Rebate Auction Mechanisms for Bidirectional Grid Balancing Using Cloud Workload Migrations
Bibliographic record
Abstract
Increasing the power-grid’s flexibility is essential for expanding the integration of renewable energy sources in modern power grids. This work presents a new rebate auction framework that allows power grids to use cloud datacenters as managed loads to provide upward/downward grid flexibility. Since the energy consumption of datacenters is proportional to their computational workload, this paper presents a rebate auction framework that can induce cloud workload migrations between datacenters to correct energy imbalances. Unlike existing datacenter-based power grid balancing approaches that have only focused on providing downward flexibility and only considered owner-operated datacenters, the proposed framework provides bidirectional flexibility and accommodates both owner-operated and public cloud datacenters. Thus, providing a general framework for creating workload migrations between datacenters to balance the power grid. Because workloads on public cloud datacenters are managed by end-users, the proposed framework uses monetary incentives (auctioned rebates) to encourage large-scale end-users to migrate their cloud workloads between datacenters to correct energy imbalances. The use of monetary rewards as an incentive hides the complexity of grid-balancing from auction participants, who only participate in the auction to lower their cost, while grid-balancing happens as a result of workload migrations. This paper presents and compares two auction implementations under the proposed framework, a strategy-proof implementation that guarantees truthful bidding as a dominant strategy, but has NP-hard computational complexity, and an alternative implementation that does not guarantee truthful bidding, but has polynomial time complexity. Simulation results show that the proposed framework is effective in incentivizing cloud workload migrations to achieve the grid balancing goal and provides positive utility to all participants.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".