Real-Time Modeling of a Solar-Driven Power Plant with Green Hydrogen, Electricity, and Fresh Water Production: Techno-Economics and Optimization
Bibliographic record
Abstract
Solar energy is important for the future as it provides a clean, renewable source of electricity that can help combat climate change by reducing reliance on fossil fuels via implementing various solar-based energy systems. In this study, a unique configuration for a parabolic-trough-based solar system is presented that allows energy storage for periods of time with insufficient solar radiation. This model, based on extensive analysis in MATLAB utilizing real-time weather data, demonstrates promising results with strong practical applicability. An organic Rankine cycle with a regenerative configuration is applied to produce electricity, which is further utilized for hydrogen generation. A proton exchange membrane electrolysis (PEME) unit converts electricity to hydrogen, a clean and versatile energy carrier since the electricity is solar based. To harness the maximum value from this system, additional energy during peak times is used to produce clean water utilizing a reverse osmosis (RO) desalination unit. The system’s performance is examined by conducting a case study for the city of Antalya, Turkey, to attest to the unit’s credibility and performance. This system is also optimized via the Grey Wolf multi-objective algorithm from energy, exergy, and techno-economic perspectives. For the optimization scenario performed, the energy and exergy efficiencies of the system and the levelized cost of products are found to be approximately 26.5%, 28.5%, and 0.106 $/kWh, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".