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<p>The power generation process in thermal power plants releases significant amounts of carbon dioxide, contributing to environmental pollution. To address the environmental challenges in power generation, the power system must evolve toward higher efficiency and reduced emissions. Carbon capture power plants (CCPPs) and wind power are expected to play increasingly important roles because of their cleaner energy characteristics. However, the high energy consumption of CCPPs and the complexity of scheduling models limit the development of low-carbon economic scheduling. To address these issues, a low-carbon economic dispatch method is proposed to coordinate the operation of wind power and CCPPs. First, a wind‒fire low-carbon economic dispatch model with CCPPs is constructed. Second, an archive self-learning-based multi-objective particle swarm optimization (AS-MOPSO) algorithm is proposed, with static scheduling results used as initial guidance particles for solving a carbon capture plant’s wind‒fire low-carbon economic dispatch model. Finally, a constraint-handling method is proposed. It repairs infeasible solutions outside the solution region to bring them within the feasible region, offering more intermediate solutions for the AS-MOPSO algorithm. The simulation results of the IEEE 39-node system show that the proposed method reduces the dispatch system’s carbon emissions and generation cost. Compared with the comparative method, the carbon emissions of AS-MOPSO decreased by about 2000 tons.</p>

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Low-carbon economic dispatch of power systems based on wind energy and flexible carbon capture devices

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月21日

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  • Guangli Chu,
  • Rongshuai Li,
  • Chong Cao,
  • Bin Li

摘要

The power generation process in thermal power plants releases significant amounts of carbon dioxide, contributing to environmental pollution. To address the environmental challenges in power generation, the power system must evolve toward higher efficiency and reduced emissions. Carbon capture power plants (CCPPs) and wind power are expected to play increasingly important roles because of their cleaner energy characteristics. However, the high energy consumption of CCPPs and the complexity of scheduling models limit the development of low-carbon economic scheduling. To address these issues, a low-carbon economic dispatch method is proposed to coordinate the operation of wind power and CCPPs. First, a wind‒fire low-carbon economic dispatch model with CCPPs is constructed. Second, an archive self-learning-based multi-objective particle swarm optimization (AS-MOPSO) algorithm is proposed, with static scheduling results used as initial guidance particles for solving a carbon capture plant’s wind‒fire low-carbon economic dispatch model. Finally, a constraint-handling method is proposed. It repairs infeasible solutions outside the solution region to bring them within the feasible region, offering more intermediate solutions for the AS-MOPSO algorithm. The simulation results of the IEEE 39-node system show that the proposed method reduces the dispatch system’s carbon emissions and generation cost. Compared with the comparative method, the carbon emissions of AS-MOPSO decreased by about 2000 tons.