A Hybrid Geothermal-Solar Power System: Optimal Design and Operation
Bibliographic record
Abstract
A model is developed for an existing plant with an organic Rankine cycle (ORC) utilizing a low-temperature geothermal brine. The model includes the performance characteristics of the ORC components. Since the size of turbines in this ORC is over-designed for available geothermal energy source, the model is reconfigured to switch the number of functioning turbines in the ORC. At a given ambient temperature, the configuration with the maximum output power is chosen as the optimal configuration. The model is validated with a set of 7200 measured data collected from one-year operation of the plant. The measured data include the net output power of the ORC as a function of ambient temperature ranging from −15 to 37 °C. The operation of the ORC is optimized maximizing the net output power of the system. The developed model is used as a basis for development of a hybrid geothermal-solar system. A hybrid geothermal-solar system in solar heating mode is analyzed and optimized. The geothermal stream has the potential to provide up to 240 MW thermal energy to the ORC and the heat transfer rate of the solar system to the cycle at nominal time is 17.6 MW. A hybridization strategy is developed that achieves a significant boost in the net output power of the system compared to the geothermal ORC. The enhancement by this approach ranges from 10%–40% as an increasing function of ambient temperature. At low ambient temperatures (Tamb ≤ 1.66 °C), two recuperators are required to be included in the hybrid system to satisfy the lower bound on temperature of geothermal brine (GB).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".