Session: 16-01-01: Government Agency Student Poster Competition

Paper Number: 150150

150150 - Multilayer Analysis of Energy Networks 

Modern interconnected and vulnerable energy infrastructures highlight the critical need for resilience and robustness to maintain the functioning of modern societies amidst growing environmental and operational challenges. This study introduces both multilayer and multiplex network analysis of Belgium’s electricity and gas systems, addressing the complex interdependence inherent in such infrastructure. The resilience is evaluated through random and targeted disruptions, while the robustness is assessed by employing the Molloy–Reed criteria for structural integrity and setting minimum operational thresholds for continuity. Multiplex layer similarities are assessed and subnetworks are identified. The coverage evolution metric quantifies the network’s adaptability under various disruption scenarios.

Belgium’s energy infrastructure is used as a case study to uncover critical nodes from different centrality perspectives whose stability is essential for overall network resilience and identify vulnerabilities to disruptions, whether random or targeted. Our insights into the structural robustness, cluster cohesion, and navigability of energy networks pave the way for more informed decisions in energy infrastructure planning and complex systems management, offering valuable lessons that can be applied globally.

Modern economies increasingly depend on sophisticated energy networks, particularly those for electricity and gas infrastructures. These networks are pivotal in ensuring operational efficiency, economic sustainability, and societal welfare. Integrating renewable energy sources, driven by environmental concerns, introduces complex new dynamics and challenges to the traditional management and optimization of these networks. Recognizing the intertwined nature of electricity and gas systems, recent advancements in multi-energy system modeling have underscored the need for innovative analytical frameworks.

The application of network science within the energy sector reveals the resilience and robustness essential for the operational stability of these systems, especially in the face of natural disruptions and targeted infrastructural attacks. Historically, electricity and gas networks have been studied in isolation. However, our research proposes a novel dual-framework analysis, employing multilayer and multiplex models to unravel the complexities inherent in Belgium’s gas and electricity networks. This approach, distinguishing unique node characteristics across separate layers (multilayer) and analyzing consistent nodes across all layers (multiplex), offers unique insights into the systems’ adaptability and interconnectedness.

Through this dual analysis, we identify critical nodes, assess patterns of interconnectivity, and evaluate the networks’ vulnerabilities to disruptions. This research fills a significant gap by comprehensively analyzing multilayer and multiplex energy networks and proposes a structured approach to enhance network resilience and robustness. Furthermore, our study underscores optimizing energy networks as a vital driver for economic and societal advancement, highlighting its importance in fostering environmental sustainability, economic viability, and alleviating energy poverty. This comprehensive approach aims to strengthen regional development and integrate sustainable energy solutions, underscoring the critical role of optimized energy distribution systems in improving societal outcomes and mitigating energy poverty.

In summary, this dual exploration of Belgium’s energy infrastructure networks introduces an innovative analytical methodology. It sheds light on essential areas for future infrastructure development and strategic management, heralding a new direction in energy network research.

Presenting Author: Muhammad Kazim North Dakota State University

Presenting Author Biography: Muhammad Kazim is a PhD student in Industrial and Manufacturing Engineering at North Dakota State University, specializing in multilayer network analysis. With a strong foundation in machine learning and data analytics, he is currently engaged in the NSF-funded EPSCoR RII Track-2 Program under grant number OIA-2119691, which aims to embed AI into North America's energy infrastructure.

Authors:

Muhammad Kazim North Dakota State University
Harun Pirim North Dakota State University
Shoumang Shi North Dakota State University
Di Wu North Dakota State University