Optimal Design and Operation of a Solar Energy Receiver and Storage
Bibliographic record
Abstract
Optimization of design and operation is presented for a solar energy receiver combined with a thermal energy storage. The concentrated solar power on-demand (CSPonD) concept, which can be described, in brief, as a volumetric solar energy receiver system combined with a modified raft thermocline concept, is considered. The CSPonD concept is assumed to be providing heat for a general cogeneration scheme where power production is the main product of the cogeneration. With a constant power production, a secondary process is assumed to consume the process heat from the CSPonD and power cycle. Models are developed for thermal analysis of the energy storage, taking into account hourly and seasonal variations in the solar energy as well as the heliostat field efficiency. Nonlinear programming (NLP) is used for optimization of the design and operation. The sequential method of optimization and a heuristic approach (parallel computing) are implemented using an equation-oriented modeling environment and gradient-based local solvers. A strategy is presented to design and operate the plant, considering the significant seasonal variations in the solar energy. Three case studies are presented. The first one optimizes the design based on a design day and a desired thermal duty. The other two address optimal yearly operation of the plant. The results of the optimization case studies show that (a) the CSPonD concept aids in handling variations (hourly, daily, and seasonal) in solar energy, (b) CSPonD is a promising concept for cogeneration, (c) the mass of salt required in the CSPonD concept is not significantly lower than the salt required in a single-tank thermal energy storage system.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".