Resilience Metrics Research Articles

Review of metrics to assess resilience capacities and actions for supply chain resilience

Efficiency and profitability are the main drivers of globalization and have led to long and complex supply chains. Recent disturbances such as COVID-19 or the Suez Canal obstruction caused severe supply disruptions and thereby unveiled the vulnerability of global trade. Resilient supply chains are characterized by the capacity to absorb, adapt to, and restore after disruptions. Building upon the established concept of the ‘resilience curve’, this article explores the interplay between resilience capacities, metrics, and actions in the state-of-the-art literature. We first analyze and harmonize the terminology used to describe capacities as well as metrics for quantifying resilience. This results in a set of 17 resilience metrics that describe all characteristics of the resilience curve and can be used as a tool to assess the resilience of a supply chain. Subsequently, we propose how these metrics can be applied to quantify the effect of resilience actions. Finally, we analyze which actions are proposed in the literature and classify those actions according to their relation to traditional supply chain planning tasks. Practitioners such as supply chain decision-makers can implement these actions to strengthen the absorptive, adaptive, and restorative capacities and are provided with mathematical formulations to quantify the strengthening effect of actions. Academic research can, inter alia, integrate the metrics into multi-criteria optimization models for decision-making and explore the interplay between economic efficiency, environmental sustainability, and resilience.

Effects of social-ecological risk factors and resilience on the relationship between metabolic metrics and mental health among young adults

The correlation between metabolic metrics and mental health remains underexplored, with few in-depth studies examining whether this association exists among college students and whether it might be moderated by socio-ecological risk factors (SERFs) and mediated by resilience. A follow-up study design investigated the association between baseline metabolic metrics, SERFs and resilience and mental health. A multivariable linear regression model using the PROCESS method established the relationship of SERFs, resilience and metabolic metrics with mental health. Participants were 794 adolescents (mean age: 18.64 [±0.90] years). In multivariable linear regression, the high-level SERFs (β = 0.124), resilience (β = −0.042), LCI (β = 0.072), and RFM (β = 0.145) were associated with higher depression symptoms, while CVH (β = 0.602), TyG (β = 0.295), TyG-BMI (β = 0.004), and RC (β = −0.041) were not. An association was also observed between SERFs, resilience, RFM and anxiety. Resilience mediated the relationship between metabolic metrics and depression and anxiety, and SERFs moderated this relationship, demonstrating the relationship between resilience, metabolic metrics, SERFs and mental health. By revealing the potential sociological mechanism underlying the relationship between metabolic metrics and adolescents’ mental health, this study provides a theoretical basis for further exploration of the biological foundations of mental health.

Defining Disadvantaged Places: Social Burdens of Wildfire Exposure in the Eastern United States, 2000–2020

This study explores the relationship between wildfire exposure, social vulnerability, and community resilience across the 26 states east of the Mississippi River. This work centers around one research question: are there spatial differences in wildfire exposure that disproportionately impact disadvantaged communities in the Eastern United States over the recent period (2000–2020)? Employing remotely sensed wildfire data and ancillary datasets, we analyze and map the extensive wildfire exposure in the Eastern United States and compare it with spatial metrics of social vulnerability and community resilience to examine the social burdens of wildfire exposure in the Eastern U.S. A discernible wildfire exposure pattern emerges, with the Southeast bearing the highest exposure levels, largely attributed to human-caused and prescribed burning. By establishing a measure of disadvantaged counties using social vulnerability and community resilience, we identify regions where wildfire exposures could have the most adverse impact—areas characterized by highly vulnerable populations and limited community capacity to respond effectively to potential events. In evaluating wildfire risk, we conclude that considering not only exposure levels but also the inclusion of disadvantaged areas (incorporating social vulnerability and community resilience) is essential for understanding the disparate impact of wildfires on individuals and the communities where they live.

