Analysis of multilayer energy networks: A comprehensive literature review.
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| Title: | Analysis of multilayer energy networks: A comprehensive literature review. |
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| Authors: | Kazim, Muhammad1 (AUTHOR) muhammad.kazim@ndsu.edu, Pirim, Harun1 (AUTHOR) harun.pirim@ndsu.edu, Yadav, Om Prakash2 (AUTHOR), Le, Chau3 (AUTHOR), Le, Trung4 (AUTHOR) |
| Source: | Applied Energy. Nov2025, Vol. 398, pN.PAG-N.PAG. 1p. |
| Subjects: | Machine learning, Smart power grids, Ecological resilience, Bibliometrics, Energy infrastructure, Interconnected power systems, Clean energy |
| Abstract: | The increasing complexity of modern energy systems, driven by renewable integration, decentralized infrastructure, and cross-sector interdependencies, necessitates advanced analytical frameworks beyond single-layer models to address interdependencies, cascading failures, and resilience. Multilayer Network Theory (MLNT) offers a robust framework for modeling interactions across diverse energy carriers (e.g., electricity, gas, and heat), providing critical insights into scalability, sustainability, and fault resilience. However, despite its potential, no comprehensive review has systematically examined MLNT's applications in multi-energy systems (MES). This paper fills this gap by synthesizing interdisciplinary research from 2014 to 2024 to assess MLNT's role in advancing energy systems. This review uses bibliometric techniques (VOSviewer) to identify dominant research themes, including integrated energy systems and resource optimization, sustainability analysis in multilayer energy networks, smart grid communication, network topology, and cascading failure mitigation. Machine learning (ML) emerges as a key enabler of MLNT, employing advanced techniques such as graph neural networks (GNNs), reinforcement learning, and hybrid models to enhance predictive accuracy, real-time adaptation, and dynamic fault detection. Practical implementations, including Integrated Energy Systems (IES) and Virtual Power Plants (VPPs), demonstrate the synergy between ML and MLNT in addressing challenges such as synchronization, fault isolation, and renewable energy variability. While MLNT has been widely applied in biology, finance, and transportation, its adoption in energy systems remains limited. Drawing insights from these domains, this review illustrates how multilayer models can improve fault detection, enhance cascading failure mitigation, and optimize cross-layer coordination in modern energy infrastructures, paving the way for more resilient and adaptive smart grids. • MLNT is framed as an advanced tool to model interdependencies, cascading failures, and resilience in MES. • ML with GNNs and RL, enhances prediction , fault detection, and real-time optimization in multilayer energy systems. • IES and VPPs show MLNT with ML helps, address synchronization, fault isolation and renewable variability. • 2014- 2024 bibliometric analysis with VOSviewer reveals key clusters: optimization, sustainability, smart grid, and cascade failures. • Insights from transport, biology, and finance show MLNT boosts fault detection, resilience, and energy efficiency. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
| Abstract: | The increasing complexity of modern energy systems, driven by renewable integration, decentralized infrastructure, and cross-sector interdependencies, necessitates advanced analytical frameworks beyond single-layer models to address interdependencies, cascading failures, and resilience. Multilayer Network Theory (MLNT) offers a robust framework for modeling interactions across diverse energy carriers (e.g., electricity, gas, and heat), providing critical insights into scalability, sustainability, and fault resilience. However, despite its potential, no comprehensive review has systematically examined MLNT's applications in multi-energy systems (MES). This paper fills this gap by synthesizing interdisciplinary research from 2014 to 2024 to assess MLNT's role in advancing energy systems. This review uses bibliometric techniques (VOSviewer) to identify dominant research themes, including integrated energy systems and resource optimization, sustainability analysis in multilayer energy networks, smart grid communication, network topology, and cascading failure mitigation. Machine learning (ML) emerges as a key enabler of MLNT, employing advanced techniques such as graph neural networks (GNNs), reinforcement learning, and hybrid models to enhance predictive accuracy, real-time adaptation, and dynamic fault detection. Practical implementations, including Integrated Energy Systems (IES) and Virtual Power Plants (VPPs), demonstrate the synergy between ML and MLNT in addressing challenges such as synchronization, fault isolation, and renewable energy variability. While MLNT has been widely applied in biology, finance, and transportation, its adoption in energy systems remains limited. Drawing insights from these domains, this review illustrates how multilayer models can improve fault detection, enhance cascading failure mitigation, and optimize cross-layer coordination in modern energy infrastructures, paving the way for more resilient and adaptive smart grids. • MLNT is framed as an advanced tool to model interdependencies, cascading failures, and resilience in MES. • ML with GNNs and RL, enhances prediction , fault detection, and real-time optimization in multilayer energy systems. • IES and VPPs show MLNT with ML helps, address synchronization, fault isolation and renewable variability. • 2014- 2024 bibliometric analysis with VOSviewer reveals key clusters: optimization, sustainability, smart grid, and cascade failures. • Insights from transport, biology, and finance show MLNT boosts fault detection, resilience, and energy efficiency. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 03062619 |
| DOI: | 10.1016/j.apenergy.2025.126357 |
