A review of district energy technology with subsurface thermal storage integration
Bibliographic record
Abstract
Abstract Renewable energies, such as solar and wind, traditionally suffer from temporal incongruity. Society’s energy demand peaks occur at different times of day than the electricity generation potential of a photovoltaic panel or, often, a wind turbine. Heat demand, in particular, is subject to a significant mismatch between the availability of heat (in the summer) and the need for heat (in the winter). Thus, a future energy system design should incorporate underground thermal energy storage (UTES) to avoid this temporal mismatch and emphasize thermal applications. Such a basis of design would introduce new methods of energy arbitrage, encourage the adoption of geothermal systems, and decrease the carbon intensity of society. UTES techniques are becoming increasingly sophisticated. These methods of storage can range from simple seasonal storage for residential structures in a grouted borehole array (BTES), to aquifer thermal energy storage (ATES), deep reservoir storage (RTES) in basins, among others. The method that each of these techniques shares is the use of the earth as a storage medium. UTES can also be characterized for electricity production, but this work largely explores applications in heating and cooling, further limited in scope to sensible heat storage (SHS). Heating and cooling processes—residential, commercial, and industrial—make up large fractions of energy demand in North America. This is also true of other locales. With the increasing concerns of climate change, exacerbated by anthropogenic greenhouse gas emissions, developers and municipal planners are strategizing to decarbonize building heating and cooling at district scales. This review covers the integration of UTES techniques with thermal energy network (TEN) technology across large districts. Though storage has long been in use for conventional district heating networks, designs are rapidly innovating, indicating broader applications of UTES integration with a TEN is advantageous from both an efficiency and economic perspective. This rapid innovation indicates the need for the integrated review offered in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".