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Contribution Type: Lightning Talk & Poster
Bridging Planning and Operation: A Coupled PyPSA and pandapipes Framework for District Heating Digital Twins
Presenter: Wonsun Song
Description: To design reliable, cost-optimal carbon-neutral urban environments, energy system planning must transition to bottom-up, physics-informed optimization, particularly in the heating sector, where thermal energy cannot be easily transported over long distances.
However, a critical bottleneck persists in current open-source toolchains: while macro-level capacity planning tools (such as PyPSA or oemof) are highly effective for energy system design, they lack the physical detail of real-world district heating networks. Conversely, detailed thermo-hydraulic tools like pandapipes are purely simulation-oriented. Uniting these two domains remains a major open challenge, primarily due to the severe computational complexity of embedding nonlinear thermo-hydraulic equations into large-scale capacity and dispatch optimization solvers.
To address this challenge and bridge the gap between optimization and simulation, this contribution presents a three-stage framework designed for the spatial planning, capacity sizing, and validation of district heating networks:
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Spatial Grid Topology Optimization: We implement a mixed-integer linear programming (MILP) model to determine cost-optimal spatial pipeline routing across building blocks, using virtual network flow constraints to prevent isolated network islands.
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Spatiotemporal Capacity Sizing: Utilizing a customized implementation of PyPSA, we co-optimize sector-coupled capacities (electricity generators, heat pumps, CHP, batteries) and 8760-hour dispatch of these assets.
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Thermo-Hydraulic Operational Validation: We couple the optimization outputs with a physical solver loop in pandapipes to verify mass flows, pressures, and thermal dynamics across the heating grid.
We apply this framework into a real-world heating system. This builds upon our prior study of the Freiburg-Dietenbach district (6900 households), which focused on the simultaneous design and control optimization of the district’s non-linear seasonal thermal energy storage system.
Background: This contribution is developed by greenventory GmbH, a high-tech energy-IT spin-off from Fraunhofer ISE and the KIT that provides municipal utilities and urban planners with high-resolution digital twins and decision-support software to design and operate climate-neutral energy systems.
Optional Links:
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Company Platform: greenventory.de
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Frameworks: PyPSA Github | pandapipes Github | NOSTES Github
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Reference Paper: Song, W., Harzer, J., Jung, C., Sander, L., & Diehl, M. (2025). Novel numerical method for simultaneous design and control optimization of seasonal thermal energy storage systems. Energy, 337, 138580. https://doi.org/10.1016/j.energy.2025.138580