Risk Of Cascading Failures Research Articles

A Non-Scheduled Multi-Stage Decision-Making Approach to Control Cascading Failures

The control research on cascading failures is critical to ensure the reliability of power supply. A path-driven multi-stage corrective control model for the whole process of cascading failures is established to eliminate the risk of cascading failure. For the cascading failure process caused by overloading, the selection criterion for subsequent outage is defined according to the mechanism of propagation of cascading failures. The path-driven constraints and power relaxation constraints are extracted based on the selection criterion for a subsequent outage. A model coupled with non-scheduled multi-stage decision-making is designed by considering the flexibility of control action implementation to optimize the sum of control cost and load-shedding risk for elimination of cascading failures. The verification results show that the proposed method can reduce the probability of outages of tripped branches and successfully eliminate cascading failure.

Decision-Making Under Uncertainty on Preventive Actions Boosting Power Grid Resilience

The growing impact of weather-related power outages on economy and society in the last decades underlines the rising need for power system resilience. Power system resilience can be boosted through adoption of probabilistic approaches and preventive actions building on smart grid capabilities. Decisions on the best-performing preventive action, however, are nontrivial and must consider the expected impact of an upcoming event, weather forecasts, fault probabilities, and their corresponding uncertainties. This article presents a three-stage decision-making methodology that is based on assessing weighted preevent and postevent performance loss and considers spatial uncertainty of fault probabilities, modeled by probability distributions. The methodology is demonstrated using preventive actions, such as additional network constraints and islanding, aiming to mitigate cascading failures in transmission networks. Their performance loss is compared to the traditional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> -1 criterion. Simultaneous faults of up to three lines are considered as initiating cause in the IEEE 30-bus network and the 489-bus German transmission network to verify potential and scalability of the methodology. Results show that the decision-making methodology effectively identifies the best-performing action to reduce the risk of cascading failures for any level of uncertainty.

Risk assessment of cascading failure of distribution network with flexible multi-state switch

With the implementation of carbon emission reduction policies in various countries, the penetration rate of distributed generation (DG) has gradually increased. Flexible multi-state switch (FMS), has attracted more attention as a new power device. This paper analyzes the cascading failure risk of flexible interconnected distribution system caused by the hidden failure of FMS protection. Firstly, based on the physical structure of FMS, the transient characteristics of AC feeder failure and the control and protection strategy of FMS are analyzed. Then, the uncertain factors causing protection misoperation are put forward and modelled. On this basis, the mechanism of cascading failures formation is analyzed. The risk index of cascading failure in flexible interconnected distribution network and the optimal load shedding based on failure consequences are established. Finally, the risk of system cascading failure is evaluated by the improved IEEE 33-bus system. Also, the influence of fault location and DG penetration on the risk is analyzed.

Critical Propagation Path Identification for Cascading Overload Failures With Multi-Stage MILP

Cascading overload failures of transmission lines are one of the dominant factors that induce catastrophic blackouts. The reliability risk of cascading overload failures is significantly underestimated in traditional adequacy assessment studies. This paper proposes an evaluation model for the assessment of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N-k</i> contingencies as cascading overloading failures. The interactions between outaged lines in the propagation path are revealed with a multi-stage optimization problem. The objective function is to maximize the probability of the propagation path of cascading overload failures. Several linearization techniques are proposed to convert the nonlinear evaluation model to mixed-integer linear programming. A rolling search algorithm is adopted to improve the capability of the proposed model to evaluate high-order contingencies. The case studies on the IEEE 118-bus systems show that the proposed model enables the identification of critical propagation paths with probabilities that are a few orders of magnitude larger than those with the traditional method.

Cascading Failure Mitigation Via Transmission Switching

After decades of research, cascading blackouts remain one of the unresolved challenges in the bulk power system operations. A new perspective for measuring the susceptibility of the system to cascading failures is clearly needed. The newly developed concept of system stress metrics may be able to provide new insights into this problem. The method measures stress as susceptibility to cascading failures by analyzing the network structure and electrical properties. This paper investigates the effectiveness of transmission switching in reducing the risk of cascading failures, measured in system stress metrics. Based on line-outage distribution factors, an algorithm is developed to identify and test the switching candidates quickly. A case study analyzing different stress metrics on the IEEE 118-bus test system is presented. The results show that transmission switching identified by our proposed algorithm could be used in preventive as well as corrective mechanisms to reduce the system's susceptibility to cascading failures. Contrary to the conventional operation wisdom that switching lines out of service jeopardizes reliability, our results suggest the opposite; system operators can often use transmission switching, when the system is under stress, as a tool to reduce the risk of cascading failures.

