Auction-Based Mechanisms for Power Grid Balancing using Cloud Datacenters
Bibliographic record
Abstract
Grid balancing (keeping equal generation and consumption levels) 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 interest in greening the energy supply by using more renewable energy sources presents new grid balancing challenges.Such volatile energy sources introduce generation-side uncertainty and cause the existing flexibility mechanisms to fall short more often on providing enough balancing capacity.It is thus essential to increase the system flexibility in order to safely accommodate larger shares of renewable input.To that end, we propose in this thesis new market-based approaches that allow cloud datacenters to be used as managed loads to provide the needed system flexibility.Datacenters are suitable to act as managed loads because they consume large amounts of energy and have flexible energy demands.However, the existing datacenter-based grid balancing approaches have only considered owner-operated datacenters and only focused on providing downward flexibility to reduce energy consumption at times of generation shortage.The work presented in this thesis introduces new demand-side management approaches that can provide upward flexibility (to combat the rising frequency of overgeneration events associated with the use of renewable sources) and is compatible with public cloud datacenters (to allow a wider range of datacenters to participate in grid balancing).The proposed systems rely on the idea of migrating the cloud workloads of large-scale cloud customers (cloud brokers, that aggregate the workloads of end-customers) between datacenters to correct energy imbalances.We start with systems that target the overgeneration problem by selling the excess energy at a reduced cost (sold as "energy credits" to differentiate it from energy sold at the regular price) to allow for its quick consumption.The proposed systems start by selling the energy credits at a fixed reduced cost, then using an auction to determine the sale price, and later using a combined auctioning-scheduling optimization formulation to ensure available capacity for the migrated cloud workloads.We then generalize BudShr ijkAmount of budget consumed in the case of shortage imbalance REC ijk Amount of recovered energy cost xvii
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".