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Design for Survival. Dynamic Infrastructures Coordination

Design for Survival. Dynamic Infrastructures Coordination. José R. Martí, Jorge A. Hollman, Carlos Ventura, Juri Jatskevich, The University of British Columbia. NSERC/PSEPC/Industry. “Develop innovative solutions to mitigate large disaster situations involving multiple infrastructure systems”.

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Design for Survival. Dynamic Infrastructures Coordination

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  1. Design for Survival. Dynamic Infrastructures Coordination José R. Martí, Jorge A. Hollman, Carlos Ventura, Juri Jatskevich, The University of British Columbia JIIRP - UBC

  2. NSERC/PSEPC/Industry “Develop innovative solutions to mitigate large disaster situations involving multiple infrastructure systems” JIIRP - UBC

  3. JIIRP Canada ($3 M) Jose Marti, University of British Columbia ($1.1 M), critical linkages in infrastructure networks Vincent Tao, York University, emergency management using geographic decision support systems Wenjun Zhang, University of Saskatchewan, models for critical infrastructure networks Benoit Robert, École Polytechnique de Montréal, interdependencies and domino effects in life-supporting networks Tamer El-Diraby, University of Toronto, interdependencies through an analysis of stakeholder needs, risks, and competencies Edward McBean, University of Guelph, resilience of water infrastructure and health response systems against waterborne diseases JIIRP - UBC

  4. UBC Team • Electrical Engineering Power Systems Communication Systems Data Security • Civil Engineering Earthquakes Damage Assessment • Software Engineering Human Decisions Metamodels • Computer Science Systems Visualization (SFU Univ.) Disaster Room Virtualization Databases Integration • Commerce Business Recovery • Geography GIS Systems • Psychology Panic Control Public Education JIIRP - UBC

  5. UBC’s Partners • British Columbia Transmission Corporation • BC Hydro • Telus Corporation • Greater Vancouver Regional District • Vancouver International Airport Authority JIIRP - UBC

  6. Design for Survival Problem Identification Problem Modelling Solution Formulation Solution Implementation JIIRP - UBC

  7. Problem Identification JIIRP - UBC

  8. “First priority during disaster situations is, and should be, human survival” JIIRP - UBC

  9. Infrastructures Recovery • During normal life, each infrastructure (power grid, telecom grid, etc.) knows how to recover from problems in its own system • Recovery times are adequate for normal life • Normal recovery assumes the other infrastructures are available • During disasters multiple infrastructures are damaged simultaneously • Recovery times are those for survival JIIRP - UBC

  10. Disaster Timeline Maslow’s Hierarchy of Needs JIIRP - UBC

  11. System Formulation Vital Survival Tokens Tokens Delivery Optimum Dispatch JIIRP - UBC

  12. Vital Survival Tokens • Water (suitable for drinking) • Food (adequate for emergency situations) • Body Shelter (breathable air, clothing, temperature, housing) • Panic Control (hope, political and religious leaders, psychologists, media) • Personal Communication (whereabouts of loved ones) • Individual Preparedness (education) • Sanitation (waste disposal, washing) • Medical Care (medicines, physicians, nurses) • Civil Order (fire fighters, police, army) JIIRP - UBC

  13. Tokens Delivery • Survival tokens need to be delivered from where they are available to where they are needed • Tokens availability and needs change as disaster evolves • Transportation channels capacity and delay changes as disaster evolves • System is time dependent JIIRP - UBC

  14. Optimum Dispatch • In general there will be more than one supply point and more than one destination point • Optimum dispatching will depend on tokens availability, needs, and transportation channels capacity and delays • Optimum dispatch needs readjustments as system conditions change (real-time) JIIRP - UBC

  15. System Modelling Cells Nodes Channels JIIRP - UBC

  16. Components • Cells: entity that performs a function • Tokens: goods needed by cells to perform function • Nodes:contain cells in same geographical location • Channels: allow transportation of tokens between separate geographical locations (from node to node) JIIRP - UBC

  17. System of Systems JIIRP - UBC

  18. Example of Cells • Hospital • Fire Hall • RCMP Station • Electrical Substation • Telecom Substation • Water Station • Residential Area • Victims Refuge Area (we identified 17 cells in UBC test case) JIIRP - UBC

  19. Modelling & Simulation Challenge • Set up “System of Systems” • … without knowing much about any of them! JIIRP - UBC

  20. Granularity • “Zoom Level”, e.g. power system • At transmission level large load centers are represented as equivalent loads • At distribution level transmission system is represented as an equivalent • Hierarchical structure • Solution in form of subsystem blocks • Blocks inside blocks JIIRP - UBC

  21. Hierarchical Solution JIIRP - UBC

  22. Token Networks • Cells, nodes and channels form token networks • Each token network has its generators, loads, and transportation channels • E.g., electric power, water, medicines • Some channels are shared, e.g., roads, airports JIIRP - UBC

  23. Lights, equipment (Load) Emergency Diesel Power Utility Cell (Gen) (Gen) Hospital Cell 1 2 D12 D13 Electric Power (token 1) 3 Residential Cell (Load) JIIRP - UBC

  24. Medicines Supplier B (Gen) Supply Room (Gen) R 1 Medicines Used By Hospital (Load) R Medicines Supplier A (Gen) D12 2 Hospital Cell D42 4 D43 Medicines (token 3) 3 Medicines used by Residential Cell (Load) JIIRP - UBC

