Congestion Management using the Circulatory System Based Optimization Algorithm

Authors

  • Gia Tue Tang Faculty of Engineering & Technology, Nguyen Tat Thanh University, Vietnam
  • Nguyen Duc Huy Bui Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Vietnam
  • Duong Thanh Long Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Vietnam
Volume: 14 | Issue: 3 | Pages: 14361-14366 | June 2024 | https://doi.org/10.48084/etasr.7204

Abstract

Congestion management is one of the most important issues in power system operation, especially in competitive electricity markets. The main aim of Congestion Management (CM) is to eliminate congestion in transmission lines. The most common technique to deal with the CM problem is re-dispatching the generator. However, finding an optimal solution for the CM problem constitutes a challenge for many researchers. Recently, a new biologically inspired metaheuristic algorithm, called Circulatory System Based Optimization (CSBO), was developed and proven to be effective in handling optimization issues. The CSBO algorithm was applied to solve the CM problem for the IEEE-30 bus system in two different cases. The former was compared with the Crayfish Optimization Algorithm (COA), Artificial Rabbits Optimization (ARO), Improved Grey Wolf Optimizer (I-GWO), and other existing methods. The simulation results revealed that the cost obtained from the proposed CSBO algorithm was lower than 14.5%, 11.31%, 9.97%, and 4% compared to PSO, FPA, FFA, and ALO. In addition, the stability of the proposed algorithm was higher than that of the other methods after 30 trials.

Keywords:

optimization algorithm, re-dispatching generator, congestion management, circulatory system based optimization, IEEE-30 bus system

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"MATPOWER." 2023, [Online]. Available: https://matpower.org/.

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How to Cite

[1]
G. T. Tang, N. D. H. Bui, and D. T. Long, “Congestion Management using the Circulatory System Based Optimization Algorithm”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14361–14366, Jun. 2024.

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