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<p>The widespread use of finite energy sources such as coal, nuclear materials, natural gas, and petroleum for electricity production presents two critical challenges: resource depletion and significant environmental harm through greenhouse gas emissions. To tackle these issues, it’s crucial to minimize fuel usage and emissions in power generation. While various optimization algorithms have been proposed to address the combined economic emission dispatch (CEED) problem, they often fail to fully satisfy technical constraints or produce optimal solutions. This study introduces a optimization algorithm with multi-objective function that merges Gbestguided artificial bee colony with elitist non-dominated sorting genetic algorithm (GMOABCNSGA-II). The researchers apply this method to test systems with 13 and 15 units, taking into account constraints like ramp rate limits, prohibited zones, spinning reserve, and transmission losses. They also extend the application to systems with 6, 14, and 40 units. The attributes containing sixteen multi-decision making techniques are used to assess the resulting nondominated solutions. The findings reveal that the GMOABC-NSGA-II algorithm outperforms previous methods, achieving optimal fuel cost and emission values without violating constraints and requiring 20% fewer iterations. Moreover, the algorithm enhances fuel cost reduction by 15% and emission reduction by 18%.</p>

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Economic emission dispatch of power plants considering ramp rate, prohibited zones, and spinning reserve using a hybrid MOABC-NSGA-II Algorithm

  • 期刊论文

  • 发表日期：

    2025年7月13日

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  • Komal Madhale,
  • Najmuddin Jamadar,
  • H. T. Jadhav

摘要

The widespread use of finite energy sources such as coal, nuclear materials, natural gas, and petroleum for electricity production presents two critical challenges: resource depletion and significant environmental harm through greenhouse gas emissions. To tackle these issues, it’s crucial to minimize fuel usage and emissions in power generation. While various optimization algorithms have been proposed to address the combined economic emission dispatch (CEED) problem, they often fail to fully satisfy technical constraints or produce optimal solutions. This study introduces a optimization algorithm with multi-objective function that merges Gbestguided artificial bee colony with elitist non-dominated sorting genetic algorithm (GMOABCNSGA-II). The researchers apply this method to test systems with 13 and 15 units, taking into account constraints like ramp rate limits, prohibited zones, spinning reserve, and transmission losses. They also extend the application to systems with 6, 14, and 40 units. The attributes containing sixteen multi-decision making techniques are used to assess the resulting nondominated solutions. The findings reveal that the GMOABC-NSGA-II algorithm outperforms previous methods, achieving optimal fuel cost and emission values without violating constraints and requiring 20% fewer iterations. Moreover, the algorithm enhances fuel cost reduction by 15% and emission reduction by 18%.