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Modelling Demand Uncertainties in Generation-Transmission Expansion planning: A case study of the Nigerian Power System
KTH, School of Electrical Engineering (EES), Electric Power Systems.
2012 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The Nigerian power system is one plagued with incessant load shedding due to inadequate generation and transmission capacities. Currently, less than 40% of the population is connected to the national grid and less than 50% of the available installed capacity is actively used in meeting demand. A new wave of energy reforms is on-going in the nation. There are proposed generation and expansion plans. These reforms have only fully taken into consideration present demands and not future energy demands. This means that even with new plants and transmission lines being constructed; there may still be inefficient generation and transmission capacities due to demand increase. This thesis models the uncertain future demands in the integrated generation-transmission planning model. An optimal investment plan is found using the deterministic optimization model of integrated generationtransmission planning. A decision analysis method was initially used to study the introduction of uncertain demand into the deterministic model. Then, a two-stage stochastic model of the generation-transmission planning taking into account uncertainties in energy demand is developed using scenario-wise decomposition method. The demand was modelled as taking discrete values with certain probabilities. These models are mixed-integer linear programming problems. They are implemented in the GAMS platform and solved using the CPLEX solver. A stylized version of the Nigerian power system is developed and tested. A thorough analysis and comparison of results from the models were carried out using the developed version of the Nigerian transmission grid.

Place, publisher, year, edition, pages
2012. , 130 p.
EES Examensarbete / Master Thesis
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-91523OAI: diva2:510601
Educational program
Master of Science - Electric Power Engineering
Available from: 2012-03-16 Created: 2012-03-16 Last updated: 2012-05-14Bibliographically approved

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