Optimal energy consumption scheduling using mechanism design for the future smart grid
Bibliographic record
Abstract
In the future smart grid, both users and power companies can benefit from real-time interactions and pricing methods which can reflect the fluctuations of the wholesale price into the demand side. In addition, smart pricing can be used to seek social benefits and to achieve social objectives. However, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. That is, users may not be willing to reveal their local information unless there is an incentive for them to do so. In this paper, we propose an efficient pricing algorithm to tackle this problem. The benefit that each user obtains from each appliance can be modeled in form of a utility function, a concept from microeconomics. We propose a Vickrey-Clarke-Groves (VCG) based mechanism for our problem formulation aiming to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each user's payment for electricity. The payment of each user is structured in such a way that it is in each user's self interest to reveal its local information truthfully. Finally, we present simulation results to show that both the energy provider and the individual users can benefit from the proposed pricing algorithm.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".