Optimizing GFMC/GFLC Capacity Configuration for Stability Enhancement in RES-Dominated Weak Grids
Bibliographic record
Abstract
Grid-following converters (GFLCs) play a crucial role in integrating renewable energy sources (RESs) into modern power systems. However, a high penetration of GFLCs can challenge the stability of weak grids with low short-circuit ratios (SCRs), necessitating the installation of grid-forming converters (GFMCs) to improve the grid strength. Although the grid-support capabilities of GFMCs have been well demonstrated in the existing literature, the optimal GFMC capacity configuration for improving stability in hybrid GFMC-GFLC systems remains relatively under-explored. Moreover, GFLCs operating under different reactive power control (RPC) schemes exhibit distinct small-signal behaviors, and it is still unclear how they affect the optimal GFMC capacity required to ensure system stability. To fill this gap, this paper identifies the main causes of instability caused by two GFLC RPC schemes—constant reactive power control (CRPC) and AC voltage control (AVC)—and explains how GFMCs address the source of instability in each case. Furthermore, a GFMC/GFLC capacity configuration strategy is proposed to determine the minimum GFMC capacity required to enhance the small-signal stability of a microgrid. Finally, the proposed method is evaluated through real-time simulations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".