Submodular Optimization of Dynamic Thermal Rating for Cascading Failure Risk Mitigation Considering Braess Paradox

Cascading failure poses a significant risk to society. One approach to mitigate failure risk is through dynamic thermal rating (DTR) sensor, placed in transmission lines to achieve both risk mitigation and investment postponement of new lines. Sensor placement, as the basis of DTR analysis, is intrinsically a combinatorial optimization problem, while traditional solving methods cannot provide optimality guarantee and suffer easily from dimensionality curse. Besides, the risk mitigation may result in Braess paradox, a counterintuitive phenomenon that line update inversely increases failure risk. This paper proposes a submodular optimization-based DTR placement model for risk mitigation considering Braess paradox. First, a model based on Markov probability and important sampling weight techniques is utilized to efficiently quantify the failure risk before and after DTR placement. Then the risk model is applied to analytically reveal the Braess paradox condition which can invalidate the submodular formulation. For this invalidation issue, a novel submodular optimization approach is established to reformulate the risk mitigation model containing estimation error. Finally, a computationally efficient solving algorithm is designed to address this nonmonotone submodular optimization, which provides a provable approximation guarantee. The benefits brough from DTR and performance of the proposed algorithms are verified by case results.

A risk‐based multi‐step corrective control method for mitigation of cascading failures

Minimizing the risk of cascading failures in a power system is vital to balance the operational economy and security. A multi-step corrective control (MSCC) method is proposed to mitigate cascading effects in terms of reducing risk of blackout by considering the multi-step propagation characteristics of cascading failures caused by overload. Based on fault chain model and the DC power flow equation, the dynamic interaction between the process of cascading failures and control schemes is first discussed. The corrective control problem is then expressed as a multi-step decision-making optimisation problem based on the defined risk indices. The MSCC model with flexibility of control timing is designed by optimizing probability weighting expected cost and overload risk of cascading failure. In this control method, generation re-scheduling and load shedding are selected as the main remedial actions, and a multi-layer coding genetic algorithm is employed to obtain the optimal remedial control schemes. The numerical results indicate that control schemes generated by the proposed method could mitigate the cascading failures and result in better performance than non-recurring corrective control (NCC) in terms of economy and operational risk.

Defense strategies against cascading failures in networks: “Too-big-to-fail” and “too-small-to-fail”

“Too-big-to-fail (TBTF)” is a controversial approach to reducing the risk of cascading failures in the financial systems. In the TBTF defense strategy, most financial supports are provided to very big companies in order to avoid the complete breakdown of the entire system. We also consider “too-small-to-fail (TSTF)” as a comparative defense strategy, in which financial supports are more focused on small companies instead. We use two types of model network and a real network based on inter-industry Input-Output Table as underlying structures for cascading failures, and examine the validity of both defense strategies with two types of bailout policy, indirect (node capacity is increased) and direct (node load is decreased). We evaluate and compare the performances of TBTF and TSTF strategies in preventing cascading failures, and demonstrate that TSTF performs better when the node capacity is increased whereas TBTF works better when the node load is decreased.

A machine learning approach for real‐time selection of preventive actions improving power network resilience

Power outages due to cascading failures which are triggered by extreme weather pose an increasing risk to modern societies and draw attention to an emerging need for power network resilience. Machine learning (ML) is used for a real-time selection process on preventive actions, such as topology reconfiguration and islanding, aiming to reduce the risk of cascading failures. Training data is obtained from Monte Carlo simulations of cascading failures triggered by extreme events. The trained ML-based decision-making process uses only predictors that are readily available prior to an extreme event, such as event location and intensity, network topology and load, and requires no further time-consuming simulations.The proposed decision-making process is compared to time-consuming but ideal decision-making and fast but trivial decision-making. Demonstrations on the German transmission network show that the proposed ML-based selection process efficiently prevents the uncontrolled propagation of cascading failures and performs similarly to an ideal decision-making process whilst being computationally three orders of magnitude faster.

A Security Scheme Based on Intranal-Adding Links for Integrated Industrial Cyber-Physical Systems.

With the advent of the Internet of Everything era, the Industrial Internet is increasingly showing mutual integration and development. Its core framework, the industrial CPS (Cyber-Physical Systems), has received more and more attention and in-depth research in recent years. These complex industrial CPS systems are usually composed of multiple interdependent sub-networks (such as physical networks and control networks, etc.). Minor faults or failure behaviors between sub-networks may cause serious cascading failure effects of the entire system. In this paper, we will propose a security scheme based on intranal-adding links in the face of the integrated and converged industrial CPS system environment. Firstly, by calculating the size of the largest connected component in the entire system, we can compare and analyze industrial CPS systems’ security performance under random attacks. Secondly, we compare and analyze the risk of cascading failure between integrated industrial CPS systems under different intranal-adding link strategies. Finally, the simulation results verify the system security strategy’s effectiveness under different strategies and show a relatively better exchange strategy to enhance the system’s security. In addition, this paper’s research work can help us design how to further optimize the interdependent industrial CPS system’s topology to cope with the integrated and converged industrial CPS system environment.

