Dynamic Simulation of Organic Rankine Cycle-Assisted Ground-Source Heat Pump Based Micro-Cogeneration System in Cold Climates: A Case Study in Canada
Bibliographic record
Abstract
Abstract As the energy needed for heating and cooling involves a substantial amount (> 80%) of residential energy utilisation in Canada, there is a demand for ultra-efficient energy systems for heating, cooling, and power generation. Two efficient systems to assist these systems are ground-source heat pumps (GSHPs) and organic Rankine cycles (ORCs). Of particular interest, this paper presents the integration of these two systems in a parallel configuration. A transient simulation model developed in TRNSYS program has been utilised to simulate the thermal performance of the combined ORC-GSHP based microco/trigeneration system. This later supplies heating and cooling to the residential load during the heating mode as required, with the capability to switch to a charging mode, where the ORC unit is directly coupled to the ground heat exchanger (GHE), which operates as a thermal energy storage and provides energy to the GSHP. The feasibility of this combined system configuration as well as its comparison with a conventional GSHP system are investigated for use in residential application in Ottawa, Canada temperature conditions. Results disclosed that the proposed micro-cogeneration system had the operating hours and performance of the GSHP improved by the addition of the ORC unit, resulting in about 11.8% reduction in hours in the colder city of Ottawa. The COP (coefficient of performance) of the GSHP system sustained a much higher value overall due to the addition of the ORC system to maintain the GHE storage temperature. In terms of net energy reduction between the conventional GSHP system and the ORC-assisted one, results revealed that Ottawa had energy usage reduction of 82.0%, demonstrating that the addition of an ORC to provide heating and recharge the GHE of a GSHP system has many advantages that could be accomplished by the end-user.
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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 | 0.001 |
| 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".