Risk Of Cascading Failures Research Articles

Cascade failure risk analysis of mixed reservoirs based on disaster spreading theory

The reservoirs in the basin are connected in series or in parallel, forming a mixed reservoir group. Failure of a single dam in a mixed reservoir group may lead to subsequent failures of downstream dams, resulting in a cascading failure of the reservoir group. However, existing studies mainly focus on the impact of the upstream dam failure on the downstream dams, and lack a comprehensive consideration of the risk level of the upstream dams, the dam's own risk, and its own warning and flood release capacity. This limitation affects the accuracy of the actual risk calculation results of mixed reservoir dams. In this paper, the cascading failure risk chain is determined using graph theory, and a cascade failure risk analysis model based on disaster spreading theory is developed by combining dam break flood simulation and reservoir flood routing theory. The cascade failure risk is assessed in terms of the probability and the duration of a cascading failure. The model is applied to six mixed reservoirs in the Wujiang River Basin, China. The sensitivity of the model parameters is analyzed by examining a specific node, and the feasibility and effectiveness of the model are verified.

National-level resilience: Innovative framework for Energy-Water-Food nexus evaluation

The escalating demand for energy, water, and food (EWF) resources, alongside external stressors such as climate change, is placing significant strain on national systems. Therefore, this study introduces a novel framework for assessing the resilience of interconnected EWF systems at a national level. The framework utilizes a hybrid approach of network systems modeling and optimization models, integrating sectoral and dependency impacts to offer a comprehensive perspective on the interconnected resilience of EWF components. Applied to Qatar's EWF nexus, the framework revealed significant interdependencies, particularly between water and energy components, that led to cascading failures and challenged nexus resilience. Under single failure scenarios, unmet demands for energy and water ranged from 9 to 59 GWh and 97,000 to 359,000 m3, respectively, with resilience indices from 0.86 to 0.99. In double failure scenarios, significantly higher unmet demands caused the resilience index to drop to 0.43. Due to limited freshwater availability, the food sector is highly vulnerable to groundwater supply disruptions, resulting in an 85.6 % reduction in total crop production under extreme scenarios. Furthermore, the study highlighted resilience-enhancement strategies, such as utilizing treated wastewater for agricultural, industrial, and commercial activities and diversifying energy sources to reduce the risk of cascading failures, which increased the nexus's resilience by 17 %. The proposed framework serves as an essential tool for identifying critical components within the EWF nexus and planning strategies to enhance both operational and structural resilience.

Hierarchical Blocking Control for Mitigating Cascading Failures in Power Systems with Wind Power Integration

The increasing uncertainty of wind power brings greater challenges to the control for mitigation of cascading failures. In order to minimize the risk of cascading failures in large-scale wind power systems at a lower economic cost, a multi-stage blocking control model is proposed based on sensitivity analysis. Firstly, the propagation mechanism of cascading failures in power systems with wind power integration is analyzed, and the propagation path of such failures is predicted. Subsequently, sensitive lines that are prone to failure are identified using the power sensitivity matrix, taking into account the effects of blocking control on the propagation path. By constraining the power flow of these sensitive lines, a multi-stage blocking control model for the predicted cascading failure path is proposed with the objective of minimizing the control cost and cascading failure probability. Based on probabilistic optimal power flow calculations, the constraints related to wind power uncertainty are transformed into opportunity constraints. To validate the effectiveness of the proposed model, the IEEE 39-node system is used as an example, and the results show that the obtained control method is able to balance economy and safety. In addition, the control costs for the same initial failure are higher as the wind power penetration rates and confidence levels increase.

