Resilience Assessment Of Systems Research Articles

An explorative Bayesian analysis of functional dependencies in emergency management systems

AbstractThe study of emergency or crisis management practices acquires strategical relevance for resilient decision‐making under uncertainty. The assessment of system resilience is an asset to identify potential design or operational improvements of a complex socio‐technical system, such as an Emergency Management (EM) system. This research aims at analyzing the functional properties of an EM system recurring to a novel integration of the Functional Resonance Analysis Method (FRAM) and Bayesian Belief Networks (BBN). The FRAM is used to model and display the actors and the interactions in the system, while the BBN, dynamically updated when new data becomes available, supports a complementary quantitative assessment. The methodology is iterated in the analysis of an EM procedure, issued by a second‐line Emergency Response organization for Oil and Gas (O&G) operators in Norwegian continental shelf. The results of the study show that the proposed stochastic methodology compensates the drawbacks of traditional FRAM modeling, via the outcomes of BBN quantitative analyses. The findings, contextualized in EM, can be transferred to different socio‐technical contexts, both military and civil ones.

Decentralized solutions for island states: Enhancing energy resilience through renewable technologies

Decentralized grid solutions could be a feasible alternative to improve resilience and mitigate cascading effects in island states. Our study explores approaches that reduce the risk of infrastructure failures and promote decentralized utility planning in islands. A novel framework is proposed to conduct a power system resilience assessment by integrating vulnerability assessments and energy system modelling approaches through network analysis. The framework is applied to an island context, where vulnerability to hydroclimatic hazards, geographic isolation, restricted access to energy sources, small population bases inadequate for substantial infrastructure investments, dependence on imported energy, lack of energy source diversification, and fragile ecosystems have exacerbated energy insecurity. As a case study, we have applied the framework to Cuba. We simulate disruptions in vulnerable network nodes in Cuba to determine the municipalities that are most impacted by the simulated cascading failures. We designed and optimized the lowest cost decentralized solutions to increase resilience either by acting as the baseload electricity source or as a complementary backup system to complement in case of a power outage. Then, the resilience of the designed system was assessed using power system resilience metrics. The study results show Regla municipality in Cuba as the most vulnerable hotspot for electricity distribution. Upon the different system comparisons, ancillary systems outperform backup systems in enhancing power system resilience, especially in the context of a disruptive event, supplying up to 53 MWh/day more, although they have higher investment costs. Based on this research, resource planners and policymakers can understand vulnerable node points and prioritize the necessary investments for the preferred system choice to alleviate impacts of energy insecurity on the Island States.

Distribution system resilience improvement against hurricanes: optimal line hardening according to reconfiguration potentials

AbstractIn recent years, there has been a notable increase in the frequency and intensity of natural disasters related to global warming. Large‐scale blackouts caused by natural disasters demonstrate the extreme vulnerability of power systems to these devastations. Resilience planning is the key to preparing power systems for natural disasters by enhancing the vital infrastructure's robustness. This paper proposes a new resilience improvement framework based on resiliency assessment and optimal hardening to improve the distribution system's resiliency against hurricanes. The main goal is to find the optimal distribution system line hardening solution according to reconfiguration potentials after the hurricane to minimize the distribution system cost of energy not supplied. Fragility curves and Monte Carlo simulations are used for the distribution system resilience assessment. Poles and conductors are vulnerable components of distribution system lines; therefore, two hardening strategies have been outlined using measures like replacing old poles with new poles, upgrading pole classes, and vegetation management. This method is modelled as an optimization program considering budget limitations and load priorities and implemented by a genetic algorithm on the IEEE 33‐bus standard network. The results show that optimal line hardening according to network reconfiguration potentials significantly increased the distribution system's resilience.

