Jared Lee Gearhart

Conceptual Framework for Developing Resilience Metrics for the Electricity, Oil, and Gas Sectors in the United States

This report has been written for the Department of Energy’s Energy Policy and Systems Analysis Office to inform their writing of the Quadrennial Energy Review in the area of energy resilience. The topics of measuring and increasing energy resilience are addressed, including definitions, means of measuring, and analytic methodologies that can be used to make decisions for policy, infrastructure planning, and operations. A risk-based framework is presented which provides a standard definition of a resilience metric. Additionally, a process is identified which explains how the metrics can be applied. Research and development is articulated that will further accelerate the resilience of energy infrastructures. Conceptual Framework for Developing Resilience Metrics for US Electricity, Oil, and Gas Sectors September, 2014 4 SAND2014-18019 ACKNOWLEDGMENTS The authors of this paper wish to acknowledge the many experts that provided invaluable effort and information for this document. Dan Ton from the Department of Energy Office of Electricity Delivery and Energy Reliability and Rima Oueid and Kate Marks, from the Department of Energy Office of Energy Policy and Systems Analysis. Thanks is given for their tremendous leadership in formalizing the development of resilience concepts and bringing ideas to fruition. Acknowledgment is also given to Henry Willis from the Rand Corporation, who provided instrumental feedback and guidance. From the Sandia Corporation, thanks to Eric Vugrin for providing foundational help in establishing this document. Thanks to Lori Parrott and Steve Conrad for both having leveraged significant accomplishments via their long-standing contributions in NISAC. Further appreciation is given to Abe Ellis for his work in electric power resilience activities and Jason Stamp for providing leadership through the development of the Energy Surety Microgrid and design methodologies. And acknowledgement is also given to Bob Hwang for his continuous efforts and guidance. We appreciate all of your efforts and look forward to collaborating in the future. Conceptual Framework for Developing Resilience Metrics for US Electricity, Oil, and Gas Sectors SAND2014-18019 5 September, 2014 CONTENTS Executive Summary ...................................................................................................................... 11 Conclusions and Recommendations ...................................................................................... 15 1. Introduction .............................................................................................................................. 17 2. What is Resilience? .................................................................................................................. 19 2.1 Literature Review .......................................................................................................... 19 2.2 Defining Resilience ....................................................................................................... 19 3. Resilience Analysis Process ..................................................................................................... 23 3.1 Define Resilience Goals ................................................................................................ 24 3.2 Define System and Resilience Metrics ......................................................................... 24 3.3 Characterize Threats ..................................................................................................... 24 3.4 Determine Level of Disruption ..................................................................................... 24 3.5 Define and Apply System Models ................................................................................ 25 3.6 Calculate Consequence ................................................................................................. 25 3.7 Evaluate Resilience Improvements ............................................................................... 25 4. Resilience Metrics .................................................................................................................... 27 4.1 Metrics: Measures of Resilience ................................................................................... 27 4.1.1 The Metric Is in Terms of Threat ..................................................................... 27 4.1.2 The Metric Is Based on Performance ............................................................... 27 4.1.3 The Metric Measures Consequence ................................................................. 28 4.1.4 The Metric Accounts for Uncertainty .............................................................. 28 4.1.5 The Metric Effectively Captures Resilience .................................................... 28 4.1.6 The Metric Is Not a Value Judgment ............................................................... 29 4.1.7 Multiple Metrics Are Often Necessary ............................................................ 29 4.1.8 System-Level Models in Resilience Metric Computation ............................... 29 4.2 Resilience Analysis Use Cases ..................................................................................... 30 5. A Framework for Developing Resilience Metrics ................................................................... 33 6. Populating Metrics ................................................................................................................... 35 7. Simplified, Anecdotal Use Case .............................................................................................. 37 7.1 Define Resilience Goals ................................................................................................ 37 7.2 Define System and Resilience Metrics ......................................................................... 37 7.3 Characterize Threats and Determine Level of Disruption ............................................ 38 7.4 Define and Apply System Models ................................................................................ 38 7.5 Calculate Consequence ................................................................................................. 39 7.6 Evaluate Resilience Improvements ............................................................................... 39 7.7 Incorporating the Resilience Improvement Process ...................................................... 40 8. Advanced Use Case Summaries .............................................................................................. 41 8.1 Electricity ...................................................................................................................... 41 8.2 Petroleum ...................................................................................................................... 41 8.3 Natural Gas ................................................................................................................... 42 9. What can be simplified today .................................................................................................. 43 9.1 How we can prepare for a more resilient framework .................................................... 43 Conceptual Framework for Developing Resilience Metrics for US Electricity, Oil, and Gas Sectors September, 2014 6 SAND2014-18019 10. Research & Development ...................................................................................................... 45 10.1 Furthering Resilience ................................................................................................... 45 10.1.1 Characterize Threats ....................................................................................... 45 10.1.2 Determine Level of Disruption ....................................................................... 45 10.1.3 Define and Apply System Models .................................................................. 46 10.1.4 Calculate Consequence ................................................................................... 46 10.1.5 Evaluate Resilience Improvements ................................................................. 46 11. Moving Forward .................................................................................................................... 47 11.1 Identifying Specific Metrics that Could Be Generally Applied Across Many Different Systems ......................................................................................................................... 47 11.2 Stakeholder and Buy-In Process .................................................................................. 47 11.3 Development of Sector-Specific System Models ......................................................... 49 11.4 Consequence Quantification ........................................................................................ 49 11.5 Conclusions and Recommendations ............................................................................. 50 Appendix A: Reliability and Other Complementary Metrics ...................................................... 53 A.1 Resource Adequacy ...................................................................................................... 54 A.1.1 Loss of Load Probability (LOLP) .................................................................... 54 A.1.2 Loss of Load Expectation (LOLE) .................................................................. 55 A.1.3 Expected Unserved Energy (EUE) or Loss of Energy Expectation (LOEE) .. 55 A.1.4 Effective Load Carrying Capability (ELCC) .................................................. 55 A.2 Transmission Reliability Indices and Measures ..............................................

