Resilience assessment of electric-thermal energy networks considering cascading failure under ice disasters

Yu, Songyuan; Wei, Chuxi; Fang, Fang; Liu, Mingxi; Chen, Yuanye

doi:10.1016/j.apenergy.2024.123533

Applied Energy

2024

DOI: 10.1016/j.apenergy.2024.123533

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Resilience assessment of electric-thermal energy networks considering cascading failure under ice disasters

Songyuan Yu,

Chuxi Wei,

Fang Fang

et al.

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Cited by 3 publications

References 23 publications

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Design and performance evaluation of a shared energy storage system integrated within combined heat and power plants in an energy network

Yuan,

Yan,

Chen

et al. 2024

Journal of Energy Storage

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Design and performance evaluation of a shared energy storage system integrated within combined heat and power plants in an energy network

Yuan,

Yan,

Chen

et al. 2024

Journal of Energy Storage

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Failure dependence and cascading failures: A literature review and research opportunities

Zhao,

Cai,

Cozzani

et al. 2025

Reliability Engineering & System Safety

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Modelling icing growth on overhead transmission lines: Current advances and future directions

Hou,

Wang,

Bai

et al. 2024

Energy Conversion and Econom

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The increasing impact of climate change raises concerns regarding the vulnerability of overhead transmission lines to ice disasters. To address this issue, this study reviews icing growth modelling in two categories: physical‐driven models (PDMs) and data‐driven models (DDMs), covering current advances and future directions. First, PDMs are summarised, focusing on the thermodynamic and fluid mechanics mechanisms. Existing PDMs are compared based on principles, analysing their advantages, disadvantages, and challenges faced. Second, the summarisation of DDMs involves four aspects: data preparation, algorithm selection, model training, and model evaluation. In data preparation, techniques such as preprocessing methods are reviewed to handle multisource data. In algorithm selection, various modelling algorithms are compared and analysed, from basic to deep learning approaches. In model training, processes are summarised to enhance practical applicability, including data partitioning, hyperparameter adjustment, generalisation capability, and model interpretability. In model evaluation, the predictive capabilities are analysed, covering both regression and classification tasks. Subsequently, based on the analyses, a comparison of PDMs and DDMs across various aspects is presented. Finally, future directions in icing growth modelling are outlined. The aim is to enhance icing assessment by understanding the underlying mechanism in attempt to reduce vulnerability and ensure reliability against adverse weather conditions.

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Resilience assessment of electric-thermal energy networks considering cascading failure under ice disasters

Cited by 3 publications

References 23 publications

Design and performance evaluation of a shared energy storage system integrated within combined heat and power plants in an energy network

Design and performance evaluation of a shared energy storage system integrated within combined heat and power plants in an energy network

Failure dependence and cascading failures: A literature review and research opportunities

Modelling icing growth on overhead transmission lines: Current advances and future directions

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