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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.21932 |
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| _version_ | 1866915434216816640 |
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| author | Hadidi, Lars Göke, Leonard Hoffmann, Maximilian Klostermeier, Mario Sasanpour, Shima Varelmann, Tim Yfantis, Vassilios Linßen, Jochen Stolten, Detlef Weinand, Jann M. |
| author_facet | Hadidi, Lars Göke, Leonard Hoffmann, Maximilian Klostermeier, Mario Sasanpour, Shima Varelmann, Tim Yfantis, Vassilios Linßen, Jochen Stolten, Detlef Weinand, Jann M. |
| contents | As renewable energy integration, sector coupling, and spatiotemporal detail increase, energy system optimization models grow in size and complexity, often pushing solvers to their performance limits. This systematic review explores parallelization strategies that can address these challenges. We first propose a classification scheme for linear energy system optimization models, covering their analytical focus, mathematical structure, and scope. We then review parallel decomposition methods, finding that while many offer performance benefits, no single approach is universally superior. The lack of standardized benchmark suites further complicates comparison. To address this, we recommend essential criteria for future benchmarks and minimum reporting standards. We also survey available software tools for parallel decomposition, including modular frameworks and algorithmic abstractions. Though centered on energy system models, our insights extend to the broader operations research field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large-Scale Linear Energy System Optimization: A Systematic Review on Parallelization Strategies via Decomposition Hadidi, Lars Göke, Leonard Hoffmann, Maximilian Klostermeier, Mario Sasanpour, Shima Varelmann, Tim Yfantis, Vassilios Linßen, Jochen Stolten, Detlef Weinand, Jann M. Optimization and Control Distributed, Parallel, and Cluster Computing Mathematical Software 90-02 (Primary) 90C06 (Secondary) As renewable energy integration, sector coupling, and spatiotemporal detail increase, energy system optimization models grow in size and complexity, often pushing solvers to their performance limits. This systematic review explores parallelization strategies that can address these challenges. We first propose a classification scheme for linear energy system optimization models, covering their analytical focus, mathematical structure, and scope. We then review parallel decomposition methods, finding that while many offer performance benefits, no single approach is universally superior. The lack of standardized benchmark suites further complicates comparison. To address this, we recommend essential criteria for future benchmarks and minimum reporting standards. We also survey available software tools for parallel decomposition, including modular frameworks and algorithmic abstractions. Though centered on energy system models, our insights extend to the broader operations research field. |
| title | Large-Scale Linear Energy System Optimization: A Systematic Review on Parallelization Strategies via Decomposition |
| topic | Optimization and Control Distributed, Parallel, and Cluster Computing Mathematical Software 90-02 (Primary) 90C06 (Secondary) |
| url | https://arxiv.org/abs/2507.21932 |