Data Center Demand Response in Deregulated Electricity Markets
Bibliographic record
Abstract
With the development of deregulated electricity markets, a customer can enter a contract with one of several competing utility companies. Meanwhile, a utility company is motivated to increase its market share by helping its customers manage their energy usage and save money through demand response programs. In this paper, we study the demand response program in deregulated electricity markets for data centers that often have significant flexibility in workload scheduling. We consider the real-time pricing and model the data centers' coupled decisions of utility company choices and workload scheduling as a many-to-one matching game with externalities. To solve such a game, we show that it admits an exact potential function, whose local minima correspond to the stable outcomes of the game. We further develop a distributed algorithm that guarantees to converge to a stable outcome. Compared with the scenario without data centers' demand response, we show through simulation that the proposed algorithm can reduce the average contract payment of data centers by 18.7% and increase the revenue of the utility companies that offer lower electricity tariffs up to 80% by attracting more data centers as customers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".