Resilience Enhancement Strategies Research Articles

Robustness evaluation method and defense strategy of electricity-heat integrated energy system

Even though electricity-heat integrated energy systems (IESs) can improve energy utilization, the faults generated by extreme events can induce more complex and wider impacts. Therefore, it is urgent to study the effective defense strategies associated with electricity-heat IES. Considering the theory of complex networks, a dynamic model was established in this paper which comprehensively considered the actual operating characteristics and cascading failure process of the electricity-heat IES. The structural and functional robustness of the electricity-heat IES after failure was evaluated by link survivability, capacity survivability and power survivability. According to the post-disaster system robustness evaluation results and system characteristics without adopting the defense strategy, the critical links of the system were identified. Also, seven defense strategies were formulated based on the cascading failure mechanism of the electricity-heat IES. The testing network coupled with an improved IEEE118-node power grid and 23-node heat supply network was used to verify the effectiveness of the proposed defense strategy and to provide a theoretical basis for the resilience enhancement strategy and defense resource allocation of the electricity-heat IES.

Climate Resilience, Vulnerability, Differential Impacts and Adaptive Capacity in Nigeria

This research investigates and promotes climate resilience in Nigeria’s vulnerable communities, which are disproportionately affected by climate change. How climate change worsens existing problems such as food insecurity, poverty, and conflict in Nigeria serve as the rationale to this study. Hence, defining climate resilience, reviewing relevant literature, and conducting a case study to generate evidence-based recommendations give insight into the objectives of this study. Therefore, reviewed literature shows the diverse impacts of climate change on disadvantaged groups, such as small-scale farmers and urban coastal dwellers. Vulnerability assessments highlight the need for adaptive capacity, integrating local knowledge in solutions. Case studies illustrate vulnerabilities in poor urban coastal areas, rural regions, and the Niger Delta. Differential impacts are examined, such as the gendered effects of climate change on women and the challenges faced by indigenous communities and urban slums. Adaptive capacity is discussed, stressing social capital, collective action, and the interrelation of adaptation, adaptive capacity, and vulnerability. Adaptive capacity assessment in Nigeria indicates difficulties in rural farming households and urban coastal communities, requiring enhanced capacities. Indigenous knowledge plays a vital role in adaptive measures, fostering resilience. Resilience enhancement strategies emphasize social justice, inclusive governance, and community-based adaptation, acknowledging the significance of local empowerment and participation. Technology and innovation provide possibilities but need context-specific methods. These strategies can help build climate resilience in Nigeria’s vulnerable communities, offering a basis for policymakers, practitioners, and stakeholders to tackle the complex challenges of climate change.

Comprehensive Evaluation of Resilience for Qinling Tunnel Group Operation Safety System Based on Combined Weighting and Cloud Model

An extensive network of tunnels has recently been constructed in the Qinling Mountains. Characterized by high and steep terrain, this network has led to frequent traffic accidents. To address this issue, this paper introduces the theory of resilience into the evaluation system of safety systems during the operation period of highway tunnel groups. Based on this, this paper establishes a resilience evaluation index system for the operation safety system of highway tunnel groups, including a human system, vehicle system, and road system. To address both qualitative and quantitative issues concerning the indicators, this paper employs the analytic hierarchy process (AHP) and entropy weight method to combine and assign weights to the resilience evaluation indicators. Subsequently, the cloud model method is utilized to quantify the level of resilience of the highway tunnel group safety system during the operation period. The study results unveiled the patterns of traffic accidents within the Qinling Tunnel Group from the perspectives of vehicle, road, and human factors. The final weight allocation reveals that the road system has the highest proportion, exerting the greatest influence as a primary level index. Moreover, by taking the Qinling Tunnel Group on the Xihan Expressway as an engineering example, the resilience level of the case project was analyzed and obtained. Proposals for enhancing resilience were put forth, taking into account the project’s unique attributes, encompassing adaptability, resistance, and recovery. Overall, this study validates the feasibility and reliability of the proposed method for assessing the resilience of highway networks, offering empirical support for transportation administrators in the implementation of resilience-enhancing strategies.

