Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy Systems
Jiaxin Wu, Xin Chen, Sobhan Badakhshan, Jie Zhang, Pingfeng Wang

TL;DR
This paper introduces a spectral graph clustering method for intentional islanding in hybrid energy systems, integrating renewable generation data to improve system resilience and reliability.
Contribution
It proposes a hierarchical spectral clustering approach that incorporates renewable generation signals for effective islanding in complex hybrid energy systems.
Findings
Effective islanding demonstrated on IEEE test systems with renewable integration
Enhanced system resilience through spectral clustering-based islanding
Inclusion of inverter-based renewable signals improves clustering accuracy
Abstract
Establishing cleaner energy generation therefore improving the sustainability of the power system is a crucial task in this century, and one of the key strategies being pursued is to shift the dependence on fossil fuel to renewable technologies such as wind, solar, and nuclear. However, with the increasing number of heterogeneous components included, the complexity of the hybrid energy system becomes more significant. And the complex system imposes a more stringent requirement of the contingency plan to enhance the overall system resilience. Among different strategies to ensure a reliable system, intentional islanding is commonly applied in practical applications for power systems and attracts abundant interest in the literature. In this study, we propose a hierarchical spectral clustering-based intentional islanding strategy at the transmission level with renewable generations. To…
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