Identifying critical nodes of cyber–physical power systems based on improved adaptive differential evolution

The identification and protection of critical nodes have a significant influence on the reliability and robustness of the cyber–physical power system (CPPS), which can effectively reduce the risk of cascading failures. Firstly, this paper establishes a one-to-one coupled cascading fault model, which considers different topological characteristics of power and cyber networks. Based on the interdependence between the two networks, the cascading fault propagation mechanism after node failures in CPPS is analyzed. Then, a critical node identification method based on improved adaptive differential evolution (JADE) is proposed to identify the sets of critical nodes that can maximize the impact on the system load of CPPS. Combined with the cascading failure characteristics of CPPS, in the population generation strategy, the test vectors that do not meet the conditions after the crossover operation are filtered and replaced to improve the search efficiency of the algorithm. Finally, in the case study, an interdependent CPPS is constructed with an IEEE 39-bus system and a scale-free network of the same nodes. The simulation results verify the effectiveness of the proposed node importance assessment strategy. In addition, it is also shown that evaluating the critical nodes in power systems is important for enhancing the robustness and ensuring the security of the power grid operation.

Cascading failure resilience analysis and recovery of automotive manufacturing supply chain networks considering enterprise roles

With the rapid development of global trade, the member enterprises in the automotive manufacturing supply chain are widely spread around the world. As a result, the scale of the automotive manufacturing supply chain network has become increasingly large, and the structure has become more complex. At the same time, the connection between enterprises is becoming closer. On one hand, the close business relationships between enterprises and the vast network structure accelerate the operation efficiency of the automotive manufacturing supply chain network. On the other hand, it also exposes the entire network system to high risks of cascading failures when facing disruptions due to enterprise production interruptions. Therefore, this work aims to establish a realistic cascading failure model for the automotive manufacturing supply chain network to explore its cascading failure characteristics. Based on this model, a two-stage recovery strategy is proposed to enhance the network’s cascading failure resilience. Experimental results demonstrate the following findings: (1) underload nodes that emerge after load redistribution are the main cause of cascading failure resilience degradation in automotive manufacturing supply chain networks; (2) cascading failure resilience is negatively correlated with the load-exponential coefficient, the capacity lower-limit coefficient, and the attenuation-tolerance coefficient; (3) among four different types of attacks, cascading failures caused by the high eigenvector centrality attack strategy and the high degree attack strategy impose the most severe damage on the automotive manufacturing supply chain network; (4) the proposed two-stage recovery strategy can significantly improve the cascading failure resilience of the automotive manufacturing supply chain network.

Data‐driven power system dynamic security assessment under adversarial attacks: Risk warning based interpretation analysis and mitigation

AbstractPower system dynamic security assessment (DSA) has long been essential for protecting the system from the risk of cascading failures and wide‐spread blackouts. The machine learning (ML) based data‐driven strategy is promising due to its real‐time computation speed and knowledge discovery capacity. However, ML algorithms are found to be vulnerable against well‐designed malicious input samples that can lead to wrong outputs. Adversarial attacks are implemented to measure the vulnerability of the trained ML models. Specifically, the targets of attacks are identified by interpretation analysis that the data features with large SHAP values will be assigned with perturbations. The proposed method has the superiority that an instance‐based DSA method is established with interpretation of the ML models, where effective adversarial attacks and its mitigation countermeasure are developed by assigning the perturbations on features with high importance. Later, these generated adversarial examples are employed for adversarial training and mitigation. The simulation results present that the model accuracy and robustness vary with the quantity of adversarial examples used, and there is not necessarily a trade‐off between these two indicators. Furthermore, the rate of successful attacks increases when a greater bound of perturbations is permitted. By this method, the correlation between model accuracy and robustness can be clearly stated, which will provide considerable assistance in decision making.

Model cascading overload failure and dynamic vulnerability analysis of facility network of metro station

The existing studies mainly focused on the cascading failure of the whole metro but rarely on stations. In this paper, we map a metro station into a directed and hierarchical network, and propose a cascading overload failure model to study the network dynamic vulnerability. The passenger behavior and node load update are considered based on impedance function and mutual flow redistribution. Without changing the network topology, a flow optimization strategy is proposed to improve the network performance against cascading failure. Yuantong station of Nanjing Metro is studied. The results show the network is vulnerable and global failure occurs easily under small node tolerance. When the node tolerance excesses a critical value, the network can transit abruptly from global failure to balance state. We obtain that the critical node tolerances are 0.17 for the largest degree-based attack(LDA) and 0.19 for the largest load-based attack(LLA). The ranges of the optimal flow redistribution coefficient are identified by the flow optimization strategy, which are [0.15 0.2] for LDA and [0.2 0.25] for LLA. The result indicates that a proper guidance of passenger flow can be beneficial to the network performance while a strong intervention to passenger flow can instead increase the cascading failure risk.

