Balanced, non-contiguous partitioning of power systems considering operational constraints

Abstract This paper presents an integer programming based partitioning of dynamic graphs that arise in power systems. The proposed approach allows for explicitly expressing power systems operational constraints in the partitioning algorithm. Balanced, non-contiguous graphs appear in several power systems applications such as network partitioning for high performance computing based parallel transient simulators and in wide area control. Quality of partition obtained using the developed algorithm compares favorably with a well known multi-level graph partitioning approach – METIS. Several test systems, ranging from a 9 bus test system to the 2383 bus western polish test system is used to demonstrate the applicability of proposed approach for power system partitioning problems. In addition, applicability of the proposed approach for a non-stationary system such as power networks is demonstrated by partitioning in real-time. Real-time in this context is defined as the interval between two dispatches as the edge weights of the dynamic graph is expected to change at every dispatch.

[1]  Bruce Hendrickson,et al.  A Multi-Level Algorithm For Partitioning Graphs , 1995, Proceedings of the IEEE/ACM SC95 Conference.

[2]  K. S. Swarup,et al.  Network partitioning using harmony search and equivalencing for distributed computing , 2012, J. Parallel Distributed Comput..

[3]  Shrirang Abhyankar.  DEVELOPMENT OF AN IMPLICITLY COUPLED ELECTROMECHANICAL AND ELECTROMAGNETIC TRANSIENTS SIMULATOR FOR POWER SYSTEMS , 2011 .

[4]  Vipin Kumar,et al.  A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..

[5]  F. Muñoz,et al.  Hopfield K-Means clustering algorithm: A proposal for the segmentation of electricity customers , 2011 .

[6]  Rubén J. Sánchez-García,et al.  Hierarchical Spectral Clustering of Power Grids , 2014, IEEE Transactions on Power Systems.

[7]  Robert E. Smith,et al.  Optimal clustering of power networks using genetic algorithms , 1994 .

[8]  I. Kamwa,et al.  Fuzzy Partitioning of a Real Power System for Dynamic Vulnerability Assessment , 2009, IEEE Transactions on Power Systems.

[9]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[10]  V. Terzija,et al.  Two-step spectral clustering controlled islanding algorithm , 2013, 2013 IEEE Power & Energy Society General Meeting.

[11]  Vipin Kumar,et al.  Multilevel Algorithms for Multi-Constraint Graph Partitioning , 1998, Proceedings of the IEEE/ACM SC98 Conference.

[12]  V. Vittal,et al.  Slow coherency-based islanding , 2004, IEEE Transactions on Power Systems.

[13]  I. Kamwa,et al.  Automatic Segmentation of Large Power Systems Into Fuzzy Coherent Areas for Dynamic Vulnerability Assessment , 2007, IEEE Transactions on Power Systems.

[14]  Alexander J. Flueck,et al.  Real-Time Power System Dynamics Simulation Using a Parallel Block-Jacobi Preconditioned Newton-GMRES Scheme , 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

[15]  Eduardo Cotilla-Sanchez,et al.  Multi-Attribute Partitioning of Power Networks Based on Electrical Distance , 2013, IEEE Transactions on Power Systems.

[16]  Xiaoming Wang,et al.  Slow coherency grouping based islanding using minimal cutsets and generator coherency index tracing using the continuation method , 2005 .

[17]  Fushuan Wen,et al.  Power system network partitioning using tabu search , 1999 .

[18]  George Karypis,et al.  Multi-threaded Graph Partitioning , 2013, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing.

[19]  Zhenyu Huang,et al.  Predictive Dynamic Simulation for Large-Scale Power Systems through High-Performance Computing , 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

[20]  Vladimir Terzija,et al.  Determination of sectionalising strategies for parallel power system restoration: A spectral clustering-based methodology , 2014 .

[21]  Carlos E. Castañeda,et al.  Partitioned modal extraction applied to power system wide-area measurements using empirical orthogonal functions , 2015 .

[22]  George Karypis,et al.  Multilevel algorithms for partitioning power-law graphs , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.