CNIP JIIRP - UBC1 Design for Survival. Dynamic Infrastructures Coordination José R. Martí, Jorge A. Hollman, Carlos Ventura, Juri Jatskevich, The University of British Columbia

CNIP JIIRP - UBC2 NSERC/PSEPC/Industry “Develop innovative solutions to mitigate large disaster situations involving multiple infrastructure systems”

CNIP JIIRP - UBC3 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

CNIP JIIRP - UBC4 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

CNIP JIIRP - UBC5 UBC’s Partners British Columbia Transmission Corporation BC Hydro Telus Corporation Greater Vancouver Regional District Vancouver International Airport Authority

CNIP JIIRP - UBC6 Design for Survival Problem Identification Problem Modelling Solution Formulation Solution Implementation

CNIP JIIRP - UBC7 Problem Identification

CNIP JIIRP - UBC8 “First priority during disaster situations is, and should be, human survival”

CNIP JIIRP - UBC9 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

CNIP JIIRP - UBC10 Disaster Timeline Maslow’s Hierarchy of Needs

CNIP JIIRP - UBC11 System Formulation Vital Survival Tokens Tokens Delivery Optimum Dispatch

CNIP JIIRP - UBC12 Vital Survival Tokens 1. Water (suitable for drinking) 2. Food (adequate for emergency situations) 3. Body Shelter (breathable air, clothing, temperature, housing) 4. Panic Control (hope, political and religious leaders, psychologists, media) 5. Personal Communication (whereabouts of loved ones) 6. Individual Preparedness (education) 7. Sanitation (waste disposal, washing) 8. Medical Care (medicines, physicians, nurses) 9. Civil Order (fire fighters, police, army)

CNIP JIIRP - UBC13 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

CNIP JIIRP - UBC14 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)

CNIP JIIRP - UBC15 System Modelling Cells Nodes Channels

CNIP JIIRP - UBC16 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)

CNIP JIIRP - UBC17 System of Systems

CNIP JIIRP - UBC18 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)

CNIP JIIRP - UBC19 Modelling & Simulation Challenge Set up “System of Systems” … without knowing much about any of them!

CNIP JIIRP - UBC20 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

CNIP JIIRP - UBC21 Hierarchical Solution

CNIP JIIRP - UBC22 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

CNIP JIIRP - UBC23 Electric Power (token 1) Power Utility Cell Emergency Diesel Hospital Cell (Load) 12 3 Lights, equipment (Load) D 12 D 13 Residential Cell (Gen)

CNIP JIIRP - UBC24 Medicines Used By Hospital (Load) Medicines (token 3) Medicines Supplier A (Gen) D 42 D 43 D 12 R R Supply Room (Gen) Medicines Supplier B (Gen) Medicines used by Residential Cell (Load) Hospital Cell

CNIP JIIRP - UBC25 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 D ik to “best” satisfy cells constraints

CNIP JIIRP - UBC26 Doctors hospital Patients from neighbourhood Electric Power Water Medicines Food Hospital Cell Each token is delivered to the cell by corresponding token network Nurses

CNIP JIIRP - UBC27 Hospital Cell Input-Output Model Cell k=2 Token 1 Token 2 Token 3 waterelectricity doctors

CNIP JIIRP - UBC28 Hospital Cell Function Node = 1 st subscript Token = 2 nd subscript x 21 = electricity used x 22 = water used x 23 = medicines x 24 = doctors used x 25 = nurses used x 26 = beds produced Vector of Tokens

CNIP JIIRP - UBC29 Hospital Cell Function Beds generated x 26 depends on availability of needed tokens. If the relationship were linear (which is not): For the general nonlinear case:

CNIP JIIRP - UBC30 Constraints 120  Beds   Doctors   Nurses  Medicines needed in 15 minutes Constraints can be modified every 5 minutes (or whatever Δt is chosen) …

CNIP JIIRP - UBC31 All System Cells One function for each cell Subject to its internal constraints

CNIP JIIRP - UBC32 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

CNIP JIIRP - UBC33 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

CNIP JIIRP - UBC34 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

CNIP JIIRP - UBC35 Channel Saturation k increases strongly with saturation

CNIP JIIRP - UBC36 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

CNIP JIIRP - UBC37 Continuity Condition (KCL) ( generated in the node – no channel delay)

CNIP JIIRP - UBC38 Solution Formulation Transportation and cell equations Dispatch Optimization

CNIP JIIRP - UBC39 System of Equations Cell Functions Transportation Equations e.g., cell 2 tokens 3 and 4

CNIP JIIRP - UBC40 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 D ij-k can be optimized for a scenario interval length, e.g. 10 hrs, and updated at each solution step, e.g., every 10 minutes

CNIP JIIRP - UBC41 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

CNIP JIIRP - UBC42 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)

CNIP JIIRP - UBC43 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

CNIP JIIRP - UBC44 MITS Real-Time Simulator Multi-Infrastructures Tokens Simulator Fast Real-Time Solutions

CNIP JIIRP - UBC45 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

CNIP JIIRP - UBC46 Software-Hardware Mapping

CNIP JIIRP - UBC47 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

CNIP JIIRP - UBC48 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

CNIP JIIRP - UBC49 Dynamic Islanding for Survival

CNIP JIIRP - UBC50 Breakup into Subsystems

CNIP JIIRP - UBC51 Islanding Strategy The network is segmented into “self- sufficient islands” to prevent cascading effects. An island is able to survive on its own for a limited time period. Beyond this period help needs to be coordinated and delivered from the external world Panic control and prevention of cascading effects requires immediate response Islanding can be less expensive than the redundancy approach

CNIP JIIRP - UBC52 Advantages Increases survivability of the network Minimizes restoration time Decreases impact of cascading events by identifying high-load nodes Dynamic definition of islands for different levels of quality service or catastrophe scenarios Optimization of network upgrades

CNIP JIIRP - UBC53 Implementation Partnerships among NCIs operators and Government Identification of cells and islands Pre-established decision hierarchy depending on emergency scenario Identification of NCIs for most critical emergency scenarios Incentives (e.g. sleeping contracts) Long term mitigation oriented plans

CNIP JIIRP - UBC54 Challenges Identification of Interdependencies (Implies disclosure of sensitive information) Cooperation among NCIs operators and Government Management of sensitive information (central vs. distributed) Panic control