Comparison of open-source linear programming solvers.

When developing linear programming models, issues such as budget limitations, customer requirements, or licensing may preclude the use of commercial linear programming solvers. In such cases, one option is to use an open-source linear programming solver. A survey of linear programming tools was conducted to identify potential open-source solvers. From this survey, four open-source solvers were tested using a collection of linear programming test problems and the results were compared to IBM ILOG CPLEX Optimizer (CPLEX) [1], an industry standard. The solvers considered were: COIN-OR Linear Programming (CLP) [2], [3], GNU Linear Programming Kit (GLPK) [4], lp_solve [5] and Modular In-core Nonlinear Optimization System (MINOS) [6]. As no open-source solver outperforms CPLEX, this study demonstrates the power of commercial linear programming software. CLP was found to be the top performing open-source solver considered in terms of capability and speed. GLPK also performed well but cannot match the speed of CLP or CPLEX. lp_solve and MINOS were considerably slower and encountered issues when solving several test problems.

Optimization-Based Probabilistic Consequence Scenario Construction for Lifeline Systems

The construction of a suite of consequence scenarios that is consistent with the joint distribution of damage to a lifeline system is critical to properly estimating regional loss after an earthquake. This paper describes an optimization method that identifies a suite of consequence scenarios that can be used in regional loss estimation for lifeline systems when computational demands are of concern, and it is important to capture the spatial correlation associated with individual events. This method is applied to a realistic case study focused on the highway network in Memphis, Tennessee, within the New Madrid Seismic Zone. This case study illustrates that significantly fewer consequence scenarios are needed with this method than would be required using Monte Carlo simulation.

Multi-objective optimization for bridge retrofit to address earthquake hazards

Protecting infrastructures against natural hazards is a pressing national and international problem. Given the current budgetary climate, the ability to determine the best mitigation strategies with highly constrained budgets is essential. This papers describes a set of computationally efficient techniques to determine optimal infrastructure investment strategies, given multiple user objectives, that are consistent with an underlying earthquake hazard. These techniques include: optimization methods for developing representative events to characterize the hazard and the post-event condition of infrastructure components, a simulation model to characterize post-event infrastructure performance relative to multiple user objectives, and a multi-objective optimization algorithm for determining protection strategies. They are demonstrated using a case study of the highway network in Memphis, Tennessee.

