Decision support for generator maintenance scheduling in the energy sector

[摘要] ENGLISH ABSTRACT: As the world-wide consumption of electricity continually increases, more and more pressure isput on the capabilities of power generating systems to maintain their levels of power provision.The electricity utility companies operating these power systems are faced with numerous challengeswith respect to ensuring reliable electricity supply at cost-e ective rates. One of thesechallenges concerns the planned preventative maintenance of a utility's power generating units.The generator maintenance scheduling (GMS) problem refers to the problem ofnding a schedulefor the planned maintenance outages of generating units in a power system (i.e. determininga list of dates corresponding to the times when every unit is to be shut down so as to undergomaintenance). This is typically a large combinatorial optimisation problem, subjected to anumber of power system constraints, and is usually difficult to solve.A mixed-integer programming model is presented for the GMS problem, incorporating constraintson maintenance windows, the meeting of load demand together with a safety margin,the availability of maintenance crew and general exclusion constraints. The GMS problem ismodelled by adopting a reliability optimality criterion, the goal of which is to level the reservecapacity. Three objective functions are presented which may achieve this reliability goal; theseobjective functions are respectively quadratic, nonlinear and linear in nature.Three GMS benchmark test systems (of which one is newly created) are modelled accordingly,but prove to be too time consuming to solve exactly by means of an o -the-shelf softwarepackage. Therefore, a metaheuristic solution approach (a simulated annealing (SA) algorithm)is used to solve the GMS problem approximately. A new ejection chain neighbourhood moveoperator in the context of GMS is introduced into the SA algorithm, along with a local searchheuristic addition to the algorithm, which results in hybridisations of the SA algorithm.Extensive experiments are performed on di erent cooling schedules within the SA algorithm,on the classical and ejection chain neighbourhood move operators, and on the modi cationsto the SA algorithm by the introduction of the local search heuristic. Conclusions are drawnwith respect to the e ectiveness of each variation on the SA algorithm. The best solutionsobtained during the experiments for each benchmark test case are reported. It is found thatthe SA algorithm, with ejection chain neighbourhood move operator and a local search heuristichybridisation, achieves very good solutions to all instances of the GMS problem.The hybridised simulated annealing algorithm is implemented in a computerised decision supportsystem (DSS), which is capable of solving any GMS problem instance conforming to the generalformulation described above. The DSS is found to determine good maintenance schedules whenutilised to solve a realistic case study within the context of the South African power system.A best schedule attaining an objective function value within 6% of a theoretical lowerbound, isthus produced.

[发布机构] Stellenbosch University