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This paper explores the optimal siting and sizing of thyristor-switched capacitors (TSCs) in distribution networks, aiming to minimize the operating costs by reducing energy losses while accounting for the annualized TSC investment costs. Within the optimization strategy, the sine-cosine algorithm (SCA) determines the optimal TSC locations and sizes, and the interior-point optimizer (IPOPT) solves the resulting optimal power flow problem, ensuring an efficient grid operation under fixed and variable reactive power scenarios. Tested on a 33-bus system, the proposed methodology was compared against the Chu & Beasley genetic algorithm (CBGA), the particle swarm optimizer (PSO), the black widow optimizer (BWO), and the SCA. According to the results, the BWO, SCA, and SCA-IPOPT methodologies reached the best solutions, reducing the objective function by 11.22%, with SCA-IPOPT further improving this value to 12.43% under a variable reactive power scenario. All numerical validations were performed using the Julia software.

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Hybrid SCA-IPOPT Approach for the Optimal Location and Sizing of TSCs in Medium-Voltage Distribution Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Oscar Danilo Montoya,
  • Víctor M. Garrido-Arévalo,
  • Walter Gil-González

摘要

This paper explores the optimal siting and sizing of thyristor-switched capacitors (TSCs) in distribution networks, aiming to minimize the operating costs by reducing energy losses while accounting for the annualized TSC investment costs. Within the optimization strategy, the sine-cosine algorithm (SCA) determines the optimal TSC locations and sizes, and the interior-point optimizer (IPOPT) solves the resulting optimal power flow problem, ensuring an efficient grid operation under fixed and variable reactive power scenarios. Tested on a 33-bus system, the proposed methodology was compared against the Chu & Beasley genetic algorithm (CBGA), the particle swarm optimizer (PSO), the black widow optimizer (BWO), and the SCA. According to the results, the BWO, SCA, and SCA-IPOPT methodologies reached the best solutions, reducing the objective function by 11.22%, with SCA-IPOPT further improving this value to 12.43% under a variable reactive power scenario. All numerical validations were performed using the Julia software.