Thermodynamic Performance Investigation of Environmentally Friendly Working Fluids in a Geothermal Integrated Pumped Thermal Energy Storage System
Bibliographic record
Abstract
Abstract Among the available energy storage technologies, pumped thermal energy storage (PTES) is emerging as a potential solution for large-scale electrical energy storage with high round-trip efficiencies and no geographical limitations. However, PTES requires a low-cost, high-temperature heat source to achieve reasonable round-trip efficiencies. Moreover, organic Rankine cycle-based PTES systems require high-performance and environmentally friendly working fluids. In this study, the thermodynamic performance of a geothermal integrated PTES system using environmentally friendly working fluids is investigated. The mathematical model of the geothermal integrated PTES system is developed using the first and second laws of thermodynamics and implemented in Engineering Equation Solver (EES). With the developed model, the thermodynamic performance of the PTES system for different working fluids, including butene, cyclopentane, isobutene, R1233zd(E), R1234ze(Z), R1224yd(Z), HFO1336mzz(Z), n-hexane, and n-pentane was investigated. For geothermal fluid outlet temperatures between 60 °C and 120 °C and geothermal fluid inlet and outlet temperature differences across the evaporator between 20 °C and 60 °C, the net power ratio, i.e., the ratio of the electrical energy discharged to the electrical energy used to run the charging cycle, is between 0.25 and 1.40. This shows that the system has the potential to give back more than 100% of the electrical energy used during charging under certain conditions. High net power ratios are obtained for a combination of high source temperatures and low geothermal fluid inlet and outlet temperature differences.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".