Optimal operation of static energy storage in fast-charging stations considering the trade-off between resilience and peak shaving
Bibliographic record
Abstract
Enhanced penetration of electric vehicles (EVs) poses several challenges to the power network, such as uncertain peak loads and resilience issues during outages. Both resilience and peak shaving functions can be achieved by using a local static battery energy storage system (BESS) in the charging stations. However, resilience and peak-shaving are contradictory, i.e. increasing one will deteriorate the other. Therefore, in this study, a resilience and peak shaving trade-off scheme is proposed to optimally utilize the static BESS. Firstly, a resilience window is formulated to determine the amount of energy to be stored in the BESS for the resilience of EVs in the case of any contingency. During peak hours of the day, more importance is given to peak-shaving, whereas in the off-peak hours, resilience is prioritized. Then, an optimization algorithm is developed to minimize the cost of the system while maintaining resilience and maximizing peak shaving. Using the proposed window method, an optimal window size has been determined via a normalization approach. Simulations have been carried out to show the effectiveness of the proposed scheme by considering the conflicting nature of resilience and peak shaving. With the help of the proposed strategy additional 3.9 % of peak shaving and 3.41 % reduction operational costs is achieved. Moreover, sensitivity analysis has been carried out by considering different factors (market price, size of EV fleet, and BESS size) that can affect the optimal size of the resilience window.
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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 | 0.001 |
| 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 | 0.000 |
| 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".