Estimation of an Origin–Destination Table for U.S. Imports of Waterborne Containerized Freight

This paper presents a probabilistic origin–destination table for waterborne containerized imports. The analysis makes use of 2012 Port Import/Export Reporting Service data, 2012 Surface Transportation Board waybill data, a gravity model, and information on the landside transportation mode split associated with specific ports. This analysis suggests that about 70% of the origin–destination table entries have a coefficient of variation of less than 20%. This 70% of entries is associated with about 78% of the total volume. This analysis also makes evident the importance of rail interchange points in Chicago, Illinois; Memphis, Tennessee; Dallas, Texas; and Kansas City, Missouri, in supporting the transportation of containerized goods from Asia through West Coast ports to the eastern United States.

Optimization of scenario construction for loss estimation in lifeline networks

Natural disasters have become a pressing national and international problem. Population growth, aging infrastructure, and climate change suggest that mounting losses will continue into the foreseeable future, hence mitigation and response planning is of increasing importance. The conduct of studies to support this type of regional planning often requires an estimation of the impacts of a single earthquake scenario on a region. This paper describes a method to identify a set of consequence scenarios that can be used in regional loss estimation for lifeline systems when computational demands are of concern, and the spatial coherence of individual consequence scenarios is important. This method is compared with Monte Carlo simulation.

Contingency Contractor Optimization Phase 3 Sustainment Software Design Document - Contingency Contractor Optimization Tool - Prototype

This document describes the final software design of the Contingency Contractor Optimization Tool - Prototype. Its purpose is to provide the overall architecture of the software and the logic behind this architecture. Documentation for the individual classes is provided in the application Javadoc. The Contingency Contractor Optimization project is intended to address Department of Defense mandates by delivering a centralized strategic planning tool that allows senior decision makers to quickly and accurately assess the impacts, risks, and mitigation strategies associated with utilizing contract support. The Contingency Contractor Optimization Tool - Prototype was developed in Phase 3 of the OSD ATL Contingency Contractor Optimization project to support strategic planning for contingency contractors. The planning tool uses a model to optimize the Total Force mix by minimizing the combined total costs for selected mission scenarios. The model optimizes the match of personnel types (military, DoD civilian, and contractors) and capabilities to meet mission requirements as effectively as possible, based on risk, cost, and other requirements.

Microgrid Design Toolkit (MDT) Technical Documentation and Component Summaries

The Microgrid Design Toolkit (MDT) is a decision support software tool for microgrid designers to use during the microgrid design process. The models that support the two main capabilities in MDT are described. The first capability, the Microgrid Sizing Capability (MSC), is used to determine the size and composition of a new microgrid in the early stages of the design process. MSC is a mixed-integer linear program that is focused on developing a microgrid that is economically viable when connected to the grid. The second capability is focused on refining a microgrid design for operation in islanded mode. This second capability relies on two models: the Technology Management Optimization (TMO) model and Performance Reliability Model (PRM). TMO uses a genetic algorithm to create and refine a collection of candidate microgrid designs. It uses PRM, a simulation based reliability model, to assess the performance of these designs. TMO produces a collection of microgrid designs that perform well with respect to one or more performance metrics.

Contingency contractor optimization.

The goal of Phase 3 the OSD ATL Contingency Contractor Optimization (CCO) project is to create an engineering prototype of a tool for the contingency contractor element of total force planning during the Support for Strategic Analysis (SSA). An optimization model was developed to determine the optimal mix of military, Department of Defense (DoD) civilians, and contractors that accomplishes a set of user defined mission requirements at the lowest possible cost while honoring resource limitations and manpower use rules. An additional feature allows the model to understand the variability of the Total Force Mix when there is uncertainty in mission requirements.

A modeling framework for investment planning in interdependent infrastructures in multi-hazard environments.

Currently, much of protection planning is conducted separately for each infrastructure and hazard. Limited funding requires a balance of expenditures between terrorism and natural hazards based on potential impacts. This report documents the results of a Laboratory Directed Research&Development (LDRD) project that created a modeling framework for investment planning in interdependent infrastructures focused on multiple hazards, including terrorism. To develop this framework, three modeling elements were integrated: natural hazards, terrorism, and interdependent infrastructures. For natural hazards, a methodology was created for specifying events consistent with regional hazards. For terrorism, we modeled the terrorists actions based on assumptions regarding their knowledge, goals, and target identification strategy. For infrastructures, we focused on predicting post-event performance due to specific terrorist attacks and natural hazard events, tempered by appropriate infrastructure investments. We demonstrate the utility of this framework with various examples, including protection of electric power, roadway, and hospital networks.