Benchmarking Realistic Synthetic Instances Against a Large-Scale District Heating Network: A Multi-Objective Optimization Study for Berlin
accepted for publication
- Decarbonizing urban energy systems requires optimization approaches capable of handling the operational complexity of large-scale district heating networks. However, existing studies typically focus on a single, real-world network, which makes results difficult to compare and limits the transferability of insights to other systems. To overcome these challenges and enable a more systematic evaluation, realistic synthetic instances play an important role: they provide controlled, reproducible environments for testing optimization algorithms independent of a specific case study while still capturing essential structural and temporal characteristics of real systems. When designed carefully, such instances enable systematic benchmarking, facilitate methodological development, and support comparative studies across different algorithms and modeling choices. In this work, we generate a suite of large-scale synthetic instances for multi-objective optimization of district heating systems. The instances are openly available in the form of the underlying network topology in a JSON format and also as a mixed integer program (MIP) as MPS files to enable modeling and algorithmic benchmarking. They are constructed using a transparent procedure that allows to recreate or extend the given instances or apply the same procedure to other network based problems. We apply this methodology to Berlin’s district heating network, the most complex in Western Europe, formulating a tri-objective mixed-integer model focused on unit commitment over a horizon of up to 25 years with a 4-hour temporal resolution. The corresponding real-world instance serves as a reference point to demonstrate that the generated datasets reflect key behaviors of actual district heating operations. A computational study provides a detailed comparison between the synthetic instances and the real-world Berlin data, showing under which conditions the generated instances reproduce realistic optimization characteristics. Furthermore, we investigate which features make the resulting models computationally challenging. The findings highlight how well-designed synthetic instances can support robust benchmarking practices and enable meaningful assessment of (multi-objective) optimization methods for large-scale district heating systems.
| Author: | Annika BuchholzORCiD, Stephanie RiedmüllerORCiD, Matthew Passage, Janina ZittelORCiD |
|---|---|
| Document Type: | In Proceedings |
| Parent Title (English): | ECOS 2026 |
| Year of first publication: | 2026 |
| ArXiv Id: | http://arxiv.org/abs/2606.02195 |

