Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility
Bibliographic record
Abstract
Smart grids are capable of two-way communication between individual user devices and the electricity provider, enabling providers to create a control-feedback loop using time-dependent pricing. By charging users more in peak and less in off-peak hours, the provider can induce users to shift their consumption to off-peak periods, thus relieving stress on the power grid and the cost incurred from large peak loads. We formulate the electricity provider's cost minimization problem in setting these prices by considering consumers' device-specific scheduling flexibility and the provider's cost structure of purchasing electricity from an electricity generator. Consumers' willingness to shift their device usage is modeled probabilistically, with parameters that can be estimated from real data. We develop an algorithm for computing day-ahead prices, and another algorithm for estimating and refining user reaction to the prices. Together, these two algorithms allow the provider to dynamically adjust the offered prices based on user behavior. Numerical simulations with data from an Ontario electricity provider show that our pricing algorithm can significantly reduce the cost incurred by the provider.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".