Collaborative defense in the Arctic: Strengthening Norway's oil sector resilience through knowledge sharing and vigilance against drone threats

AbstractNorway's oil sector faces significant security challenges due to rapid technological advancements and heightened geopolitical tensions, particularly concerning threats from Russian drones. The sector maintains a vigilant approach, continually monitoring and adapting to evolving threats. This study investigates strategies employed by sector companies to mitigate challenges, emphasizing collaboration, effective network utilization, and learning from past incidents to enhance operational resilience. Our analysis aligns with three thematic areas from the Generic Resilience Management Guidelines developed under the EU‐funded H2020 project DARWIN: enhancing coordination in emergency response operations, managing adaptive capacities, and updating procedures and checklists. The study underscores the role of task forces, interdisciplinary teams, and collaborative efforts among industry operators in enhancing adaptability to threats. Findings indicated that although a willingness to draw insights from past incidents exists, a predominant reactive approach to learning prevails. Furthermore, a culture of secrecy prevalent in the sector inhibits knowledge sharing, potentially challenging overall resilience. In this complex context, proactive and transparent security approaches are imperative for Norway's oil sector to enhance resilience. Organizations should adopt a dynamic, technology‐integrated risk management approach, involving continuous policy revision and stakeholder engagement, as suggested in contemporary resilience enhancement strategies.

Risk assessment and resilience enhancement strategies for urban power supply-demand imbalance affected by extreme weather: A case study of Beijing

Extreme weather and climate events (EWCE) risk assessment and system resilience enhancement are important supports for urban power systems to cope with global climate change and realize green and low-carbon transformation. However, the existing risk assessment methods tend to ignore the impact of cumulative damage events, and lack the systematic analysis of the power supply process. Moreover, there is a deficiency in researches on resilience enhancement strategies with application analysis in practical scenarios. This study established a risk assessment methodology with the occurrence probability of extreme weather, the deviation state of power supply-demand and the state duration as the first-level indexes. The methodology was applied to the assessment of three EWCE scenarios using the urban power structure of Beijing as a case study. Finally, the power supply resilience of EWCE was defined, and the power supply resilience curves with severe impacts and cumulative damages were characterized, and then resilience enhancement strategies were proposed to guide the case applications. The results of the study suggest that coupled extreme weather scenarios and an increase in the percentage of electricity supplied from renewable sources would increase the level of supply imbalance risk. System resilience could be improved by extending the time for supply level minimums to occur and by increasing the level of supply capacity minimums. Beijing would need to focus on the resilience enhancement for extreme low temperature and heavy rainfall superimposed on heat wave. This study could provide theoretical reference for the construction of resilient cities under climate change.

An Evaluation of Sustainable Power System Resilience in the Face of Severe Weather Conditions and Climate Changes: A Comprehensive Review of Current Advances

Natural disasters pose significant threats to power distribution systems, intensified by the increasing impacts of climate changes. Resilience-enhancement strategies are crucial in mitigating the resulting social and economic damages. Hence, this review paper presents a comprehensive exploration of weather management strategies, augmented by recent advancements in machine learning algorithms, to show a sustainable resilience assessment. By addressing the unique challenges posed by diverse weather conditions, we propose flexible and intelligent solutions to navigate disaster complications effectively. This proposition emphasizes sustainable practices that not only address immediate disaster complications, but also prioritize long-term resilience and adaptability. Furthermore, the focus extends to mitigation strategies and microgrid technologies adapted to distribution systems. Through statistical analysis and mathematical formulations, we highlight the critical role of these advancements in mitigating severe weather conditions and ensuring the system reliability.

