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<p>The integration of renewable energy into modern power systems requires scheduling strategies that balance economic efficiency with low-carbon objectives. This study develops an optimal scheduling framework for virtual power plant (VPP) operating under carbon trading and green certificate mechanisms. First, the model coordinates the dispatch of gas turbines, wind power, photovoltaic units, and energy storage systems, incorporating market incentives and emission constraints into a unified optimization problem. Second, a Q-learning enhanced particle swarm optimization algorithm (QPSO) is designed, which adaptively adjusts inertia weights and learning factors according to search states to improve convergence stability and solution quality. Finally, comparative analyses with the standard particle swarm optimization algorithm and independent Q-learning demonstrate significant improvements: under the dual-market scenario, net profit increases by 89.9%, renewable energy utilization rises by 19.9%, and carbon emissions are reduced by 39.4%. These results indicate that combining dual-market participation with adaptive optimization provides a feasible and effective approach to enhancing both the economic and environmental performance of VPP operations.</p>

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Optimal scheduling method of carbon-green certificate trading virtual power plant via Q-learning-enhanced particle swarm algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

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  • Jiansheng Jin,
  • Xinfu Pang,
  • Baoshi Wang,
  • Duo Wang,
  • Zhedong Zheng

摘要

The integration of renewable energy into modern power systems requires scheduling strategies that balance economic efficiency with low-carbon objectives. This study develops an optimal scheduling framework for virtual power plant (VPP) operating under carbon trading and green certificate mechanisms. First, the model coordinates the dispatch of gas turbines, wind power, photovoltaic units, and energy storage systems, incorporating market incentives and emission constraints into a unified optimization problem. Second, a Q-learning enhanced particle swarm optimization algorithm (QPSO) is designed, which adaptively adjusts inertia weights and learning factors according to search states to improve convergence stability and solution quality. Finally, comparative analyses with the standard particle swarm optimization algorithm and independent Q-learning demonstrate significant improvements: under the dual-market scenario, net profit increases by 89.9%, renewable energy utilization rises by 19.9%, and carbon emissions are reduced by 39.4%. These results indicate that combining dual-market participation with adaptive optimization provides a feasible and effective approach to enhancing both the economic and environmental performance of VPP operations.