Bibliographic record
Abstract
Improving system efficiency and reliability is motivating countries to design and execute different types of time of use demand response programs. However, certain deficiencies prevent these programs from reaching their goals. Smart meters as a mechanism could help the electric system to reach the highest demand-side management goals which are inaccessible through today’s methods. On the other hand, realization of smart meters in a system would be very costly. In this situation, identifying the most influential buses to implement the infrastructures of a smart grid is of highest importance. In this paper, after a short overview of demand response programs and problems facing them, a smart meter is introduced as a solution to these problems. As a test grid, the IEEE 57-bus network has been chosen to compare the results of the execution of a normal time of use program and real-time pricing program available in a smart grid. In order to execute the mentioned programs in this system, 10 buses have been selected as the most influential buses using a generation shift factor method. The execution of time of use and real-time pricing programs on the selected buses have been simulated using a demand response model. Finally, the time of use program and the real-time pricing program in a smart grid environment have been compared with respect to load shape modification, load factor, price curve, and ‘Expected Power Not Supplied’.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".