Possible Approaches to Trade Non-Electric Energy Sources in the Next Generation Smart Grids
Bibliographic record
Abstract
It is known that the energy trading strategy followed in existing smart grids is built based on transferring electricity between entities. Because there is a big portion of non-electric energy forms are also employed in daily uses, such as logs, solar, and biogas water heaters, so still there is a deficiency associated with these grids. There is an attempt to trade these non-electrical energy forms in the next generation smart grids. However, the option discussed in that study is not well established, and it is built based on only transferring hot waters through pipelines connected between entities. Actually, this approach has some limitations and it could be a non-optimal choice to trade nonelectrical energy forms in terms of minimum losses, maximum profit, less risks, more flexibility, environment friendly, aesthetics, etc. This study discusses multiple possible options to locally trade these non-electric energy forms in the next generation smart grids. Therefore, the best option can be selected based on a tradeoff or a single/multi-objective approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".