Planning for a network system with renewable resources and battery energy storage, focused on enhancing resilience

The growing significance of network resilience underscores the importance of research in integrating Renewable Energy Resources (RESs) and battery energy storage Systems (BESS) with electrical networks. This paper presents a real-time simulation for systematically integrating renewable energy sources (RESs) and battery energy storage systems (BESS) in electrical networks, focusing on resilience metrics that involve a multi-objective optimization approach that considers the relative battery capacity to the total system cost. In addition, the Demand Response Program (DRP) is considered an additional layer to manage seven different loads that provide a comprehensive and accurate representation of energy consumption during 24 h. The scheduling model, which incorporates the IDR strategy, reduces the total load of data center performance by 40 %. Additionally, optimizing battery capacity can lead to an 89 % reduction in total costs, representing a substantial success in enhancing the economic resilience metric. This study has been conducted on the IEEE 24-bus RTN to assess the required investment for deploying BESS and to gauge its effectiveness in enhancing the resilience of the electrical network. The results confirm the effectiveness of the proposed model for enhancing the economic and performance metrics of resilience.

ENHANCING COMMUNITY RESILIENCE: ELECTRIC SCHOOL BUS V2G AS PORTABLE ENERGY SOLUTION

This study investigates the potential of utilizing electric school buses equipped with vehicle-to-grid (V2G) technology as a portable energy solution to enhance community resilience during and after power outages caused by natural disasters and shortages. The first objective of this research is to advance our understanding of disaster resilience, particularly during the critical response period—from the onset of the disruption event to the stabilization of recovery efforts. The second objective is to translate fundamental insights gained from this understanding into practical applications. To achieve these objectives, the study encompasses various analyses. Firstly, economic losses resulting from power outages are quantified, providing insights into the broader impacts of energy disruptions. Additionally, the performance of power grid systems is evaluated to assess their resilience and effectiveness in responding to and recovering from adverse events. Furthermore, the study includes calculations for mobilization (response) time, crucial for assessing the timeliness and efficiency of emergency response efforts. Finally, the viability of employing electric bus V2G technology as an emergency power solution is thoroughly analyzed. This encompasses assessing factors such as response time, load recovery or supply, system performance pre- and post-recovery, and other resilience metrics. By integrating these multifaceted analyses, this research aims to offer comprehensive insights into the potential of electric school buses as a resilient energy solution for communities facing power disruptions.

Enhancing urban system resilience to earthquake disasters: Impact of interdependence and resource allocation

During the post-disaster recovery process of the urban system (US), it is critical to understand the interdependencies of critical infrastructure systems (CISs) and strategically allocate resources among them. However, due to the complexity of the problem and the limitations of the perspective, the existing research usually ignores the implicit impact of interdependence and resource allocation on urban resilience. To bridge this gap, this study establishes a multilayer network-based methodological framework to characterize various types of interdependencies between different CISs and integrate the US as a complex “system of systems”. Then, the system functionality of the US under different resource allocation strategies is quantified and optimized by resilience metrics. This proposed framework was demonstrated in a virtual US including a transportation subsystem (TS), an electric power supply subsystem (EPSS), and a community subsystem (CS) under catastrophic earthquakes. The sensitivity of urban resilience to interdependencies is investigated, and the corresponding results reveal that urban resilience is most sensitive to the interdependence between TS and EPSS. In particular, when there exists strong interdependence between the TS and EPSS, the optimal resource allocation strategy to maximize urban resilience is assigning resource allocation coefficients of 0.1, 0.8, and 0.1 for the TS, EPSS, and CS, respectively. These results can be effectively applied in future planning and investment in urban resilience.

A Review of Resilience Metrics and Modeling Methods for Cyber-Physical Power Systems (CPPS)

A Review of Resilience Metrics and Modeling Methods for Cyber-Physical Power Systems (CPPS)

Cross-jurisdictional disaster preparedness: A Nigeria-USA data-analytical approach

