Electric Generator Maintenance Scheduling

Abstract

The complexity of electric power systems has resulted in the development of software-based techniques for solving operational problems. Generator maintenance scheduling (GMS) is one of those problems. The objectives of this paper are to present a review of the problem formulation, model development and the solution techniques. GMS is a large-scale, nonlinear and stochastic optimization problem with many constraints and conflicting objective functions. It is formulated in such a way as to define the time sequence of maintenance for a set of generating units so that all operational constraints are satisfied and the objective function obtains a minimum value. GMS constraints are related to the maintenance technology of the generating units, power system requirements and manpower and material resources. The technology defines the most desirable period, duration, sequence and uninterrupted duration of the maintenance work. It must be understood that GMS is a multiobjective optimization problem. This paper shows that the most widely used approach involves the minimization of the fuel and maintenance costs. The reserve and reliability criteria are also used by assigning some weighting coefficients. There are many algorithms and approaches that are suitable for the solution of the GMS problem. These are grouped in a number of categories such as heuristic search algorithms, mathematical programming methods, expert systems, fuzzy logic approach, simulated annealing, maintenance scheduling and tabu search methods. Each method has been successfully applied to a specific problem or network. However, it is important to note that there is no general consensus or agreement about the most suitable method. Future research may address the issues and impact of the independent power producer (IPP) on GMS problems. Constraints on transmission systems also have to be included and considered in GMS formulation.