  25. Dispatching Decisions • Dispatching decisions determine how much power is sent to the hospital and how much to the neighbourhood • Dispatching decisions determine how many medicines are sent to the hospital and how many medicines are sent to the residential neighbourhood • Optimum dispatch problem: Determine dispatching amounts Dik to “best” satisfy cells constraints JIIRP - UBC

  26. Hospital Cell Nurses Food Doctors hospital Each token is delivered to the cell by corresponding token network Electric Power Patients from neighbourhood Water Medicines JIIRP - UBC

  27. electricity Cell k=2 water Token 1 Token 2 doctors Token 3 Hospital Cell Input-Output Model JIIRP - UBC

  28. Node = 1st subscript Token = 2nd subscript x21 = electricity used x22 = water used x23 = medicines x24 = doctors used x25 = nurses used x26 = beds produced Hospital Cell Function Vector of Tokens JIIRP - UBC

  29. Hospital Cell Function • Beds generated x26 depends on availability of needed tokens. If the relationship were linear (which is not): • For the general nonlinear case: JIIRP - UBC

  30. Constraints 120  Beds  150 10  Doctors  15 15  Nurses  20 500 Medicines needed in 15 minutes Constraints can be modified every 5 minutes (or whatever Δt is chosen) … JIIRP - UBC

  31. All System Cells • One function for each cell • Subject to its internal constraints JIIRP - UBC

  32. Cell’s Wellness • The cell’s wellness at a given moment can be expressed as a function of the cell’s current operating capacity versus its needed capacity. In the hospital case • Cell wellness can be used to put weight in constraints • Other political, environmental, etc. constraints can also add weights to constraints JIIRP - UBC

  33. Channel Model • Transportation channels have capacity limits and time delays • Some channels (e.g., roads, airports) may be shared by multiple token networks and only road/airport people can provide best routes and channel delays JIIRP - UBC

  34. Channel Model D(t) = dispatched token amount x(t) = received token amount g = conductance of channel m = magnitude loss (usually = 1) k = time delay, e.g., 2 hours Channel capacity = constraint on D JIIRP - UBC

  35. Channel Saturation k increases strongly with saturation JIIRP - UBC

  36. Channel Damage • E.g., medicines truck route involves broken road, to be repaired in 3 hours, plus 2 hours for travelling time • E.g., power line will be down for 4 hours JIIRP - UBC

  37. Continuity Condition (KCL) ( generated in the node – no channel delay) JIIRP - UBC

  38. Solution Formulation Transportation and cell equations Dispatch Optimization JIIRP - UBC

  39. System of Equations e.g., cell 2 tokens 3 and 4 • Cell Functions • Transportation Equations JIIRP - UBC

  40. LTI Discrete Time System with Nonlinear Constraints • Transportation equations are linear with one to Nth-order delays • Cell functions impose nonlinear constraints • Equations can be solved step by step at ∆t (delay-one) intervals using MATE/EMTP techniques • Dispatching values Dij-kcan be optimized for a scenario interval length, e.g. 10 hrs, and updated at each solution step, e.g., every 10 minutes JIIRP - UBC

  41. Optimum Tokens Dispatch • Diagonalize transportation equations taking sparsity into consideration • Solve the TPBV problem to meet the cell requirements • The shooting method (Perkins, Martí, Dommel, 1995) or the waveform relaxation method (Wang, Martí, 1996) can be implemented with step by step solution of the difference equations JIIRP - UBC

  42. Optimum Power Flow Problem • Dommel & Tinney, 1968, solved OPF problem with Newton’s method and sparsity with very fast results • System 300x80 = 2,400 eqns was solved in 4 min on IBM 7040 (1.3 MHz 2 CPU) JIIRP - UBC

  43. Optimum Tokens Dispatch • Real-time solutions are possible • A case with 100 cells and 50 tokens: 100x50 = 5,000 eqns can take about 5 minutes for a 10-hour scenario updating every Δt=10 minutes using a dual-processor 3 GHz PC • PC-Cluster architecture (Hollman, DeRybel, Marti, 2003, 2005) can linearly escalate the computational power JIIRP - UBC

  44. MITS Real-Time Simulator Multi-Infrastructures Tokens Simulator Fast Real-Time Solutions JIIRP - UBC

  45. MITS Simulator • Based on our MATE (Multi-Area Thevenin Equivalent) real-time simulator • Each token has its corresponding transportation system (matrix sub-block) • All tokens come together at cells subsystem and must satisfy the cell functions JIIRP - UBC

  46. Software-Hardware Mapping JIIRP - UBC

  47. Solution Lock-Up • A large area system may well “lock-up” and we may not be able to find feasible dispatching solutions for given disaster situation • What can be done at planning stage? • Add resources • Reallocate resources and loads • Split system into ISLANDS JIIRP - UBC

  48. Conclusions • Analytical tool to study disaster scenarios • Useful for • Resilient system design • Disaster mitigation plans • Real time disaster room scenarios • Real-time solutions for what-if scenarios • Based on proven tools for discrete-time solutions and optimum dispatching solutions • Easy to interface with human layer JIIRP - UBC

  49. Dynamic Islanding for Survival JIIRP - UBC

  50. Breakup into Subsystems JIIRP - UBC

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