Disasters pose significant challenges globally, demanding effective preparedness strategies to mitigate their impact. This study proposes a novel approach to disaster preparedness through cross-jurisdictional collaboration between Nigeria and the United States, leveraging data analytics for enhanced readiness. Nigeria, with its vulnerability to various disasters, and the United States, known for its advanced disaster management systems, offer a unique opportunity for mutual learning and collaboration. This research aims to bridge the gap in disaster preparedness between these two countries by employing a data-driven analytical framework. Through the collection, analysis, and comparison of disaster-related data from both nations, this approach seeks to identify commonalities, differences, and best practices in disaster preparedness strategies. By leveraging the strengths of both countries, such as Nigeria's community resilience and the USA's technological advancements, this approach aims to develop comprehensive disaster preparedness models tailored to the specific needs and contexts of each jurisdiction. Key components of this approach include data collection from various sources, including historical disaster records, socio-economic indicators, infrastructure data, and community resilience metrics. Advanced analytical techniques, such as machine learning algorithms and spatial analysis, will be employed to identify patterns, assess vulnerabilities, and predict potential disaster scenarios. The findings will inform the development of targeted preparedness strategies, including early warning systems, evacuation plans, resource allocation, and capacity building initiatives. Furthermore, this research emphasizes the importance of multi-stakeholder collaboration and knowledge exchange in disaster preparedness. By involving government agencies, non-governmental organizations, academic institutions, and local communities from both Nigeria and the USA, this approach aims to foster a holistic and inclusive preparedness framework. Ultimately, this study seeks to contribute to the global discourse on disaster risk reduction by demonstrating the effectiveness of cross-jurisdictional collaboration and data-driven approaches in enhancing disaster preparedness. By leveraging the strengths and experiences of Nigeria and the USA, this research aims to develop scalable and adaptable models that can be applied in diverse contexts to build resilience and mitigate the impact of disasters.

Resilience changes of carbon stocks to quantify the long‐term effects of ecological engineering projects in subtropical forests of China based on satellite‐derived net ecosystem production time series and inventory data

AbstractUnderstanding how resilient forests are to ecological engineering projects (EEPs) is essential to forest management and ecosystem health. Despite growing evidence that EEPs achieve increasing carbon stocks, whether such benefits can be sustainable and what are the consequences of EEPs on forest health remain unclear. This study aimed to investigate the long‐term effects of EEPs using forest resilience from aspects of resistance and recovery, by applying a change detection algorithm (breaks for additive seasonal and trend; BFAST) spatially on net ecosystem production (NEP) (proxy for carbon stocks) time series (1981–2019) in red soil hilly region (RSHR) of subtropical China. The spatial parameters (e.g., the number, magnitude, and time of changes) used to construct resilience metrics were generated based on BFAST‐derived breakpoints. These metrics were then utilized to analyze the dynamics of forest resilience in relation to EEPs factors in terms of plantation area, forest type, and stand age. Our results observed 92.77% of breakpoints in NEP after 2000, which corresponds well with the periods that multiple EEPs were conducted. NEP resilience showed great variability during 2001–2019, with a positive increasing trend in resistance (R2 = 0.72) and a continuous decline (R2 = 0.37) in recovery, indicating an unhealthy ecosystem in RSHR. Our findings revealed that forest resistance was strongly associated with plantation area (R = 0.71), and the presence of monoculture and young coniferous forest may be the potential factors for the decline in recovery. This suggested that forest resilience in RSHR is mainly modulated by large‐scale EEPs.

Towards adaptive resilience for the future of integrated water systems planning

Abstract The integrated water systems (IWSs) concept involves managing water quantity and quality through dynamic interactions. This paper reviews the terrestrial water cycle, focusing on resilience and adaptive planning (AP) approaches within IWSs. We examine how integrating these approaches can improve IWS management and planning, addressing their inherent complexities. Using a performance-based resilience definition, we consider the system’s ability to absorb, recover from and adapt to adverse events. The AP focuses on flexible management pathways for uncertain future conditions. Although both resilience and AP aim to enhance water system performance and address uncertainties, they differ in their assessment and implementation approaches. We propose an Adaptive Resilience Planning (ARP) framework that merges both approaches. The ARP uses resilience metrics for performance assessment and incorporates AP’s methods for conceptualising uncertainties and optimising management portfolios. Implementing the ARP framework raises four research questions: (1) holistic characterisation of uncertainties and options in IWSs, (2) using resilience metrics for IWS adaptation, (3) balancing trade-offs among management goals through optimal portfolio selection and (4) monitoring portfolio performance and uncertainties for informed adaptation. The ARP framework offers a structured method for dynamic and adaptive resilience planning, enhancing IWS management’s responsiveness to evolving challenges.

