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

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.

[1]  Rudolf Krawczyk,et al.  Interval operators of a function of which the Lipschitz matrix is an interval M-matrix , 1983, Computing.

[2]  V. M. da Costa,et al.  Interval arithmetic in current injection power flow analysis , 2012 .

[3]  A. Sarić,et al.  Integrated fuzzy state estimation and load flow analysis in distribution networks , 2003 .

[4]  S.T. Lee,et al.  Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion , 2004, IEEE Transactions on Power Systems.

[5]  Eldon Hansen,et al.  Bounding the solution of interval linear equations , 1992 .

[6]  Jürgen Garloff,et al.  Interval Gaussian Elimination with Pivot Tightening , 2008, SIAM J. Matrix Anal. Appl..

[7]  E. Hansen,et al.  Bounding solutions of systems of equations using interval analysis , 1981 .

[8]  Alfredo Vaccaro,et al.  A Range Arithmetic-Based Optimization Model for Power Flow Analysis Under Interval Uncertainty , 2013, IEEE Transactions on Power Systems.

[9]  George J. Cokkinides,et al.  A new probabilistic power flow analysis method , 1990 .

[10]  Biswarup Das Radial distribution system power flow using interval arithmetic , 2002 .

[11]  Edmond Chow,et al.  Approximate Inverse Preconditioners via Sparse-Sparse Iterations , 1998, SIAM J. Sci. Comput..

[12]  Alfredo Vaccaro,et al.  An Affine Arithmetic-Based Methodology for Reliable Power Flow Analysis in the Presence of Data Uncertainty , 2010, IEEE Transactions on Power Systems.

[13]  D. Shirmohammadi,et al.  A compensation-based power flow method for weakly meshed distribution and transmission networks , 1988 .

[14]  Kevin Tomsovic,et al.  Boundary load flow solutions , 2004 .

[15]  A. Vaccaro,et al.  Radial Power Flow Tolerance Analysis by Interval Constraint Propagation , 2009, IEEE Transactions on Power Systems.

[16]  R.N. Allan,et al.  Evaluation Methods and Accuracy in Probabilistic Load Flow Solutions , 1981, IEEE Transactions on Power Apparatus and Systems.

[17]  Fernando L. Alvarado,et al.  Interval arithmetic in power flow analysis , 1991 .

[18]  K. Prasad,et al.  Use of interval arithmetic to incorporate the uncertainty of load demand for radial distribution system analysis , 2006, IEEE Transactions on Power Delivery.

[19]  B. Das Consideration of input parameter uncertainties in load flow solution of three-phase unbalanced radial distribution system , 2006, IEEE Transactions on Power Systems.

[20]  Felix F. Wu,et al.  Network reconfiguration in distribution systems for loss reduction and load balancing , 1989 .