AI 中文总结
针对虚拟电厂可行域聚合方法通用性与适应性不足的问题，提出数据驱动逆优化方法，采用虚拟电池模型并基于多场景数据确定参数，数值测试验证了方法准确性，助力利用分布式能源灵活性。
AI 中文摘要
系统运营商可能允许虚拟电厂（VPP）提交其可行域（FR）以进行市场出清和调度。虚拟电厂需要根据其内部分布式能源资源（DERs）的单个运行模型，确定要提交的整体可行域，这是一个可行域聚合问题。现有的可行域聚合方法依赖于分析方法，存在通用性和适应性方面的问题。本文提出一种数据驱动方法来近似虚拟电厂的能量调节可行域，该方法采用虚拟电池模型近似虚拟电厂的聚合可行域，并基于原始运行模型生成的多场景运行数据，通过逆优化确定模型参数。数值测试验证了所提方法的准确性，我们认为本研究有助于更好地利用分布式能源资源的灵活性。
英文摘要
System operators will probably allow virtual power plants (VPPs) to submit their feasible region (FR) for market clearing and dispatch. A VPP needs to determine its FR to submit as a whole based on the individual operation model of its internal distributed energy resources (DERs), which is an FR aggregation problem. Existing FR aggregation approaches rely on analytical methods, which have issues with generality and adaptability. In this paper, we propose a data-driven approach to approximate the energy-regulation FR of VPPs. It adopts the virtual battery model to approximate the aggregate FR of a VPP and determines the model parameters through inverse optimization based on generated multi-scenario operation data using the original operation model. Numerical tests verified the accuracy of the proposed method. We believe that our work helps to better leverage the flexibility of DERs.
CommentsPublished in: 2024 IEEE Power & Energy Society General Meeting (PESGM)
DOI：10.1109/PESGM51994.2024.10689111
