Over-Voltage Regulation of Distribution Networks by Coordinated Operation of PV Inverters and Demand Side Management Program
Bibliographic record
Abstract
The increase of Photovoltaics (PV) units’ penetration factor in the power grids might create overvoltage over the network buses. The active power curtailment (APC) and the reactive power provision methods use inverters to regulate their output active and reactive powers for high PV-penetrated grids. However, the mentioned solutions would reduce the maximum injectable active solar power to the grid, not financially acceptable. Continuous employment of the maximum apparent power capacity of the inverters will practically decrease the inverters’ lifetime, require special design considerations, and make the control system complex. To overcome those issues, a feasible solution would be increasing the load consumption within the time intervals in which the grid faces the over-voltage problem. In this research, the demand response (DR) program is employed. Load shifting techniques are exerted to move a portion of loads from the peak hours to when further power consumption is expected for voltage level reduction purposes. A new long-term strategy based on the coordinated operation of the PV inverters and load shifting techniques is proposed to resolve the over-voltage issue in the network. Consequently, the PV inverter’s contribution to voltage control is reduced; a new sight of DR potential is implemented, and also the under-voltage level in peak times is decreased significantly.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".