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1.浙江大学电气工程学院,浙江省杭州市 310063
2.华电电力科学研究院有限公司,浙江省杭州市 310030
3.内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司,内蒙古自治区呼和浩特市 010020
Received:12 September 2024,
Revised:2024-12-13,
Published Online:27 March 2025,
Published:10 July 2025
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钱立群,魏卿,胡鹏飞等.基于数据驱动预测控制的低感知度配电网在线无功优化方法[J].电力系统自动化,2025,49(13):61-69.
QIAN Liqun,WEI Qing,HU Pengfei,et al.Online Reactive Power Optimization Method Based on Data-driven Predictive Control for Distribution Network with Low Situation Awareness[J].Automation of Electric Power Systems,2025,49(13):61-69.
钱立群,魏卿,胡鹏飞等.基于数据驱动预测控制的低感知度配电网在线无功优化方法[J].电力系统自动化,2025,49(13):61-69. DOI: 10.7500/AEPS20240912001.
QIAN Liqun,WEI Qing,HU Pengfei,et al.Online Reactive Power Optimization Method Based on Data-driven Predictive Control for Distribution Network with Low Situation Awareness[J].Automation of Electric Power Systems,2025,49(13):61-69. DOI: 10.7500/AEPS20240912001.
分布式电源的广泛接入使得配电网的潮流波动和电压越界问题愈加突出,特别是给拓扑结构未知、观测不足、数据质量低的低感知度配电网的无功优化带来了巨大挑战。文章引入数据驱动预测控制算法,提出了一种低感知度配电网在线无功优化方法。首先,利用历史运行数据构造配电网等效代理模型;然后,使用正则化技术对配电网在线无功优化问题建模;最后,在滚动优化过程中使用求解器求解,并给出无功优化策略。该方法基于行为系统理论,具有良好的可解释性,无需事前的模型训练,求解效率高。同时,还可在线实时追踪配电网状态并更新等效代理模型,具有较强的鲁棒性,适用于开关状态多变的低感知度配电网。通过改进的IEEE 33节点系统的算例仿真验证了所提方法的有效性。
The extensive access to distributed generators makes the problems of power flow fluctuation and voltage violation of the distribution network more and more prominent
especially bringing great challenges to the reactive power optimization of distribution networks with low situation awareness and unknown topology
insufficient observation
and low data quality. This paper introduces a data-driven predictive control algorithm and proposes an online reactive power optimization method for distribution networks with low situation awareness. First
the historical operation data is utilized to construct an equivalent agent model of the distribution network. Then
the online reactive power optimization problem for distribution networks is modeled using regularization techniques. Finally
a solver is used to solve and give reactive power optimization strategies during the rolling optimization process. The proposed method based on behavioral system theory has good interpretability
requires no prior model training
and has high solving efficiency. It can also track the distribution network state and update the equivalent agent model online in real time
which is robust and suitable for distribution networks with low situation awareness and variable switching states. The effectiveness of the proposed method is verified by the case simulation of a modified IEEE 33-bus system.
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