Optimal operation of static energy storage in fast-charging stations considering the trade-off between resilience and peak shaving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it