Toward Developing Metrics for Power System Resilience

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, and power plants) destructions. This calls for developing control and operation methods and planning strategies to improve grid resilience against such events. The first step toward this goal is to develop resilience metrics and evaluation methods to compare planning and operation alternatives and to provide techno-economic justifications for resilience enhancement. Although several power system resilience definitions, metrics, and evaluation methods have been proposed in the literature, they have not been universally accepted or standardized. This paper provides a comprehensive and critical review of current practices of power system resilience metrics and evaluation methods and discusses future directions and recommendations to contribute to the development of universally accepted and standardized definitions, metrics, evaluation methods, and enhancement strategies. This paper thoroughly examines the consensus on the power system resilience concept provided by different organizations and scholars and existing and currently practiced resilience enhancement methods. Research gaps, associated challenges, and potential solutions to existing limitations are also provided.

The electric power system is one of the most important critical infrastructures of a country. Recently, the number of natural and man‐made disasters is increased, which can impose extensive damages and costs to the power system. A resilient power system can withstand against, adapt to and recover from these disasters. Power system resilience is quantified by mathematical tools which are called “resilience metrics”. Currently, a lot of resilience metrics are proposed in the power system literature. In this paper, based on the extensive research in the critical infrastructure resilience literature which specifically concentrates on the “area‐based” resilience metrics, a new area‐based resilience metric is proposed which can measure the power system resilience considering the government policymaker criteria, which are rarely noticed before. The proposed and conventional area‐based resilience metrics are evaluated based on the real data from the 2012 Superstorm Sandy in the USA, which led to significant damage to the power distribution system. The simulation results show that the proposed area‐based resilience metric is very simple, can successfully address actual power system performance curves and is more meaningful and tangible than the conventional area‐based metrics for the government policymaker. The proposed area‐based resilience metric has also a general form and can be used for other critical infrastructures.

Resilience has been defined as the ability of a system to withstand stressors while preserving its structure and functions. Various resilience assessment frameworks and metrics have been developed for understanding individual water system behaviour. However, in coupled human-water systems, the increased complexity presents new challenges in the application of these frameworks. This exploratory study first conducted a literature review on system performance indicators, failure thresholds, and resilience metrics, across urban water supply, drainage, wastewater, groundwater, and river systems. Challenges are identified in intercomparison between system performance indicators, robustness of thresholds selection, and resilience metrics synthesis as well as their applicability to inform water management. Based on the insights, a bottom-up resilience assessment framework for coupled human-water systems is developed. This framework sets double thresholds to characterise the vulnerable and critical systems state during a disruptive period. Four shape-based resilience metrics are designed and uniformly applied to various performance indicators to facilitate intercomparison between subsystems. The application of the metrics crosses temporal scales, from event-level assessments for understanding system behaviour to annual-level evaluations of system reliability, which are ultimately synthesised at the system level for multi-stakeholder decision-making. The efficacy of this framework is demonstrated through its application with the integrated water system model (WSIMOD) in Luton, UK, serving as a case study. The findings highlight river water quality as the least resilient subsystem that needs prioritised management. Sensitivity analysis is conducted to examine the robustness of results, with subsequent interpretation linking these metrics to specific design variables for enhanced management. This framework can be further applied with stakeholder engagement and multi-criteria analysis for more effective decision-making to achieve better system performance under deep uncertainties.   

Recently, there has been a focus on natural and man-made disasters with a high-impact low-frequency (HILF) property in electric power systems. A power system must be built with “resilience” or the ability to withstand, adapt and recover from disasters. The resilience metrics (RMs) are tools to measure the resilience level of a power system, normally employed for resilience cost–benefit in planning and operation. While numerous RMs have been presented in the power system literature; there is still a lack of comprehensive framework regarding the different types of the RMs in the electric power system, and existing frameworks have essential shortcomings. In this paper, after an extensive overview of the literature, a conceptual framework is suggested to identify the key variables, factors and ideas of RMs in power systems and define their relationships. The proposed framework is compared with the existing ones, and existing power system RMs are also allocated to the framework’s groups to validate the inclusivity and usefulness of the proposed framework, as a tool for academic and industrial researchers to choose the most appropriate RM in different power system problems and pinpoint the potential need for the future metrics.

