Optimal scheduling of virtual power plant with battery degradation cost
Bibliographic record
Abstract
This study proposes a novel optimal generation scheduling model for virtual power plant (VPP) considering the degradation cost of energy storage system (ESS). The VPP is generally formed by a mix of distributed energy resources, and the ESS is an important installation for flexible VPP dispatch due to its controllable and schedulable behaviours. For the operations of battery storage systems, the ambient temperature and depth of discharge have significant impacts on the wear and tear of the ESS as well as battery degradation cost. Furthermore, the battery degradation cost is modelled and approximated by a piecewise linear function, and then incorporated into the proposed optimal VPP scheduling model. Consequently, the optimal VPP scheduling problem is formulated as a two‐stage stochastic mixed‐integer linear programming in order to maximise the expected profits of the VPP. The proposed model has been successfully implemented and tested through a representative case study, and the influence of battery degradation cost on optimal VPP scheduling has also been thoroughly analysed and demonstrated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".