The Resilient Infrastructure initiative increases understanding of cascading and escalating impacts among critical infrastructure based on comprehensive analysis of upstream, internal, and downstream dependencies.
Resilient Infrastructure Initiative
Delivering Science and Technology to Enable Resilient Design
Argonne’s Resilient Infrastructure Initiative focuses on delivering science and technology to enable the resilient design of future infrastructure systems, thereby reducing risk to lives and property.
On February 13, 2013, the White House released Presidential Policy Directive (PPD) 21 – Critical Infrastructure Security and Resilience. Its objective is to advance “a national unity of effort to strengthen and maintain secure, functioning, and resilient critical infrastructure.” Our society depends on the provision of reliable, secure, and efficient services by that infrastructure. However, it is rapidly aging and growing more insecure, becoming more vulnerable to natural disasters, accidents, and deliberate destructive acts. Further, the demands on our infrastructure are ever changing and thus, our infrastructure must adapt to 21st century requirements. According to PPD 21, innovation should include:
Promoting R&D to enable the secure and resilient design and construction of critical infrastructure and more secure accompanying cyber technology
Argonne’s Resilient Infrastructure Initiative:
- Increases understanding of cascading and escalating impacts among infrastructure systems based on comprehensive analysis of dependencies and interdependencies,
- Serves as an “integrator” and streamlined path in delivering to various stakeholders a critical combination of experimentation, computation, engineering, and analysis tools essential to building a secure, resilient, and cost effective infrastructure, and
- Drives the development of resilient infrastructure materials and technologies through simulations and standards development to transform the design of future infrastructure systems.
“Good design and advanced materials can improve transportation and energy, water, and waste systems, and also create more sustainable urban environments.”
– National Academies
“Research and development should be funded at the federal level to develop new, more efficient methods and materials for building and maintaining the nation’s infrastructure.”
– American Society of Civil Engineers
Resilient Infrastructure Capabilities
As described below, Argonne National Laboratory offers a wide range of resiliency-related capabilities, tools, techniques and engineering methods to optimize interdependencies and respond to rapidly changing needs.
Resilient Infrastructure Publications
Argonne National Laboratory researchers have published a wide range of resiliency-related reports, papers and articles, some of which are shown below.
Huang, W., Sun, K., Qi, J., and Xu, Y., “Voronoi Diagram Based Optimization of Dynamic Reactive Power Sources,” 2015 IEEE Power & Energy Society General Meeting.
Ju, W., Qi, J., and Sun, K., “Simulation and Analysis of Cascading Failures on an NPCC Power System Test Bed,” 2015 IEEE Power & Energy Society General Meeting.
Portante, E., Craig, B., Talaber Malone, L., Kavicky, J., and Folga, S., 2011, EPFast: A Model for Simulating Uncontrolled Islanding in Large Power Systems.
Portante, E., Craig, B., and Folga, S., 2007, NGFast: A Simulation Model for Rapid Assessment of Impacts of Natural Gas Pipeline Breaks and Flow Reductions at U.S. Stat Borders and Import Points.
U.S. Department of Energy, Energy Assurance and Interdependency Workshop, December 2-3, 2013.
Campos, E., and Wang, J., 2015, “Numerical Simulation and Analysis of the April 2013 Chicago Floods,” Journal of Hydrology 531(2), 454-474 (2015).
Chen, C., Wang, J., Qiu, F., and Zhao, D., “Resilient Distribution System by Microgrids Formation after Natural Disasters,” IEEE Transactions on Smart Grid 7(2), 958-966 (2015).
Chiang N.Y., and Zavala, V.M., “Large-scale Optimal Control of Interconnected Natural Gas and Electrical Transmission Systems,” Applied Energy 168, 226-235 (2016).
Li, Z., Wang., J., Sun, H., and Guo, H., “Transmission Contingency Screening Considering Impacts of Distribution Grids,” IEEE Transactions on Power Systems 31(2), 1659-1660 (2015).
Mousavian, S., Valenzuela, J., and Wang, J., “A Probabilistic Risk Mitigation Model for Cyber-Attacks to PMU Networks,” IEEE Transactions on Power Systems 30(1), 156-165 (2014).
Petit, F., Wallace, K., and Phillips, J., “An Approach to Critical Infrastructure Resilience,” p.17 in The CIP Report(January 2014).
Qi, J., Mei, S., and Liu, F., “Blackout Model Considering Slow Process,” IEEE Transactions on Power Systems28(3), 3274-3282 (2013).
Qi, J., and Pfenninger, S., “Controlling the Self-organizing Dynamics in a Sandpile Model on Complex Networks by Failure Tolerance,” Europhysics Letters 111(3) (2015).
Qi, J., Sun, K., and Mei, S., “An Interaction Model for Simulation and Mitigation of Cascading Failures,” IEEE Transactions on Power Systems 30(2), 804-819 (2014).
Qi, J., Wang, J., Liu, H., and Dmitrovski, A., “Nonlinear Model Reduction in Power Systems by Balancing of Empirical Controllability and Observability Covariances,” IEEE Transactions on Power Systems (2016).
Qiu, F., Li, Z., and Wang, J., “A Data-Driven Approach to Improve Wind Dispatchability,” IEEE Transactions on Power Systems (2016).
Qiu, F., and Wang, J., “Chance-Constrained Transmission Switching With Guaranteed Wind Power Utilization,” IEEE Transactions on Power Systems 30(3), 1270-1278 (2014).
