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Investment Maximization Transmission Expansion Planning

Arabali, Amir Saman | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 41042 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Hosseini, Hamid
  7. Abstract:
  8. Trasmission system is one of the most important parts of the power system which not only creates an interface between generation and distribution section, but also provides a reliable and non-descriminated environment between them. In recent years, restructuring is introduced in many countries of the world. The competition is begun in generation section while, the transmission section was remained monopoly. Private investors don't have enough incentive to invest in this section, because, the presence of uncertainties have made decision making hard for them and increased the investment risk. On the other hand, with increase in consumption and generation, need to invest in this section seems to be essential. In restructured environment, the necessity of this matter seems to be more essencial. Deregulation in power system introduced new challenges and uncertainties and escalated the previous ones. All of these factors together, cause that investment in new transmission projects dose not create enough incentive for private investors. In this thesis, a new multi-objective transmission expansion planning algorithm is presented considering power system physical limitations, security criteria and market conditions to determine the most attractive transmission projects from the viewpoint of private investors. Uncertainties in load and energy bids are modeled probabilistically. To conduct this optimization, non-dominated sorting genetic algorithm (NSGA) is used. The results consist of a set of non-dominated solutions called Pareto optimal solutions. These Pareto solutions show the trade-off region between objective functions. Eventually, the final solution is found using a min-max fuzzy decision making approach. This algorithm is implemented on the IEEE-24 bus test system to evaluate the effectiveness of the approach
  9. Keywords:
  10. Uncertainty ; Genetic Algorithm ; Transmission Expansion Planning ; Private Capital Absorption ; Investment Risk

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