Modelling and Simulation in Engineering (Jan 2024)

A Novel Method for Dynamic Distribution Network Reconfiguration Utilizing Capacitor and DG Allocation

  • Sana Sadeghi,
  • Alireza Jahangiri,
  • Ahmad Ghaderi Shamim

DOI
https://doi.org/10.1155/2024/9617622
Journal volume & issue
Vol. 2024

Abstract

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Because of the low voltage level and high current in the distribution system, as well as the high ohmic resistance of the conductors, the majority of power system losses are attributed to the distribution system. As a result, it is necessary to address the issue of reducing distribution system losses. To reduce losses in distribution systems, a wide variety of methods and algorithms have been proposed and are still being developed. The allocation of capacitors in a distribution system is one of the earliest and most effective methods for reducing system losses. With the growth of distributed generations and the diversity of their productive power, the use of capacitors in distribution systems has decreased slightly. Distributed generation resource allocation has replaced this method. Conversely, the reconfiguration of distribution systems has proven to be one of the easiest and cheapest methods for reducing distribution system losses in the past half-century. Distribution system reconfiguration has been the subject of many studies, each with different objectives. Many studies have also reconfigured the distribution system on a daily and hourly basis. A novel method for dynamic distribution network reconfiguration is presented in this study, which utilizes capacitors and distributed generation resources simultaneously. This method divides 24 h into several periods instead of considering real-time and hourly data. By reconfiguring and allocating capacitors and distributed generation resources during these periods, the voltage profile is improved and losses are reduced. An IEEE 33 bus network is used to test the simulations. A genetic algorithm was used to reduce losses and improve the voltage profile using MATLAB software. Based on the simulation results, this model provides better results than previous approaches.