Resilient Distributed Real-Time Demand Response via Population Games
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The proliferation of high powered electric devices is a driving force in the rising of peak power demand from electric power utilities. One way to accommodate these rising consumption patterns involves the deployment of high capacity dispatchable, but largely unsustainable peak generation systems. To avert these extravagant costs and the likelihood of grid overload, demand response (DR) strategies can be employed to curtail overall consumption, thus reducing peak patterns. In this paper, we propose a distributed real-time DR approach. The proposed method fosters seamless cooperation between DR participants for rapid convergence to expected aggregate load curtailment, while accounting for individual consumer satisfaction needs. We assess this paper through theoretical analysis based on population game theory and simulations to demonstrate its inherent flexibility, scalability, and resilience making it attractive for practical widespread deployment.
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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 | 0.000 | 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 | 0.001 |
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 it