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

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.

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

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

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

Case Simulator Statistical Reasoning Causal Reasoning

Online Simulator

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.

North America Power System interconnections

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

North America power system grid

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.

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?

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?

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 control Juhwan 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

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: 2 On 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.