Robust resilience enhancement strategy for gas‐electricity integrated energy system considering multiple reinforcement levels

AbstractNatural disasters are extreme events with low probability but strong destructive ability, which have a serious impact on the power system. The gas‐electricity integrated energy system is more likely to be affected by natural disasters due to its complex multi‐energy coupling characteristic. In order to improve the resilience of the gas‐electricity integrated energy system under natural disasters, this paper proposes a three‐layer defence‐attack‐defence robust resilience improvement model with the objective of minimizing the load loss in the gas‐electricity integrated energy system. Firstly, in order to cope with various levels of disaster and improve the economy of line/pipeline reinforcement in the gas‐electricity integrated energy system, a multi‐level line/pipeline reinforcement model is proposed on the basis of load weight in the upper layer. Then, information entropy is introduced to represent the impact of natural disasters in the middle layer. Based on the operation constraints of the gas‐electricity integrated energy system, the optimal allocation of the mobile electricity storage and liquefied gas storage is established in the lower layer. The three‐layer resilience improvement model is solved by the nested column and constraint generation algorithm. Finally, the case study proves the feasibility of the model and the effectiveness of the solution method.

Study on Resilience Factors and Enhancement Strategies in Prefabricated Building Supply Chains

Prefabricated building holds promise for quality, efficiency, and sustainability when compared to traditional techniques. However, realizing prefabricated building work hinges on strengthening supply chain resilience. This research assesses interdependent risks undermining prefab network continuity during disruption. Questionnaire data from industry experts informed a structural equation model quantifying pathways between component production, construction, information, and other uncertainties. Findings confirm that project delays can be traced to manufacturing and on-site risks, with information gaps broadly propagating impacts. Meanwhile, organizational risks have an insignificant influence, suggesting partnership networks readily reconfigure around operational contingencies. Robust information infrastructures and coordination, therefore, offer crucial leverage. Accordingly, a multidimensional resilience enhancement strategy is formulated, prioritizing supply chain transparency, digital integration, inventory buffering, contingencies planning, and transportation flexibility. Our mixed-methods approach advances the construction literature by demonstrating the applicability of structural equation modeling for diagnostic resilience analytics. Industry leaders also gain actionable, evidence-based guidance on strategic investments to stabilize project flows. This dual theoretical and practical contribution underscores the versatility of tailored statistical assessments in furthering construction innovation objectives within complex, uncertain environments.

Enhancing Resilience With Electric Vehicles Charging Redispatching and Vehicle-to-Grid in Traffic-Electric Networks

Electric vehicles (EVs) can be viewed as both electric loads and a type of mobile storage resource. Effective management and utilization of EVs can yield significant benefits for networks. This paper proposes a novel resilience enhancement method for coupled traffic-electric networks with a high penetration of EVs, incorporating two specific strategies in response to contingencies. The first strategy involves rerouting and reselecting charging stations for power-deficient EVs, while the second entails redispatching power-sufficient EVs to the most suitable nearby charging stations to provide grid support through vehicle-to-grid technology. A carefully designed pricing mechanism, which includes imposing additional road congestion tolls and charging station charging fees, is incorporated to facilitate rerouting and charging station reselection for EVs under the user equilibrium principle. Furthermore, a formulation incorporating the two proposed resilience enhancement strategies for coupled traffic-electric networks is presented. This formulation enables coordination between the two networks to capitalize on flexibility derived from sector coupling. A series of linearization techniques are then introduced to transform the original problem into a mixed-integer quadratically constrained programming model. Numerical results based on a coupled network with 20 traffic roads and 21 electric buses are presented to demonstrate the effectiveness of the proposed resilience enhancement method.

Using Autonomous Learning Mode to Improve Students’ Learning Willingness and Resilience

In China, English teaching has become a key part of the education system at all levels. However, despite the significant resources invested in the education system, many students lack the necessary motivation and perseverance to learn. The interactive autonomous learning mode is a newly emerged educational approach that aims to enhance students’ motivation and resilience in learning through interaction and self-motivation. Existing works mostly focus on qualitative analysis and case studies; however, few have comprehensively considered the multiple factors or the future trend. To address this research gap, this paper proposes three novel research methods: predicting future learning willingness using a grey prediction model, evaluating the effectiveness of a learning resilience enhancement strategy using the TOPSIS analysis method, and analyzing the relationship between learning willingness and learning resilience enhancement using an improved grey relation projection method. These methods not only provide a comprehensive and accurate analytical framework, but they also contribute to educational reform and teaching practice.