Multi-Indicator Fused Resilience Assessment of Power Grids Considering Wind-Photovoltaic Output Uncertainty during Typhoon Disasters

Extreme weather events such as typhoons pose a serious threat to the safe operation of power grids. In the field of power system resilience assessment during typhoon disasters, a parametric typhoon wind field model combined with actual historical meteorological data has not been well adopted, and the conventional renewable energy uncertainty modeling methods are not suitable for typhoon disaster periods. In this paper, a multi-indicator fused resilience assessment strategy considering wind-photovoltaic uncertainty and component failure during typhoon disasters is proposed. Firstly, based on the actual historical meteorological data of typhoons, an uncertainty model of typhoon wind speed is established by a rolling non-parametric Dirichlet process Gaussian mixture model. Then, a spatial–temporal contingency set is constructed by considering the best-fit wind field model and stress–strength interference model for failure probability of transmission lines. On this basis, a holistic resilience assessment framework is established from the perspectives of priority, robustness, rapidity, and sustainability, and the entropy weight method combined with the technology for order preference by similarity to an ideal solution is leveraged to obtain the comprehensive resilience indicator. Finally, numerical studies are performed on the IEEE-30 bus test system to identify vulnerable lines and improve system resilience during typhoon disasters.

Resilience modeling and improvement of metro systems considering statistical behaviors of passenger mobility

As an indispensable infrastructure in modern society, the metro system is threatened by unexpected events (e.g., natural disasters and terrorist attacks). To improve the safety and reliability of the metro system, we need not only to strengthen the management of accident prevention, but also formulate remarkable emergency repair strategies. Nevertheless, there are few research on resilience modeling and improvement methods considering the statistical behaviors of passenger mobility, such as passenger flow and additional travel distance due to certain disaster. Besides, unreasonable repair work can also lead to waste of the metro system performance for multi-station failure under the effects of some hazards. In this paper, we develop a new performance indicator to model the resilience of the metro system, considering the passenger flow, travel distance and potential transfer solution. An emergency operation strategy is proposed for the operation of the normal section following accidents based on the developed resilience model. The station importance is formulated, and the system performance influencing factors are analyzed. According to the metro system resilience assessment results, we improve the repair sequence. Finally, we demonstrate the proposed method by a real case study. The results show that the proposed optimal strategy can improve system resilience by up to 20%.

Redundancy-Based Resilience Optimization of Multi-Component Systems

Systems are damaged due to various disturbances, and the reliability of the systems is reduced. Measures to improve system resilience need to be studied since many systems still need to operate normally after suffering damage. In this paper, the whole process of the disturbance and recovery of the system is considered, and a resilience optimization model of a multi-component system is proposed. Firstly, a system resilience assessment method is proposed based on system reliability, and the system resilience loss is used as the resilience assessment index. Secondly, two component importance indexes, loss importance and recovery importance, are proposed for the system disturbance phase and recovery phase, respectively. The two importance indexes are weighted to obtain the weighted importance so as to measure the change law of system resilience and determine the influence degrees of components on system reliability. Then, under the constraint of maintenance time, an optimization model is established to determine a redundancy strategy to maximize system resilience. Finally, through an example analysis of a wind turbine system with its main components, it is verified that the redundancy strategy proposed with this method can reduce the loss of system resilience and effectively improve system reliability.

Microgrids and distribution system resilience assessment: A multi-objective robust-stochastic optimization approach considering social behavior using a deep learning modeling

In this paper, a new multi-objective two-stage robust-stochastic (MOTSRS) optimization approach for assessing microgrids and distribution system resilience is proposed. A novel approach based on deep learning is presented to model social behavior, specifically Social Reactions to Disasters (SRDs), in assessing microgrid and distribution system resilience. It is initially demonstrated that the proposed robust-stochastic approach is efficient for assessing the resilience of microgrids and distribution systems. A new resilience criterion is also proposed to be used for sensitivity analyses at the conservative level of the model. Subsequently, the effect of SRDs on system resilience is investigated through a supervised deep regression method. First, a large dataset is generated through a proposed nested Monte Carlo simulation. A numerical feature and five binary social behavior features are included in the dataset. Next, a feedforward neural network is utilized to predict the load during both normal and disaster circumstances based on the six features. The proposed supervised deep regression model with mean squared errors between 2e-5 and 2e-6 is capable of predicting electrical load during normal and disaster events. It is concluded that the resilience metrics of MGs, as well as expected operation costs and regret values, are influenced by the SRD effect.

