Demonstration of HVAC chiller control for power grid frequency regulation—Part 2: Discussion of results and considerations for broader deployment
Bibliographic record
Abstract
Secondary frequency regulation is an electric grid ancillary service that balances the electric power system supply and demand on short time intervals. A companion article (Su and Norford 2015) developed and experimentally demonstrated a practical controller to modify chiller power demand to provide secondary frequency regulation with sufficient performance to meet market qualification requirements. This article compares experimental results to model predictions and to other sources of secondary frequency regulation. Delay time, ramp-rate limits, minimum power, and variable coefficient of performance are identified as factors that contribute to chiller power transient behavior and should be considered when predicting performance. This article additionally introduces results from a second building to assess the applicability of similar chiller control strategies for other HVAC systems. The second building produced less positive results due to communication delays associated with a modified setup and due to less favorable chiller characteristics, such as ramp-rate limitations and compressor cycling. While these constraints may be lessened with changes to chiller settings, the significant variations between the two buildings suggest that for a given site, observations are necessary in addition to design information to ascertain a chiller's suitability for providing secondary frequency regulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".