Modelling and optimization of a hybrid photovoltaic-parabolic trough concentrated solar power plant: Technical, economic, and environmental
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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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".