1. Modeling and Simulation of Critical Infrastructure Interdependencies H.S. Jason Min, Walter Beyeler, and Theresa Brown Sandia National Laboratories Critical Infrastructures Modeling and Simulation Group -The National Infrastructure Simulation and Analysis Center (NISAC) is joint program at Sandia National Laboratories and Los Alamos National Laboratory, funded and managed by the Department of Homeland Security’s (DHS) Preparedness Directorate. - Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04- 94AL85000.

2. National Infrastructure Simulation and Analysis Center (NISAC) –DHS, Information Analysis and Infrastructure Protection –Jon MacLaren, Program Manager –Los Alamos National Lab Critical Infrastructure Protection Decision Support System (CIP/DSS) –DHS, Science and Technology –Paul Domiche, Program Managers –Los Alamos National Lab and Argonne National Lab Sandia DHS Infrastructure Protection Programs

3. Mission Provide fundamentally new modeling and simulation capabilities for the analysis of critical infrastructures, their interdependencies, vulnerabilities, and complexities. These advanced capabilities improve the robustness of our Nation’s critical infrastructures by aiding decision makers in the areas of policy assessment, mitigation planning, education, training, and near real-time assistance to crisis response organizations. Transportation Emergency Services Energy Water Postal & Shipping Monuments & Icons Defense Industrial Base Government Public Health Agriculture Food Information & Telecommunications Banking & Finance Chemical Industry & Hazardous Shipments

4. Quantify the potential impacts to Infrastructures for major events  Threats  Natural Disasters  Policy Analyses  Planning Exercises Analysis for Homeland Security NISAC provides comprehensive, quantitative analyses of the nation’s infrastructures and their interdependencies against all threats (e.g., natural, accidental and malevolent) in support of homeland security concerns for DHS Prioritization and Prioritization Methodologies Knowledge Management Systems Analysis Tools Infrastructure Disruption Models

5. Multiple Viewpoints High-fidelity models - individual infrastructure elements Systems models of aggregate supply - demand dynamics Decreasing detail, computation and development time Generic, highly abstracted network models Data on system elements RealisticAbstract Only know what is measured or monitored limited to specific set of conditions Event Lib CFS Census Detailed simulation of changes in conditions or behaviors UIS Suite N-ABLE NSMART R-NAS IEISS Air Model WISE Effects of conditions and limitations on system operation Port Simulators Petroleum System CIP/DSS National CIP/DSS Urban Simulation and identification of vulnerabilities of different network topologies to disruptions and effective mitigation PolyNet

6. Modeling and analyzing Interdependencies of Critical Infrastructures Importance - analyzing cascading effects of failure of one infrastructure across other infrastructures Difficulties - Data acquisition is difficult - Each individual infrastructure is complicated - Infrastructures are evolving - Governing regulations are changing Methodologies used - System Dynamics - IDEF0 - Decision Support System (Optimization tool)

7. National Infrastructure Interdependency Model - SD The Critical Infrastructure Protection Decision Support System (CIP/DSS) is a joint project between Argonne, Los Alamos and Sandia National Laboratories, in the Critical Infrastructure Protection Portfolio, of the DHS Science & Technology Directorate

8. One Particular Model: Power generation system dynamics model

9. National Infrastructure Interdependency Model – IDEF0

10. One Particular Model: Power generation system IDEF0

11. Case Study: Power Disruption Scenarios

12. Notation Used in the Study: Li = capacity loss of critical infrastructure i; CIi = total available products/services of critical infrastructure i; αij = satisfaction rate of desired consumption of Product/Service i for demand sector j; Dij = desired consumption of product/service i for demand sector j; Iij = available inventory of product/service i for demand sector j; ERj = economic revenues of demand sector j Rij = relative availability of product/Service i for sector j; LA = labor availability; APij = allocated product/service i for demand sector j; i = p(Power), f(Fuel), g(Natural Gas), w(Drinking Water), c(Communication); j = R(Residential), C(Commercial), I(Industrial), T(Transportation).

13. Disruption Experiments and results Scenarios: –Scenario 1: No disruption –Scenario 2: a. A power disruption starts at time 60 hour and ends at time 108 hour. b. 40% (Lp = 0.4 )of power generation capacity is lost at time 60 hour. c. market allocation algorithm of each critical infrastructure determined the allocation of each infrastructure materials/services. –Scenario 3: a. the same conditions with Scenario 2 used. b. the allocation of power is determined by the DSS using simulation based nonlinear optimization algorithm, and allocation of other infrastructure materials/services is determined by the market allocation of each critical infrastructure.

14. Results of different policies

15. Purchases transaction in residential sector

17. Conclusion Case Study: Based on the results of experiments, the intervention of market of the critical infrastructure may be necessary in the case of disruptions or damages in order to minimize global economic impact. Modeling and analyzing interdependencies of infrastructures is important The framework of the study successfully integrates the existing individual models together with system dynamics, functional models, and decision support system.

18. Questions?