A Novel Hierarchical/Decentralized AGC Scheme for Power Systems Integrated With Large-Scale Solar Power Plants
Bibliographic record
Abstract
As the penetration level of large-scale solar power plants (LSSPPs) in transmission systems increases, their contribution to the stability of networks cannot be overlooked. Theoretically, such resources can be considered akin to traditional power plants in preserving network stability. Moreover, diverse frequency regulation resources exert varying levels of system complexity, capacity, and response speed, thereby posing challenges to appropriate performance automatic generation control (AGC). As a remedy, a new hybrid (hierarchical/decentralized) scheme is proposed to improve the performance of traditional AGC mechanisms in the presence of LSSPPs and utilize maximum potential capability to ensure network stability. First, a new method is employed to calculate the spinning reserve for LSSPPs considering the performance of AGC for traditional power plants, the dynamics of the DC-link voltage in LSSPPs, the critical operating point related to the most severe disturbance, and the load model. Following this, the decentralized AGC system works hierarchically and in parallel with the centralized algorithm to regulate the frequency and tie lines exchange power. Furthermore, a simple and accurate index (ΔPIPS_x) is provided to estimate the amount of active power changes after the disturbance in an interconnected power system (IPS). The simulation results are conducted in IEEE 39-bus and PST-16 test systems using DIgSILENT PowerFactory software. The simulation results verify the efficacy and performance of our proposed scheme to improve the AGC system performance and system stability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".