A Novel Framework for Centralized Remote Power Smoothing as a Prospective Ancillary Service
Bibliographic record
Abstract
The increasing integration of intermittent renewable energy sources and the large load variations introduce fast power fluctuations. These fluctuations can impact grid stability and lead to significant frequency deviations, emphasizing the need for effective power smoothing strategies to ensure reliable grid operation. This paper introduces a novel centralized remote power smoothing (CRS) framework, where smoothing facilities (SF) are located away from the sources of power fluctuations. The CRS is proposed as a novel market ancillary service that can be fully integrated within the existing automatic generation control (AGC) of power system operators. The feasibility of the CRS is evaluated in comparison to conventional on-site smoothing and increasing AGC reserve capacity. First, a real-world proof of concept is demonstrated in the electricity system of Ontario, Canada, where a 2-MW flywheel storage facility remotely smooths the output power of a 200-km away transmission-connected wind plant. Subsequently, several simulation studies are conducted on the New England IEEE 39-Bus System, using real high-resolution renewable power and load profiles with 2–second granularity.The proposed CRS achieves up to 84% of the on-site smoothing performance using only 56% of its SFs' capacities. Yet, CRS large-scale practical implementation could be limited to 80% ofits theoretical performance.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".