Dynamic Cascading Failure Model for Blackout Risk Assessment of Power System With Renewable Energy

To assess the blackout risk of power system with high penetration of renewable, the existing cascading failure models need to be improved for capturing the dynamics and relays of renewable generation. In this paper, a dynamic model of cascading failure considering the utility-scale and distributed renewable energy is proposed. With the solution of dynamic equations for power system, the logics of relays are simulated for components such as transmission lines, conventional generators and renewable generations. The failure interactions among sources, networks, and loads are analyzed more comprehensively. In the proposed model, to capture the impact of renewable energy on the system dynamics, the dynamic equations for the utility-scale renewables are constructed with the second generic generation model of WECC (Western Electricity Coordinating Council), and the interactions among distributed renewables and the transmission system are considered in the amount of net load at buses. And to capture the tolerance of renewables for disturbances, the simulation logic is constructed for the voltage relays and frequency relays of utility-scale renewables and the anti-islanding relay of distributed renewables. The presented model is verified on the IEEE 39-bus system. The results show that renewable energy has a significant influence on the cascading failure risk.

Power Network Component Vulnerability Analysis: A Machine Learning Approach

The U.S. has defined a number of critical infrastructures, the disruption of which “would have a debilitating impact on security, national economic security, national public health or safety, or any combination of those matters.” Among these critical infrastructures is the electric power network that has a crucial role in enabling the operation of societies and industries. In the past decades, the functionality of the power network has been vulnerable to numerous disruptive events, including natural hazards, human-made events, or common failures. This work leverages several publicly available big data sets to lay the foundation for a comprehensive characterization and analysis of the U.S. power network in order to propose a network component vulnerability measure adopting machine learning techniques. The non-linear machine learning model is implemented to create smarter component cataloging for vulnerability analysis based on its geographic location and criticality. The findings could be useful by the grid stakeholder and policymakers to (i) evaluate network stability, (ii) understand the risk of cascading failure, and (iii) improve the resilience of the overall network and moving toward resilient smart grids.

Vulnerability Assessment of the Urban Rail Transit Network Based on Travel Behavior Analysis

Vulnerability assessment plays a crucial role in the management of complex and large-scale urban rail transit networks (URTNs). Even a temporary disruption may paralyze an entire public transportation network under a cascading failure. In the terms of vulnerability of the URTN, most studies have focused on changes in URTN topological characteristics and travel costs under disturbances, thereby neglecting the impact of alternative public transportation modes on passenger flow redistribution within the URTN. Therefore, in this study, considering the impact of changes in travel costs and modes on passenger flow redistribution, a novel model is proposed to analyze the cascading failure of weighted URTNs. Furthermore, adopting the estimated operation interruption time of the station (EOITS) to characterize the system disturbance intensity, the dynamic vulnerability of the URTN is simulated and analyzed. The results show that the EOITS exerts a major impact on the vulnerability of URTNs. With increasing of EOITS, the travel behavior of passengers becomes more diverse, and the process of cascading failure in URTNs also becomes more challenging to control. This research has implications for managers in terms of implementing of protective measures to mitigate the cascading failure risk and avert extreme catastrophes.

Probabilistic modeling of cascading failure risk in interdependent channel and road networks in urban flooding

This paper presents a probabilistic model for assessing risk of cascading failures in co-located road and channel networks. The proposed Bayesian network analysis framework integrates network structural properties and empirical flood propagation data to model the spread of flooding. The model was tested in a multiple watershed scenario in Harris County, Texas (USA), using historical flood data from past events. The results show the capability of the proposed Bayesian network model to quantitatively characterize the failure (i.e., inundation) of road network considering the cascading failure (i.e., overflow) from the channel network. The proposed model also enables simulating the risk of flood cascades (i.e., flood propagation) on the road network with high accuracy. The generic design of the algorithm also enables the adaptation of the proposed framework in other cities and regions. Accordingly, the proposed model provides a new tool to help decision-makers prioritize infrastructure protection plans and emergency response actions.

Risk of Cascading Blackouts Given Correlated Component Outages

Cascading blackouts typically occur when nearly simultaneous outages occur in $k$ out of $N$ components in a power system, triggering subsequent failures that propagate through the network and cause significant load shedding. While large cascades are rare, their impact can be catastrophic, so quantifying their risk is important for grid planning and operation. A common assumption in previous approaches to quantifying such risk is that the $k$ initiating component outages are statistically independent events. However, when triggered by a common exogenous cause, initiating outages may actually be correlated. Here, copula analysis is used to quantify the impact of correlation of initiating outages on the risk of cascading failure. The method is demonstrated on two test cases; a 2383-bus model of the Polish grid under varying load conditions and a synthetic 10 000-bus model based on the geography of the Western US. The large size of the Western US test case required development of new approaches for bounding an estimate of the total number of $N-3$ blackout-causing contingencies. The results suggest that both risk of cascading failure, and the relative contribution of higher order contingencies, increase as a function of spatial correlation in component failures.

