上海交通大学学报

上海交通大学学报

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基于分布式模型预测控制的自适应二次调频策略（网络首发）

  

  1. 1. 清华大学清华深圳国际研究生院；2. 山东大学智能创新研究院；3. 国网浙江省电力有限公司电力科学研究院
  • 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

  1. （1. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; 2. Academy of Intelligent Innovation, Shandong University, Jinan 250101, China; 3. State Grid Zhejiang Electric Power Co., Ltd. Research Institute, Hangzhou 310006, China）

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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（distributed model predictive control，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（recursive least square，RLS）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（area control error，ACE）最小为目标，利用DMPC优化机组出力，实现二次调频控制。最后，通过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: Aiming at the problem of the decreasing adaptability of secondary frequency regulation caused by the change of power system parameters, a self-adaptive secondary frequency regulation scheme based on distributed model predictive control (DMPC) is proposed. First, the model of a multi-area interconnected power system is built. According to the frequency response trajectory, a parameter identification model of each area is established. Then, the recursive least square (RLS) method is used to solve the parameter identification model, and the parameters of each area are updated online. Additionally, with the objective to minimize the area control error (ACE), the DMPC is adopted to optimize the power of generators for secondary frequency regulation. Finally, a case study is conducted to verify the effectiveness of the proposed scheme.

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

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