Less is More: Real-time Failure Localization in Power Systems

Cascading failures in power systems exhibit nonlocal propagation patterns, which make the analysis and mitigation of failures difficult. In this work, we propose a distributed control framework inspired by the recently proposed concepts of unified controller and network tree-partition that offers strong guarantees in both the mitigation and localization of cascading failures in power systems. In this framework, the transmission network is partitioned into several control areas which are connected in a tree structure, and the unified controller is adopted by generators or controllable loads for fast timescale disturbance response. After an initial failure, the proposed strategy always prevents successive failures from happening, and regulates the system to the desired steady state where the impact of initial failures are localized as much as possible. For extreme failures that cannot be localized, the proposed framework has a configurable design, that progressively involves and coordinates more control areas for failure mitigation and, as a last resort, imposes minimal load shedding. We compare the proposed control framework with Automatic Generation Control (AGC) on the IEEE 118-bus test system. Simulation results show that our novel framework greatly improves the system robustness in terms of the N − 1 security standard, and localizes the impact of initial failures in majority of the load profiles that are examined. Moreover, the proposed framework incurs significantly less load loss, if any, compared to AGC.

[1]  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.

[2]  I. Dobson,et al.  A LOADING-DEPENDENT MODEL OF PROBABILISTIC CASCADING FAILURE , 2005, Probability in the Engineering and Informational Sciences.

[3]  D. Jayaweera,et al.  Value of Security: Modeling Time-Dependent Phenomena and Weather Conditions , 2002, IEEE Power Engineering Review.

[4]  Elwood S. Buffa,et al.  Graph Theory with Applications , 1977 .

[5]  Adilson E. Motter,et al.  Stochastic Model for Power Grid Dynamics , 2006, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[6]  Enrique Mallada,et al.  Distributed plug-and-play optimal generator and load control for power system frequency regulation , 2018, International Journal of Electrical Power & Energy Systems.

[7]  Daniel S. Kirschen,et al.  Criticality in a cascading failure blackout model , 2006 .

[8]  Chen Liang,et al.  Monotonicity properties and spectral characterization of power redistribution in cascading failures , 2017, 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[9]  Adam Wierman,et al.  Failure Localization in Power Systems via Tree Partitions , 2018, 2018 IEEE Conference on Decision and Control (CDC).

[10]  Paul Hines,et al.  Controlling cascading failures with cooperative autonomous agents , 2007, Int. J. Crit. Infrastructures.

[11]  Paul D. H. Hines,et al.  Cascading Power Outages Propagate Locally in an Influence Graph That is Not the Actual Grid Topology , 2015, IEEE Transactions on Power Systems.

[12]  Gil Zussman,et al.  Power grid vulnerability to geographically correlated failures — Analysis and control implications , 2012, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[13]  Ufuk Topcu,et al.  Design and Stability of Load-Side Primary Frequency Control in Power Systems , 2013, IEEE Transactions on Automatic Control.

[14]  I. Dobson,et al.  Initial review of methods for cascading failure analysis in electric power transmission systems IEEE PES CAMS task force on understanding, prediction, mitigation and restoration of cascading failures , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[15]  V. E. Lynch,et al.  Critical points and transitions in an electric power transmission model for cascading failure blackouts. , 2002, Chaos.

[16]  Daniel Bienstock,et al.  Using mixed-integer programming to solve power grid blackout problems , 2007, Discret. Optim..

[17]  Massimo Marchiori,et al.  A topological analysis of the Italian electric power grid , 2004 .

[18]  Enrique Mallada,et al.  Optimal Load-Side Control for Frequency Regulation in Smart Grids , 2014, IEEE Transactions on Automatic Control.

[19]  Jiajia Song,et al.  Dynamic Modeling of Cascading Failure in Power Systems , 2014, IEEE Transactions on Power Systems.

[20]  Dorian Mazauric,et al.  Analysis of Failures in Power Grids , 2017, IEEE Transactions on Control of Network Systems.

[21]  E A Leicht,et al.  Suppressing cascades of load in interdependent networks , 2011, Proceedings of the National Academy of Sciences.

[22]  Edmund M. Yeh,et al.  Resilience to Degree-Dependent and Cascading Node Failures in Random Geometric Networks , 2011, IEEE Transactions on Information Theory.

[23]  Anna Scaglione,et al.  A Markov-Transition Model for Cascading Failures in Power Grids , 2012, 2012 45th Hawaii International Conference on System Sciences.

[24]  Nasir Ghani,et al.  Stochastic Analysis of Cascading-Failure Dynamics in Power Grids , 2014, IEEE Transactions on Power Systems.

[25]  Na Li,et al.  Connecting Automatic Generation Control and Economic Dispatch From an Optimization View , 2016, IEEE Trans. Control. Netw. Syst..

[26]  Enrique Mallada,et al.  A unified framework for frequency control and congestion management , 2016, 2016 Power Systems Computation Conference (PSCC).

[27]  Hadi Saadat,et al.  Power Systems Analysis , 2002 .

[28]  Jun Yan,et al.  Cascading Failure Analysis With DC Power Flow Model and Transient Stability Analysis , 2015, IEEE Transactions on Power Systems.

[29]  Na Li,et al.  Connecting Automatic Generation Control and Economic Dispatch From an Optimization View , 2014, IEEE Transactions on Control of Network Systems.