A Game Theoretic Approach to Real-Time Robust Distributed Generation Dispatch
Bibliographic record
Abstract
Power demands are rising at an exponential pace due to the increasing proliferation of high-energy consuming devices such as plug-in hybrid electric vehicles. It is well known that scaling traditional power generation systems to accommodate these soaring demands will be excessively costly and may lead to negative environmental ramifications. One approach to supplement increasing energy needs involves diversifying the generation mix to incorporate a large number of local distributed generators (DGs) for economical and sustainable operation. However, such an approach remains an open challenge due to the inherent generation variability of DGs. In this paper, we propose a distributed generation dispatch strategy that can effectively coordinate a large number of DGs to meet consumer demand in real time. Through theoretical analysis based on population games and simulation studies, we demonstrate that our dispatch strategy is scalable and allows for the seamless integration of alternative energy resources into the grid in a robust and an optimally cost-effective manner.
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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 | 0.000 | 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 | 0.000 |
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".