K-means and agglomerative clustering for source-load mapping in distributed district heating planning

• Data-driven clustering optimizes heat source placement in Stockholm's district heating network. • Distributed sources supply 54% of annual heat demand (7.7 TWh/year) from data centers, water bodies, and supermarkets. • Marginal heat costs (12.7–20 EUR/MWh) are below market prices, confirming economic feasibility. • Scalable framework offers sustainable solutions for district heating in global urban contexts. • Findings guide policy for cost-effective, low-carbon energy systems. This study introduces a high-resolution, data-driven approach for optimizing district heating networks using source-load mapping, focusing on Stockholm as a case study. The methodology integrates detailed building energy performance data (2014–2022) with geographic data from the Swedish Survey Agency, employing advanced clustering techniques such as K-means Clustering, Agglomerative Clustering, DBSCAN, Spectral Clustering, and Gaussian Mixture Model (GMM) Clustering to identify optimal locations for distributed heat sources, including data centers, supermarkets, and water bodies. Quantitative results show that these environmentally friendly sources could supply 54 % of Stockholm’s total annual heat demand of 7.7 TWh/year, equating to 4.2 TWh from residual heat sources. Data centers contribute 0.48 TWh, water bodies provide 3.4 TWh, and supermarkets contribute 0.3 TWh annually. Economic analysis further reveals that 98 % of residual heat sources are economically viable, with marginal costs of heat (MCOH) for data centers, supermarkets, and water bodies estimated at 12.7 EUR/MWh, 16.0 EUR/MWh, and 20.0 EUR/MWh, respectively—well below the Open District Heating (ODH) market price of 22.0 EUR/MWh. The policy implications of these findings are profound. Policymakers can leverage this methodology to identify economically viable heat sources, enabling the creation of regulations that incentivize the integration of distributed heat sources into existing district heating networks. This can lead to reduced energy costs, enhanced sustainability, and more resilient energy systems. Practically, urban planners and energy utilities can use clustering insights to optimize the placement of new infrastructure, such as data centers, ensuring they are strategically located in high-demand zones. Furthermore, the study’s methodology can be replicated in other urban contexts, offering cities worldwide a scalable tool for improving the efficiency and sustainability of their heating networks. These findings support the transition to low-carbon energy solutions and provide actionable recommendations for the long-term development of urban energy systems.