The increasing frequency and severity of extreme weather events pose significant threats to power systems, particularly at the distribution level. The most detrimental consequence of such events is observed in critical loads due to high outage costs. As a result, there is a pressing need for utilities to invest in enhancing system resilience, which requires a comprehensive resilience investment framework and metrics to evaluate system performance. This paper proposes a distribution system resilience assessment framework to guide strategic investment decisions. The framework incorporates a mathematical model that estimates system restoration time after an extreme event, considering the criticality of loads, the interdependence of component failures and repair sequences, and the availability of repair crews. In addition, two new resilience metrics—disconnected load point hours (DLH) and normalized DLH (NDLH)—are introduced, which provide a more comprehensive view of system resilience by reflecting both vulnerability and the ability to withstand and recover from extreme events. Case studies are performed on a modified IEEE 69-bus test system utilizing the developed framework. The results evaluate the effectiveness of different resilience investment strategies, including infrastructure hardening, distributed energy resources management, and repair process coordination, in improving the system resilience for maintaining the critical loads and the overall distribution system.

Background/Objectives: The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure. Traditional static resilience metrics may not capture dynamic system states, leading to inaccurate assessments and ineffective responses to cyber threats. This work aims to develop a data-driven, adaptive method for resilience metric learning. Methods: We propose a data-driven approach using inverse reinforcement learning (IRL) to learn a single, adaptive resilience metric. The method infers a reward function from expert control actions. Unlike previous approaches using static weights or fuzzy logic, this work applies adversarial inverse reinforcement learning (AIRL), training a generator and discriminator in parallel to learn the reward structure and derive an optimal policy. Results: The proposed approach is evaluated on multiple scenarios: optimal communication network rerouting, power distribution network reconfiguration, and cyber–physical restoration of critical loads using the IEEE 123-bus system. Conclusions: The adaptive, learned resilience metric enables faster critical load restoration in comparison to conventional RL approaches.

The occurrence of natural disasters such as cyclones, earthquakes, and floods has increased worldwide, and their effect is most intense in localized regions. These events expose weaknesses in the power system infrastructures and show how well-prepared the system is to operate its services in a resilient way. During and after an extreme event, the outage and service disruption happens due to the inability of the affected part of the grid to cope with disruptions, which leads to insufficient resilience of the system. It is becoming more critical to enhance the resilience of electrical networks to extreme weather occurrences through suitable hardening processes and smart operational techniques. A reliable prevention approach necessitates a quantitative resilience metric that can estimate the effects of the future extreme events on distribution systems and assess the possible benefits of various planning strategies. This study aims to address the issue of lack of resilience metrics and proposes probabilistic metrics for evaluating the performance of distribution systems resilience in case of extreme weather events. Specifically, this study introduces active and passive resilience concepts to provide insights into system response. The proposed resilience metrics for various weather scenarios are quantified for the IEEE 33-bus system. The simulation framework also analyzes the effects of different operational and structural resilience improvement approaches on the proposed resilience metrics.

Microgrids with advanced management and control strengthen power system reliability and resilience. In normal operation, economy and reliability are the objectives. However, for adverse events, economy would not be the top concern and focus moves to resiliency and providing energy to critical loads. The resource dispatch strategy in normal economic mode would not be suitable for resilient mode given adverse events. In this work, energy resource dispatch strategy is proposed for an islanded microgrid with hydro-diesel-battery for economic and resilient mode. Resiliency metric is utilized to guide the resource coordination. RT-RMS, a real time resilience management and visualization tool is developed and integrated for the resource coordination driven by resiliency metric. A simplified real-world islanded microgrid model with hydro-diesel-battery is used for validation of the proposed resource coordination. Simulation results indicate that the proposed dispatch method in this work is effective in improving resilience of microgrid by strategically pre-event switching to resilient mode given a predicted tsunami event.

