Dynamic Pricing Mechanism in Smart Grid Communications Is Shaping Up
Bibliographic record
Abstract
An efficient smart grid communication system is a key enabling technology for modernized utility that can adjust electricity usage to balance generation and demand in real time. However, the utility industry is not able to meet the power demand for the wholesale market during overloading or emergency situations. To mitigate power shortage pressures, utility providers need to employ a dynamic pricing policy to enforce higher pricing than pre-estimated statistic pricing to motivate consumers to reduce their power consumption. In this letter, the time of use (TOU) approach is proposed to regulate price variances considering desired power demand. In this mechanism, the consumers deliver their TOU electricity demands and subsequent control signals to utility providers through communication network infrastructure. The utility provider creates an hourly demand profile for each consumer by predicating individual requests over short- and long-time frames. The control signaling exchanged and prior arrangement of services enable utility providers to evaluate the status of wholesale market demand and assign prices considering dynamic changes in electricity demand. Numerical analysis study was carried out to validate the advantages of the proposed mechanism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".