Multiobjective in-core fuel management optimisation for nuclear research reactors

Thesis (PhD)--Stellenbosch University, 2016.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.AFRIKAANSE OPSOMMING : Die doeltreffendheid en doelmatigheid van brandstofverbruik in 'n tipiese kernreaktor word deur die spesieke rangskikking van beskikbare brandstofelemente in die laaiposisies van die reaktor beinvloed. Hierdie rangskikking staan bekend as 'n brandstof herlaaikongurasie en word gewoonlik opnuut bepaal vir elke operasionele siklus van 'n reaktor. Die gelyktydige optimering van veelvuldige doele word dikwels tydens die ontwerp van 'n herlaaikongurasie nagestreef, veral binne die konteks van navorsingsreaktore. Die doelwit van meerdoelige binne-kern brandstofbeheeroptimering (MBKBBO) is om 'n Pareto optimale versameling van herlaaikongurasieafruilings te identiseer. So 'n versameling mag dan vir oorweging (deur byvoorbeeld 'n kernreaktoroperateur) voorgele word sodat 'n voorkeurkongurasie gekies kan word. In die eerste gedeelte van hierdie proefskrif word 'n skalariseringsgebaseerde metodologie vir MBKBBO voorgestel om verskeie tekortkominge in die gewilde gewigverswaringsmetode aan te spreek. Laasgenoemde metode word gereeld in die literatuur gebruik om die MBKBBO probleem op te los. Die voorgestelde metodologie is in 'n reaktorsimulasiestelsel, bekend as die OSCAR-4 stelsel, geimplementeer. Om die praktiese toepasbaarheid daarvan te demonstreer, word die metodologie gebruik om 'n aantal MBKBBO probleemgevalle binne die konteks van twee navorsingsreaktore op te los. In die tweede gedeelte van die proefskrif word 'n uitgebreide ondersoek ingestel om die geskiktheid van verskeie meerdoelige optimeringsalgoritmes vir die oplos van die beperkte MBKBBO probleem te bepaal. Die berekeningstyd wat vir die ondersoek benodig word, word verminder deur die gebruik van kunsmatige neurale netwerke, wat in die proefskrif gekonstrueer word, om doelfunksies en beperkings te evalueer. Agt meerdoelige metaheuristieke word binne die konteks van verskeie MBKBBO toetsprobleemgevalle vergelyk wat op die SAFARI-1 navorsingsreaktor in Suid-Afrika gebaseer is. Toetse dui daarop dat die NSGA-II, die P-ACO algoritme en die MOOCEM oor die algemeen die beste oor al die toetsprobleemgevalle presteer. Die MOVNS algoritme presteer ook goed in die konteks van tweedoelige probleemgevalle. 'n Vermenigvuldigende boetefunksie (VBF) beperkinghanteringstegniek word ook voorgestel en vergelyk met 'n bestaande tegniek bekend as beperkte dominasie. Daar word bevind dat the VBF tegniek 'n mededingende alternatief tot beperkte dominasie is. 'n Poging word aangewend om die vlak van algemeenheid waarmee MBKBBO uitgevoer word, te verhoog, asook om potensieel die kwaliteit van die optimeringsresultate te verbeter. 'n Meerdoelige hiperheuristiek, bekend as die AMALGAM metode, word in die nastreef van hierdie twee doelwitte oorweeg. Die metode funksioneer deur middel van die gelyktydige insluiting van 'n aantal metaheuristieke deel-algoritmes. Toetse dui daarop dat the AMALGAM metode beter resultate vir die meerderheid van toetsprobleme lewer, en dus word die bogenoemde twee doelwitte bereik. Die metode is ook in the OSCAR-4 stelsel ge mplementeer en word gebruik om 'n aantal MBKBBO gevallestudie probleemgevalle (binne die konteks van twee navorsingsreaktore) op te los. Sodoende word die praktiese toepasbaarheid van die metode gedemonstreer. In die derde deel van die proefskrif word 'n konseptuele raamwerk laastens vir 'n optimeringsgebaseerde persoonlike besluitsteunstelsel gemik op MBKBB, voorgestel. Hierdie raamwerk mag as grondslag dien vir die ontwikkeling van 'n gerekenariseerde hulpmiddel vir kernreaktoroperateurs om aanvaarbare herlaaikongurasies te ontwerp.Doctora

