Auction-Based Scheduling of Excess Energy Consumption to Enhance Grid Upward Flexibility
Bibliographic record
Abstract
Grid balancing is an essential requirement for power grid systems. This requirement has traditionally been fulfilled by existing flexibility mechanisms that provide voltage and frequency regulation. However, the recent interests in greening the energy supply by using more renewable energy sources present new grid balancing challenges. Such volatile energy sources introduce generation-side uncertainty and cause the flexibility mechanisms to fall short more often on providing enough balancing capacity. In this paper, we target the problem of balancing surplus energy from renewable sources by selling it in an auction to allow for its quick consumption. Our solution uses cloud datacenters as managed loads by incentivizing inter-datacenter cloud workload migrations through the auction sale of excess energy. It leverages the programmability and energy-demands flexibility of cloud datacenters, and uses incentivized cloud workload migrations to increase the energy consumption of a datacenter in a certain location to consume its excess energy. We propose an integrated auctioning-scheduling mechanism that auctions the surplus energy and schedules its consumption on a cloud datacenter. The auction part incentivizes the inter-datacenter cloud workload migrations, while the scheduling part ensures that migrated workloads do not exceed the destination datacenter capacity. Existing datacenter-based grid balancing approaches have focused on providing downward flexibility and only considered the case of owner-operated datacenters. In contrast, our system focuses on providing upward flexibility to target the increasingly frequent problem of excess energy from renewables, and it uses public cloud datacenters to increase the participation of datacenters in providing demand-side flexibility. Conducted simulation experiments show the effectiveness of our approach in ramping up the energy consumption of a target datacenter to minimize the time needed to consume the excess energy by as much as 75%. Plus, selling excess energy in an auction was shown to salvage 55%-65% of its original cost and provide 10% of cost savings to buyers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".