Fast Power System Cascading Failure Path Searching With High Wind Power Penetration

Liu, Yuxiao; Wang, Yi; Yong, Pei; Zhang, Ning; Chen, Kang; Lu, Dan

doi:10.1109/tste.2019.2953867

Cited by 26 publications

(7 citation statements)

References 34 publications

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“…In this work, we simulate the power system cascading failures based on the ORNL-PSerc-Alaska (OPA) [25], [26] model, which is widely adopted and developed in [7], [10], [11], [19], [27]. The OPA model simulates the cascading failures triggered by branch failures in the power system.…”

Section: B Power System Cascading Failures Simulationmentioning

confidence: 99%

“…Yao et al proposed the risk estimation index to quantify the priority of each search attempt [10]. Liu et al accelerated the cascading failure simulation by simplifying multiple related DC optimal power flow problems into affine calculation problems [11]. Nonetheless, the Markov chain search still suffers from high computational complexity from the large search space of network cascading failures.…”

Section: Introductionmentioning

confidence: 99%

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Guiding Cascading Failure Search with Interpretable Graph Convolutional Network

Liu,

Zhang,

et al. 2020

Preprint

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Power system cascading failures become more time variant and complex because of the increasing network interconnection and higher renewable energy penetration. High computational cost is the main obstacle for a more frequent online cascading failure search, which is essential to improve system security. In this work, we show that the complex mechanism of cascading failures can be well captured by training a graph convolutional network (GCN) offline. Subsequently, the search of cascading failures can be significantly accelerated with the aid of the trained GCN model. We link the power network topology with the structure of the GCN, yielding a smaller parameter space to learn the complex mechanism. We further enable the interpretability of the GCN model by a layer-wise relevance propagation (LRP) algorithm. The proposed method is tested on both the IEEE RTS-79 test system and China's Henan Province power system. The results show that the GCN guided method can not only accelerate the search of cascading failures, but also reveal the reasons for predicting the potential cascading failures.

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Section: B Power System Cascading Failures Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Guiding Cascading Failure Search with Interpretable Graph Convolutional Network

Liu,

Zhang,

et al. 2020

Preprint

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“…[11] used the MC method for scenario sampling and analyzed the system reliability based on scenario power flow. For the uncertainty of high penetration of wind power in the power system, the path search problem was modeled as a Markov chain search problem [12], which reduced the computational burden. Ref.…”

Section: Introductionmentioning

confidence: 99%

Fast assessment method of power outage risks caused by extreme cold disasters based on probabilistic graph model

Wang

Yan

Cui

et al. 2023

J. Phys.: Conf. Ser.

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The extremely cold weather may cause cascading failures of power grid components, resulting in large-scale outages and economic losses. Thus, to make better anti-disaster preparations, it is necessary to assess the system-level risk of the power grid efficiently with extreme weather predictions. This paper proposes a risk assessment method for power grids based on the probabilistic graphical model (PGM). First, the time-varying failure probabilities of power lines are estimated with consideration of the cascading effects of the direct impact of disasters and power flow transfer. Then, load supply reliabilities are fast inferred by solving the probability graph model of power component failure propagations. Finally, the overall outage risk is evaluated by considering the capacity and importance of different loads, which helps to improve anti-disaster decisions. Through theoretical analysis and case studies, the efficacy of the proposed method is verified.

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“…A new wind power probabilistic interval prediction model is proposed to describe the uncertain scenario of wind power (Yang et al, 2020). Moreover, an efficient framework that enables a fast cascading path searching under high penetration of wind power (Liu et al, 2020a) is proposed. The universal generating functions are used to evaluate the time-varying reliabilities of power systems with high wind power penetration (Ding et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of Power Systems with High Renewable Energy Penetration Using Shadow Price and Impact Increment Methods

et al. 2021

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With the ever-increasing penetration of renewable resources, more complexities and uncertainties are introduced in power system reliability assessment. This entails an enormous number of contingency states to represent the characteristics of renewable energy. As a result, the unbearable computation burden is the main challenge toward the efficiency of the state enumeration (SE) method. To address that, this paper proposes an improved reliability evaluation approach that can not only increase the accuracy but also accelerate the analysis. In detail, the impact increment method is first employed to decrease the proportion of higher-order contingency states, leading to accuracy improvement. Then, the shadow price is used to solve the optimal power flow (OPF) problem in a faster manner. This shadow price (SP) method allows us to obtain the optimal load curtailment directly from linear functions rather than the time-consuming OPF algorithms. In addition, one hundred percent criterion is used to match shadow-price-based linear functions with system states. Case studies are performed on the RTS-79 system and IEEE 118-bus system, in which test scenarios include loads, photovoltaics (PV), and wind turbines (WT). Results indicate that the proposed method can significantly ease the computation burden and outperform traditional reliability assessment methods in terms of both computing time and accuracy.

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Fast Power System Cascading Failure Path Searching With High Wind Power Penetration

Cited by 26 publications

References 34 publications

Guiding Cascading Failure Search with Interpretable Graph Convolutional Network

Guiding Cascading Failure Search with Interpretable Graph Convolutional Network

Fast assessment method of power outage risks caused by extreme cold disasters based on probabilistic graph model

Reliability Assessment of Power Systems with High Renewable Energy Penetration Using Shadow Price and Impact Increment Methods

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