Global maximum flow based time-domain simulation method for evaluating power system resilience

With climate change, extreme weather is occurring frequently, which can cause serious damage to energy supply systems. It is of great significance to study the performance of power transmission system under extreme weather for prevention and reinforcement before and after disasters. This paper proposes a time-domain simulation model for quantifying power system resilience under extreme typhoon scenario. Firstly, the power system resilience assessment method based on global maximum flow (GMF) is presented, which is applied to map the overall system operation performance via creating a network indicator while considering component fragility, system topology, and power flow. A specific resilience expression (RES) is proposed to describe percentage improvement of each component in the resilience metrics by considering the overall system performance. Secondly, power system vulnerability model and time-domain simulation methodology are established. The proposed method is demonstrated by four case studies using the IEEE-30 node test system under specific typhoon scenarios. From the GMF based RES index, the component repair strategy is formulated accordingly. The proposed restoration strategy, compared with the traditional expert strategy can effectively measure the resilience contribution of each component to the whole power system, and minimizes the power system performance losses due to extreme typhoon. The proposed approach quantitatively evaluates real-time system resilience to extreme typhoons, and it can also identify the system critical areas and assess the benefits of different resilience enhancement strategies. The resilience assessment framework can also be applied to other extreme scenarios and contribute to planning and construction of strong smart grid in the future.

A review of power system resilience assessment and enhancement approaches by focusing on wind farms and wind turbines

AbstractRecently, due to the increase of the frequency of high impact low probability (HILP) events (manmade and natural events), assessment and enhancement of resilience has become very important in the operation and planning procedures of future power systems. In this regard, several researches have been conducted on operation and planning strategies of the power system to improve resilience. Here, assessment and enhancement approaches of power system resilience by focusing on the role of wind farms and wind turbines are reviewed. To do so, a comprehensive review of the related metrics, hazard types, modelling approaches, assessment methods, and enhancement solutions is presented. Then, the assessment and enhancement methods of power system resilience are categorized in three phases before event (preventive proactive actions), during event (corrective active actions) and after event (restorative reactive actions) from the point of view of operation and planning actions. Finally, the current challenges, research gaps and future trends are discussed.

Data-driven static and dynamic resilience assessment of the global liner shipping network

As a critical infrastructure system of modern society, the global liner shipping network (GLSN) has become increasingly complex and thus vulnerable to disruptions. This study proposes a resilience assessment framework for the GLSN across two dimensions, including static resilience and dynamic resilience. First, by leveraging high-frequency vessel movement data, the GLSN is constructed. Then, with the clique percolation method (CPM), overlapping community structures and key nodes can be identified. The static resilience assessment is initially conducted using simulation techniques, with nodes attacked through pre-designed scenarios. Then, a network disintegration method is employed to consider the impact of traffic flow on system resilience assessment, which separates the weighted GLSN into different layers for evaluation. The results show that both overlapping community structure and traffic flow significantly impact the resilience evaluation of the GLSN. Finally, to assess the dynamic resilience of the GLSN, we propose an innovative, knock-on effect simulation model with tailored, locally weighted flow redistribution rules. It provides a method for predicting the impacts of potential global disruptions (e.g., the COVID-19 pandemic) and critical maritime infrastructure failures (e.g., the Suez Canal obstruction) on the shipping network, which are of great concern not only to academia but also to industry.

