Integrating renewable energy into district heating creates a heat supply–demand mismatch that High-Temperature Aquifer Thermal Energy Storage (HT-ATES) can help address. However, the potential greenhouse gas emission reduction and financial benefits of HT-ATES have received limited attention. Additionally, the interplay between the demand, supply components, and HT-ATES has been overlooked, while the assessment of integrating HT-ATES into a district heating system is crucial to understanding the benefits of the HT-ATES implementation. This study evaluates the integration of HT-ATES into a district heating system, focusing on both economic and environmental performance indicators. It novelly accounts for the dynamic operational interactions between HT-ATES and other system components, enabling a more realistic assessment of operational choices. The model is applied to a case study of a simplified district heating system. The results show that the relative size of the heat supplier compared to heat demand is a key determinant of the cost-effectiveness of HT-ATES. In the case study, a geothermal doublet reduced the levelized cost of heat by 25–37 €/MWh compared to a gas boiler, while also reducing reliance on fossil fuels. In contrast, HT-ATES had a limited impact on total system costs, regardless of whether it operated when stored heat was available or was used for peak shaving. Nevertheless, HT-ATES increased the renewable energy share by 9%–18% across all scenarios. Furthermore, the optimal geothermal capacity differed depending on whether HT-ATES was included. Finally, while a high renewable energy share can be cost-effective, achieving 100% renewable heat was found to be highly cost-ineffective in this case. These results support informed decision-making on HT-ATES implementation under appropriate system design conditions.

District heating systems must decarbonize by replacing fossil fuel-based heat sources with sustainable alternatives. To fully utilize the capacity of renewable sources, seasonal thermal energy storage is necessary due to seasonal supply–demand mismatches. High-Temperature Aquifer Thermal Energy Storage (HT-ATES) offers a promising solution, but its cost-effective deployment requires coordinated sizing with the sustainable heat source, which has received limited attention in literature. This study presents a techno-economic and renewable share analysis of district heating systems incorporating deep geothermal heat, solar thermal collectors, HT-ATES, and gas boilers. We identified representative heat demand profiles for different climates by clustering to ensure broader applicability of the findings. We show that the demand profile is important for the cost-effectiveness of district heating. The results show that HT-ATES is cost-effective in most scenarios compared to natural gas boilers, particularly when paired with a geothermal source. Geothermal energy was generally more economically favorable than solar thermal collectors. Achieving 100% renewable heat supply is cost-inefficient because it requires large additional capacity for limited additional load, increasing costs by 15% compared to 99% renewable share. However, 90% renewable share can be reached with only 5% cost increase compared to the optimum, using geothermal energy. These insights provide guidance for district heating designers, operators and policymakers on optimal component sizing and promote the informed use of HT-ATES to support cost-effective decarbonization of district heating. Representative demand profiles are expected to be used often in research, as they proved influential on the levelized cost of heat.

High-Temperature Aquifer Thermal Energy Storage (HT-ATES) has the potential to significantly increase the renewable heat share in heating systems. However, HT-ATES has not been implemented in the current energy system models because the widely applied numerical models for HT-ATES are computationally expensive. This leads to a lack of HT-ATES assessment from an energy system perspective. Therefore, an accurate and computationally efficient model that is widely applicable is needed to facilitate such implementation. This research aimed to develop a novel data-driven model that generates the temperature profile of an HT-ATES accurately and computationally efficiently. A trained machine learning algorithm predicts the recovery efficiency for an HT-ATES system, which, combined with other parameters, enables a nearest neighbor search to identify a suitable temperature profile. As a result, the temperature profile generated by the data-driven model has a root mean square error of 1.22 °C compared to the numerical model output. This error was shown to be larger for lower recovery efficiency values compared to higher values. The machine learning algorithm used to predict the recovery efficiency has a root mean square error of 1.45 percentage points. The data-driven model has a computation time of less than half a second, which is more than 180,000 times faster than the numerical model that was used to generate the data. This model is, therefore, suitable for integration in larger energy system models.

