The Integration of Heat Pumps and Thermal Storage for Residential Demand Side Management
Bibliographic record
Abstract
Space heating and cooling accounts for approximately 65% of residential secondary energy consumption, of which almost 40% is met using electricity in Canada.This demand places a significant peak load on the electrical grid that must be managed to reduce the required generating and transmission capacity within the grid.It is proposed that this demand side management can be achieved using thermal storage coupled with a heat pump to charge during off-peak periods and use the stored heating and cooling during peak periods.Through this work, TRNSYS Types were validated to model the performance of a liquid to liquid heat pump, medium temperature chiller and using sensible storage tanks and stratified cold thermal storage.A new TRNSYS Type was developed and validated to model a compact ice storage system to store cooling potential for peak periods.These components were combined, and the coupled performance of the heat pump connected to both hot and cold thermal storage was determined.It was found that higher flow rates resulted in better performance, when compared to using low flow rates that resulted in the stratification in the storage tanks.The complete system was then integrated into a house modelled situated in Ottawa and different combinations were examined for the total energy consumption, peak consumption, annual costs, and greenhouse gas emissions.In all cases, total consumption, energy costs and greenhouse gas emissions increased, although certain combinations showed greater potential, with the greatest potential being the offsetting of peak cooling loads, when compared to heating loads.This system was then implemented in locations across North America, with different rate structures and climactic conditions.It was found that locations with a high difference between the peak and off-peak rate, and high cooling loads had the greatest potential for reducing peak consumption and reducing iii utility costs.When looking at life cycle costs, rate increases selected over the 15-25-year period examined greatly influenced the economics.In conclusion, the system is technically feasible, where a large portion of cooling peak loads can be offset, but to be economically feasible, government incentives or a high annual rate of utility cost increases must occur.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".