Vulnerability assessment for cascading failures in electric power systems

Baldick, Ross; Chowdhury, Badrul; Dobson, Ian; Dong, Zhaoyang; Gou, Bin; Hawkins, David; Huang, Zhenyu; Joung, Manho; Kim, Janghoon; Kirschen, Daniel; Lee, Stephen; Li, Fangxing; Li, Juan; Li, Zuyi; Liu, Chen‐Ching; Luo, Xiaochuan; Mili, Lamine; Miller, Stephen; Nakayama, Marvin K.; Papic, Milorad; Podmore, Robin; Rossmaier, John; Schneider, Kevin P.; Sun, Hongbin; Sun, Kai; Wang, David; Wu, Zhigang; Yao, Liangzhong; Zhang, Pei; Zhang, Wenjie; Zhang, Xiaoping

doi:10.1109/psce.2009.4839939

Cited by 56 publications

(29 citation statements)

References 51 publications

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“…Many studies used centrality measures to perform the vulnerability analysis of electric power systems [12][13][14], and this study applies two of them: degree centrality for nodes and betweenness centrality for edges.…”

Section: Centrality Measuresmentioning

confidence: 99%

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Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Konishi

Takahashi

2017

Energies

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There is a growing need to connect renewable energy systems (REs), such as photovoltaic systems (PVs), to the power grid for solving environmental problems such as global warming. However, an electricity grid with RE is vulnerable to problems of power shortage and surplus owing to the uncertainty of RE outputs and grid failures. Energy storage systems (ESSs) can be used to solve supply reliability problems, but their installation should be minimized considering their high costs. This study proposes a method to optimize the allocations of PVs and ESSs based on vulnerability analysis, and utilizes our proposed concept of "slow" and "fast" ESSs, which can reflect the influences of both uncertainties: PV outputs and grid failures. Accordingly, this paper demonstrates an optimal allocation of PVs and ESSs that minimizes the amount of ESSs while satisfying the PV installation target and the constraints on supply reliability indices for power shortage and power surplus in the event of a grid failure.

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Section: Centrality Measuresmentioning

confidence: 99%

“…It is known that a failure on an edge with higher c bet l could result in cascading failures [12]. Moreover, such a transmission line has higher risks of cascading failures if it is connected to a bus with lower c deg i .…”

Section: Centrality Measuresmentioning

confidence: 99%

Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Konishi

Takahashi

2017

Energies

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“…The sequence of cascading outage is well described in [1], [6]. One component outage may create new operating conditions that trigger another outage, which can bring sequential outages such as line tripping, generator tripping, or load shedding.…”

Section: Characteristics Of Cascading Outagesmentioning

confidence: 99%

“…A typical type of protective scheme among protection devices for generators and loads is the use of under-frequency relays. When the system frequency drops below a threshold value for a pre-specified time period, the protective relay may be triggered and activated [6]. A simulation that combines a steady state model with at least some representation of a dynamic model is therefore needed.…”

Section: Introductionmentioning

confidence: 99%

Sequential Outage Checkers for Analyzing Cascading Outages and Preventing Large Blackouts

Hur¹,

Joung²,

Baldick³

2011

Journal of Electrical Engineering and Technology

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-This paper presents the use of sequential outage checkers to identify the potential cascading processes that might lead to large blackouts. In order to analyze cascading outages caused by a combination of thermal overloads, low voltages, and under-frequencies following an initial disturbance, sequential outage checkers are proposed. The proposed sequential outage checkers are verified using the AEP 9-bus system, New England 39-bus system, and IEEE 118-bus system.

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“…In the last two decades, a number of large blackouts occurred all over the world due to the loss of synchronization caused by cascading failures [1]. Insufficient online implementations and lack of timely emergency controls, such as load shedding, generator tripping and proactive islanding, are said to be the common causes of those accidents [2].…”

Section: Introductionmentioning

confidence: 99%

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

Liu¹,

Yang²,

et al. 2016

J. Mod. Power Syst. Clean Energy

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A high-performance predictor for critical unstable generators (CUGs) of power systems is presented in this paper. The predictor is driven by the MapReduce based parallelized neural networks. Specifically, a group of back propagation neural networks (BPNNs), fed by massive response trajectories data, are efficiently organized and concurrently trained in Hadoop to identify dynamic behavior of individual generator. Rather than simply classifying global stability of power systems, the presented approach is able to distinguish unstable generators accurately with a few cycles of synchronized trajectories after fault clearing, enabling more in-depth emergency awareness based on wide-area implementation. In addition, the technique is of rich scalability due to Hadoop framework, which can be deployed in the control centers as a high-performance computing infrastructure for real-time instability alert. Numerical examples are studied using NPCC 48-machines test system and a realistic power system of China.

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Vulnerability assessment for cascading failures in electric power systems

Cited by 56 publications

References 51 publications

Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Sequential Outage Checkers for Analyzing Cascading Outages and Preventing Large Blackouts

High-performance predictor for critical unstable generators based on scalable parallelized neural networks

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