Planning distributed energy systems with low-grade heat sources integration: Geography-Based clustering and risk-informed thermal pipeline layout design approaches

Distributed energy systems represent a key development trend in regional energy supply, driven by the efficient utilization of local low-grade heat sources such as industrial waste heat, data center waste heat, and domestic wastewater heat. However, planning such systems presents significant challenges due to the dispersed geographical distribution of heat sources, varying source output and load demands. At present, the planning process largely relies on empirical expertise, in the absence of a comprehensive and scientifically grounded methodological framework. To address this challenge, this paper proposes an integrated approach that combines geography-based clustering with risk-informed pipeline layout design for distributed energy system planning incorporating low-grade heat. Specifically, the Recursive Partitioning Clustering Tree (RPCT) algorithm is employed to effectively divide heating areas into clusters, optimizing resource allocation by considering heat resource potential, supply-demand matching and geographical distribution. Then, two pipeline layout strategies are optimized using Minimum Spanning Tree (MST) methodologies and implemented on the QGIS platform, including a shortest-line connection approach and a road-network-based optimization, while a redundant pipeline design approach is introduced to enhance system robustness by accounting for heating interruption costs across different thermal load types. Moreover, the pipeline layout comprehensive results effectively reflects the clustering performance, updating weight and linking two problems of cluster partitioning and pipeline design. Finally, a case study in Jiangsu Province, China, validates the effectiveness of the proposed approach, offering valuable insights into the design and implementation of sustainable and efficient heating solutions.