Real-Time Cascading Failure Risk Evaluation With High Penetration of Renewable Energy Based on a Graph Convolutional Network

When renewable energy penetration increases and the power network becomes more complex, vulnerability and security concerns typically also become more prevalent. A proper evaluation method focused on risk prediction and failure identification is required in modern power systems. In our work, a novel evaluation framework with high efficiency and accuracy is proposed, where the influence of over/under-voltage on renewable energy generators is considered during the cascading failure risk evaluation process. A graph convolutional network and a long short-term memory network are applied to describe the complex network topological structure and extract the system's electrical features, respectively. The neural network model trained with offline simulation data can reduce the numerical effort of evaluating cascading failure risk in real-time. Finally, the proposed method is verified based on the benchmark of both an IEEE 39-bus test system and a real-world French power system. The results show that the proposed cascade neural network model can achieve better evaluation results with shorter training and evaluation times.

Geometric deep learning for online prediction of cascading failures in power grids

Past events have revealed that widespread blackouts are mostly a result of cascading failures in the power grid. Understanding the underlining mechanisms of cascading failures can help in developing strategies to minimize the risk of such events. Moreover, a real-time detection of precursors to cascading failures will help operators take measures to prevent their propagation. Currently, the well-established probabilistic and physics-based models of cascading failures offer low computational efficiency, hindering them to be used only as offline tools. In this work, we develop a data-driven methodology for online estimation of the risk of cascading failures. We utilize a physics-based cascading failure model to generate a cascading failure dataset considering different operating conditions and failure scenarios, thus obtaining a sample space covering a large set of power grid states that are labeled as safe or unsafe. We use the synthetic data to train deep learning architectures, namely Feed-forward Neural Networks (FNN) and Graph Neural Networks (GNN). With the development of GNNs, improved performance is achieved with graph-structured data, and GNNs can generalize to graphs of diverse sizes. A comparison between FNN and GNN is made and the GNNs inductive capability is demonstrated via test grids. Furthermore, we apply transfer learning to improve the performance of a pre-trained GNN model on power grids not seen in the training process. The GNN model shows accuracy and balanced accuracy above 96% on selected test datasets not used in the training. Conversely, the FNN shows accuracy above 85% and balanced accuracy above 81% on test datasets unseen during training. Overall, the GNN model is successful in determining, if one or several simultaneous outages result in a critical grid state, under specific grid operating conditions.

A dual perspective of organizational resilience (OR) and information technology systems resilience (ITSR): an analysis of interdependencies and tensions

Contemporary organizations face a rising incidence of disasters, extreme events and crises (Boin and Lodge 2021). In parallel, the socioeconomic landscape is increasingly complex which intensifies inter-organizational dependencies and the risk of cascading failures (Ansell et al. 2021). To survive and perhaps thrive, organizations must cultivate organizational resilience (OR). However, their capacity to do so is currently curtailed. Although it is widely recognized that the performance of organizational processes, functions and capabilities is closely aligned to the effectiveness of associated information technology systems (ITS) (Schultze and Orlikowski 2004); models of OR have yet to elucidate mechanisms by which ITS contribute to resilience capabilities (Annarelli and Nonino 2016). In this conceptual paper, which is a work in progress, we reflect upon the nature of interdependencies and tensions between ITS resilience (ITSR) and OR. We adopt a deductive, qualitative approach to systematically compare OR & ITSR. Our comparative analysis is informed by OR models described by Duchek (2020), Sheffi and Rice Jr (2005) and Weick and Sutcliffe (2011), while for ITSR, we employ the Reactive Manifesto as interpreted by Bonér et al. (2014) and Debski et al. (2017). Figs 1, 2 illustrate our interpretation of each resilience construct. As may be seen on Fig. 1 we interpret OR as a three-phase process including preemptive, proactive & recovery phases each possessing associated resilience capabilities. The process may occasion three operational outcomes (or levels of resilience maturity). 1st , 2nd and 3rd order resilience denote organizations that when a disruption occurs, merely maintain key operations, rapidly achieve a return to normal operations or that capitalize on a disruption to achieve an improved post-shock trajectory respectively. merely maintain key operations, rapidly achieve a return to normal operations or that capitalize on a disruption to achieve an improved post-shock trajectory respectively. ITSR is interpreted via the notion, reactive scalability which describes an organizational system that is both responsive and scalable (Debski et al. 2017) i.e., which is able to rapidly achieve (responsive) appropriately dimensioned (scalable) adaptations to intra and extra-organizational changes. Our analysis employs a multi-level approach. The preliminary results of the analysis are presented in Table 1. Upon completion of the analysis, we will elaborate theoretical propositions pertaining to the relationship between the OR and ITSR constructs to guide subsequent empirical research to bridge the theoretical divide between these in reality, indissociable resilience constructs. This comprises the main anticipated contribution.

