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Reports of Resilient Performance: Investigating Operators' Descriptions of Safety-producing Behaviors in the Aviation Safety Reporting System
While many existing taxonomies and frameworks provide a common vocabulary for describing how human operators fail in the context of sociotechnical systems, at present, there is no common vocabulary to describe how humans succeed. Such a framework would facilitate systematically collecting and analyzing data on how human performance can produce safety, not just how it can reduce safety. One potentially rich source of currently available information for exploring desired performance is the reports submitted to NASA’s Aviation Safety Reporting System (ASRS). These de-identified, confidential, and voluntary narrative reports are submitted by pilots, controllers, ground operators, and others within aviation operations. While these reports are primarily submitted to describe safety risks, incidents, and problems, they also often describe how those risks were mitigated, and provide a window into aspects of everyday work in aviation. This paper describes an analysis of ASRS narratives to understand how operators talk about their own resilient behaviors during adverse safety conditions and events. Guided by Erik Hollnagel’s Resilience Assessment Grid framework (i.e., anticipate, monitor, respond, learn), we illustrate our approach and methodology with examples from reports. We also highlight some of the challenges and how further research is needed in developing a taxonomy of operators’ descriptions of resilient performance.
Reports of Resilient Performance: Investigating Operators' Descriptions of Safety-producing Behaviors in the Aviation Safety Reporting System
While many existing taxonomies and frameworks provide a common vocabulary for describing how human operators fail in the context of sociotechnical systems, at present, there is no common vocabulary to describe how humans succeed. Such a framework would facilitate systematically collecting and analyzing data on how human performance can produce safety, not just how it can reduce safety. One potentially rich source of currently available information for exploring desired performance is the reports submitted to NASA’s Aviation Safety Reporting System (ASRS). These de-identified, confidential, and voluntary narrative reports are submitted by pilots, controllers, ground operators, and others within aviation operations. While these reports are primarily submitted to describe safety risks, incidents, and problems, they also often describe how those risks were mitigated, and provide a window into aspects of everyday work in aviation. These reports can be searched in a variety of ways. This paper describes methods for systematically examining ASRS narratives to understand how operators talk about their own resilient behaviors during adverse safety conditions and events. Guided by Erik Hollnagel’s Resilience Assessment Grid framework (i.e., anticipate, monitor, respond, learn), various approaches and tools for such inquiries are described. The approach, process, challenges, and suggestions to building an operator-based description of resilient performance are discussed, and tools to facilitate data analysis to maximize learning from these reports are described.
Analysis of Historical Power Outages of the United States and the National Risk Index
Several works have been documented in the literature to study the societal effect of power outages and to analyze their correlation with the Social Vulnerability Index (SVI). However, the relationship between National Risk Index (NRI) and power outages is yet to be explored. This work analyzes the NRI indices such as Risk, Expected Annual Loss, Social Vulnerability, and Community Resilience with several resilience metrics such as event duration, impact duration, recovery duration, impact level, impact rate, recovery rate, recovery to impact ratio, and area under the outage curves to see the correlation of NRI indices with the resilience metrics. The results show that NRI indices such as Risk and Expected Annual Loss increase with the increase of event duration, impact duration, and recovery duration. All Other metrics are indifferent to the change in the Risk and EAL ratings. The results also show that there is no strong relationship between all the metrics and community resilience and social vulnerability. This work also performed the sensitivity analysis of the extreme event selection process. This sensitivity analysis reveals that the way of identifying extreme events has a significant impact on the evaluation of the events.
Role of Renewables in Grid Resilience
This chapter focuses on revealing the impact of renewable energies, especially distributed energy resources, on modern power system resilience. It is widely recognized that the increasing adoption of renewable energy is bringing more uncertainty and variability, posing new challenges to grid resilience. However, both supply and demand are becoming more active in grid resilience management. The communities themselves would benefit from the utilization of local resources and flexible management to gain resilience during the extreme events.
