A dual time-scale optimal dispatch algorithm for PV systems: Integrating centralized optimal power dispatch with distributed power deviation absorption in DC smart grids
Bibliographic record
Abstract
The integration of Photovoltaic (PV) systems into DC smart grids faces challenges due to solar power’s inherent unpredictability. Traditional dispatch methods struggle to effectively manage PV power deviations in real-time. This paper proposes a dual time-scale strategy integrating centralized optimization with distributed consensus. On the long-term scale, a convex relaxation-based optimal power flow model minimizes line losses and stabilizes voltages. For short-term adjustments, a distributed consensus algorithm dynamically allocates power deviations among PV sources using reserve capacity, eliminating the need for probabilistic uncertainty modeling. The approach is validated through IEEE 14-node simulations and hardware-in-loop (HIL) tests, with comparisons against centralized methods considering forecast errors. The results demonstrate enhanced voltage stability, highlighting the framework’s practicality for real-time grid management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".