Data-Physical Hybrid Driven Distribution Network Linear Power Flow Considering Non-Smooth Constraints

被引:0
作者
Ju, Yuntao [1 ]
Zhang, Tianlei [2 ]
Wang, Lei [1 ]
Huang, Yan [3 ]
Yu, Zongmin [3 ]
Ma, Yuxuan [2 ]
机构
[1] North China Univ Technol, Sch Energy Storage Sci & Engn, Beijing 100144, Peoples R China
[2] North China Univ Technol, Sch Elect & Control Engn, Beijing 100144, Peoples R China
[3] Tsinghua Univ, Sichuan Energy Internet Res Inst, Chengdu 610213, Peoples R China
基金
中国国家自然科学基金;
关键词
Mathematical models; Load flow; Distribution networks; Computational modeling; Accuracy; Reactive power; Voltage; Data-physical hybrid driven; distribution network; droop control; linear power flow; non-smooth characteristic; FORMULATION; MODEL;
D O I
10.1109/TIA.2024.3462682
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Existing liner power flow (LPF) models do not consider the non-smooth constraint characteristics of the voltage source converter (VSC) and on-load tap changer (OLTC) that contain operation limit and dead zones, which limits its application. Thus, a three-phase LPF model for distribution networks is proposed, which considers non-smooth constraint characteristics. Firstly, smoothing functions are used to effectively fit the non-smooth constraint characteristics, resulting in control functions that are continuous and differentiable. Then, based on the first-order Taylor series expansion, the three-phase power flow equations are physically linearized, and the terms to compensate errors are obtained through the partial least squares (PLS) method. Compared with the model without considering the non-smooth constraints, the three-phase LPF model considering the non-smooth constraints can accurately represent the operating characteristics of the VSC and the OLTC in the actual distribution network, thus providing more reliable power flow calculation results. Based on a typical 42-node distribution network, the proposed model is compared and analyzed against other LPF models. The results indicate that the proposed model has the ability to handle non-smooth constraints, with higher computational accuracy compared to existing data-physical hybrid driven models.
引用
收藏
页码:1748 / 1756
页数:9
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