Modelling and optimization of a hybrid photovoltaic-parabolic trough concentrated solar power plant: Technical, economic, and environmental
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Bibliographic record
Abstract
• Response surface methodology was employed to optimize a hybrid PV-CSP plant. • SAM and PVsyst were used for simulating the CSP and PV systems, respectively. • The optimization procedure was carried out through the Design-Expert software. • The results indicated that the optimal configuration comprises 38.6 % CSP and 61.4 % PV. This research presents detailed guidelines for modeling and optimizing an integrated photovoltaic-concentrated solar power (PV-CSP) plant using response surface methodology (RSM), tailored to the climate of Sharjah, UAE. Five factors are considered in the optimization, which are the percentage share of PV/CSP (A), PV tilt angle (B), PV spacing (C), CSP solar multiple (D), and thermal storage size (E), with corresponding ranges of 10–90% (equivalent to 10 to 90 MW), 20–40°, 1–7 m, 2.5–7.5, and 5–20 h, respectively. The research utilizes three software tools: System Advisor Model (SAM) for CSP, PV syst for PV, and Design-Expert for RSM. Based on the analysis of variance (ANOVA), seven factors (A, C, E, D², AC, AE, and DE) are significant for energy output, while eight (A, C, D, E, AC, AD, AE, and DE) are significant for LCOE. Through multi-objective optimization aimed at maximizing energy production while minimizing LCOE and land area, the results indicate that the optimal configuration comprises 38.6% CSP and 61.4% PV. This configuration achieves an energy output of 3.64 × 10 8 kWh/year, a LCOE of $0.033/kWh, and a land area of 743.46 acres. These results were achieved with B, C, D, and E of 27.18°, 5.45 m, 4.41, and 15.49 h, respectively.
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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 | 0.000 |
| 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 it