Fairness-Aware Game Theoretic Approach for Demand Response in Microgrids
Bibliographic record
Abstract
Demand response programs are implemented by the utilities to manage the energy consumption at consumer side. Demands are managed in response to supply conditions as opposed to the traditional grid. The existing programs focus mainly on achieving system objectives such as minimizing peaks and reducing the cost of power generation. Consequently, users' electrical bills will be reduced but the question of fairness has barely been discussed in the literature. In this paper, the issue of fairness within demand response programs is addressed. In this context, a fair pricing model based on the contribution of each user toward attaining the aggregated system cost is proposed. In addition, the proposed system considers a more realistic scenario that consists of multiple energy sources within a micro grid as opposed to existing ones that use only one shared energy source. We first employ the concept of Shapley value in the pricing model to evaluate the fairness of existing works. Then, we use an approximation of this value to drive our proposed demand response program. Finally, we evaluate the use of the approximate Shapley within our proposed demand response algorithm and compare it to existing works.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".