上海交通大学学报

上海交通大学学报 >

2025 , Vol. 59 >Issue 2: 186 - 199

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.240

新型电力系统与综合能源

支持虚拟电厂提供灵活爬坡服务的云边协同市场模型

  • 彭超逸 ,
  • 陈文哲 ,
  • 徐苏越 ,
  • 李建设 ,
  • 周华锋 ,
  • 顾慧杰 ,
  • 聂涌泉 ,
  • 孙海顺
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  • 1.中国南方电网电力调度控制中心,广州 510530
    2.华中科技大学 强电磁工程与新技术国家重点实验室,武汉 430074
彭超逸(1990—),博士,从事电力系统优化、电力市场、电力系统运行与控制等方向的研究.
孙海顺,教授,博士生导师,电话(Tel.):027-87543228;E-mail:haishunsun@hust.edu.cn.

收稿日期: 2023-06-13

  修回日期: 2023-07-24

  录用日期: 2023-08-09

  网络出版日期: 2023-09-20

基金资助

中国南方电网有限公司科技项目(000000KK52200035)

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Modeling of Cloud-Edge Collaborated Electricity Market Considering Flexible Ramping Products Provided by VPPs

  • PENG Chaoyi ,
  • CHEN Wenzhe ,
  • XU Suyue ,
  • LI Jianshe ,
  • ZHOU Huafeng ,
  • GU Huijie ,
  • NIE Yongquan ,
  • SUN Haishun
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  • 1. China Southern Power Grid Dispatching and Control Center, Guangzhou 510530, China
    2. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Received date: 2023-06-13

  Revised date: 2023-07-24

  Accepted date: 2023-08-09

  Online published: 2023-09-20

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摘要

虚拟电厂(VPP)拥有一定的负荷时移能力和功率调节能力,具备参与电力市场并提供灵活性爬坡服务(FRPs)的潜能,但受系统需求不确定影响,VPP难以进行准确的市场申报.因此,提出支持VPP参与电力市场并提供FRPs的云边协同市场架构,建立了对应的分布式优化交易模型.市场出清过程由系统运营商和VPP协同交互完成,能够准确引导VPP优化用电曲线并提供灵活爬坡服务.分布式优化模型采用目标级联分析方法迭代求解,并引入启发式约束条件来提升算法的收敛性.最后,在“鸭形曲线”典型场景中验证了所提市场交易模型的有效性,算例结果表明所提模型能有效降低系统运营费用,同时提升可再生能源消纳.

关键词： 虚拟电厂; 灵活性爬坡服务; 云边协同; 分布式优化

本文引用格式

彭超逸 , 陈文哲 , 徐苏越 , 李建设 , 周华锋 , 顾慧杰 , 聂涌泉 , 孙海顺 . 支持虚拟电厂提供灵活爬坡服务的云边协同市场模型[J]. 上海交通大学学报, 2025 , 59(2) : 186 -199 . DOI: 10.16183/j.cnki.jsjtu.2023.240

Abstract

Due to its load time shifting and power regulation capabilities, virtual power plants (VPPs) have the potential to participate in the electricity market and provide flexible ramping products (FRPs). However, it is hard for VPPs to make accurate bidding in the market, due to the uncertainty of their dispatching capability and system requirements. Therefore, a cloud-edge collaborated market architecture supporting VPPs participation in the electricity market and providing FRPs services is proposed, and the corresponding distributed optimization trading model is established. The market clearing process is completed through the collaborative interaction between the independent system operator and VPPs, which can accurately guide VPPs to optimize the electricity consumption and provide flexible climbing services. The distributed optimization model is iteratively solved using the analytical target cascading (ATC) method, and heuristic constraints are introduced to improve the convergence of the algorithm. Finally, the proposed method is evaluated by the simulation results of typical cases featuring the “duck-curve” net load, which demonstrate that the cloud-edge collaborated market can effectively reduce operating costs and promote the consumption of renewable energy.

Key words： virtual power plant (VPP); flexible ramping product (FRP); cloud-edge collaboration; distributed optimization

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