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<p>Under the background of the “Carbon Peak and Carbon Neutrality” strategy and the “new electricity reform”, the increasing penetration rate of distributed renewable energy in virtual power plants (VPPS) poses new challenges to their flexible operation. This paper proposes a low-carbon operational strategy based on a virtual power plant cluster (VPPC). To safeguard the rights and interests of each VPP, an efficient three-tier coordinated energy management system is developed for energy-saving scheduling. In the first stage, an internal pricing model is established to optimize returns for both individual VPPs and the cluster as a whole, utilizing a bidding strategy model that focuses on day-ahead optimal scheduling and VPPC returns. In the second stage, we employ a Model Predictive Control (MPC) method based on rolling time domains to address real-time rescheduling issues faced by each VPPP. This approach accommodates internal “source-load” uncertainties inherent in VPP operations while minimizing costs. In the third stage, we introduce a penalty and benefit distribution mechanism aimed at addressing operating deviations of individual VPPs. This mechanism ensures overall benefits are maximized while maintaining control accuracy across the VPPC. Finally, simulation analyses were conducted in an electricity spot market environment using data from a VPPC located in East China to validate the effectiveness of our proposed methods and models.</p>

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Low-Carbon Scheduling Strategy for Multi-Virtual Power Plant Cluster Energy Sharing Collaborative Participation in Market Trading

  • 期刊论文

  • 发表日期：

    2026年1月7日

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  • Fang Liu,
  • Yun Xu,
  • Xiao-Ming Zheng,
  • Si-Ming Wei,
  • Cheng-Peng Tang,
  • Zong Guo

摘要

Under the background of the “Carbon Peak and Carbon Neutrality” strategy and the “new electricity reform”, the increasing penetration rate of distributed renewable energy in virtual power plants (VPPS) poses new challenges to their flexible operation. This paper proposes a low-carbon operational strategy based on a virtual power plant cluster (VPPC). To safeguard the rights and interests of each VPP, an efficient three-tier coordinated energy management system is developed for energy-saving scheduling. In the first stage, an internal pricing model is established to optimize returns for both individual VPPs and the cluster as a whole, utilizing a bidding strategy model that focuses on day-ahead optimal scheduling and VPPC returns. In the second stage, we employ a Model Predictive Control (MPC) method based on rolling time domains to address real-time rescheduling issues faced by each VPPP. This approach accommodates internal “source-load” uncertainties inherent in VPP operations while minimizing costs. In the third stage, we introduce a penalty and benefit distribution mechanism aimed at addressing operating deviations of individual VPPs. This mechanism ensures overall benefits are maximized while maintaining control accuracy across the VPPC. Finally, simulation analyses were conducted in an electricity spot market environment using data from a VPPC located in East China to validate the effectiveness of our proposed methods and models.