Load shaping via grid wide coordination of heating-cooling electric loads: A mean field games based approach
Bibliographic record
Abstract
Pressure on ancillary reserves in power systems has significantly mounted due to the recent generalized increase of the fraction of (highly fluctuating) wind and solar energy sources in grid generation mixes. Dedicated energy storage devices have seen their role reaffirmed as potentially low carbon print, if expensive tools, for smoothing the resulting generation/demand imbalances. However, a hitherto under utilized, relatively inexpensive energy storage alternative is that formed by the tiny energy wells of electric origin attached to millions of individual customer electric thermal loads. A hierarchical mean field games approach is proposed for shaping their collective load, whereby the top level sets system optimal mean aggregate temperature target trajectories. In turn based on a local state and a mean field dependent cost function, each individual load develops a decentralized local control law such that the aggregate load can meet the set targets. This control law is to be followed only as long as local comfort and safety constraints are secured, thus guaranteeing acceptability by customers. The corresponding mathematical theory is developed and numerical results are reported. Acknowledgments: The authors gratefully acknowledge the support of Natural Resources Canada. Les Cahiers du GERAD G–2015–68 1
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".