Qiu, F., and Wang, J., “Distributionally Robust Congestion Management with Dynamic Line Ratings,” IEEE Transactions on Power Systems 30(4), 2198-2199 (2014).
Qiu, F., Wang, J., Chen, C., and Tong, J., “Optimal Black Start Resource Allocation,” IEEE Transactions on Power Systems 31(3), 2493-2494 (2015).
Sun, H., Wang, Z., Wang, J., Huang, Z., Le Carrington, N., and Liao, J., “Data-Driven Power Outage Detection by Social Sensors,” IEEE Transactions on Smart Grid (2016).
Sun, K., Qi, J., and Kang, W., “Power System Observability and Dynamic State Estimation for Stability Monitoring Using Synchrophasor Measurements,” Control Engineering Practice 53, 160-172 (2016).
Taha, A.F., Qi, J., Wang, J., and Panchal, J.H., “Risk Mitigation for Dynamic State Estimation Against Cyber Attacks and Unknown Inputs,” IEEE Transactions on Smart Grid (2016).
Verner, D., and Petit, F., “Resilience Assessment Tools for Critical Infrastructure Systems,” p.2 in The CIP Report(December 2013).
Wang, Y., Chen, C., Wang, J., and Baldick, R., “Research on Resilience of Power Systems under Natural Disasters—A Review,” IEEE Transactions on Power Systems 31(2), 1604-1613 (2015).
Wang, Z., Chen, B., Wang, J., and Chen, C., “Networked Microgrids for Self-Healing Power Systems,” IEEE Transactions on Smart Grid 7(1), 310-319 (2015).
Wang, Z., and Wang, J., “Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids,” IEEE Transactions on Power Systems 30(6), 3139-3149 (2015).
Xin, S., Guo, Q., Sun, H., and Zhang, B., “Cyber-Physical Modeling and Cyber-Contingency Assessment of Hierarchical Control Systems,” IEEE Transactions on Smart Grid 6(5), 2375-2385 (2015).
Yuan, W., Wang, J., Qiu, F., Chen, C., Kang, C., and Zeng, B., “Robust Optimization-Based Resilient Distribution Network Planning Against Natural Disasters,” IEEE Transactions on Smart Grid (2016).
Zhang, C., Ramirez-Marquez, J.E., and Wang, J., “Critical Infrastructure Protection Using Secrecy – A Discrete Simultaneous Game,” European Journal of Operational Research 242(1), 212-221 (2015).
Zhao, J., Zhang, G., La Scala, M., and Dong, Z.Y., “Short-Term State Forecasting-Aided Method for Detection of Smart Grid General False Data Injection Attacks,” IEEE Transactions on Smart Grid (2015).
Evans, N., Petit, F., and Joyce, A., “Assessment of Critical Infrastructure Cyber Dependencies,” George Mason University Center for Infrastructure Protection & Homeland Security (October 2015).
Clifford, M., December 2015, “National Call to Action: The Resilient Infrastructure Initiative,” George Mason University Center for Infrastructure Protection & Homeland Security (December 2015).
Clifford, M., Lewis, L., Petit, F., Verner, D., and Wall, T., “Closing the Gap between Climate Science and Critical Infrastructure Adaptation,” George Mason University Center for Infrastructure Protection & Homeland Security(August 2015).
Phillips, J., Porod, C., and Petit, F., “Resilience and the Electric Grid,” The Military Engineer (May-June 2015).
Portante, E., Craig, B., Kavicky, J., Talaber, L. and Folga, S., May-June 2016, “Modeling Electric Power and Natural Gas Systems Interdependencies,” George Mason University Center for Infrastructure Protection & Homeland Security (May-June 2016).
Carlson, L., Bassett, W., Buehring, W., Collins, M., Folga, S., Haffenden, R., Petit, F., Phillips, J., Verner, D., and Whitfield, R., Resilience: Theories and Applications, ANL/DIS-12-1, Argonne National Laboratory (2012).
Clifford, M., and Macal, C., 2016, Advancing Infrastructure Dependency and Interdependency Modeling: A Summary Report from the Technical Exchange, Argonne National Laboratory (2016).
Folga, S., Portante, E., Shamsuddin, S., Tompkins, A., Talaber, L., McLamore, M., Kavicky, J., Conzelmanm, G., and Levin, T., U.S. Natural Gas Storage Risk-Based Ranking Methodology and Results, ANL-16/19., Argonne National Laboratory (2016).
Petit, F., Bassett, G., Black, R., Buehring, W., Collins, M., Dickinson, D., Fisher, R., Haffenden, R., Huttenga, A., Klett, M., Phillips, J., Thomas, M., Veselka, S., Wallace, K., Whitfield, R., and Peerenboom, J., Resilience Measurement Index: An Indicator of Critical Infrastructure Resilience, ANL/DIS-13-01, Argonne National Laboratory (2013).
Petit, F., Verner, D., Brannegan, D., Buehring, W., Dickinson, D., Guziel, K., Haffenden, R., Phillips, J., and Peerenboom, J., Analysis of Critical Infrastructure Dependencies and Interdependencies. ANL/GSS-15/4, Argonne National Laboratory (2015).
Wang, J., Advanced Distribution Management Systems for Grid Modernization — Importance of DMS Distribution Grid Modernization, ANL/ESD-15/16, Argonne National Laboratory (2015).