Realistic Synthetic Benchmark Instances for Multi-Objective Optimization of District Heating Systems
Authors/Creators
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1.
Zuse Institute Berlin
Description
This dataset provides 100 large-scale synthetic benchmark instances for lexicographic multi-objective optimization of district heating systems, based on the Berlin network. The instances capture both the network topology and the underlying MIP structures of the unit commitment problem, enabling detailed analysis of operational planning decisions.
Optimization is performed lexicographically across three objectives:
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Minimize operational cost
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Minimize emissions (subject to the minimum cost solution)
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Maximize delivered heat (subject to the previous objectives)
Each instance is available both as:
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JSON files (graph-based network representation with topology, parameters, and time series)
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MPS files (complete mixed-integer programming formulations for direct solver use, provided for each objective)
Instance design
The dataset varies key parameters across instances:
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time horizon T (10 or 25 years, 4-hour resolution)
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number of demand nodes and production sites
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number of converter units
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inclusion of storage
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number of fuel markets
Instances are grouped into configurations (10 per group), with stochastic variations in time series and technical parameters.
| Instance | Timesteps | Demand | Prod. Sites | Converters | Storage | Fuel Markets |
| uc_000-009 | 54750 | 3 | 5 | 20 | 0 | 2 |
| uc_010-019 | 54750 | 1 | 5 | 20 | 0 | 2 |
| uc_020-029 | 54750 | 3 | 3 | 10 | 0 | 2 |
| uc_030-039 | 54750 | 3 | 5 | 20 | 0 | 2 |
| uc_040-049 | 21900 | 3 | 5 | 20 | 0 | 4 |
| uc_050-059 | 54750 | 3 | 5 | 20 | 1 | 2 |
| uc_060-069 | 54750 | 5 | 5 | 20 | 1 | 2 |
| uc_070-079 | 54750 | 3 | 5 | 20 | 1 | 4 |
| uc_080-089 | 54750 | 1 | 5 | 20 | 1 | 6 |
| uc_090-099 | 21900 | 3 | 5 | 20 | 1 | 2 |
Complexity: The number of variables range from 5.3 million to 15.4 million and the number of constraints ranges from 6.6 million to 18.8 million, with 19.3 million to 64.4 million non-zeros.
Files
uc_instances_json.zip
Additional details
Funding
- Federal Ministry of Education and Research
- Research Campus MODAL 05M2025
References
- Annika, B., Riedmüller, S., Passage, M., Zittel, J. (2026). Benchmarking Realistic Synthetic Instances Against a Large-Scale District Heating Network: A Multi-Objective Optimization Study for Berlin. The 39th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2026).