上海交通大学学报 ›› 2025, Vol. 59 ›› Issue (3): 333-341.doi: 10.16183/j.cnki.jsjtu.2023.352

• 新型电力系统与综合能源 • 上一篇    下一篇

基于分布式模型预测控制的自适应二次调频策略

曹永吉1,2, 张江丰3, 王天宇3, 郑可轲3, 吴秋伟1()   

  1. 1.清华大学 清华深圳国际研究生院,广东 深圳 518055
    2.山东大学 智能创新研究院,济南 250101
    3.国网浙江省电力有限公司电力科学研究院,杭州 310006
  • 收稿日期:2023-08-24 修回日期:2023-09-20 接受日期:2023-12-15 出版日期:2025-03-28 发布日期:2025-04-02
  • 通讯作者: 吴秋伟,教授,博士生导师; E-mail: quiwudtu@163.com.
  • 作者简介:曹永吉(1992—),副研究员,从事电力系统稳定分析与控制、可再生能源并网及储能技术应用研究.
  • 基金资助:
    国家自然科学基金(52177096);山东省自然科学基金(ZR2021QE133);国家电网公司科技资助项目(5108-202219050A-1-1-ZN)

Self-Adaptive Secondary Frequency Regulation Strategy Based on Distributed Model Predictive Control

CAO Yongji1,2, ZHANG Jiangfeng3, WANG Tianyu3, ZHENG Keke3, WU Qiuwei1()   

  1. 1. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China
    2. Academy of Intelligent Innovation, Shandong University, Jinan 250101, China
    3. State Grid Zhejiang Electric Power Co., Ltd. Research Institute, Hangzhou 310006, China
  • Received:2023-08-24 Revised:2023-09-20 Accepted:2023-12-15 Online:2025-03-28 Published:2025-04-02

摘要:

针对电力系统参数变化导致二次调频适应性降低的问题,提出一种基于分布式模型预测控制(DMPC)的自适应二次调频策略.首先,构建多区域互联系统的二次调频模型,进而基于频率响应轨迹建立各区域系统的参数辨识模型.其次,采用递推最小二乘法求解参数辨识模型,在线更新区域系统的参数.然后,以区域控制偏差最小为目标,利用DMPC优化机组出力,实现二次调频控制.最后,利用算例分析验证了所提方法的有效性.

关键词: 分布式模型预测控制, 二次调频, 参数辨识, 区域控制偏差, 优化决策

Abstract:

To address the issues of reduced adaptability of secondary frequency regulation caused by changes in power system parameters, a self-adaptive secondary frequency regulation strategy based on distributed model predictive control (DMPC) is proposed. First, a model of a multi-area interconnected power system is built. Based on the frequency response trajectory, a parameter identification model for each area of the system is established. Then, the recursive least square method is used to solve the parameter identification model and update the parameters of each area online. Additionally, with the objective to minimize the area control error (ACE), DMPC is adopted to optimize the power of generators for secondary frequency regulation. Finally, a case study is conducted to demonstrate the effectiveness of the proposed strategy.

Key words: distributed model predictive control (DMPC), secondary frequency regulation, parameter identification, area control error (ACE), optimized decision-making

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