Probabilistic interval power flow calculation method for distribution networks considering the correlation of distributed wind power output

被引:6
|
作者
Liao, Xiaobing [1 ]
Zhang, Yiming [1 ]
Li, Zicheng [1 ]
Wei, Hanqi [1 ]
Ding, Hua [2 ]
机构
[1] Wuhan Inst Technol, Coll Elect & Elect Engn, Wuhan 430073, Hubei, Peoples R China
[2] State Grid Integrated Energy Serv Grp Co Ltd, Beijing 100052, Peoples R China
基金
中国国家自然科学基金;
关键词
Distribution network; Probabilistic interval power flow; Correlation; Affine optimization model; Evidence theory; NONPROBABILISTIC CONVEX MODEL; LOAD FLOW; UNCERTAINTY ANALYSIS;
D O I
10.1016/j.ijepes.2024.109827
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In order to more accurately simulate the impact of distributed wind farm access on the uncertain power flow distribution of distribution networks, this paper proposed a probabilistic interval power flow calculation method for distribution networks that considers correlation. Firstly, by dividing the output interval of distributed wind farms into several sub-intervals and assigning corresponding probabilities to the sub-intervals, a focal element model of distributed wind farm output is constructed. By combining affine arithmetic, the probabilistic interval power flow model of the distribution networks based on the focal element model is transformed into an affine optimization model to be solved. The parallelogram model, convex polygon model, and ellipsoid model are used to describe the correlation of distributed wind farm output. Through coordinate transformation, the correlation model is transformed into affine constraint conditions and embedded into the affine optimization model for simultaneous solution. Finally, based on the synthesis rules of evidence theory, the probabilistic boundary of the distribution networks probabilistic interval power flow solution is obtained. The simulation of two distribution networks shows that the established probabilistic interval power flow model can describe the uncertainty of power flow solutions through maximum and minimum probabilities, and the ellipsoidal model is more accurate in modeling correlation than the other two kinds of methods.
引用
收藏
页数:16
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