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  1. Home
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  5. Optimization of distributed generation placement and capacity in distribution system using genetic algorithm method
 
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Optimization of distributed generation placement and capacity in distribution system using genetic algorithm method

Date Issued
2022
Author(s)
Arnawan Hasibuan
Handle (URI)
https://hdl.handle.net/20.500.14170/2640
Abstract
Population growth, economic growth, and industrial development can lead to an increase in the need for electrical energy. The continuous increase in the load on the electric power system can result in a decrease in the capacity of the existing power grid. This decrease can be shown by increasing power losses, decreasing voltage so that the system capacity decrease. One solution is to add a source of electrical energy directly to the load side by installing a distributed generator (DG) in the system. The injection of distributed generators into the power system can significantly affect the system. The effect that occurs depends on the location and size of the DG, In this study, the Genetic Algorithm approach is used to determine the location and optimum capacity of a distributed generator (DG). How long the system's capability last after the installation of distributed generation (DG) is also assessed. Proper placement and optimal DG capacity can reduce system power losses and increase bus voltage. This new approach has been tested on the IEEE 30 standard bus system. The study was applied to the 20 buses and 21 lines distribution system in Medan Indonesia. The results obtained are that the DG installation is applied to bus 2 with active and reactive power losses by 676.6612 MW and 240.2443 MVAR. There was a decrease in power losses by 52.55% from 718.16 MW to 340.77 MW and the voltage profile became well above 0.9500 p.u. The capability of the system after installation of the distributed generator is estimated to be up to 10 years.
Subjects
  • Distribution system

  • Electric power system...

  • Distributed generatio...

File(s)
Page 1-24.pdf (504.54 KB) Full Text.pdf (1.88 MB) Declaration Form.pdf (305.33 KB)
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5
Acquisition Date
Nov 19, 2024
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Acquisition Date
Nov 19, 2024
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