Cascading Failure Propagation and Mitigation Strategies in Power Systems

In this article, we investigate the local and nonlocal failure propagation patterns in power systems and propose effective mitigation strategies. It is widely acknowledged that the sequence of failure events of component overloading is determined by power flow redistribution. To understand the power flow redistribution process, we analyze the prefault power flow condition using network flow theory and by clustering the system into several regions with balanced local power flow. We observe that faults triggering local power imbalance would lead to remote power transfer and faults causing no local power imbalance would lead to local power redistribution. The improved understanding of the failure process permits us to develop effective mitigating strategies consisting of adaptive power balance restoration and selective edge protection for cascading failure propagation. In addition, we propose a set of indexes to measure the local and nonlocal propagation risks of cascading failures. Numerical simulations on the IEEE 118- and 300-bus systems demonstrate that the proposed mitigation strategies can significantly reduce the blackout sizes and are tolerant to incorrect transmission line tripping.

Evaluation for Risk of Cascading Failures in Power Grids by Inverse-Community Structure

Recently, the development of the Internet of Things (IoT) enables more comprehensive and intelligent analysis and defense for cascading failures in power grids. This paper summarizes power grids as Temporal Weighted Networks (TWN) which are different from conventional temporal networks. For TWN, the topological structure is fixed but weight distribution is time-varying. Then it is noted that for different operating states represented by different weight (power flow) distribution at different time sections, the risks of cascading failures would be completely different. Inspired by analysis of inter-subnetworks power shifts in cascading failures, Inverse-Community (IC) structure is proposed in TWN to intuitively identify the risk of cascading failures. IC describes a structure in weighted networks with several communities in which the weighted interaction between communities is significantly stronger than that within the same community. Furthermore, the conventional modularity is upgraded as Inverse Modularity (IM) to quantify the characteristic of IC structure in power networks. Subsequently, considers the risk of cascading failures represented by IM and the cost of power network operation, a security/economic dispatch (SED) method is designed to handle the optimal power dispatch issues. Simulation results prove a positive correlation between IM of power flow distribution and risk of cascading failures. Furthermore, the results on the IEEE 118-bus system demonstrate the effectiveness of the proposed SED method in mitigating cascading failure risks.

Coordination control method to block cascading failure of a renewable generation power system under line dynamic security

The large application of renewable energy generation (REG) has increased the risk of cascading failures in the power system. At the same time REG also provides the possibility of new approaches for the suppression of such failures. However, the capacity and position of the synchronous generator (SG) involved in regulation limit the power regulation speed (PRS) of REG to the overload line which is the main cause of cascading failures, while the PRS of SG is related to the position and shedding power. REG and SGs have difficulty in achieving effective cooperation under constraints of system power balance. Particularly, the dynamic variation of line flow during power regulation causes new problems for the accurate evaluation of line thermal safety under overload. Therefore, a new strategy for quantitatively coordinating shedding power and power regulation to block cascading failures in the dynamic security domain is proposed in this paper. The control capability and dynamic security domain of the overload line are modeled, and the coordination control method based on power regulation is then proposed to minimize shedding power. The algorithm for the optimal control scheme considers the constraints of load capacity, power source capacity and bus PRS. The correctness of the proposed method is verified using case studies.

