Leveraging Aquifer Thermal Storage in Shallow Aquifers with Sustainable Low Temperature Resources
Bibliographic record
Abstract
Abstract Residential heating and hot water demand often rely on fossil fuels, increasing CO₂ emissions. Geothermal energy has emerged as a renewable alternative, with seasonal thermal storage playing a key role. Aquifer thermal energy storage (ATES) stores excess energy underground during peak supply periods and extracts it when needed. This paper explores ATES's potential to improve efficiency and lower emissions by utilizing solar heat and surplus geothermal energy for sustainable residential heating and hot water supply. This study utilizes a numerical model of the Mannville Aquifer to simulate energy storage dynamics. Heated water from solar thermal and excess deep geothermal heat is injected into the reservoir to charge it during low-demand periods. The numerical model examines temperature fluctuations in the aquifer during charge and discharge cycles, assessing its effectiveness. Additionally, it explores the feasibility of integrating solar thermal energy into an open-loop shallow aquifer storage system, aiming to improve efficiency and sustainability in geothermal energy utilization through a deeper investigation into solar thermal inclusion. Numerical results demonstrate that shallow aquifers play a crucial role in regulating heat production from deep geothermal reservoirs. By balancing energy extraction between solar energy sources and shallow and deep geothermal reservoirs during peak demand, the system maintains efficiency while stabilizing storage temperatures. Excess heat stored in shallow aquifers during high supply periods (e.g., abundant solar energy in summer) reduces temperature decline and preserves thermal energy for future use. This approach enhances sustainability by reducing overall energy consumption while optimizing energy utilization. Findings indicate that integrating excess deep geothermal heat storage with solar energy enhances long-term efficiency and system stability. Utilizing shallow aquifer storage allows urban energy solutions to harness solar energy in summer and geothermal energy in winter, reducing reliance on conventional heating systems while refining renewable energy management. The numerical model presents a practical method for integrating such energy distribution, making it more effective for large-scale urban applications. This study underscores the potential of shallow aquifer storage in advancing sustainable energy systems, providing a promising avenue for efficient solar and geothermal energy utilization in densely populated areas. Results of this study showcase the effectiveness of combining solar and geothermal technologies to optimize renewable energy use and support sustainable urban heating solutions. The insights from the modeling workflow can be used to improve energy storage and extraction methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".