Complex-Array-Operation Newton Solver for Power Grids Simulations

This paper presents a robust and efficient technique for performing repeated power flow simulations of power networks. The method relies on a vector-based formulation of the power balance equations combined with a complex-array operation Newton solver. It is shown how the method is suitable for advanced simulations of power grids, such as probabilistic analyses, where a large number of scenarios have to be explored in reasonable simulation times. Applications to benchmark single phase networks as well as to unbalanced three phase grids are provided.

[1]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .

[2]  Hongwei Li,et al.  A Loop-Analysis Theory Based Linear Power Flow Method for Three-Phase Distribution Power System , 2019, IEEE Access.

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

[4]  Hamidreza Zareipour,et al.  Probabilistic Power Flow by Monte Carlo Simulation With Latin Supercube Sampling , 2013, IEEE Transactions on Power Systems.

[5]  Maarouf Saad,et al.  A Monitoring Technique for Reversed Power Flow Detection With High PV Penetration Level , 2015, IEEE Transactions on Smart Grid.

[6]  Wenyuan Li,et al.  Probabilistic Power Flow Analysis for Hybrid HVAC and LCC-VSC HVDC System , 2019, IEEE Access.

[7]  Francois Vallee,et al.  Development of a probabilistic tool using Monte Carlo simulation and smart meters measurements for the long term analysis of low voltage distribution grids with photovoltaic generation , 2013 .

[8]  Giambattista Gruosso,et al.  Uncertainty-Aware Computational Tools for Power Distribution Networks Including Electrical Vehicle Charging and Load Profiles , 2019, IEEE Access.

[9]  R. Billinton,et al.  Probabilistic Power Flow Analysis Based on the Stochastic Response Surface Method , 2016, IEEE Transactions on Power Systems.

[10]  Phil Blythe,et al.  A probabilistic approach to combining smart meter and electric vehicle charging data to investigate distribution network impacts , 2015 .

[11]  Igor Simone Stievano,et al.  An Iterative Scheme for the Power-Flow Analysis of Distribution Networks based on Decoupled Circuit Equivalents in the Phasor Domain , 2020 .

[12]  Luca Daniel,et al.  Stochastic Testing Method for Transistor-Level Uncertainty Quantification Based on Generalized Polynomial Chaos , 2013, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[13]  Jianwei Liu,et al.  An Efficient Power Flow Algorithm for Distribution Systems with Polynomial Load , 2002 .

[14]  Antonello Monti,et al.  Dimension-Adaptive Sparse Grid Interpolation for Uncertainty Quantification in Modern Power Systems: Probabilistic Power Flow , 2016, IEEE Transactions on Power Systems.

[15]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[16]  Gabriela Hug,et al.  Robust Power Flow and Three-Phase Power Flow Analyses , 2018, IEEE Transactions on Power Systems.

[17]  Dongbin Xiu,et al.  The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations , 2002, SIAM J. Sci. Comput..

[18]  Balasubramaniam Natarajan,et al.  Probabilistic Voltage Sensitivity Analysis (PVSA)—A Novel Approach to Quantify Impact of Active Consumers , 2018, IEEE Transactions on Power Systems.

[19]  Whei-Min Lin,et al.  Multiple-frequency three-phase load flow for harmonic analysis , 2004, IEEE Transactions on Power Systems.