A Power Outage Data Informed Resilience Assessment Framework
Catastrophic impacts to power systems due to disruptive events have increased significantly during the last decade. These events highlight the need to develop approaches to assess the resilience of power systems against extreme events. However, the availability of data that capture power system performance during and after disruptive events is scarce. This paper proposes an assessment framework to evaluate the performance aspects of the grid system during extreme outage events using the Environment for Analysis of Geo-Located Energy Information (EAGLE-I) data. EAGLE-I includes information related to the number of impacted customers, duration, and location of power outages in the United States. Statistical analyses were conducted to extract resilient-based outage data and derive probability distribution functions of their impact and recovery characteristics. A list of extreme events is identified based on few predetermined threshold values. Metrics from other power outage assessments were used to measure the characteristics of each event, including impact rate and duration, recovery rate and duration, and impact level. A probability distribution function is obtained for each metric. The obtained results provide a representation of national grid performance during extreme events, which can be applied as a framework to evaluate various resilience enhancement techniques.
A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks: Preprint
As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution systems use to prepare for extreme events is to form multiple microgrids and thereby isolate themselves from the grid supply by using local generation as much as possible to supply critical loads. But partitioning an existing system into multiple feasible islands capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithmbased approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, USA.
Strategic Placement and Sizing of Distributed Generation for Resilience Enhancement of Distribution Grids With Microgrid Formation
The rise in frequency and severity of extreme weather events highlights the need for resilient power distribution networks. Microgrids can help improve the resilience of distribution grids by providing continuous power supply using local distribution generation (DG) when the distribution grid fails. In this paper, we propose an approach for optimal placement and sizing of DG to form multiple microgrids throughout the distribution network by restoration actions such as switching operations in case of distribution grid outages caused by extreme weather events. Considering the randomness of damaged distribution lines, the DG placement and sizing problem is formulated as a two-stage stochastic mixed-integer program, with the first stage determining the placement and size of DG, and the second stage focusing on minimizing the amount of load shedding through network restoration and microgrid formations for each scenario. Due to the large number of scenarios, the sample average approximation (SAA) method is employed to solve the problem. The results of case studies on a modified IEEE 33 bus distribution grid demonstrate the effectiveness of the proposed DG placement and sizing strategy in improving the resilience of distribution grids by allowing the formation of multiple microgrids. In addition, the robustness and accuracy of the SAA method are validated through various case studies.
Uncovering Hazards Using a Multi-Objective Optimization to Explore the Faulty State-Space
Considering resilience when designing complex engineered systems is crucial to ensure the system is safe under unexpected hazardous scenarios. Traditional risk-based approaches, such as Failure Modes and Effects Analysis (FMEA) are useful for designing the system to mitigate hazardous scenarios that can be identified by the designer, but often require experience or prior knowledge of system failures to generate. More recently, researchers have developed simulation tools that enable the designer to model large sets of hazardous scenarios (driven by both internal faults and external factors) through simulation. While these tools enable a wider scope of fault modes to be evaluated (e.g., by injecting combined set of fault modes or injecting modes at different times), the resulting assessments (like FMEA) still require knowledge of the specific modes to be evaluated. However, failure to analyze a wide variety of fault scenarios can lead to an incomplete picture of the system resilience, especially to "surprise events'' which may be difficult for the designer to identify and predict beforehand. To overcome this challenge, previous work developed a fault sampling approach for resilience simulations which would procedurally-generate a wide variety of potential faults by systematically perturbing the health states of the system. While the resulting fault modes generated covered a much larger space hazards than would be otherwise considered (and identified many unique failure trajectories which would not have otherwise been identified), it also significantly increased the computational cost of the analysis and resulted in the simulation and analysis of a large set of essentially duplicate scenarios. Additionally, as the number of dimensions in the faulty state-space increases, the full elaboration of possible modes becomes computationally infeasible, justifying the use of a more targeted search. To resolve this limitation, this work proposes the use of a multiobjective optimization algorithm to search the health state space for potential fault modes that are both (1) hazardous and (2) unique. To solve this type of problem, this work proposes the use of a cooperative co-evolutionary algorithm. To demonstrate this approach, it will be applied to a model of an autonomous rover which uses line markings to navigate, focusing on potential hazards in the drive system which could cause the rover to crash. To determine the merit of the approach, it will further be compared with the previously-presented range elaboration approach and a random mode generation approach on the basis of computational efficiency and found modes.
