Resilient Infrastructure Initiative

Delivering Science and Technology to Enable Resilient Design


The Resilient Infrastructure initiative increases understanding of cascading and escalating impacts among critical infrastructure based on comprehensive analysis of upstream, internal, and downstream dependencies.

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

Designing Resilient Infrastructure: The Argonne Approach

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.


Electrical Power Network Modeling (EPfast)

EPfast is an electric power infrastructure modeling tool used to examine the impacts of power outages on large electric grid systems. The tool models the tendency of power systems to “island” after either man-made or natural disturbances, which, in turn, can lead to regional power network deficiencies (i.e., blackouts) due to an imbalance between power supply and demand. Example applications include: analysis results that enable utility operators to identify system vulnerabilities and implement preventative measures; critical power infrastructure, resiliency and vulnerability analyses; and system dependency/interdependency analyses with non-power infrastructure systems. Argonne’s EPfast model produces data that enable local utility operators to spot system vulnerabilities and implement preventative measures, as well as contributes to critical power infrastructure, resiliency, and vulnerability analyses.

Electric Grid Resilience Improvement Program (EGRIP)

EGRIP is an AC power flow-based cascading failure/outage and integrated power system restoration optimization tool. The cascading failure module, which is a high-fidelity, event-driven power system simulation tool, considers system monitoring, protection, and control and simulates the most important cascading mechanisms, including cascading overloads, protection operation and mis-operation, transient instability, voltage collapse, small signal instability, and lack of situational awareness. It provides the capability for cascading risk analysis and generates credible cascading scenarios for restoration drills. The integrated system restoration module is a set of optimization tools designed to support restoration planning and operational decision-making in both transmission systems and distribution systems. At the transmission level, it simultaneously optimizes system sectionalization and generator startup to produce an effective recovery plan. EGRIP can also be used to optimize black-start resource procurement and finds the restoration plan that minimizes the overall power system restoration time. Using a security-constrained ACOPF approach, each restoration plan is simulated to validate its operational feasibility and effectiveness. In addition, the model includes physical repair constraints and optimizes repair task scheduling and truck routing. At the distribution level, EGRIP includes a distribution system restoration module that maximizes load served by re-routing power in a damaged distribution network. EGRIP can also prioritize critical loads to be restored within a given time window and can be used to analyze intentional islanding for storm preparation.

Hurricane Electrical Assessment Damage Outage Model (HEADOUT)

Argonne’s HEADOUT model quickly (within minutes) produces an estimation of the potential number of electric customers that will experience a loss of commercial electrical power as a storm makes landfall.  The tool uses real-time forecasts from the National Hurricane Center (NHC) and develops detailed wind speed contours and applies a fragility curve. This fragility curve is a proxy for determining the damage to the electrical distribution grid and potential number of customers without electricity, without detailed knowledge of the electric distribution network in the area of storm landfall. HEADOUT generates detailed results providing customer outage estimates and identifies assets at risk from storm surge.

Urban Scale Renewable Resource Integration and Resilient Microgrid Planning

Using state-of-the-art modeling tools, Argonne provides neutral, 3rd party, techno-economic analysis of renewable generation (PV solar and wind) and energy storage (thermal and battery) installations to help optimize the placement and sizing of those resources to provide economic and resiliency benefits.  Argonne also analyzes and advises the design of microgrids for increased grid reliability and resiliency. The use of these tools allows city planners and district managers to make better investments in renewable generation, energy storage, and microgrid creation. The tools will help reduce costs, improve economic performance, reduce environmental impacts, and increase reliability and resiliency.

Natural Gas Pipeline Network Modeling (NGfast)

NGfast is a natural gas pipeline network modeling tool that enables the rapid assessment of impacts from disruptions and flow reductions in the nation’s natural gas distribution network. Argonne uses NGfast as an analysis and information management tool to provide understanding of information on normal system operations, pipeline structure and load connectivity, and spare mitigating capacity for each state served by pipelines. Example applications include: critical infrastructure analysis; risk analysis; and dependency and interdependency analysis. Argonne’s NGfast model analyzes disruptions at import points, production fields, and pipeline breaks to identify affected states and local distribution centers and to supply information to help conduct cascading impact analyses that focus on interdependent sectors (such as the electric power sector).

Petroleum, Oil, and Lubricants Pipeline Model (POLfast)

POLfast is a pipeline network model that enables rapid, first-stage assessments of the impacts from major pipeline breaks and the reductions in flow from import points, production points, and petroleum refineries. Example applications include: critical infrastructure analysis; system dependency and interdependency analysis; risk analysis; critical power infrastructure, resiliency, and vulnerability analyses.  Argonne’s POLfast model estimates potential regional supply shortfalls, determines mitigating measures, and estimates the potential increase in the price of petroleum and related impacts on the regional gross domestic product (GDP).