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.

Supply Chain Resilience Enhancement Strategies in the Context of Supply Disruptions, Demand Surges, and Time Sensitivity

Supply chain disruptions, including trade disputes, regional conflicts, and the COVID-19 pandemic, have highlighted the importance of supply chain resilience. This study focuses on investment decisions related to supply chain resilience, including considerations of supply disruptions, demand disruptions, and demand time sensitivity. The study uses a Stackelberg game model, with the retailer as the leader and the supplier as the follower, to analyze and compare optimal strategies for supply chain participants under different levels of risk. The results show that the severity of risk events impacts the optimal retail price, optimal wholesale price, supply chain recovery time, and expected profits for each participant. The investment in supply capacity enhancement not only expands the ability to meet demand but also encourages suppliers to expedite supply chain recovery, elevate demand fulfillment rates, and enhance the overall return on investment in supply chain resilience. As a result, the resilience of the entire supply chain is fortified. Similarly, it has been found that centralized decision-making leads to lower prices, shorter recovery times and greater supply chain resilience than decentralized decision-making. This study offers theoretical underpinning for corporations to effectively address risky occurrences, ensure supply security, enhance supply chain resilience, and imparts support for governments involved in humanitarian relief efforts.

Deep reinforcement learning-based resilience enhancement strategy of unmanned weapon system-of-systems under inevitable interferences

Unmanned weapon system-of-systems (UWSoS) is a collection of unmanned weapon systems providing multiple interdependent capabilities to support the mission completion. The soaring number of interconnected systems makes UWSoS vulnerable in the face of inevitable uncertain interferences. Thus, devising an effective resilience enhancement strategy is critical to handling inevitable disruption events. However, preventive and protective methods are not all-inclusive because of the high uncertainty operation environment and the lack of autonomy. Hence, we studied the resilience enhancement problem of UWSoS from the recovery perspective based on deep reinforcement learning (DRL). First, a DRL-based resilience enhancement strategy framework is proposed, combining the graph convolution network and proximal policy optimization algorithm to extract the entities’ representation features and autonomously learn the resilience enhancement strategy to handle various interferences scenarios better. Subsequently, a collaboration action resilience contribution index-guided proximal policy optimization algorithm is proposed to improve training efficiency. Finally, extensive simulation experiments and comparisons with five similar algorithms demonstrate the effectiveness, adaptability, and superiority of the proposed strategy. This work could provide valuable scheduling schemes for decision-makers to guide the reliable operation of UWSoSs.

Enhancing distribution system resilience against extreme disasters considering sequential action of multi-type switches

With the increasing frequency and intensity of extreme disasters, enhancing distribution system (DS) resilience has been an urgent need for smart grid. Among various resilience enhancement measures, network reconfiguration is the most commonly used one. Multiple switches are involved in distribution network reconfiguration, e.g., circuit breakers (CBs), fuses (FUs), remote-controlled switches (RCSs), and manual switches (MSs). According to the characteristics of various switches, it is vital to establish an analytical switch action model for generating feasible and effective DS resilience enhancement strategies. In this paper, an analytical model is developed to characterize the automatic tripping process of switches under multiple faults caused by extreme disasters. A novel DS resilience enhancement method is further proposed, which considers the sequential actions of multiple types of switches to enhance the prevention and rapid restoration capability of DSs against extreme disasters. Before the strike of a disaster, the topology of DS is reconfigured based on RCSs, MSs, and CBs to improve the survivability of loads. The automatic tripping process of CBs and FUs is simulated when faults occur. After the disaster, the DS is reconfigured based on RCSs and CBs after events to isolate faults and restore critical loads. The proposed method is formulated as a mixed-integer linear programming (MILP) problem, which can be efficiently solved using commercial solvers. Case studies conducted on the modified IEEE 123-bus test systems demonstrate the effectiveness of the proposed method for DS resilience enhancement.