As federal agencies and businesses rely more on cyber infrastructure, they are increasingly vulnerable to cyber attacks that can cause damages disproportionate to the sophistication and cost to launch the attack. In response, regulatory authorities call for focusing attention on enhancing infrastructure resilience. For example, in the USA, President Obama issued an Executive Order and policy directives focusing on improving the resilience and security of cyber infrastructure to a wide range of cyber threats. Despite the national and international importance, resilience metrics to inform management decisions are still in the early stages of development. We apply the resilience matrix framework developed by Linkov et al. (Environ Sci Technol 47:10108–10110, 2013) to develop and organize effective resilience metrics for cyber systems. These metrics link national policy goals to specific system measures, such that resource allocation decisions can be translated into actionable interventions and investments. In this paper, a number of metrics have been identified and assessed using quantitative and qualitative measures found in the literature. We have proposed a generic approach and could integrate actual data, technical judgment, and literature-based measures to assess system resilience across physical, information, cognitive, and social domains.

The shipboard power system of an all-electric ship can be characterized as an isolated microgrid system with limited capacity and no support from external grid. Its resilience problem is more prominent when confronting some extreme events, compared to land-based power system. Currently, there are few researches concerning about the shipboard power system's resilience. It's urgent to fill the comprehensive evaluation need of ship power system's resilience. This paper proposes a comprehensive evaluation framework for all-electric ship's power system resilience. Furthermore, evaluation metrics of shipboard power system resilience comprising resistance, security, self-healing ability, environmental friendless and personnel quality are established. Based on Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation methods, a comprehensive assessment framework is performed to evaluate the resilience of shipboard power system. Numerical results demonstrate that the proposed method can provide guidance for all-electric ship's fault recovery and resilience improvement strategy.

Dealing with major power disruption during natural disasters is one of the most notable concerns in power systems. In this regard, the optimal application of microgrids as a potential solution in increasing and sustaining the distribution system resilience is considered. Furthermore, this purpose is pursued while maintaining the resilience of each DC microgrid connected to the distribution system, which is an essential and challenging issue. In the proposed method of this paper, a novel modeling strategy is formulated as a multi-period two-stage scenario-based stochastic mixed-integer linear programming (MPTSS-MILP) based on a multi-objective optimization problem (MOOP). In this framework, the operation associated with emergency and normal conditions, according to the influences of each situation on another one, is managed in multi-microgrids coordinately. In this regard, the technical constraints correlated to the operation of microgrids as well as the distribution system are satisfied simultaneously in specific to each condition which covers normal and critical operating under all uncertainty scenarios. Through introducing two evaluation criteria and also a resilience metric in microgrids and distribution systems, the efficiency of the proposed method is demonstrated. Meanwhile, innovative modeling is executed based on the subjective behavior of people affected by the disaster. The proposed method is implemented on a test system that involves a 34-bus distribution system with three distinct DC microgrids. In this regard, the impact of plug-in electric vehicles, as well as social behavior affected by severe events, demonstrate significant results according to the resilience criteria and resilience metric of microgrids and distribution system in three case studies based on the proposed approach.

Microgrids with advanced management and control strengthen power system reliability and resilience. In normal operation, economy and reliability are the objectives. However, for adverse events, economy would not be the top concern and focus moves to resiliency and providing energy to critical loads. The resource dispatch strategy in normal economic mode would not be suitable for resilient mode given adverse events. In this work, an energy resource coordination strategy is proposed for a hydro–diesel-battery islanded microgrid, which improves distribution system resiliency by preevent control and switching to resilient mode from economic mode. Resiliency metric is used to verify and quantify the effectiveness of the selected strategy among available ones. Real-time resiliency management system (RT-RMS), a real time resilience management and visualization tool is developed and the resiliency driven resource coordination is integrated into it. Finally, a 14-nodes isolated power system model and a simplified 55-nodes real-world islanded microgrid model are used for validation and demonstrate the effectiveness of proposed resource coordination method.

