Abstract: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, limiting comparability and the transferability of insights. To address this, realistic synthetic instances provide controlled, reproducible environments for testing optimization algorithms independent of specific case studies while preserving key structural and temporal characteristics of real systems. Such instances enable systematic benchmarking, methodological development, and comparative studies across 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 as network topologies in JSON format and as mixed-integer programs (MPS files) for benchmarking. They are constructed via a transparent procedure that allows reproduction, extension, and transfer to other network-based problems. We apply the method to Berlins district heating network, the most complex in Western Europe, formulating a tri-objective mixed-integer model for unit commitment over up to 25 years with 4-hour temporal resolution. 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.
From: Annika Buchholz [view email]
[v1]
Mon, 1 Jun 2026 12:50:31 UTC (637 KB)
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