Limiting the Failure Impact of Interdependent Power-Communication Networks via Optimal Partitioning

The mutual dependency between the power grid and communication network increases the risk of large-scale cascading failure, making the mitigation of outages a difficult task. In this paper, we use the concept of joint partitioning of the power and communication subsystems by identifying the most vulnerable typed-graphlets. This results in higher-order partitions, which prevent cascading failures from propagating globally in the system. For this purpose, we first formulate the problem of partitioning the interdependent system as a large-scale optimization problem with the objective of minimizing the overall load shedding subject to: i) power flow convergence, ii) power stability, iii) partition connectivity, iv) communication congestion/delay control, and v) a high partition quality. Then, using Benders’ method, we solve the problem by decomposing it into a relaxed master problem that consists of constrained spectral clustering, and a linear subproblem that consists of minimizing the load shedding subject to power flow convergence, stability, and congestion/delay control. Simulations are conducted on an IEEE 118-bus supported by a 118-node communication network. Our investigations reveal an average of 62% decrease in damage when the system is optimally partitioned.

A multi-utility and dynamic approach for the upgrade of an aged water distribution network

Water Distribution Networks (WDNs) along with other utilities are among the most important assets in every industrialized society. The rehabilitation and upgrade of WDNs refer to a very complex and multi-criteria problem which needs a comprehensive decision support system providing optimal renewal plans for the asset managers. There is a multitude of real-world problems faced with such activities. In this regard, this study aims to propose a novel method to cope with three practical challenges in WDNs rehabilitation activities including 1-budget limitation, 2- hydraulic deficiency and 3- correlation of WDNs with other infrastructures bringing the risk of cascading failures to a multiplex network. This results in a multi-objective optimization problem with three objectives which are cost, hydraulic and decoupled reliabilities. The problem is solved dynamically with the contribution of a nature inspired optimization algorithm, the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The method is applied to a deteriorated water pipe network, and the results are compared with the ones obtained only by the consideration of two objectives, costs and hydraulic reliability showing how much the objectives are conflicted with each other.

Scenario inference model of urban metro system cascading failure under extreme rainfall conditions

Metro systems have become high-vulnerability entities due to the increasing frequency and severity of urban flooding. Flood events may cause cascading failure to metro systems; therefore, exploring the cascading failure risk of the metro system is a prerequisite for urban flooding prevention and risk management. This study presented a Rank-Order Centroid (ROC) based CIA-ISM (Cross-Impact Analysis, and Interpretive Structural Modeling) method to accurately assess the reliability of emergency management in metro systems under extreme rainfall conditions. We applied this approach to a metro flooding case in Zhengzhou on July 20, 2021. The reliability results show that efficient rescue and timely shutdown notification are the most critical causal events in the cascading failure scenarios. The events of system vulnerability that have the most significant impact on casualties, property losses, and social panic are, respectively, timely notification of the shutdown, humanitarian aid, and public opinion guidance. In forecast scenarios with emergency management measures in effect, the probability of casualties, property losses, and social panic decrease by 96.3%, 58.58%, and 64.28%, respectively. Moreover, a comparison with Bayesian Network (BN) model verified the effectiveness of the ROC-based CIA-ISM approach. Based on the study, we suggest the metro companies release a timely notification of the shutdown. This study can provide scientific data for decision-makers to reasonably develop emergency strategies, significantly reducing flood losses and promoting cities’ sustainable development.

