Optimal Start-Up Policies for a Solar Thermal Power Plant
Bibliographic record
Abstract
Sustainability and depletion of fossil fuels have propelled the use of renewable energy sources to meet energy demands. Solar radiation is perhaps the most economical and widely available alternative energy source. Energy in the form of solar radiation can be recovered using either photovoltaic or thermal processes. Nowadays, both approaches can only capture a small fraction of the available solar radiation. In this work, we have addressed the dynamic optimal operation of thermal solar plants during start-up. During a normal operating day when solar radiation becomes available, power should be available as soon as possible to meet consumer demands. One of the major problems related to thermal solar plants is the lack of power when solar radiation is off. To overcome this problem, energy storage tanks are considered in the design of the thermal plant. We assume that a conventional Rankine cycle can be used for power generation from low-temperature energy sources. In this work, a dynamic optimization framework is deployed to identify optimal dynamic start-up policies in thermal solar plants. Since the dynamic plant model is composed of a set of partial and differential equations, we have deployed the method of lines and the direct transcription approach for spatial and time discretization, respectively. The results indicate that fast optimal control policies result in power production in a more efficient fashion than simple heuristic-based start-up policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".