A Novel Solution Method for the Distribution Network Reconfiguration Problem based on an Objective Function and considering the Cost of Electricity Transmission

Authors

  • Nguyen Tung Linh Faculty of Control and Automation, Electric Power University, Vietnam https://orcid.org/0000-0003-2645-4599
  • Pham Vu Long Institute of Energy, 6 Ton That Tung, Hanoi, Vietnam | Faculty of Control and Automation, Electric Power University, Vietnam
Volume: 13 | Issue: 6 | Pages: 12366-12372 | December 2023 | https://doi.org/10.48084/etasr.6568

Abstract

The problem of distribution reconfiguration is an important issue in the optimal operation of distribution networks. Nowadays, with the diverse development of renewable energy sources, the uncertainty of the load becomes more complex, and the need for competitive retail electricity markets is more evident. This paper presents an optimal solution to this problem, utilizing the global advantage of the simulated annealing algorithm, improving the time parameter, and combining it with the rapid mutation ability of the genetic algorithm. Simultaneously, the Zbus, EBE, and PS models were integrated to optimize the costs, considering transmission costs under constraints related to taxes and economic indicators when connected to the distribution grid. The proposed method was simulated and tested on the IEEE 33-node standard power grid with three different scenarios. The simulation results showed that the proposed method provides optimal results, which can be applied to calculate the optimal operation of the distribution grid when participating in retail electricity markets.

Keywords:

simulation annealing algorithm, genetic algorithms, distribution network reconfiguration, transmission system usage, transmission cost allocation

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How to Cite

[1]
Linh, N.T. and Long, P.V. 2023. A Novel Solution Method for the Distribution Network Reconfiguration Problem based on an Objective Function and considering the Cost of Electricity Transmission. Engineering, Technology & Applied Science Research. 13, 6 (Dec. 2023), 12366–12372. DOI:https://doi.org/10.48084/etasr.6568.

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