Initial review of methods for cascading failure analysis in electric power transmission systems IEEE PES CAMS task force on understanding, prediction, mitigation and restoration of cascading failures

Baldick, Ross; Chowdhury, Badrul; Dobson, Ian; Dong, Zhaoyang; Gou, Bin; Hawkins, David; Huang, Hao; Joung, Manho; Kirschen, Daniel S.; Li, Fangxing; Li, Juan; Li, Zuyi; Liu, Chen‐Ching; Mili, Lamine; Miller, Stephen; Podmore, Robin; Schneider, Kevin P.; Sun, Kai; Wang, D.; Wu, Zhigang; Zhang, Pei; Zhang, Wenjie; Zhang, Xiaoping

doi:10.1109/pes.2008.4596430

Cited by 193 publications

(95 citation statements)

References 70 publications

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“…Cascading effects of contingencies in the power grid are very complex phenomenona, and identifying the typical mechanisms of cascading failures and understanding how these mechanisms interact during blackouts is an important research area [58][59][60][61][62][63]. Potential mechanisms that might be modeled include overloaded line tripping by impedance relays due to the low voltage and high current operating conditions, line tripping due to loss of synchronism, the undesirable generator tripping events by overexcitation protection, generator tripping due to abnormal voltage and frequency system condition, and under-frequency or under-voltage load shedding.…”

Section: Integrated Simulation Framework For Security Of Supply Analysismentioning

confidence: 99%

Development of a Simulation Framework for Analyzing Security of Supply in Integrated Gas and Electric Power Systems

et al. 2017

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Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consists of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to liquefied natural gas (LNG) terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.

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Section: Integrated Simulation Framework For Security Of Supply Analysismentioning

confidence: 99%

Development of a Simulation Framework for Analyzing Security of Supply in Integrated Gas and Electric Power Systems

et al. 2017

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“…There is also an established body of literature on the risk analysis of accidental cascading failure in power systems, of which the IEEE Cascading Failure Working Group have produced a number of authoritative surveys [5], [61], [62]. The state of the art here is sophisticated [63]: modern simulation tools e.g [64] can simulate many of the dynamics underlying cascading failures with impressive granularity.…”

Section: Risk Analysismentioning

confidence: 99%

A Comparison of Malicious Interdiction Strategies Against Electrical Networks

Cuffe

2017

IEEE J. Emerg. Sel. Topics Circuits Syst.

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“…Modelling the dynamic evolution of system-wide cascading failure processes poses a number of challenges due to the diversity of mechanisms which can trigger the initial failure and influence the subsequent propagation of breakdowns in the power system [27]. Various cascading failure models have been proposed; these can be divided into two main categories: those based on complex network theory analysis and those using power flow analysis, often including optimal economic power dispatch after each failure in the propagation, e.g., by linear optimal power flow (OPF) [29].…”

Section: Models Of Cascading Failure Considered In This Workmentioning

confidence: 99%

“…Some studies [23], [25], [27] have provided qualitative comparisons between complex network theory models and power flow models -identifying similarities and differences, and evaluating advantages and disadvantages. Most recently, Correa and Yusta conclude on the appropriateness of graph theory techniques for the assessment of electric network vulnerability by comparison to physical power flow models [28].…”

Section: Introductionmentioning

confidence: 99%

Comparing Network-Centric and Power Flow Models for the Optimal Allocation of Link Capacities in a Cascade-Resilient Power Transmission Network

Fang¹,

Pedroni²,

Zio³

2017

IEEE Systems Journal

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Abstract-In this study, we tackle the problem of searching for the most favourable pattern of link capacities allocation that makes a power transmission network resilient to cascading failures with limited investment costs. This problem is formulated within a combinatorial multi-objective optimization framework and tackled by evolutionary algorithms. Two different models of increasing complexity are used to simulate cascading failures in a network and to quantify its resilience: a complex network model (namely, the Motter-Lai (ML) model) and a more detailed and computationally demanding power flow model (namely, the ORNL-Pserc-Alaska (OPA) model). Both models are tested and compared on a case study involving the 400kV French power transmission network. The results show that cascade-resilient networks tend to have a non-linear capacity-load relation: in particular, heavily loaded components have smaller unoccupied portions of capacity, whereas lightly loaded links present larger unoccupied portions of capacity (which is in contrast with the linear capacity-load relation hypothesized in previous works of literature). Most importantly, the optimal solutions obtained using the ML and OPA models exhibit consistent characteristics in terms of phrase transitions in the Pareto fronts and link capacity allocation patterns. These results provide incentive for the use of computationally-cheap network-centric models for the optimization of cascade-resilient power network systems, given the advantages of their simplicity and scalability.Index Terms-power transmission network, cascading failures, complex network theory model, power flow model, capacity optimization, evolutionary algorithm

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Initial review of methods for cascading failure analysis in electric power transmission systems IEEE PES CAMS task force on understanding, prediction, mitigation and restoration of cascading failures

Cited by 193 publications

References 70 publications

Development of a Simulation Framework for Analyzing Security of Supply in Integrated Gas and Electric Power Systems

Development of a Simulation Framework for Analyzing Security of Supply in Integrated Gas and Electric Power Systems

A Comparison of Malicious Interdiction Strategies Against Electrical Networks

Comparing Network-Centric and Power Flow Models for the Optimal Allocation of Link Capacities in a Cascade-Resilient Power Transmission Network

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