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<p>With the increasing demand for electricity and environmental concerns, efficient and sustainable power system operation is crucial. This study addresses the dynamic economic load dispatch (DELD) problem, considering both fuel cost and emission objectives, and incorporates an energy storage unit (ESU) to enhance operational flexibility. The main challenge lies in handling time-dependent demand, generator constraints, and multi-objective optimization in a realistic dynamic environment. To tackle this, a Genetic Algorithm (GA) is applied to simultaneously minimize fuel costs and emissions over a 24-hour horizon in a 10-unit power system. Comparative analysis with Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Shuffled Frog Leaping Algorithm (SFLA) demonstrates that the employed GA consistently achieves lower fuel costs and emissions, while maintaining computational efficiency. Simulation results show that the inclusion of the ESU reduces total fuel cost from $360,922.7 to $356,820.4 and total emissions from 3,125,786,452&#xa0;kg to 3,091,517,356&#xa0;kg, demonstrating improved system efficiency. The originality of this work lies in combining dynamic multi-objective optimization with energy storage integration, rarely addressed in the literature. These findings provide practical insights for power system operators aiming to reduce operational costs and emissions while improving reliability and sustainability.</p>

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Solving the dynamic economic load dispatch of power plants considering energy storage units

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月26日

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  • Ali Akbar Ghezi,
  • Mohammad Ebrahim Hajiabadi,
  • Hossein Lotfi

摘要

With the increasing demand for electricity and environmental concerns, efficient and sustainable power system operation is crucial. This study addresses the dynamic economic load dispatch (DELD) problem, considering both fuel cost and emission objectives, and incorporates an energy storage unit (ESU) to enhance operational flexibility. The main challenge lies in handling time-dependent demand, generator constraints, and multi-objective optimization in a realistic dynamic environment. To tackle this, a Genetic Algorithm (GA) is applied to simultaneously minimize fuel costs and emissions over a 24-hour horizon in a 10-unit power system. Comparative analysis with Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Shuffled Frog Leaping Algorithm (SFLA) demonstrates that the employed GA consistently achieves lower fuel costs and emissions, while maintaining computational efficiency. Simulation results show that the inclusion of the ESU reduces total fuel cost from $360,922.7 to $356,820.4 and total emissions from 3,125,786,452 kg to 3,091,517,356 kg, demonstrating improved system efficiency. The originality of this work lies in combining dynamic multi-objective optimization with energy storage integration, rarely addressed in the literature. These findings provide practical insights for power system operators aiming to reduce operational costs and emissions while improving reliability and sustainability.