Cascading Failure Vulnerability Analysis in Interdependent Power Communication Networks

Future smart grids represent interdependent cyber-physical systems that involve multiple interactions between a power grid and a communication network. Such a mutual dependence increases the risk of large-scale cascading failures. Thus, it is important to conduct a joint vulnerability analysis. Unfortunately, existing works failed to identify the combination of power nodes and communication nodes that present the greatest damage impact. In this article, we first present a model that captures the interdependence between the two subsystems using stochastic geometry; then, we conduct a joint vulnerability analysis. We propose an attack strategy that targets the most influential nodes in both subsystems. We show that, based on six joint targeted nodes, this strategy presents a comparable and, in some cases, larger damage impact compared to the traditional exhaustive search attack and with very low complexity. Our investigations reveal that 1) the proposed joint attack strategy presents a large damage impact at a significantly small simulation runtime and 2) attacking the most influential communication nodes solely or the most influential power nodes solely achieves a comparable damage impact to that of the proposed joint attack.

Interdependent effects of critical infrastructure systems under different types of disruptions

Modern critical infrastructure systems (CISs) are becoming increasingly dependent on each other for proper operation, which also exposes the systems to higher risks of cascading failures. Prior research has attempted to quantify the interdependent effect (IE) of CISs under random or intentional disruptions. However, those efforts are limited with respect to both the methodological approaches for simulating intentional disruptions and CISs failure propagation, and the granularity and quantification method of the simulated disruption impacts. To address these limitations, this study proposes a new framework for analyzing the IE of CISs. The framework first models interdependent CISs with a high level architecture (HLA)-based simulation approach that integrates existing knowledge, data, and models specific to each infrastructure domain. It then co-simulates the failure propagation through interdependent CISs under three different types of disruptions, namely random, topology-based and flow-based disruptions. Based on the simulation outputs, the framework quantifies the IE by comparing the disruption-induced system performance losses, which are measured with three different performance metrics, under both interdependent and independent conditions. A case study of two interdependent water and power supply systems was conducted to demonstrate the efficacy of the proposed framework. The case study results revealed a few new insights into the vulnerability of interdependent CISs under different disruption scenarios, which have important implications for the risk assessment and resilience management of interdependent CISs in practice.

Application of multi-factor dynamic interaction graph in vulnerability assessment and online monitoring of transmission lines

The use of interaction graph to evaluate and obtain vulnerable transmission lines is an effective means of preventing cascading failure. However, the continuous changes of the key parameters that affect the cascading failure during the operation of the power system will affect the actual vulnerability of the lines. Especially with the increase in the penetration rate of renewable energy, the impact of frequent uncertainty and randomness brought by it can no longer be ignored. Therefore, the assessment results obtained from the static interaction graph cannot meet the accuracy requirements in the engineering, and also limit the scope of its application. How to realize the dynamization of the static interaction graph is the key to solving the above problems, which is also the difficulty that the current research has been facing. This paper proposes a construction method of multi-factor dynamic interaction graph based on generalized fault chains. With data-driven as the core, it realizes true dynamic and more accurate vulnerability assessment with the help of parallel distributed computing, uninterrupted data stream and sliding window. On this basis, according to the characteristics of generalized fault chains and the dynamic interaction graph, we propose and realize online monitoring for the classification of vulnerability lines for the first time. This function provides strong support for the implementation of measures with real-time characteristics, and can also more effectively reduce the risk and harm of cascading failure. Finally, this paper combines the IEEE 118 node system and deliberate attacks to prove the significance and effectiveness of the research.

Risk assessment of cascading failures in power systems with increasing wind penetration

The ever-growing penetration of renewable based generation is leading to significant increases in the risk of cascading failures in low-inertia, and interconnection-rich, power systems. This paper proposes a framework for quantifying the risk of cascading failures in renewable-rich power systems with fast frequency response (FFR) services. This is achieved by developing a novel time-based dynamic model of cascading failures to capture the transient behaviours of the system response and quantify the risk of blackout. This dynamic cascading failure model is complemented by unit commitment for representative dispatch and includes the provision of FFR. The proposed framework is illustrated using a modified version of the Illinois 200-bus synthetic system. As key outputs, the results emphasize the importance of accurate modelling of ancillary services associated with frequency regulation in cascading failure analysis, and indicate that increased wind penetration can lead to a higher probability of power outages. Adopting FFR services can help to mitigate the cascading risk but may introduce rotor angle instability issues.