Optimal Dynamic Pricing for Binary Demands in Smart Grids: A Fair and\n Privacy-Preserving Strategy
Bibliographic record
Abstract
Motivated by demand-side management in smart grids, a decentralized\ncontrolled Markov chain formulation is proposed to model a homogeneous\npopulation of users with binary demands (i.e., off or on). The binary demands\noften arise in scheduling applications such as plug-in hybrid vehicles.\nNormally, an independent service operator (ISO) has a finite number of options\nwhen it comes to providing the users with electricity. The options represent\nvarious incentive means, generation resources, and price profiles. The\nobjective of the ISO is to find optimal options in order to keep the\ndistribution of demands close to a desired level (which varies with time, in\ngeneral) by imposing the minimum price on the users. A Bellman equation is\ndeveloped here to identify the globally team-optimal strategy. The proposed\nstrategy is fair for all users and also protects the privacy of users.\nMoreover, its computational complexity increases linearly (rather than\nexponentially) with the number of users. A numerical example with 100 users is\npresented for peak-load management.\n
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".