Interval radial power flow using extended DistFlow formulation and Krawczyk iteration method with sparse approximate inverse preconditioner

被引:44
作者
Ding, Tao [1 ,2 ]
Li, Fangxing [2 ]
Li, Xue [2 ]
Sun, Hongbin [1 ]
Bo, Rui [3 ]
机构
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN 37996 USA
[3] Midcontinent Independent Transmiss Syst Operator, Eagan, MN USA
基金
中国国家自然科学基金;
关键词
load flow; iterative methods; approximation theory; inverse problems; minimisation; matrix algebra; Interval radial power flow; extended DistFlow formulation; Krawczyk iteration method; sparse approximate inverse preconditioner; renewable energy source; voltage magnitude; simplified DistFlow formulation; interval linear equation; Frobenius norm minimisation; parallel implementation; dense approximate inverse matrix; dropping strategy; sparse representation; 33-bus system; interval LU decomposition; interval Gauss elimination method; Monte Carlo simulation; 123 bus system; 69-bus system; SYSTEMS;
D O I
10.1049/iet-gtd.2014.1170
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
Confronted with uncertainties, especially from large amounts of renewable energy sources, power flow studies need further analysis to cover the range of voltage magnitude and transferred power. To address this issue, this work proposes a novel interval power flow for the radial network by the use of an extended, simplified DistFlow formulation, which can be transformed into a set of interval linear equations. Furthermore, the Krawczyk iteration method, including an approximate inverse preconditioner using Frobenius norm minimisation, is employed to solve this problem. The approximate inverse preconditioner guarantees the convergence of the iterative method and has the potential for parallel implementation. In addition, to avoid generating a dense approximate inverse matrix in the preconditioning step, a dropping strategy is introduced to perform a sparse representation, which can significantly reduce the memory requirement and ease the matrix operation burden. The proposed methods are demonstrated on 33-bus, 69-bus, 123-bus, and several large systems. A comparison with interval LU decomposition, interval Gauss elimination method, and Monte Carlo simulation verifies its effectiveness.
引用
收藏
页码:1998 / 2006
页数:9
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