Bibliographic record
Abstract
Abstract There are very few published data comparing performance and cost of thermal and photovoltaic (PV) based solar power generations. With recent intense technology and business developments there is a need to establish a comparison between these two solar energy options. We have developed a simple model to compare electricity cost using these two options without any additional fuel source of hybridization. Capital along with operation and maintenance (O&M) costs and other parameters from existing large scale solar farms are used to reflect actual project costs. To compete with traditional sources of power generation, solar technologies need to provide dispatchable electric power to respond to demand during peak hours. Different solutions for energy storage are available. In spite of their high capital cost, adding energy storage is considered a better long term solution than hybrid solar systems for large scale power plants. For this reason, a comparison between the two solar options is also provided that include energy storage. Although electricity storage is more expensive than thermal storage, PV power remains a competitive option. Expenses related to O&M in solar thermal plant are about ten times higher than PV, an important factor resulting in higher energy cost. Based on data from proven commercial technologies, this study showed that PV holds a slight advantage even when energy storage is included. Copyright © 2010 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".