Browsing by Author "Schlunz, Evert Barend"

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    Decision support for generator maintenance scheduling in the energy sector
    (Stellenbosch : Stellenbosch University, 2011-12) Schlunz, Evert Barend; Van Vuuren, J. H.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics.
    ENGLISH ABSTRACT: As the world-wide consumption of electricity continually increases, more and more pressure is put 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 challenges with respect to ensuring reliable electricity supply at cost-e ective rates. One of these challenges concerns the planned preventative maintenance of a utility's power generating units. The generator maintenance scheduling (GMS) problem refers to the problem of nding a schedule for the planned maintenance outages of generating units in a power system (i.e. determining a list of dates corresponding to the times when every unit is to be shut down so as to undergo maintenance). This is typically a large combinatorial optimisation problem, subjected to a number of power system constraints, and is usually difficult to solve. A mixed-integer programming model is presented for the GMS problem, incorporating constraints on maintenance windows, the meeting of load demand together with a safety margin, the availability of maintenance crew and general exclusion constraints. The GMS problem is modelled by adopting a reliability optimality criterion, the goal of which is to level the reserve capacity. Three objective functions are presented which may achieve this reliability goal; these objective 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 software package. Therefore, a metaheuristic solution approach (a simulated annealing (SA) algorithm) is used to solve the GMS problem approximately. A new ejection chain neighbourhood move operator in the context of GMS is introduced into the SA algorithm, along with a local search heuristic 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 cations to the SA algorithm by the introduction of the local search heuristic. Conclusions are drawn with respect to the e ectiveness of each variation on the SA algorithm. The best solutions obtained during the experiments for each benchmark test case are reported. It is found that the SA algorithm, with ejection chain neighbourhood move operator and a local search heuristic hybridisation, achieves very good solutions to all instances of the GMS problem. The hybridised simulated annealing algorithm is implemented in a computerised decision support system (DSS), which is capable of solving any GMS problem instance conforming to the general formulation described above. The DSS is found to determine good maintenance schedules when utilised 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, is thus produced.
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    Multiobjective in-core fuel management optimisation for nuclear research reactors
    (Stellenbosch : Stellenbosch University, 2016-12) Schlunz, Evert Barend; Van Vuuren, Jan Harm; Bokov, Pavel M.; Stellenbosch University. Faculty Economic and Management Science. Dept. of Logistics.
    ENGLISH SUMMARY : The efficiency and effectiveness of fuel usage in a typical nuclear reactor is influenced by the specific arrangement of available fuel assemblies in the reactor core positions. This arrangement of assemblies is referred to as a fuel reload configuration and usually has to be determined anew for each operational cycle of a reactor. Very often, multiple objectives are pursued simultaneously when designing a reload configuration, especially in the context of nuclear research reactors. In the multiobjective in-core fuel management optimization (MICFMO) problem, the aim is to identify a Pareto optimal set of compromise or trade-off reload configurations. Such a set may then be presented to a decision maker (i.e. a nuclear reactor operator) for consideration so as to select a preferred configuration. In the first part of this dissertation, a secularization-based methodology for MICFMO is pro- posed in order to address several shortcomings associated with the popular weighting method often employed in the literature for solving the MICFMO problem. The proposed methodology has been implemented in a reactor simulation code, called the OSCAR-4 system. In order to demonstrate its practical applicability, the methodology is applied to solve several MICFMO problem instances in the context of two research reactors. In the second part of the dissertation, an extensive investigation is conducted into the suitability of several multiobjective optimization algorithms for solving the constrained MICFMO problem. The computation time required to perform the investigation is reduced through the usage of several artificial neural networks constructed in the dissertation for objective and constraint function evaluations. Eight multiobjective metaheuristics are compared in the context of a test suite of several MICFMO problem instances, based on the SAFARI-1 research reactor in South Africa. The investigation reveals that the NSGA-II, the P-ACO algorithm and the MOOCEM are generally the best-performing metaheuristics across the problem instances in the test suite, while the MOVNS algorithm also performs well in the context of bi-objective problem instances. As part of this investigation, a multiplicative penalty function (MPF) constraint handling technique is also proposed and compared to an existing constraint handling technique, called constrained-domination. The comparison reveals that the MPF technique is a competitive alternative to constrained-domination. In an attempt to raise the level of generality at which MICFMO may be performed and potentially improve the quality of optimization results, a multiobjective hyperheuristic, called the AMALGAM method, is also considered in this dissertation. This hyperheuristic incorporates multiple metaheuristic sub-algorithms simultaneously for optimization. Testing reveals that the AMALGAM method yields superior results in the majority of problem instances in the test suite, thus achieving the dual goal of raising the level of generality and of yielding improved optimization results. The method has also been implemented in the OSCAR-4 system and is applied to solve several MICFMO case study problem instances, based on two research reactors, in order to demonstrate its practical applicability. Finally, in the third part of this dissertation, a conceptual framework is proposed for an optimization-based personal decision support system, dedicated to MICFM. This framework may serve as the basis for developing a computerized tool to aid nuclear reactor operators in designing suitable reload configurations.