Generation maintenance scheduling in virtual power plants
Bibliographic record
Abstract
In an active network, as a virtual power plant (VPP), periodic maintenance of distributed generators (DGs) is critically vital for the reliable operation of the power system. To prevent unexpected failure of DGs and avoid deterioration of the grid's reliability, coordination of maintenance scheduling is indispensable. In this study, maintenance management of a VPP is proposed for scheduling the planned outage of DGs, in order to preserve their useful lifespan. In addition to conventional DGs and the upstream power grid, renewable generation including wind turbines and photovoltaic systems, energy storage systems, and curtailable loads are considered as components of the VPP. The proposed maintenance scheduling scheme provides different advantages in viewpoints of cost and reliability. Moreover, risk management is also investigated to lower the risk of maintenance scheduling due to the uncertainty in price in an energy market by adopting the conditional value at risk as a measure of risk. The overall cost is minimised considering the power loss in the grid as well as the security constraints such as DGs operational constraints, voltage magnitude, and transmission lines’ power flow limit. The effectiveness of the proposed scheme is illustrated using numerical studies with short‐ and long‐term scheduling.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".