A Novel Cost Minimizing Strategy for Cooperative Relay and Edge-Computing-Based Wind Power Communication Network
Bibliographic record
Abstract
Wind power frequency regulation relies on reliable communication between wind farms and power dispatch center (PDC), which is critical for ensuring the accuracy of frequency regulation. However, data transmission errors and delays may lead to deviations in control commands, increasing the reliance on ancillary services and thereby raising the operational costs of PDC. Therefore, this paper proposes a communication-enhancing strategy that integrates cooperative relay and edge computing (CRE) to improve communication reliability. Edge node (EN) devices with computational capabilities are employed as relays to enable data preprocessing at edge-side. Furthermore, a bandwidth compression model is developed based on Amdahl’s law, hardware constraints of EN, and thermal wall effect, to characterize the coupling between computing power and bandwidth and support efficient resource allocation. On this basis, a cost model incorporating packet loss, frequency regulation response time, and control errors is established, and the cost minimization problem is formulated as a Stackelberg game to jointly optimize resource allocation and pricing strategy. Simulation results demonstrate that the proposed strategy improves resource utilization efficiency. Compared with benchmark schemes, it reduces the total cost of PDC by 33.82% and increases the profit of EN by 21.53%, while exhibiting strong scalability across different wind farm scales.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".