Climate variability in agroecosystems: A quantitative assessment of stakeholder-defined policies for enhanced socio-ecological resilience

This paper identifies and evaluates issues in traditional resource adequacy (RA) assessment practices, and how adjusting these practices may affect and depend on existing institutional arrangements for planning and procurement. The paper proposes a technical-institutional roadmap that would allow regulators in vertically-integrated jurisdictions and system planners and operators in restructured jurisdictions to revise RA practices across a range of components. First, we compile a critical review of current RA assessment practices based on (1) interviews with RA practitioners and (2) a review of recent technical literature. We find that (i) RA may need to expand beyond capacity adequacy to ensure energy adequacy – relevant for energy-limited resources such as storage – and potentially some form of ancillary service adequacy (e.g. enough ramping-up and ramping-down capability in the system); (ii) chronological hourly simulations for all hours in the year are the current best practice; (iii) metrics and models used do not reflect economic criteria in system operation and loss of load; and (iv) there is a need to improve representation of weather dependencies and weather data. Second, we review planning and RA reports for several private and public entities that plan generation and/or transmission infrastructure in the continental U.S. to look for existing practices involving resilience assessments. We find no systematic treatment of the costs of extreme weather and other hazards, the benefits of resilience, and resilience metrics in planning analyses and no systematic treatment of resilience metrics, methods, and outcomes for resource adequacy purposes. Third, we create a technical framework for probabilistic RA assessment and use it to study how key choices about how to model power system operations affect the values that are obtained for RA metrics. We find that (i) non-economic dispatch schemes that ignore economic objectives can lead to accurate RA assessments when coordinated with detailed operational strategies; (ii) multi-year data is critical to capture a wide variety of system conditions; (iii) not incorporating transmission limits into RA assessment could lead to substantial underestimation of traditional “expected value” RA metrics; and (iv) new RA metrics that capture event-specific shortfall characteristics should be used as supplements to traditional metrics. Finally, we examine RA assessments and use this information to propose a guide of evolving industry standards for resource adequacy assessments in resource planning and transmission planning. We report minimum, best, and frontier practices for temporal resolution of assessments, metrics and targets, weather data, load forecasting, characterization of variable renewable resources, characterization of transmission and market transactions, RA modeling and integration with planning processes, and capacity accreditation.

Power distribution systems face increasing threats from high-impact, low-probability (HILP) events caused by extreme weather conditions such as floods, typhoons, droughts, and heatwaves. These events often lead to power outages worldwide, highlighting the need for effective strategies to mitigate their impact. This work proposes an emergency response strategy that integrates network reconfiguration (NR) with the deployment of mobile emergency generators (MEGs) to enhance system resilience. The objective is to maximize power supply availability following HILP events. To achieve this, mixed-integer quadratic constraint programming (MIQCP) is used to optimize MEG deployment and network restoration. Additionally, improved quantitative resilience metrics are introduced to assess system degradation, pre-recovery, and recovery phases, enabling continuous resilience measurement and informed decision-making. Furthermore, an optimal capacity deployment strategy (OCDS) is proposed to ensure that MEGs are deployed with suitable capacities based on the specific needs of outage-affected areas. The effectiveness of the proposed strategy is demonstrated through tests on the IEEE 118 bus system. The results show a significant improvement of up to 100% in system resilience, reducing power outages and accelerating restoration. The findings confirm that integrating NR with optimized MEG deployment enhances service restoration, providing an effective approach for utilities to manage power disruptions.