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Mahmoodzadeh, Zahra | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44483 (05)
  4. University: Sharif University Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Ehsan, Mehdi
  7. Abstract:
  8. Losses are unavoidable in any parts of power systems, from generation to receiving nodes and occur in transmission and distribution networks. However, the main loss component is produced in distribution networks. Energy losses in distribution networks are about 70% of technological transport energy losses.
    Energy losses in distribution networks are an important indicator for the planning and operation of the system. A fast, reliable and accurate energy loss calculation method is required for optimal operation of the distribution networks.
    Energy loss depends on network's operating conditions. Loads values and loads factors are time variables; therefore, methods based on probability theory and mathematical statistics are among the most effective methods for estimation of energy loss in distribution networks. Due to the random nature of the loads, the stochastic methods can drastically reduce computational effort.
    Energy loss calculation can be carried out on the base of load flow calculation. This is the common way used by transmission networks operators. However, energy loss computation in electrical distribution networks by means of load flow calculations requires a large amount of data and great amount of computational time.
    An improved stochastic simulation method for calculating current dependent energy losses in distribution networks is proposed in this thesis. The method gives accurate results several times faster than other existing methods. Therefore, it is proper for considering losses in optimization and decision making purposes in operating and planning of distribution networks.
    In order to evaluate the capability of the proposed method to solve operating and planing problems of the distribution networks, the method is used in optimal sizing and placement of DGs in distribution networks
  9. Keywords:
  10. Loss Reduction ; Distributed Networks ; Loss Distribution ; Stochastic Simulation ; Dispersed Generation Optimal Placement ; Statistical Methods ; Energy Loss ; Optimal Placement

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