the resolving of the GMS. This paper proposes a new GMS model, which is suitable to be solved by the non-dominated sorting genetic algorithm-II (NSGA-II). In the GMS model, the maintenance status of a generator is encoded into an integer variable and both the online status and the start-up status are represented by the generation variables. The GMS model on the IEEE reliability test system is solved by NSGA-II with a set of Pareto-optimal solutions obtained. The simulation results show that the GMS can be efficiently solved by NSGA-II. The simulation results also show that one producer's profit conflicts with another one's, and that the reliability objective is independent of the other objectives.