Resilience assessment and improvement for electric power transmission systems against typhoon disasters: A data-model hybrid driven approach

In response to the damage to electric power transmission systems caused by typhoon disasters in coastal areas, a planning-targeted resilience assessment framework that considers the impact of multiple factors is established to accurately find the weak links of the transmission system and improve the system resilience. Firstly, this paper constructs the attenuation model of the wind field and the comprehensive failure model of the system, in which the model drive to establish the cumulative failure model of the transmission system is adopted, and multiple data-driven schemes to correct the cumulative failure rate by relying on the feature factor information of the transmission system is adopted. At the same time, an analytic hierarchy process-weighted arithmetic averaging (AHP-WAA) method is introduced to select the optimal data-driven evaluation scheme. Secondly, this paper adopts the impact-increment-based state enumeration (IISE) method to establish resilience indicators for systems and corridors separately. On this basis, the optimal promotion strategy is selected according to the construction difficulty, resilience improvement ability, and cost analysis. Finally, simulations on the IEEE RTS-79 system have been carried out considering the influence of the real typhoon scenarios, micro-topographic and transmission corridor information factors. The results demonstrate that the hybrid-driven system resilience assessment and improvement method can assist planners in accurately judging the resilience level of the system against typhoon disasters and selecting the best resilience improvement strategy based on the cost-effectiveness ratio.

A test-based methodology for the probabilistic assessment of system resilience under random disturbances

Considering the random characteristics of the disturbances that may occur on the engineering system, this paper proposes a test-based probabilistic resilience assessment methodology. It is a sampling-based test method, including five main steps: (1) the bottom-up disturbance identification; (2) the sample size determination according to the law of large numbers and the central limit theorem; (3) the sample stratification and selection based on proportion stratified sampling method; (4) resilience test and performance data collection; and (5) probabilistic resilience assessment, consisting of point estimates and confidence intervals. Besides, two probabilistic resilience measures are defined to reflect the average and the percentile characteristics of the system resilience under random disturbances, and the traditional performance normalization method is extended to adapt to different types of parameters. A wireless DC servo motor is used to verify the effectiveness of our methodology, and this generic methodology can be further applied in other engineering systems.

Integration of Electric Vehicle Evacuation in Power System Resilience Assessment

Electric vehicle growth is creating a larger share of mobile load in power systems. This can affect system stress in potentially constrained areas of the grid, during a period that may already be an emergency. This paper proposes a novel model to reflect the behavior and spatiotemporal charging demand of electric vehicles during a natural disaster evacuation. The paper integrates the model in an algorithmic approach enabling operators to anticipate grid impacts from the charging of electric vehicles during a wildfire and subsequent evacuation. Geospatial visualization of evacuation charging demand provides a means to enhance operator awareness of spatiotemporal impacts and indicate critical areas of the grid for proactive mitigation. Results for a case study using the RTS-GMLC test system suggest that electric vehicle evacuation can result in dynamic changes to load patterns, adding stress to network components with the most significant load changes occurring during the initial evacuation period.

Alleviating Fractal and Ill-Conditioning Problems of the AC Power Flow Using a Polynomial Form

Power system resilience assessment, and mitigation, which deals with high-impact, low probability events, is gaining a great deal of attention among power researchers. To perform such an analysis, the availability of tools able to analyze modern power grids under normal and extreme disturbances is imperative. One such tool is the AC power flow algorithm. The latter should exhibit a numerical stability and an excellent convergence property under a broad range of operating conditions of a power system. One major difficulty that needs to be overcome stems from the nonlinearity and non-convexity of the AC power flow equations. As shown by Thorp and Naqavi, the Newton-Raphson (NR) method suffers from a fractal behavior, and the power flow Jacobian matrix becomes singular at points located on the fractal hypersurface, preventing that algorithm to converge. Furthermore, the Jacobian matrix becomes ill-conditioned for power systems that have relatively short lines or are heavy loaded. To address these problems, we propose a transformation of the AC power flow equations from a sinusoidal form to a polynomial form, which is solved using a modified Newton's method. The new polynomial form, whose order is equal to the number of solutions, encompasses node-based equations, Pythagorean equations, and loop-based equations. Furthermore, our approach is quadratic in three cases. Simulation results performed on several power systems reveal the good performance of the proposed method.

Power system resilience assessment considering critical infrastructure resilience approaches and government policymaker criteria

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.