A Security Resilience Metric Framework Based on the Evolution of Attack and Defense Scenarios

The frequent attacks show that no information system is absolutely safe and the security capabilities are relative. Security resilience becomes a complementary priority for improving information systems’ continuous service and security capabilities in the face of attacks such as unknown vulnerabilities and backdoors. Endogenous security defense technology has become an important research aspect to improve the security resilience of information systems. However, there are some limitations in the research of the information system security resilience evaluation model, such as lacking indexes to characterize the system security resilience under an attack environment. In this paper, a security resilience enhancement strategy based on dynamic defense is constructed to improve the security performance of the system through IP port hopping and attack surface conversion. For the adversarial behaviors of attackers and defenders, we propose a security resilience metric framework based on the evolution of attack and defense scenarios, which is evaluated using a resilient security evaluation model based on the fuzzy Choquet integral. In the model, the weights of evaluation indicators are calculated based on the DEMATEL method. The 2-addable fuzzy measures of each indicator are calculated secondarily. Then the security performance of the system is calculated using the fuzzy Choquet integral. Absorptive capacity, adaptive capacity, and resilience factor are proposed to better supervise the metric framework’s validity. Finally, four groups of control cases were created by building the Web service system after the endogenous security transformation as the experimental simulation scenario. Experimental simulation results show the superiority of the proposed metric model.

Resilience of hydrogen fuel station-integrated power systems with high penetration of photovoltaics

Hydrogen fuel stations (HFSs) may be connected to power systems; to absorb electricity, produce hydrogen and supply hydrogen demands. To the best of the authors' knowledge, the resilience of HFS-integrated power systems has not been addressed in the literature. In this research, a novel strategy is proposed for resilience enhancement of PV-rich HFS-integrated power systems, considering the uncertainties. In the proposed strategy, fuel cells (FCs) and hydrogen tanks are added to HFSs and mobile batteries are added to the power system. A two-stage stochastic model is developed in which the location of mobile batteries are the first-stage here-and-now decision variables and other variables are second-stage wait-and-see decision variables. The model is formulated as a multi-objective problem as the ability of the system to supply both electricity and hydrogen demands are considered. Expected load not served (ELNS) is used as resilience metric. Case study is a modified IEEE 24-bus power system with 5 HFSs. The results confirm that with the proposed resilience enhancement strategy, hurricane does not result in any shed for hydrogen demands. As per results, mobile batteries are the most efficient tools in resilience enhancement of the studied system; they cause 84 % improvement in ELNS of electricity and 43 % improvement in ELNS of hydrogen. The results also show that besides mobile batteries, hydrogen storage tanks and FCs are the most efficient components in resilience enhancement of the system. Hydrogen tanks improve ELNS of electricity by 1.8 % and remove the need of system operator to shed hydrogen demands. A sensitivity analysis is done to see how the results of the developed model are sensitive to its parameters.

Equilibrium Allocation of ESSs in Multiple UIESs-Accessed Distribution Networks Considering the Resilience and Economic Benefits

Recently, extreme disasters considered as high-impact low-probability (HILP) events have caused severe power outages. User-level integrated energy systems (UIESs) is being widely formed and accessed to distribution networks (DNs), which has the potential to enhance the resilience of DNs. Therefore, multiple UIESs-accessed DNs against such HILP events are required in order to enhance the resilience of the distribution network. In this paper, from a resilience-oriented perspective, an equilibrium allocation method of multiple energy storage systems (ESSs) is proposed considering the planning of distribution network with multiple UIES for improving the resilience of distribution network. Firstly, the topology and operation modes of multiple UIESs-accessed DNs are analyzed. Secondly, a quantitative evaluation method of UIES power support capability is formulated, in which the differences in type, quantity and scale of coupling equipment in different scenarios are considered. An allocation model of multiple ESSs is then established using the Nash equilibrium method to balance the distribution network resilience and economics. Finally, simulation tests verify the superiority of the proposed equilibrium allocation model of multiple ESSs compared with other resilience enhancement strategies.