POLARIS – Integrated Transportation Systems Model

Developed by Argonne, POLARIS is an activity-based microsimulation of individual behavior used to simulate large-scale regional transportation systems. POLARIS is unique as the tool integrates models of components of traffic operations, travel demand, and social travel behavior into a comprehensive modeling platform. Synthetic individuals representing every traveler in the modeled region are created and governed by a complex series of behavioral rules and models, which determine what the agent needs to accomplish on a given day, how the individual expects to go about accomplishing those needs, and how s/he travels through the system to accomplish those plans in coordination/competition with other such simulated agents. This structure allows POLARIS to be used for a wide range of applications, including regional evacuation modeling; network demand response and congestions; emergency preparedness; critical infrastructure identification, operations; planning for adaptation to climate change; infrastructure dependency and interdependency studies; and freight modeling. In addition, when combined with Autonomie vehicle energy consumption tool, also developed by Argonne, the toolkit can be used to assess the impact of a large number of scenarios on both mobility and energy. This platform enables the simulation and modeling of surface transportation networks (down to the level of individual vehicles/drivers) to evaluate the operational impacts of network disruptions, congestion, travel times, evacuation scenarios, emergency response, energy and the operational aspects of transportation systems.

Downscaled, Regional Climate Model Data and Guides

Climate change risk for cities and their infrastructure is a key concern for the planning and development of cities. Argonne is providing and using ultra high spatial resolution (12km x 12km resolution) climate projections using a regional scale climate model (dynamic downscaling) for the North American continent. Time slices for the current, mid-century and the end of the century are available. The model results are documented in peer reviewed literature and used for impact assessments. The model data is useful for estimating means, variance, extremes, and inter-annual variability at the metro-regional scale.  Argonne products can be used for developing estimates of the risk envelopes for topics such as air quality, flooding, and heat waves. This enables stakeholders and decision-makers to quantify and plan for future climate change impacts at specific locations.

Economic Impact Analyses

Argonne’s economic impact analyses examine upstream and downstream losses in regional employment and labor income that result from the disruption of individual facilities or specific supply chains. Example applications include: system dependency and interdependency studies; cascading failures analysis; risk and consequence analysis; infrastructure decision making; and strategic planning. The economic impact analyses use facility-specific information in conjunction with economic input-output models, such as IMPLAN©, to determine changes in costs and economic activity as a result of infrastructure disruption.

Interdependent Repair and Restoration Processes (Restore©)

Argonne’s Restore© is a stochastic model of the complex sets of steps required to restore a system following an incident that affects critical infrastructure. Restore© offers insights into outage restoration times at critical infrastructure facilities by modeling the complex sets of steps that are needed to accomplish a goal, such as repairing a ruptured natural gas pipeline or drinking water distribution network that affect an infrastructure system’s or a region’s restoration and recovery processes. Example applications include: critical infrastructure analysis, cyber infrastructure analysis, risk analysis, emergency preparedness and response. Restore offers insights into outage restoration times at critical infrastructure facilities that can inform regional response and recovery activities. Considered within a regional context, Restore© can provide insights into dependencies/interdependencies among systems and identify the “most active path” through the network of tasks, which can ultimately lead to reduced recovery times.

The Resilience Measurement Index (RMI)

Argonne’s Resilience Measurement Index (RMI) captures the fundamental aspects of critical infrastructure resilience, considers all-hazards and characterizes a facility in terms of its preparedness, mitigation measures, response capabilities, and recovery mechanisms. The value of the RMI ranges between 0 (low resilience) and 100 (high resilience). The RMI enhances the ability of facility owners and operators to manage investments by allowing comparisons among different options that can increase the resilience of their facility. All the data and levels of information used for the calculation of the RMI are presented on an interactive, Web-based tool called the “dashboard,” which allows owners and operators to take the information that emerges from calculating the indices and use it for day-to-day operations, as well as investment justification and strategic planning.

Global Security Geospatial Information Systems

Argonne maintains web mapping applications, services, and datasets related to the assessment of critical infrastructure risk and consequences as they apply to natural and man-made events. These web-based, risk-based spatial analytical tools and products contain integrated spatial models which enhance the analysis of the resiliency of critical infrastructure using the analytical insights provided by the exploitation of real or near-real time spatial data. Infrastructure model output, sensor data, live meteorological data, and law enforcement and intelligence data are utilized in these systems to assess the potential impacts of external events on the infrastructure.

Argonne would apply relevant spatial modeling systems and web applications to enhance the city’s assessment of critical infrastructure and dependencies of critical services such as gas, water, and electricity services. Additionally, Argonne’s analytical team would assist in developing the methodology and application deployment to help urban law enforcement entities track and predict crime related phenomena such as illicit trafficking of people, drugs, or other materials. These systems have been deployed by Argonne for U.S. government agencies and Interpol.

The applications, tools, services, and methodologies provided by Argonne are useful in assessing potential cascading impacts on city infrastructure. Combining detailed city networks such as water, natural gas, traffic, and others with the integrated toolsets and applications will provide unique insight into potential impacts and consequences of a variety of threats and hazards. Additionally, law enforcement entities may view crime and trafficking data in a unique, spatial/temporal manner which may create a unique analytical view into urban crime patterns.

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.

Conference and Workshop Papers

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.

Journal Articles

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).

Other Publications

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).