Exact approaches towards solving generator maintenance scheduling problems
Van Niekerk, T.K.
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The holistic management of a national power system is faced with multiple long and short-term scheduling challenges. One of the key long-term scheduling problems is referred to as the generator maintenance scheduling problem (GMS). The GMS problem is a complex combinatorial scheduling problem with the main focus of finding an optimal schedule for execution of planned maintenance while satisfying operating, maintenance, financial and national grid demand constraints. Three different deterministic mathematical modelling formulations were applied to try and solve realistic industry-sized GMS scenarios, within an acceptable time frame, while maximising net present value (NPV) over the selected planning horizon. The first being the well-known time index formulation which is frequently applied in the literature to solve GMS problems. The main drawback of employing mathematical deterministic approaches is increased computational complexity with enlarged solution search space. For this reason, network flow and graph theory formulations were considered as possible alternatives to the general time index formulation. Graph theory and network flow formulations are collectively known as resource flow formulations. A general resource flow formulation was employed which showed promising results for improved computational efficiency compared to the time index formulations, however, it was not capable of accounting for variability of resources, demand etc. over the planning horizon. To counteract this drawback a novel resource flow formulation was developed. A comparative study was done where all three deterministic formulations were applied to solve multiple GMS case studies of varying size. Both the resource flow formulations were proven to be computationally superior in most cases. There were some instances where the time index formulations were computationally faster than the resource flow formulations, but ultimately the resource flow formulations were preferred when solving realistic industry-sized GMS scenarios.
- Engineering