Distribution network resilience enhancement strategy considering spatial‐temporal migration of flexible resources on supply and demand sides

AbstractExtreme events bring great challenges to the safety and resilience of a distribution network, which makes it difficult for traditional static resources to provide enough operation support. The continuous enrichment of spatial‐temporal flexibility resources on both the power demand and supply sides provides a new idea for the dynamic dispatching of distribution network. This paper proposes a spatial‐temporal migration method of mobile power sources (MPSs) on the supply side and internet data centres (IDCs) on the demand side, based on the power‐transportation and power‐computation network coupling models respectively. Then, the dynamic dispatching potential of MPSs and IDCs is evaluated, and the dispatching models against extreme events of MPSs and IDCs are established. With the objective of maximizing system resilience and minimizing dispatching costs, an optimization model considering spatial‐temporal migration of data loads and MPSs dynamic dispatching strategy is formulated. Finally, the proposed method is tested in the modified IEEE 33‐node test system and IEEE 69‐node test system, and the economy and resilience enhancement are verified with comparison of different dispatching schemes.

Multi-stage collaborative resilient enhancement strategy for coupling faults in distribution cyber physical systems

Due to the strong interdependence of cyber systems and physical systems, cyber-physical systems (CPS) often face serious coupling faults in case of extreme natural disasters. To improve the ability of the distribution CPS to withstand extreme events and rapidly resume normal operation, it is critical to consider effective recovery processes and formulate the collaborative resilience enhancement strategy of CPS. In this paper, a sequential multi-stage collaborative recovery strategy from CPS faults caused by natural disasters is proposed, considering the interdependence of the cyber system and physical system at the layers of facilities, topologies and functions, and the coupling relationship of power grid integration in the recovery process. In the implementation of this strategy, on the cyber side, based on the demand of power grid integration recovery, the communication optical fiber aims to be repaired as quickly as possible to control the power grid Remote Controlled Switches (RCS). On the physical side, the power grid is restored with the objective to reduce loss of load, considering the whole process of degradation, response and recovery during fault handling in coordination. Resilience metrics based on the proposed method are introduced to evaluate the resilience level, as the absorption rate, adaptation rate and energy supply rate. IEEE-33 system is used as an example to show that compared with the traditional recovery strategy that considers only the power grid, the proposed strategy can significantly improve the system's ability to face disasters at the early stage, maintain power supply after the disaster, and speed up the overall load recovery.

An integrated resilience assessment model of urban transportation network: A case study of 40 cities in China

The transportation network is essential in providing daily accessibility and essential service to societies. An acceptable level of service needs to be maintained in critical infrastructures, even in the case of disruptions. We develop an integrated model to assess the resilience of urban transportation networks when exposed to different disruptions. Resilience is evaluated from perspectives of both traffic network topology and traffic flow. This model contains three parts: (1) a traffic flow simulation model to get flow distributed throughout the whole network, (2) different real-world disruptions are abstracted as random, localized, and flood disturbances, (3) functional and topological resilience are accessed by analyzing variations in travel time and connected components before and after a disruption occurs. 40 major cities are chosen to conduct resilience assessments. As the intensity of the disruption increases, common trends in the changes of functional and topological resilience values are observed and analyzed across all studied cities. In most cases, flood disruption is the least disruptive one of all three types of disruptions. Regression models are developed to estimate functional and topological resilience after random, localized, and flood disturbances. Possible resilience enhancement strategies are discussed based on analyses of regression models. This study could aid stakeholders in gaining a clear understanding of resilience not only topologically but also from the perspective of traffic flow and offer them practical strategies to enhance resilience in face of disruptions.