Decision support for generator maintenance scheduling in the energy sector

Thesis (MSc)--Stellenbosch University, 2011.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.AFRIKAANSE OPSOMMING: Met die wêreldwye elektrisiteitsverbruik wat voortdurend aan die toeneem is, word daar al hoe meer druk geplaas op die vermoë van kragstelsels om aan kragvoorsieningsaanvraag te voldoen. Nutsmaatskappye wat elektrisiteit opwek, word deur talle uitdagings met betrekking tot betroubare elektrisiteitsverskaffing teen koste-e ektiewe tariewe in die gesig gestaar. Een van hierdie uitdagings het te make met die beplande, voorkomende instandhouding van 'n nutsmaatskappy se kragopwekkingseenhede. Die generator-instandhoudingskeduleringsprobleem (GISP) verwys na die probleem waarin 'n skedule vir die beplande instandhouding van kragopwekkingseenhede binne 'n kragstelsel gevind moet word ('n lys van datums moet tipies gevind word wat ooreenstem met die tye wanneer elke kragopwekkingseenheid afgeskakel moet word om instandhoudingswerk te ondergaan). Hierdie probleem is tipies 'n groot kombinatoriese optimeringsprobleem, onderworpe aan 'n aantal beperkings van die kragstelsel, en is gewoonlik moeilik om op te los. 'n Gemengde, heeltallige programmeringsmodel vir die GISP word geformuleer. Die beperkings waaruit die formulering bestaan, sluit in: venstertydperke vir instandhouding, bevrediging van die vraag na elektrisiteit tesame met 'n veiligheidsgrens, die beskikbaarheid van instandhoudingspersoneel en algemene uitsluitingsbeperkings. Die GISP-model neem as optimaliteitskriterium betroubaarheid en het ten doel om die reserwekrag wat gedurende elke tydperk beskikbaar is, gelyk te maak. Drie doelfunksies word gebruik om laasgenoemde doel te bereik (naamlik doelfunksies wat onderskeidelik kwadraties, nie-lineêr en lineêr van aard is). Drie GISP-maatstaftoetsstelsels (waarvan een nuut geskep is) is dienooreenkomstig gemodelleer, maar dit blyk uit die oplossingstye dat daar onprakties lank gewag sal moet word om eksakte oplossings deur middel van kommersiële programmatuur vir hierdie stelsels te kry. Gevolglik word 'n metaheuristiese oplossingsbenadering ('n gesimuleerde temperingsalgoritme (GTA)) gevolg om die GISP benaderd op te los. 'n Nuwe uitwerpingsketting-skuifoperator word in die konteks van GISP in die GTA gebruik. Verder word 'n lokale soekheuristiek met die GTA vermeng om 'n basteralgoritme te vorm. Uitgebreide eksperimente word uitgevoer op verskeie afkoelskedules binne die GTA, op die klassieke en uitwerpingsketting-skuifoperators en op die verbasterings van die GTA meegebring deur die lokale soekheuristiek. Gevolgtrekkings word oor elke variasie van die GTA se e ektiwiteit gemaak. Die beste oplossings vir elke toetsstelsel wat gedurende die eksperimente verkry is, word gerapporteer. Daar word bevind dat die GTA met uitwerpingsketting-skuifoperator en lokale soekheuristiek-verbastering baie goeie oplossings vir die GISP lewer. Die verbasterde GTA word in 'n gerekenariseerde besluitsteunstelsel (BSS) geïmplementeer wat 'n gebruiker in staat stel om enige GISP van die vorm soos in die wiskundige programmeringsmodel hierbo beskryf, op te los. Daar word bevind dat die BSS goeie skedules lewer wanneer dit gebruik word om 'n realistiese gevallestudie binne die konteks van die Suid-Afrikaanse kragstelsel, op te los. 'n Beste skedule met 'n doelfunksiewaarde wat binne 6% vanaf 'n teoretiese ondergrens is, word ondermeer bepaal

An optimisation-based decision support system framework for multi-objective in-core fuel management of nuclear reactor cores

The notion of in-core fuel management (ICFM) involves decision making in respect of the specific arrangement of fuel assemblies in a nuclear reactor core. This arrangement, referred to as a reload configuration, influences the efficiency and effectiveness of fuel usage in a reactor. A decision support system (DSS) may assist nuclear reactor operators in improving the quality of their reload configuration designs. In this paper, a generic optimisation-based DSS framework is proposed for multi-objective ICFM, with the intention of serving as a high-level formalisation of a computerised tool that can assist reactor operators in their complex ICFM decisions