Fifth generation district heating and cooling (5GDHC) networks enable the integration of low temperature sources and the utilization of synergies between heating and cooling demands. To implement them in urban areas a modular approach is suitable which implies the partition of the respective area into smaller cluster networks. This study addresses the effects of participation uncertainty in potential networks and the lack of comparison of clustering methods in the context of 5GDHC networks. Within this work the performance of different clustering methods is measured by optimization metrics that capture the requirements for a suitable partition, such as compact and balanced clusters with sufficient energy supply. This enables the comparison of the selected methods K-Means, K-Medians, DBSCAN and Single Linkage Clustering (SLC). To investigate the robustness with respect to participation uncertainty, a scenario-based approach that combines the minimax regret method with a cluster viability analysis is developed. The developed methodology is applied to the city centre of Amsterdam. The findings indicate that the participation rate influences the viability of the cluster networks. Nevertheless, K-Means, K-Medians and SLC are robust in their performance measured by the averages of the optimization metrics. Additionally, K-Means leads to the lowest variance in performance and is the best performing and most robust method in terms of average cluster compactness and demand fulfilment. Moreover, promising starting clusters for the implementation in the city centre of Amsterdam are identified. The study gives insights into the behaviour of the tested clustering methods and how they may be an advantageous alternative to state-of-the-art planning tools. Finally, the proposed approach to analyse the robustness of clustering methods contributes to the planning of district heating and cooling networks.

As the Netherlands aims to phase out gas by 2050, alternative heating solutions are urgently needed, especially in densely populated areas like Amsterdam's city center. Solutions like hydrogen and green gas are infeasible due to limited availability.
A potential solution is a fifth-generation district heating and cooling (5GDHC) network, an energy system that provides both heating and cooling to buildings using external energy. This is possible due to a low temperature heat carrier in combination with bidirectional operation. Heating and cooling can be exchanged between buildings within the network, reducing the need for external energy. A 5GDHC network for a large city requires clustering of the buildings to create smaller networks, which can later be connected. Clustering allows for a bottom-up approach, reducing both the investment risks and the overload risk. A benefit of 5GDHC is the possibility to use aquifer thermal energy storage (ATES), where thermal energy is stored underground. However, research on 5GDHC, particularly on large-scale implementation, is scarce. Dividing buildings into clusters is crucial for large-scale implementation, since it reduces the investment and overload risk, but the best methods are unknown.
Additionally, the possibility of implementing ATES is not considered in research during the clustering process. This research investigates a method to cluster buildings and integrate ATES systems within these clusters. The goal is to create compact clusters to minimize the use of space in the already crowded subsurface. The clusters must also have a high demand fulfillment, meaning the heating and cooling demands must be fulfilled as much as possible within the cluster, either through energy
exchange between buildings or through the use of storage. A higher demand fulfillment reduces the need for external energy. The developed method clusters buildings based on their geographical location using both k-means and equal size k-means to ensure compactness.After this, ATES systems are implemented in the clusters, which can only be placed in available open space. The model results indicate that implementing ATES systems can increase the demand fulfillment from 1.4% to 58.6% for the entire center of Amsterdam, if buildings are insulated to a level fit for low temperature heating. The results show that very compact areas struggle more to meet heating demand. k-means outperforms equal size k-means in both compactness and demand fulfillment, making it the recommended method. The findings suggest that a 5GDHC network with ATES implementation has significant potential, with the most potential for clusters in the east and northwest. The identified method is robust, adaptable to various geographical regions, and particularly effective in densely populated urban areas with limited space and high demand. In conclusion, this research contributes to the fields of aquifer thermal energy storage and fifth generation district heating and cooling. It not only fills critical gaps in existing literature but also provides a practical methodology for optimizing urban energy systems. This approach holds promise for supporting initiatives like the ‘High-hanging fruit’ project by the AMS Institute, assisting sustainable urban heating solutions in Amsterdam and beyond.