Decision support for the production and distribution of electricity under load shedding

 

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dc.contributor.advisor Durbach, Ian en_ZA
dc.contributor.advisor Nyirenda, Juwa en_ZA
dc.contributor.author Rakotonirainy, Rosephine Georgina en_ZA
dc.date.accessioned 2016-07-28T12:15:03Z
dc.date.available 2016-07-28T12:15:03Z
dc.date.issued 2016 en_ZA
dc.identifier.citation Rakotonirainy, R. 2016. Decision support for the production and distribution of electricity under load shedding. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/20942
dc.description.abstract Every day national power system networks provide thousands of MW of electric power from generating units to consumers. This process requires different operations and planning to ensure the security of the entire system. Part of the daily or weekly operation system is the so called Unit Commitment problem which consists of scheduling the available resources in order to meet the system demand. But the continuous growth in electricity demand might put pressure on the ability of the generation system to sufficiently provide supply. In such case load shedding (a controlled, enforced reduction in electricity supply) is necessary to prevent the risk to system collapse. In South Africa at the present time, a systematic lack of supply has meant that regular load shedding has taken place, with substantial economic and social costs. In this research project we study two optimization problems related to load shedding. The first is how load shedding can be integrated into the unit commitment problem. The second is how load shedding can be fairly and efficiently allocated across areas. We develop deterministic and stochastic linear and goal programming models for these purposes. Several case studies are conducted to explore the possible solutions that the proposed models can offer. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Statistical Sciences en_ZA
dc.title Decision support for the production and distribution of electricity under load shedding en_ZA
dc.type Thesis / Dissertation en_ZA
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Statistical Sciences en_ZA
dc.type.qualificationlevel Masters en_ZA
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image


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