Organic Rankine Cycle-Ground Source Heat Pump with Seasonal Energy Storage Based Micro-Cogeneration System in Cold Climates: The Case for Canada
Bibliographic record
Abstract
In cold climatic regions such as those located across Canada, it is necessary to implement heating system technology that is ultra-efficient and that has near-zero rates of emissions. Such systems would satisfy consumers’ energy needs and also comply with environmental standards, especially because the systems would account for more than 80% of residential energy use. This paper investigates two complementary efficient systems that can support these heating systems; ground-source heat pumps (GSHPs) and organic Rankine cycle systems (ORCs). The study proposes to couple these two systems in a parallel configuration. A dynamic simulation model created in TRNSYS platform has been deployed to assess the performance of the combined ORC-GSHP based micro-cogeneration system. This former provides heating to a residential house during the heating mode as needed. It has the capacity to switch to a charging mode, during which the ORC system is directly coupled to the ground heat exchanger (GHE), which works as a thermal energy storage and supplies energy to the GSHP. The feasibility of this combined system arrangement, and its comparison with a conventional GSHP system are examined for use in residential buildings in three cities across the varied climatic regions within Canada, namely Edmonton (AB), Halifax (NS), and Vancouver (BC). Results showed that the proposed micro-cogeneration system recorded less energy use of over 80%. The addition of the ORC system had a definite effect on the performance of the GSHP in that it decreased the operating hours from 11–58% compared to the conventional GSHP case and maintained consistently higher COP values. These results may help to specify viable ORC-GSHP based micro-co/trigeneration systems in cold climatic applications and should be useful for prototype design and development.
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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 | 0.000 | 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 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".