Optimal Scheduling of Merchant-Owned Energy Storage Systems With Multiple Ancillary Services
Bibliographic record
Abstract
Electrical energy storage (EES) can improve the flexibility and reliability of electric power systems. At the same time, they can supply different ancillary services. The profit of the energy storage operation can be maximized by deciding the best level of each service. Merchant-owned facilities require a profit-maximizing formulation for grid-connected energy storage systems with multiple ancillary services. This paper proposes a new linear profit-maximizing formulation for grid-connected merchant-owned energy storage systems operating with multiple ancillary services. All technical characteristics of EES have been modelled, including cycle life loss due to fast charge/discharge and low depth of discharge operations. A piece-wise linear model of an EES converter's capability curve has also been included for reactive power modeling. The model was assessed considering a battery EES, a flywheel EES, and a compressed air EES, with the results demonstrating the benefits of the formulation. From case studies, it is clear that the merchant-owned battery EES and the flywheel EES can generate profits, especially from voltage regulation and frequency regulation services. The case studies prove that the proposed model can be used as an optimal planning and operation tool for any type or size of EES.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".