Polygeneration Grid Including Atmospheric Water Generation, Renewable Sources and Storage Technologies: Modelling and Real-Time Management
Bibliographic record
Abstract
The aim of this paper is the modelling of a smart polygeneration grid and the development of its Energy Management System (EMS) for real-time optimal operations, minimizing variable costs.The proposed grid includes two different demands: water and cooling energy.While water can be obtained from the aqueduct or from an Atmospheric Water Generator system (AWG), cooling energy can be produced from a devoted cooler or from the chilled air of the AWG.The integration with renewable sources in the form of photovoltaic panels is also proposed.Moreover, the grid includes a water tank and a thermal storage system to have additional management flexibility and to avoid money losses in case of extra-productions.While the optimized management is common in electrical energy smart grids, the application of EMS tools in such polygenerative grids including water generation is quite rare.Following the development and validation of component models, attention is focused on the EMS details and its application aspects.To assess the EMS performance, the results obtained with the application of this new tool have been compared with a simple traditional management, showing the obtained economic and environmental benefits.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".