This study investigates the thermal performance of closed-loop advanced geothermal systems under the influence of groundwater flow in deep sedimentary formations. By integrating advective heat transport into a 3D numerical model, we evaluate the combined effects of groundwater flow in deep sedimentary aquifers and geothermal heat transport and extraction using U-shaped closed-loop geothermal wells. The model is developed to simulate heat-transfer dynamics, incorporating well design with realistic casing and cement layers, layered geology with associated petrophysical uncertainties, and varying operational conditions. As study area, we selected the Midyan basin in Saudi Arabia, characterized by thick sedimentary formations and an elevated geothermal gradient. The results show that the advective heat transfer, induced by groundwater flow, significantly enhances system efficiency. Improvement in thermal power output increases by up to 27% over a 40-year operational period compared to conduction-only scenarios, particularly if groundwater flow is perpendicular to the lateral section of the wellbore. Sensitivity analysis reveals that geothermal gradient and reservoir depth are the most impactful geological parameters. Operational parameters such as injection rates (10—100 kg/s) and injection temperatures (25—45 °C) can be adjusted to further optimize the system performance, with 30 kg/s identified as the optimal injection rate that balances energy extraction and parasitic pumping losses. Well-design parameters, including diameters (0.114–0.245 m) and lateral length (0.5–3 km), also play a critical role, with longer lateral sections and larger diameters increasing the overall power output. These findings show the potential of U-shaped closed-loop advanced geothermal systems in sedimentary basins with dynamic groundwater flow and provide insights for optimizing geothermal energy systems in similar geological settings.

Direct Use Geothermal Systems (DUGS) are rapidly and densely deployed to meet the growing demand for renewable energy with less carbon emissions globally. The simulation of DUGS can provide a reservoir-scale understanding of geothermal resource assessment, where the geothermal system's lifetime and the injection well Bottom Hole Pressure (BHP) are used as performance indicators. However, there are inherent errors from numerical simulations of any engineering problems, due to approximating continuous partial differential equations by their discretized approximation in time and space. In this work, we establish an optimal numerical setup with reduced errors across the homogeneous, stratified and heterogeneous models for the simulation of a geothermal system. Next, we develop a standardized method for calculating recoverable Heat In Place (HIP) and an analytical solution for evaluating the HIP recovery factor across various geological models using a single forward simulation. We present reference examples on the design of DUGS simulations using the open-source software Delft Advanced Research Terra Simulator (open-DARTS). The open-DARTS platform enables accurate and efficient sensitivity and uncertainty analysis. Using Distance-Based Generalized Sensitivity Analysis (DGSA), we identify reservoir depth and discharge rate as the most influential parameters for geothermal projects across all three types of geological models.

Geothermal energy extraction through deep mine systems offers the potential to reduce the cost of geothermal systems while meeting the cooling needs of deep mines. However, the injection of cold water into the subsurface triggers strongly coupled thermo-hydro-mechanical (THM) processes that can affect the stability of underground excavations. This study evaluates the impact of geothermal energy extraction on the temperature and stability of a deep mine. By quantifying the sensitivity of the mine temperature and stability to various parameters, we propose a scheme to optimize geothermal energy production, while achieving rapid mine cooling and maintaining stability. We first evaluate the impact of geothermal operations on mine temperature and stability through THM numerical modeling. The simulations show that poro-elastic stress quickly affects mine stability, while thermal stress has a more significant impact on the long-term stability. We then use Distance-based Generalized Sensitivity Analysis (DGSA) to quantify parameter sensitivity. The analysis identifies the distance between the mine system and the geothermal system as the most influential factor. Other important parameters include the injection rate, injection temperature, well spacing, coefficient of thermal expansion, permeability, Young's modulus, and heat capacity. Finally, we propose a DGSA-based optimization framework that accounts for subsurface uncertainty and validate the optimized results. Our results indicate that, with favorable geological conditions, a rational selection of system design parameters can enhance geothermal energy production while ensuring rapid mine cooling and stability. This study provides essential insights for the optimization of deep mine geothermal systems and supports effective decision-making.