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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.19412 |
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| _version_ | 1866918069114241024 |
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| author | Danner, Philipp de Meer, Hermann |
| author_facet | Danner, Philipp de Meer, Hermann |
| contents | The global energy transition towards distributed, smaller-scale resources, such as decentralized generation and flexible assets like storage and shiftable loads, demands novel control structures aligned with the emerging network architectures. These architectures consist of interconnected, self-contained clusters, commonly called microgrids or energy communities. These clusters aim to optimize collective self-sufficiency by prioritizing local energy use or operating independently during wide-area blackouts. This study addresses the challenge of defining optimal clusters, framed as a community detection problem. A novel metric, termed energy modularity, is proposed to evaluate community partitions by quantifying energy self-sufficiency within clusters while incorporating the influence of flexible resources. Furthermore, a highly scalable community detection algorithm to maximize energy modularity based on the Louvain method is presented. Therefore, energy modularity is calculated using linear programming or a more efficient simulation-based approach. The algorithm is validated on an exemplary benchmark grid, demonstrating its effectiveness in identifying optimal energy clusters for modern decentralized energy systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19412 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Community Detection in Energy Networks based on Energy Self-Sufficiency and Dynamic Flexibility Activation Danner, Philipp de Meer, Hermann Social and Information Networks The global energy transition towards distributed, smaller-scale resources, such as decentralized generation and flexible assets like storage and shiftable loads, demands novel control structures aligned with the emerging network architectures. These architectures consist of interconnected, self-contained clusters, commonly called microgrids or energy communities. These clusters aim to optimize collective self-sufficiency by prioritizing local energy use or operating independently during wide-area blackouts. This study addresses the challenge of defining optimal clusters, framed as a community detection problem. A novel metric, termed energy modularity, is proposed to evaluate community partitions by quantifying energy self-sufficiency within clusters while incorporating the influence of flexible resources. Furthermore, a highly scalable community detection algorithm to maximize energy modularity based on the Louvain method is presented. Therefore, energy modularity is calculated using linear programming or a more efficient simulation-based approach. The algorithm is validated on an exemplary benchmark grid, demonstrating its effectiveness in identifying optimal energy clusters for modern decentralized energy systems. |
| title | Community Detection in Energy Networks based on Energy Self-Sufficiency and Dynamic Flexibility Activation |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2506.19412 |