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Prototypical warm-starts for demand-robust LP-based energy system optimization

Please always quote using this URN: urn:nbn:de:0297-zib-101242
  • The expressiveness of energy system optimization models (ESOMs) depends on a multitude of exogenous parameters. For example, sound estimates of the future energy demand are essential to enable qualified decisions on long-term investments. However, the enormous demand fluctuations even on a fine-grained scale diminish the computational performance of large-scale ESOMs. We therefore propose a clustering-and-decomposition method for linear programming based ESOMs that first identifies and solves prototypical demand scenarios with the dual simplex algorithm, and then composes dual optimal prototype bases to a warm-start basis for the full model. We evaluate the feasibility and computational efficiency our approach on a real-world case study, using a sector-coupled ESOM with hourly resolution for the Berlin-Brandenburg area in Germany, based on the oemof framework.

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Author:Lukas Mehl, Niels LindnerORCiD, Karolina Bartoszuk, Janina ZittelORCiD
Document Type:ZIB-Report
MSC-Classification:90-XX OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
CCS-Classification:J. Computer Applications
Date of first Publication:2025/08/25
Series (Serial Number):ZIB-Report (25-15)
ISSN:1438-0064
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