Generation of Large District Heating System Models Using Open-Source Data and Tools: An Exemplary Workflow
Jan Stock, Till Schmidt, André Xhonneux, Dirk Müller
TL;DR
The paper addresses transforming existing district heating systems toward sustainable heat supply by leveraging open-source data and tools. It proposes a workflow to create digital, graph-based representations of DH networks by integrating heterogeneous public datasets and estimation tools, enabling scenario analysis and tool benchmarking. Two large-scale case studies, Bottrop and Essen, demonstrate how the workflow can produce thousands of connected buildings and facilitate infrastructure-adaptation analyses, including network separation. The authors discuss applicability and limitations, emphasizing data quality and the need for validation with DH operators, and highlight the workflow’s potential to support open-data driven planning and testing of analytical tools.
Abstract
District heating (DH) systems play a pivotal role in decarbonizing the building sector's heat supply. While innovative low-exergy DH and cooling systems are increasingly adopted in new developments, the transformation of existing DH systems remains critical, as many still depend on fossil-based heating plants. Achieving a sustainable heat supply necessitates integrating renewable energy and waste heat sources into current DH systems and enhancing operational efficiency through measures such as reduced supply temperatures and advanced control algorithms. These improvements can reduce costs and CO2 emissions but may require infrastructure adaptations, including pipe replacements and building-level system adjustments. This paper introduces a workflow for generating DH models using publicly available data and open-source tools. Such models enable comprehensive analyses of existing DH systems, allowing for the evaluation of sustainable heat integration, operational improvements, and the testing of analytical tools, such as simulation and optimization models. The workflow, detailed in this study, combines general structural data with computational estimations to create digital representations of DH systems. These models facilitate scenario-based analyses, tool benchmarking, and the identification of necessary infrastructure adaptations. Two example DH models generated using the proposed workflow are presented, followed by a discussion of the methodology's applicability and limitations. This study demonstrates how leveraging open data and tools can advance the transformation of DH systems, supporting the transition to a sustainable heat supply infrastructure.