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<p>To address the challenges of slow convergence, susceptibility to local optima, and inadequate low-carbon constraints in virtual power plants (VPPs) scheduling, this paper proposes a multi-energy coupled low-carbon optimization scheduling model. The model integrates a tiered carbon trading mechanism and demand response strategies, covering multi-energy flow balance constraints across electricity, heat, gas, hydrogen, and carbon emissions to achieve coordinated economic and environmental optimization. An enhanced hybrid metaheuristic algorithm incorporating differential evolution into a hierarchical population-based search framework is introduced to enhance global search capability and avoid premature convergence. Case study results demonstrate that the proposed enhanced hybrid metaheuristic algorithm significantly outperforms several benchmark algorithms in evolutionary generations, convergence time, and solution accuracy. Specifically, compared to the base algorithm, the proposed algorithm reduces evolutionary generations by 36.2 (46% reduction), shortens convergence time by 2.5&#xa0;s (20% reduction), and lowers VPP operating costs by 6,800 CNY (9.6% reduction). Against literature algorithms, it achieves cost reductions of 11,100 to 62,100 CNY (14.7–50.0%). Simulations of typical daily multi-energy flow scheduling validate the model’s effectiveness in reducing carbon emissions, improving energy utilization efficiency, and enhancing economic performance.</p>

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Low-carbon optimization scheduling of multi-energy coupled virtual power plants based on an enhanced hybrid metaheuristic integrating hierarchical search and differential evolution

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Jia Lu,
  • Junjie Wang,
  • Jijun Liu,
  • Youwu Liu

摘要

To address the challenges of slow convergence, susceptibility to local optima, and inadequate low-carbon constraints in virtual power plants (VPPs) scheduling, this paper proposes a multi-energy coupled low-carbon optimization scheduling model. The model integrates a tiered carbon trading mechanism and demand response strategies, covering multi-energy flow balance constraints across electricity, heat, gas, hydrogen, and carbon emissions to achieve coordinated economic and environmental optimization. An enhanced hybrid metaheuristic algorithm incorporating differential evolution into a hierarchical population-based search framework is introduced to enhance global search capability and avoid premature convergence. Case study results demonstrate that the proposed enhanced hybrid metaheuristic algorithm significantly outperforms several benchmark algorithms in evolutionary generations, convergence time, and solution accuracy. Specifically, compared to the base algorithm, the proposed algorithm reduces evolutionary generations by 36.2 (46% reduction), shortens convergence time by 2.5 s (20% reduction), and lowers VPP operating costs by 6,800 CNY (9.6% reduction). Against literature algorithms, it achieves cost reductions of 11,100 to 62,100 CNY (14.7–50.0%). Simulations of typical daily multi-energy flow scheduling validate the model’s effectiveness in reducing carbon emissions, improving energy utilization efficiency, and enhancing economic performance.