A Two-Step Restoration Scheme with DER Controllability for Resilient Distribution Systems
Modern distribution systems have increasing integration of distributed energy resources (DERs). A large number of distribution system DERs are interfaced through a power electronic converter. During grid contingency scenarios these resources have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid's restoration after an adverse event and hence enhancing resilience. In distribution systems, system restoration is an important problem, especially in face of high-impact low-frequency events. This paper proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in DERs for mitigating voltage problems. Simulation studies are performed on the IEEE-123 node feeder system to demonstrate the effectiveness of the proposed method for various cases.
Quantitative Power System Resilience Metrics and Evaluation Approach
Power system resilience is an emerging topic and plays an essential role in helping the power industry understand and respond to the increasing threats of extreme weather events. The first step of power system resilience analysis is to introduce metrics to quantify the resilience reasonably. Existing resilience metrics are typically restrained by the limited data for extreme event modeling and fall short in terms of physical interpretation and comparability. This paper develops novel quantitative metrics to evaluate power system resilience in pre- and post-event contexts. The developed metrics illustrate clear physical meanings and can be effectively used to compare resilience across different systems under different extreme events. Moreover, the developed metrics can be applied to both transmission and distribution systems. Simulation on a distribution system is employed to validate the effectiveness of the proposed resilience metrics and resilience evaluation approach.
AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)
The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.
Radiation-Induced Noise Resilience of Neuromorphic Architectures
Neuromorphic event-based networks use asynchronous time-dependent information to extract features from input data that can allow for edge-based distributed applications such as object recognition. The noise resilience properties of such networks, especially in the context of space applications, are yet to be explored. In this paper, we use the hierarchy of time surfaces (HOTS) algorithm, which is one of the neuromorphic algorithms, to understand the least and most resilient modules in a neuromorphic network. The HOTS algorithm relies on the computing of time surfaces that maps the temporal delays between neighboring pixels into normalized features that involve many computations that are also found in other neuromorphic networks such as exponential decays, distance computations, etcetera. We implemented HOTS on a Digilent PYNQ board with a Xilinx Zynq 7020 system on a chip, and we subjected the boards running the HOTS network inference to neutron radiation at the Los Alamos Neutron Science Center. Furthermore, we used simulation models from our previous similar experiments on the event-based sensor to create a neutron induced noise model to quantify the effect of this noise on the overall performance of the network. This experiment provides the preliminary measurements of the reliability of the HOTS algorithm and proposes methods to create a more reliable HOTS architecture in future spacecraft missions.
A Resilient Integrated Resource Planning Framework for Transmission Systems: Analysis and Optimization
This article presents a resilient Integrated Resource Planning (IRP) framework designed for transmission systems, with a specific focus on analyzing and optimizing responses to High-Impact Low-Probability (HILP) events. The framework aims to improve the resilience of transmission networks in the face of extreme events by prioritizing the assessment of events with significant consequences. Unlike traditional reliability-based planning methods that average the impact of various outage durations, this work adopts a metric based on the proximity of outage lines to generators to select HILP events. The system’s baseline resilience is evaluated by calculating load curtailment in different parts of the network resulting from HILP outage events. The transmission network is represented as an undirected graph. Graph-theoretic techniques are used to identify islands with or without generators, potentially forming segmented grids or microgrids. This article introduces Expected Load Curtailment (ELC) as a metric to quantify the system’s resilience. The framework allows for the re-evaluation of system resilience by integrating additional generating resources to achieve desired resilience levels. Optimization is performed in the re-evaluation stage to determine the optimal placement of distributed energy resources (DERs) for enhancing resilience, i.e., minimizing ELC. Case studies on the IEEE 24-bus system illustrate the effectiveness of the proposed framework. In the broader context, this resilient IRP framework aligns with energy sustainability goals by promoting robust and resilient transmission networks, as the optimal placement of DERs for resilience enhancement not only strengthens the system’s ability to withstand and recover from disruptions but also contributes to efficient resource utilization, advancing the overarching goal of energy sustainability.
