A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917892634705920 |
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| author | Ryu, Minseok Byeon, Geunyeong Kim, Kibaek |
| author_facet | Ryu, Minseok Byeon, Geunyeong Kim, Kibaek |
| contents | We propose a GPU-accelerated distributed optimization algorithm for controlling multi-phase optimal power flow in active distribution systems with dynamically changing topologies. To handle varying network configurations and enable adaptable decomposition, we advocate a componentwise decomposition strategy. However, this approach can lead to a prolonged computation time mainly due to the excessive iterations required for achieving consensus among a large number of fine-grained components. To overcome this, we introduce a technique that segregates equality constraints from inequality constraints, enabling GPU parallelism to reduce per-iteration time by orders of magnitude, thereby significantly accelerating the overall computation. Numerical experiments on IEEE test systems ranging from 13 to 8500 buses demonstrate the superior scalability of the proposed approach compared to its CPU-based counterparts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08293 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems Ryu, Minseok Byeon, Geunyeong Kim, Kibaek Distributed, Parallel, and Cluster Computing Optimization and Control We propose a GPU-accelerated distributed optimization algorithm for controlling multi-phase optimal power flow in active distribution systems with dynamically changing topologies. To handle varying network configurations and enable adaptable decomposition, we advocate a componentwise decomposition strategy. However, this approach can lead to a prolonged computation time mainly due to the excessive iterations required for achieving consensus among a large number of fine-grained components. To overcome this, we introduce a technique that segregates equality constraints from inequality constraints, enabling GPU parallelism to reduce per-iteration time by orders of magnitude, thereby significantly accelerating the overall computation. Numerical experiments on IEEE test systems ranging from 13 to 8500 buses demonstrate the superior scalability of the proposed approach compared to its CPU-based counterparts. |
| title | A GPU-Accelerated Distributed Algorithm for Optimal Power Flow in Distribution Systems |
| topic | Distributed, Parallel, and Cluster Computing Optimization and Control |
| url | https://arxiv.org/abs/2501.08293 |