Time Series Ecological Coastal Resilience in Ende City, Indonesia

Ende city is a coastal area that is one of cities in East Nusa Tenggara province. There are risk that threatens the city and coastal area originating from natural disasters and human activity. It is necessary to assess and monitor coastal resilience of this city since abrupt change of city landscape has impact on ecosystem resilience. Therefore, the aim of this research is to measure and analyze ecological coastal resilience from 2016 to 2021. Coastal resilience can be assessed from ecosystem-ecology approach so that sustainability of coastal community can be strengthening. Landscape parameter of typology was used to measure ecological resilience in coastal ecosystem of Ende City. One of widely used approach to measure ecological resilience is applying a variety of different indicator or metric of resilience. Parameter of resilience is scored and classified for each typology in coastal area of Ende City. Research method includes field survey, in depth interview and focus group discussions. One of method to determine coastal resilience classification is from resilience index. Based on result, coastal resilience is mostly moderate class in Ende City. The lowest class of resilience index is in volcanic coast typology.

Resilience analysis and improvement strategy of microgrid system considering new energy connection.

With the increasing demand for electricity, microgrid systems are facing issues such as insufficient backup capacity, frequent load switching, and frequent malfunctions, making research on microgrid resilience crucial, especially to improve system power supply reliability. This paper proposes a method for analyzing the resilience metric of new energy grid-connected microgrid system, and proposes optimization strategies to improve resilience. Firstly, a measurement method for the resilience of the microgrid system is established based on the operating characteristics of the system components. Secondly, the sensitivity relationship between system resilience and parameters is established, and an optimization model for resilience improvement strategies of microgrid systems based on parameter sensitivity is constructed. Finally, simulation verification is conducted based on the IEEE 37-node microgrid system. The results show that the proposed measurement method can scientifically and reasonably measure the resilience of the microgrid system, and the resilience improvement strategy significantly improves the operational resilience, verifying the effectiveness and robustness of the proposed analysis method.

A Logic-Based Resilience Metric for Water Resource Recovery Facilities

This study develops quantifiable metrics to describe the resilience of Water Resource Recovery Facilities (WRRFs) to extreme and exceptional shock and stress events, such as those posed by long-term challenges...

Review of Resilience Evaluation Methods in Operational Highway Tunnel

The research on tunnel resilience has garnered increasing attention in recent years. Owing to prolonged exposure to natural or anthropogenic factors, the resilience level of many highway tunnels is continuously declining, rendering them susceptible to sudden accidents and challenging to restore postincident. Currently, although several scholars have employed diverse evaluation methods in their research on tunnel resilience, there is a lack of summarization and integration of these methods. In addition, there is also a dearth of a unified evaluation index system and framework for different types of natural or man‐made disasters, which are crucial for advancing the development of tunnel resilience evaluation. This study commences with an introduction to the origin of resilience and the definition of tunnel resilience, and comprehensively summarizes commonly employed evaluation methods. Subsequently, this study centers on the resilience evaluation methods in tunnel engineering and analyzes their strengths and weaknesses. Besides, the distribution of resilience metrics in current researches is analyzed and the detailed explanations for the diverse choices are provided. According to the results and deficiencies of existing research, combined with the author’s perspectives, the index systems, evaluation frameworks, and resilience improvement strategies are proposed, which can be applied to the resilience evaluation of various operational highway tunnels under diverse disaster scenarios. Furthermore, this study also presents a case study on the evaluation of tunnel fire resilience to validate the applicability of the research findings. These findings aim to provide a guide for the operation and maintenance management of the operating tunnels and improve the scientific decision‐making level of tunnel maintenance.

Designing a Use Case for Supply Chain Resilience Based on Process Mining

Amidst global disruptions, the significance of Supply Chain Resilience (SCR) has surged in scholarly and practical discourse. This paper endeavors to craft an industry-neutral conceptual dashboard, tailored for in-house consultants, to measure and oversee SCR. Drawing from the SCOR model's resilience metrics, Key Resilience Areas (KRAs), and Accenture’s SCR application, this study presents a practical use case for deploying such a dashboard. Expert evaluation by process mining and supply chain specialists highlighted potential enhancements for the dashboard.