Assessing flood resilience of urban drainage system based on a ‘do-nothing’ benchmark

To deal with the threat of urban flooding, it is necessary to assess the flood resilience of urban drainage systems at the planning and design stage. This study proposes a system resilience assessment methodology based on a ‘do-nothing’ benchmark. In this new benchmark, the number of flooded nodes used in computation of mean flood duration in the system is that observed under a ‘do-nothing’ scenario (i.e. with no intervention), irrespective of the scenario under evaluation. This methodology is demonstrated using a case study in Chizhou city, China, a simple stormwater drainage network with seven subcatchments. Schemes of interventions (with distributed storage tanks) that aim to mitigate flooding are then produced by zero-one integer programming and schemes sampling. The results show that the proposed method can compute the mean flood duration and system resilience reasonably and helps identify effective intervention schemes. Compared with traditional methods, this resilience assessment method based on a ‘do-nothing’ scenario can correctly indicate the change in trend of system resilience provided by different schemes, and aids understanding of different interventions to improve system resilience to urban flooding. This study also provides a new way to test different interventions and to explore which provide the greatest improvement in system resilience.

Assessment methods of urban system resilience: From the perspective of complex adaptive system theory

With the continuous development of urbanization, and increasing uncertainties and risks, resilience has become an important criterion for urban safety. As a dynamic and open spatial system, the urban system presents typical complex features. Thus, the understanding of urban resilience from the perspective of complex systems theory is helpful to achieve a full understanding of the composition and functioning mechanism of urban systems, and to then improve the scientific nature of urban system cognition and research. In this article, the literature on urban resilience is first reviewed, and it is found that the related research on resilience assessment has yielded some assessment methods. Then, based on complex adaptive systems (CAS) theory and combined with its seven basic characteristics, the basic characteristics of complex urban systems are summarized from the perspective of the principle of the urban system action mechanism. On this basis, a framework of complex urban systems is constructed from three aspects, namely the system environment, system elements and system structure, and the assessment methods of the urban system resilience for each aspect are explored. This study not only expands the research on urban system resilience assessment, but also provides a reference for urban safety development and resilience improvement.

Quantification of the Benefits for Power System of Resilience Boosting Measures

Severe natural events leading to wide and intense impacts on power systems are becoming more and more frequent due to climate changes. Operators are urged to set up plans to assess the possible consequences of such events, in view of counteracting them. To this aim, the application of the resilience concept can be beneficial. The paper describes a methodology for power system resilience assessment and enhancement, aimed at quantifying both system resilience indicators evaluated for severe threats, and the benefits to resilience brought by operational and grid hardening measures. The capabilities of the methodology are demonstrated on real study cases.

A framework for the resilience analysis of electric infrastructure systems including temporary generation systems

A quantitative framework is developed to assess the resilience of an electric infrastructure system, including the contributions of temporary service systems. The framework incorporates five dimensions of resilience – robustness, resourcefulness, redundancy, rapidity, and readjust-ability – under various setups for supplementary generation. It differentiates and prioritizes affected end-users. Notional examples illustrate the framework. The formula-based framework demonstrates the resilience contribution of temporary distributed generation (DG) technologies, and shows how differentiating among affected end-users supports the assessment of system resilience.

Electrical Power System Resilience Assessment: A Comprehensive Approach

Extreme weather events such as earthquake and hurricane have disastrous consequences on power systems. Due to the inherent nature of these events, as high-impact low-probability (HILP) events, selection of an appropriate method that can provide the effects of weather conditions on the power system behavior still remains a significant challenge. Resilience is a new concept that focuses on mitigating the destructive effects of such disastrous events on power systems. This article provides a fundamental framework for quantifying and modeling of power systems resilience, with focus on high wind incidence. The algorithm composes of four steps. In the first step, the prerequisites of the analysis are described. Disaster modeling is carried out in step second and the transmission line status and system topology obtaining are outlined in the third step. In the last step, based on optimal power flow tracing, a novel resilience index is introduced based on axiomatic design concepts. The core of the main algorithm involves a novel matrix-based approach to count automatically all possible routes from generators to loads, considering load importance, before and after a disruption. The effectiveness of the proposed approach is illustrated by the IEEE 14-bus system. The proposed methodology is original and a comprehensive approach.