Sizing and control optimization of thermal energy storage in a solar district heating system
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Bibliographic record
Abstract
Solar district heating systems have shown significant promise to facilitate the large scale adoption of solar energy technologies and thus substantially reduce greenhouse gas emissions. Given the mismatch between solar energy and district heating demand, energy storage devices play a critical role given their capacity to stockpile solar energy in both the short-term (hours to days) and long-term (months). However, the integration, sizing and control of energy storage technologies is far from simple. This paper investigates sizing and controlling thermal energy storage from the perspective of its performance within a district heating system, highlighting the close link between design and control. A 52-house Canadian solar district heating system, the Drake Landing Solar Community (DLSC), was used as a case study. This system uses solar collectors as main energy supply, borehole thermal energy storage (BTES) for seasonal storage and two 120-m3 water tanks for short-term thermal storage (STTS). The effect of (a) storage sizing (STTS volume) and (b) storage control (rate at which energy is either injected or extracted from the BTES) was evaluated. A control-oriented model, calibrated and validated with operational data at 10-min intervals, was used along with an optimal rule-based control to gauge system primary energy use. Different scenarios were tested, with STTS volumes ranging from 120 m3 to 480 m3, and BTES loop nominal flow rates between 2.5 and 4.5 L/s. An optimization routine was developed to calculate the optimum parameters of the rule-based control strategy. Results show that, in comparison with the design and control in place, primary energy savings of 13%–30% (with BTES flow rates of 2.5–4.5 L/s) could have been obtained with the proposed rule-based control strategy. By decreasing the STTS volume to 120-m3, energy savings up to 6% could still be achieved; savings could reach 27%–36% by increasing the STTS size to 360-m3.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it