Nipping Blackouts in the Bud: Introducing a Novel Cascading Failure Network

Cascading blackouts are the oldest major unsolved technical problem in power system engineering, highly visible since the Northeast Blackout of 1965 in North America. In this article, we review some cascading blackouts and past work on the problem. New metrics measure network stress to identify and reduce the risk of cascading failures.

Reducing the risk of cascading failure in active distribution networks using adaptive critic design

In this study, a technique for developing a distribution management system (DMS), which possesses the flexibility to take both preventive and corrective actions against thermal overloading of branches in active distribution networks (ADNs), has been demonstrated. An ADN comprises microgrids that consist of photovoltaic and battery energy storage systems (BESSs). The DMS primarily minimizes the hourly cumulative cost incurred by loads due to energy pricing of utility, by effectively dispatching the BESSs. Besides, the DMS regulates BESS state of charge and bus voltages within their limits. It also controls loading of branches by taking corrective measures during overloading or preventive measures during critical loading conditions. This DMS has been designed using a reinforcement learning based technique, namely, adaptive critic design (ACD). This study elaborates the formulation of ACD algorithm so that an effective performance of the controller can be achieved. As case study, a modified IEEE 5-bus system along with a microgrid and its controllers have been modelled in detail and simulated in real-time by developing a simulation-in-the-loop testbed using OPAL-RT and DSpace. This testbed facilitates simulation of the detailed model along with its power electronic components, such that both transient and steady-state performance of the system can be observed.

Cost-effective retrofits of power grids based on critical cascading failure scenarios identified by multi-group non-dominated sorting genetic algorithm

Power supply network is one of the most critical infrastructure networks of urban communities, but prone to the risk of cascading failures. In efforts toward disaster risk reduction of urban communities, it is thus important to identify critical cascading failure scenarios of the power supply networks and prepare effective countermeasures based on the identified scenarios. While addressing these issues, previous research efforts focused on cascading failure scenarios induced by a single component although those induced by multiple components may occur under natural or man-made disaster events. A major challenge in this problem is high computational cost required for simulating cascading failure scenarios and solving large-size optimization problems. This paper first presents an effective method to identify multi-component failure combinations entailing critical cascading failures in the network by using the overload cascading model, the multi-group non-dominated sorting genetic algorithm (MG-NSGA), and the concept of critical zone. Based on the identified critical scenarios, cost-effective retrofit combinations against the risk of cascading failures are also identified using the proposed ‘elite set updating’ method. The proposed disaster risk reduction decision-making methods are demonstrated and tested by numerical examples of two complex power supply networks.

Cascading failures in networks with the harmonic closeness under edge attack strategies

Abstract The definition of the edge load is usually confined to the degree and the betweenness. To overcome the limitation, we adopt the harmonic closeness to define the initial load on the edge whose strength is controlled by a tunable parameter θ. It is found that in Barabasi-Albert networks (BA networks), Erdos-Renyi networks (ER networks) with θ ≈ 7.6, and Newman-Watts networks(NW networks) with θ ≈ 7, the robustness is the strongest for the different average degrees . We furthermore explore the relationship between the proportion of attacked edges f and the optimal value of θ under the random attack (RA) and the intentional attack (IA). In order to prove the advantage of the harmonic closeness, our method is compared with the definitions concerning the degree, the betweenness of nodes and edges, the PageRank and the communicability angle. Simulation results show that in comparison with other methods, our method leads to less risk of cascading failures regardless of f in artificial and real networks under RA and BA networks under IA. A key finding is that regardless of , the artificial network with our method is more robust than those with other methods. In addition, the failed edge has the less impact on artificial and real networks with the harmonic closeness. These findings may be useful not only for the development of the research on cascading failures, but also for the reasonable distribution of the loads on edges in infrastructure networks.

Power system security improvement using an OPA model and IPSO algorithm

The growing demand for power generation makes power systems increasingly more complex to operate and less secure against outages, so the risk of extensive blackouts is growing and needs to be addressed. Cascading failures are the main reason for extensive blackouts, so to investigate this effect a new method including a standard blackout model, named ORNL-PSerc-Alaska (OPA), and static synchronous series compensator (SSSC) placement using an improved particle swarm optimization (IPSO) algorithm is proposed. This study provides two optimization approaches and presents optimal corrective actions. The results will help operators to implement corrective actions like optimal generation and load redispatching to return the system to a stable operating condition. The effectiveness of the proposed model is demonstrated using a realistic transmission network. Simulation results show a significant effect of SSSC on decreasing the risk of cascading failures and the ability of the proposed method to prevent blackout.