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<p>With the increasing demand for electricity and financial constraints on building new power plants, power systems face challenges such as excessive transmission losses and high generation fuel costs. To address these issues, this paper presents an efficient multi-objective optimization approach utilizing the artificial bee colony (ABC) algorithm for minimizing generation fuel cost and transmission loss through the optimal placement and sizing of flexible AC transmission system (FACTS) controllers. The study evaluates the performance of the ABC algorithm against particle swarm optimization and differential evolution, analyzing key performance metrics, including standard deviation and computation processing time unit. The optimization framework employs a Pareto-optimal solution approach using a fuzzy decision-making tool, ensuring a balanced trade-off between cost-effectiveness and system performance. A detailed mathematical formulation of FACTS devices, including thyristor-controlled series compensator, static VAR compensator, and thyristor-controlled phase shifter, is provided, with applications to the IEEE-30 and IEEE-57 bus systems. The proposed multi-objective artificial bee colony (MOABC) algorithm demonstrates substantial efficiency gains, reducing generation costs and active power losses. Specifically, in the IEEE-30 bus system, the MOABC approach decreases the generation cost per dollar per hour from 991.35 to 859.786 and reduces active power loss from 11.74 to 6.56&#xa0;MW. Similarly, in the IEEE-57 bus system, generation costs drop from 8260.088 to 6019.9105 per dollar per hour, while active power losses decline from 16.97 to 13.66&#xa0;MW. These findings underscore the effectiveness of FACTS controllers in modern power networks and highlight the superiority of the MOABC algorithm in optimizing power system performance.</p>

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Efficient Multi-objective Approach Using ABC Algorithm for Minimizing Generation Fuel Cost and Transmission Loss Through FACTS Controllers Placement and Sizing

  • 期刊论文

  • 发表日期：

    2025年5月22日

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  • Jagannath Patra,
  • Ashiwani Yadav,
  • Rohit Kumar Verma,
  • Nitai Pal,
  • Scion Samantaray,
  • Kunja Bihari Sahu,
  • Pragya Singh,
  • Ramesh Singh Parihar,
  • Anil Kumar Panda

摘要

With the increasing demand for electricity and financial constraints on building new power plants, power systems face challenges such as excessive transmission losses and high generation fuel costs. To address these issues, this paper presents an efficient multi-objective optimization approach utilizing the artificial bee colony (ABC) algorithm for minimizing generation fuel cost and transmission loss through the optimal placement and sizing of flexible AC transmission system (FACTS) controllers. The study evaluates the performance of the ABC algorithm against particle swarm optimization and differential evolution, analyzing key performance metrics, including standard deviation and computation processing time unit. The optimization framework employs a Pareto-optimal solution approach using a fuzzy decision-making tool, ensuring a balanced trade-off between cost-effectiveness and system performance. A detailed mathematical formulation of FACTS devices, including thyristor-controlled series compensator, static VAR compensator, and thyristor-controlled phase shifter, is provided, with applications to the IEEE-30 and IEEE-57 bus systems. The proposed multi-objective artificial bee colony (MOABC) algorithm demonstrates substantial efficiency gains, reducing generation costs and active power losses. Specifically, in the IEEE-30 bus system, the MOABC approach decreases the generation cost per dollar per hour from 991.35 to 859.786 and reduces active power loss from 11.74 to 6.56 MW. Similarly, in the IEEE-57 bus system, generation costs drop from 8260.088 to 6019.9105 per dollar per hour, while active power losses decline from 16.97 to 13.66 MW. These findings underscore the effectiveness of FACTS controllers in modern power networks and highlight the superiority of the MOABC algorithm in optimizing power system performance.