A techno-economic model for application of geothermal heat pump systems
Bibliographic record
Abstract
For more than five decades, geothermal heat pumps have been used to provide low grade geothermal energy for heating/cooling purposes. Though the geothermal energy of the earth is free, the heat pump in charge of upgrading the lower grade heat runs on relatively expensive electric power. Therefore, in short-and-long-term, the economic feasibility of each geothermal heating project is directly dependent on the balance between energy savings and energy costs; heat versus electricity. While energy savings are usually evaluated based on the heat flux provided on the resource side, the electricity consumption is significantly affected by the overall Coefficient of Performance of the heat pump. Also, an important factor affecting the operating costs and therefore the economic feasibility of a geothermal heat pump will be the price gap between heat and electricity. On another aspect, the economic feasibility of a geothermal heat pump system can drastically vary based on its site-specific requirements. In the absence of proper guidelines for analyzing the feasibility of geothermal heat pump systems, application of such systems may lead to huge financial risks. Therefore, there is a fundamental need for development of a comprehensive but simple engineering tool with which the feasibility of application of geothermal heat pump systems can be evaluated. The present study identifies non-dimensional parameters based on which the feasibility of a geothermal project can be effectively assessed. The selected set of parameters is then used to develop an economic model to quantify the economic indicators such as energy costs, savings, revenues and internal rates of return. Field data from a real-life engineering project is incorporated into the model to evaluate the techno-economic feasibility of this case. Based on the results of this study, geothermal heat pump applications are categorized into infeasible, weakly-feasible, fairly-feasible and strongly-feasible.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".