Techno-Economic Optimization of a Borehole Thermal Energy Storage and a District Heating System Using a New Open Data Framework for Integrated District Planning

Sustainable district heating systems (DHS) will play a key role in the decarbonization of the heating sector in Germany and Northwestern Europe. When designing these complex systems, the interaction between producers, consumers, storages and the network must be optimized. Borehole thermal energy storages (BTES) are among the most promising seasonal storage solutions to address the asynchronous fluctuations of heat supply and demand. Mixed-integer linear programming (MILP) is a powerful approach for optimizing such DHS. This paper demonstrates the automated coupling of a non-linear BTES model with a techno-economic MILP optimization tool for DHS. For the BTES model, the open-source python tool GHEDesigner, based on the g-function approach, is chosen. It allows for the determination of the optimal number of boreholes and their optimal depth for given ground parameters while ensuring that the temperatures of the ground and the working fluid in the boreholes do not exceed predefined boundaries. So far, such detailed BTES models have primarily been used for isolated dimensioning or operational optimizations of single building energy systems or DHS neglecting the grid but focusing on the heat production site only. In our work, however, BTES are integrated into an optimization scheme for the design stage of an entire DHS including the grid. A case study demonstrates the automated creation of a DHS model based on publicly available data (Open Data) using the newly developed Open District Hub (ODH) planning framework developed in the ODH@Jülich project. This also includes the automated generation of load profiles for individual buildings. The system is dimensioned, simulated, and optimized, including a BTES system. This paper also includes a sensitivity analysis regarding important parameters such as soil thermal conductivity or drilling and electricity costs, which have a significant impact on the heat generation costs of the overall system.