Comparison of topological functionality-based resilience metrics using link criticality

Resilient road networks ensure that transportation systems can withstand and return to normal performance fast after disruptions. Ensuring a better network structure for the system promises better resilience, irrespective of demand variations. Comparison between the ability of different topological functionality-based metrics in literature to measure resilience is an unexplored area. This study will bridge the gap by evaluating four such metrics. The distinguishing feature of the study is that link criticality approach and traffic functionality measures are used for comparing the metrics. The links are removed according to the order of their criticality ranking, and network performance is measured using Global Importance Index. The advantages of the proposed methodology are twofold. First, the position of critical links provides insight into the network characteristics measured by the metric. Second, the usage of traffic functionality-based performance measure for comparison ensures that the metric captures traffic perspective also. The position of critical links obtained using the four metrics for the networks considered in the study provides interesting observations about the network property that influences them. Also, the networks degrade the maximum when links are removed according to number of independent paths ranking when the network is not already majorly disrupted. Hence, it is more effective at measuring resilience in such networks. For networks that are already disrupted, removing links based on Global efficiency affected the network more.

Merging trait-based ecology and regime shift theory to anticipate community responses to warming.

Anthropogenic warming is altering species abundance, distribution, physiology, and more. How changes observed at the species level alter emergent community properties is an active and urgent area of research. Trait-based ecology and regime shift theory provide complementary ways to understand climate change impacts on communities, but these two bodies of work are only rarely integrated. Lack of integration handicaps our ability to understand community responses to warming, at a time when such understanding is critical. Therefore, we advocate for merging trait-based ecology with regime shift theory. We propose a general set of principles to guide this merger and apply these principles to research on marine communities in the rapidly warming North Atlantic. In our example, combining trait distribution and regime shift analyses at the community level yields greater insight than either alone. Looking forward, we identify a clear need for expanding quantitative approaches to collecting and merging trait-based and resilience metrics in order to advance our understanding of climate-driven community change.

Resilience enhancement of distribution networks based on demand response under extreme scenarios

AbstractIn the context of rare but high‐impact extreme scenarios, such as natural disasters, it is crucial to utilize all available resources, including microgrids and distributed power sources, to restore critical loads in the distribution network as much as possible. This paper proposes a two‐stage resilience enhancement strategy for distribution networks considering post‐disaster topology reconstruction and demand response, aiming to facilitate the recovery of critical loads after disasters. In the first stage, a heuristic algorithm is introduced to determine the post‐disaster topology of the distribution network. In the second stage, by employing a step‐wise elastic load curve, user demand response is incorporated to maximize the socio‐economic value of the restoration, and resilience metrics are computed. Finally, the effectiveness of the proposed resilience restoration strategy is validated through simulations on Case33 and Case69 systems.

Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues

In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.

Enhancing the resilience of zero-carbon energy communities: Leveraging network reconfiguration and effective load carrying capability quantification

The integration of distributed energy resources (DERs) into contemporary energy communities (ECs) has revolutionized power systems, fostering sustainable and clean energy infrastructures. This paper focuses on the effective load-carrying capability (ELCC) to enhance grid resilience in the presence of DERs. We introduce an innovative network topology-based optimization framework that seamlessly integrates economic and resilience metrics within ECs while reducing carbon emissions. The Pareto front of non-dominated solutions for the proposed three-objective optimization problems is extracted, providing a comprehensive visualization of the trade-off between economic, resilience, and emission objectives, enabling informed decision-making. Analytical results, validated on the IEEE 33-bus test system, demonstrate the effectiveness of DER-based ELCC quantification in managing load supply during emergencies. Case studies show how the synergy between economic and resilience-based metrics significantly enhances grid resilience. The proposed framework has diverse applications, including enhancing grid adaptability to climate change, promoting sustainable energy integration, optimizing demand response strategies, and supporting the transition to a decarbonized energy community. This work addresses the challenges and opportunities in the evolving energy landscape, emphasizing the importance of our approach in achieving a cleaner and more resilient energy future.