Energy Credits Auction Mechanism for Enhancing the Grid’s Upward Flexibility Using Datacenters
Bibliographic record
Abstract
Since modern utility grids lack large-scale energy storage capabilities, their supply and demand levels must always be balanced to maintain a reliable operation. This requirement has traditionally been fulfilled through several existing techniques. However, recent interests in greening the utility grids require using more renewable energy sources, which makes the balancing task more challenging due to the unpredictability of such sources. This necessitates enhancing the grid's flexibility (energy balancing capabilities) to ensure reliable grid operation. In this work, we propose a new energy auctioning mechanism that enhances the grid's upward flexibility by using cloud datacenters as managed loads to quickly balance excess renewable energy. The auction process increases the energy consumption at a certain datacenter by incentivizing workload migrations to it in order to consume the excess energy. We present the formulation of our system model, introduce the used auction mechanism and show through simulation that it achieves efficient grid balancing, generates revenue on the sale of the excess energy and guarantees positive utility for all auction participants.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".