Current Practices in Distribution Utility Resilience Planning for Winter Storms
This report is part of a series of hazard-focused case studies examining common practices in electric utility resilience planning. We use standard terminology defining resilience as the ability to anticipate, withstand, absorb, and recover from hazards that cause long duration outages. We distinguish between reliability and resilience using Institute of Electrical and Electronics Engineers (IEEE) 1366-2022, which defines major events as an event that exceeds reasonable design and/or operational limits of the electric power system. Resilience planning is focused on major event days and reliability planning is focused on nonmajor event days. Utility resilience plans are assessed according to common resilience components identified in existing resilience frameworks. The focus of this report is on winter storms in which the primary hazards are heavy snowfall, freezing rain, ice, extreme cold, severe wind, and flooding. These hazards can also contribute to generation shortages, resulting in bulk power system impacts that have consequences for the distribution system, such as load shedding. Stand-alone reports focusing on wildfires and nonwinter storms have been published in parallel with this report. This report can be used as a starting point for understanding potential investment prioritization processes and investment options. This report is intended to improve utility resilience planning by supporting constructive dialogue among utilities, regulators, and other stakeholders.
Data-Based Resilience Enhancement Strategies for Electric-Gas Systems Against Sequential Extreme Weather Events
Some extreme weather events, such as the hurricane, pass through an area sequentially and thus are called sequential extreme weather events (SEWEs). This paper proposes a data-based robust optimization (RO) model to enhance the resilience of the integrated electricity and gas system (IEGS) against SEWEs. Specifically, the SEWE strikes the IEGS sequentially. After each attack, the system state is adjusted immediately to minimize the maximized expected system cost caused by the SEWE. The attack-defense procedures are repeated alternatively during the SEWE. Preventive measures, hardening, are made in advance to reduce the impact of sequential attacks. The entire process is formulated as a multi-period RO model. Furthermore, it is proved that the most effective resilience enhancement strategies for this model are the same as those for a two-stage RO model, which can be solved by the nested column-and-constraint generation (C&CG) algorithm. In addition, the property of SEWEs, sequentially endangering limited regions of the IEGS, is incorporated to build a data-based uncertainty set and reduce its conservativeness. Simulation results on two IEGSs validate the effectiveness of the proposed model.
Resiliency-Driven Proactive Distribution System Reconfiguration With Synchrophasor Data
Measuring and enabling resiliency of electric distribution systems with increasing weather and cyber events are important. Some of the extreme events (e.g. Earthquakes, Hurricanes) and associated paths are predicted and monitored closely in advance and allow to take pre-event proactive control actions. The Distribution Phasor Measurement Units (D-PMUs) provide new opportunities and supporting such proactive actions. Here, a synchrophasor based resiliency driven pre-event reconfiguration can ensure minimizing impact of the expected event on the power distribution system and associated performance. However, the D-PMUs will also face challenges in terms of data quality similar to the transmission PMUs. The focus of this paper is to provide data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data. Results are validated for real industrial feeders and test cases with satisfactory response
Repurposing Inactive Oil and Gas Wells for Energy Storage: Maximizing the Potential Via Optimal Drivetrain Control: Preprint
In recent years, there has been a growing emphasis on utilizing energy storage to enhance grid resilience against disruptive events. While renewable energy supply continues to expand, gravity-based solutions like pumped hydro remain dominant in the commercial space. However, their geographical limitations constrain availability, scalability, and increase costs for co-locating solar and wind energy. An alternative approach proposes repurposing idle oil and gas wells located closer to existing grid infrastructure, offering a promising and cost-effective solution. This paper addresses the optimization and control of a regenerative drive system coupled to an interior permanent magnet synchronous machine in a 300-meter well with a 100-Newton weight. The study employs a dynamic MATLAB/Simulink model to simulate the operation of the electric drivetrain system during storage and discharge operations. The results demonstrate an initial round-trip efficiency of 85.9% for the electrical system alone and identify crucial factors for maximizing efficiency. The optimized operation and control of the electromechanical drivetrain system hold great potential for minimizing the levelized cost of storage while maximizing efficiency and revenue generation.