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Dynamic Islanding for Survival

Dynamic Islanding for Survival. UBC Group Joint Infrastructure Interdependencies Research Program. J.R. Marti, KD Srivastava, J. Jatskevich, J. Hollman. Objective .

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Dynamic Islanding for Survival

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  1. Dynamic Islanding for Survival UBC Group Joint Infrastructure Interdependencies Research Program J.R. Marti, KD Srivastava, J. Jatskevich, J. Hollman

  2. Objective • As stated in our Proposal the objective of our project is to study decision making for critical linkages in infrastructure networks. • By modelling critical interdependencies between networks we can account for the impact of each network on the other network’s risk management and restoration processes. • By coordinating actions among networks we can minimize down times and maximize the survival chances of networks and the people who use them.

  3. Islanding-to-Survive Paradigm • In Power Systems, the concept of Islanding is well known and applied to improve the survivability of the network. • The network is segmented into “self- sufficient islands” to prevent cascading effects. • Islands can survive by themselves for a period of time. • In epidemics islanding of the infected victims is essential to prevent spreading. • An island’s extended survivability depends on its critical links to the other networks • Restoration of vital links to the islands constitutes the recovery process • Islanding is much less expensive than the redundancy approach

  4. Survivability Parameters • Survival time Sk(t): how long island k can survive before its links are re-established • Link restoration time lki(t): time needed to restore link i in island k • Sk(t) = f{lki(t)} • lki(t) = f{lki(t)} for all k≠i} • System Survivability Index SSI: composite index reflecting total system strength

  5. Methodology • Surveying of infrastructure operators • Identification of critical links • Identification of Islands • Risk assessment of each infrastructure must include dependencies with other infrastructures • Process of restoration includes • self-sufficient actions (internal to each utility) • links restoration (coordinated effort) • Network simulation • Interdependencies simulation • Coordinated restoration simulation

  6. Case Simulator StatisticalReasoning CausalReasoning

  7. Online Simulator

  8. Identification of Power System Islands • Critical to minimize the restoration time, and survivability of the network. • Decrease impact of Cascading events by identifying hi-load nodes • Dynamic definition of islands for different levels of quality service or catastrophe scenarios.

  9. North America Power System interconnections

  10. Identification of interdependencies • Critical to extend the survivability of network islands. • Improves action coordination. • Optimizes future network upgrades.

  11. North America power system grid

  12. Key tasks • Identify Vulnerabilities in each network. • Identify Interdependencies among networks. • Simulate different scenarios. • Select actions to cluster the networks into self sufficient islands, resulting in mitigating the impact of the catastrophe. • Select actions to restore the networks, first at Island level, then as groups of Islands and finally as a unified network.

  13. Tentative survey questions • Can you identify a group of weak or critical points in your infrastructure? • Can you define areas in your network capable of providing service without dependency on the rest of the network? • Are there any other particular infrastructures of critical importance for your infrastructure? • Is your infrastructure of critical importance to any other infrastructures? • Can you identify any critical interdependency points in your network? • Do you have any specific time constraints to interact with those interdependencies?

  14. Tentative survey questions (cont.) • Do you have in place any specific protocol of coordinated actions with other infrastructures to manage critical situations? • What kind of specific critical situations do you have contingency plans for? • Can you identify any area or group of elements in your infrastructure as critical to provide basic emergency service? • Do you have in place a restoration protocol? • Does the restoration protocol require coordinated actions with other infrastructures?

  15. References • Structural vulnerability of the North American power grid Reka Albert, Istvan Albert, and Gary L. Nakarado, Phys. Rev. E 69, 025103 (2004) • Cascade Control and Defense in Complex Networks Adilson E. Motter, Phys. Rev. Lett. 93, 098701 (2004) • Self-healing in power systems: an approach using islanding and rate of frequency decline-based load shedding, Haibo You; Vittal, V.; Zhong Yang; Power Systems, IEEE Transactions on. Volume 18,  Issue 1,  Feb. 2003 Page(s):174 – 181 • Toward self-healing energy infrastructure systems, Amin, M.; Computer Applications in Power, IEEE Volume 14,  Issue 1,  Jan. 2001 Page(s):20 – 28 • Multi-agent technology for vulnerability assessment and controlJuhwan Jung; Chen-Ching Liu;Power Engineering Society Summer Meeting, 2001. IEEE Volume 2,  15-19 July 2001 Page(s):1287 - 1292 vol.2 • EPRI, P39.006 Simulation of Islanding Scenarios for Operator Training (052166) • Solution for the crisis in electric power supply. Heydt, G.T.; Liu, C.C.; Phadke, A.G.; Vittal, V.; Computer Applications in Power, IEEE Volume 14,  Issue 3,  July 2001 Page(s):22 - 30 • Splitting strategies for islanding operation of large-scale power systems using OBDD-based methods. Kai Sun; Da-Zhong Zheng; Qiang Lu; Power Systems, IEEE Transactions on Volume 18,  Issue 2,  May 2003 Page(s):912 - 923 • Determination of generator groupings for an islanding scheme in the Manitoba Hydro system using the method of normal forms. Vittal, V.; Kliemann, W.; Ni, Y.-X.; Chapman, D.G.; Silk, A.D.; Sobajic, D.J.; Power Systems, IEEE Transactions on. Volume 13,  Issue 4,  Nov. 1998 Page(s):1345 - 1351

  16. References (cont.) • System islanding considerations for improving power system restoration at Manitoba Hydro. Archer, B.A.; Davies, J.B.; Electrical and Computer Engineering, 2002. IEEE CCECE 2002. Canadian Conference on. Volume 1,  12-15 May 2002 Page(s):60 - 65 vol.1 • Real-time EMTP-based transients simulation Marti, J.R.; Linares, L.R.; Power Systems, IEEE Transactions on Volume 9,  Issue 3,  Aug. 1994 Page(s):1309 – 1317 • Distributed Heterogeneous Simulation of Large-Scale Dynamical Systems.Lucas, C. E., Waiters, E. A., & Jatskevich, J. (2003, April 7-9). 13-th International Ship Control Systems Symposium, Orlando, FL. • Real time network simulation with PC-cluster. Hollman, J.; Marti, J.; Power Systems, IEEE Transactions on, May 2003 Volume: 18,  Issue: 2On page(s): 563- 569 • Ovni: Integrated software/hardware solution for real-time simulation of large power systems. Marti, J.; Linares, L.; Hollman, J.; Moreira, F.; 14th Power Systems Computer Conference. Seville: PSCC’02, 2002. • An enhanced network interconnect for the new ovni real-time simulator. De Rybel, T.; Hollman, J.; Marti, J.; Power Systems Computer Conference, Liege, Belgium: PSCC’05, 2005. • Infrastructure Adaptability and Survivability for Dependable and Reliable Services. Wilikens, M. (2000). The report from the meeting held in Brussels on 23rd May 2000. Brussels: EC Joint Research Center. • Building the energy internet. The Economist, March 13, 2004,  U.S. Edition, Technology Quarterly.

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