Cooperative Control of Wind Power Generator and Electric Vehicles for Microgrid Primary Frequency Regulation
Bibliographic record
Abstract
Wind generators and plug-in hybrid electric vehicles (PHEVs) are increasing rapidly in modern power grids. Despite all their merits, these two classes of sources are limited by some practical constraints which disqualify each of them from effectively contributing separately to the primary frequency regulation in power grids with reduced inertia, such as microgrids. However, when combined with proper control and coordination, wind generators and PHEVs can compensate for the individual drawbacks of each source and effectively participate in the frequency regulation. A cooperative control strategy that considers the practical limits of both sources is not available in the literature. To fill this gap, in this paper, small-signal analysis is employed to investigate which frequency regulation method, droop or virtual inertia, is more suitable for such cooperation. The centralized and distributed control structures are examined as two possible coordination methods to ensure that the wind generator and PHEVs constraints are not violated and also that the communication system delay is considered. Based on a detailed analysis, the advantages, disadvantages, and appropriate applications of the centralized and distributed structures are discussed. Time-domain simulation results, obtained by using a typical microgrid system, validate the analytical results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".