Mobile Energy Storage Scheduling and Operation in Active Distribution Systems
Bibliographic record
Abstract
A mobile (transportable) energy storage system (MESS) can provide various services in distribution systems including load leveling, peak shaving, reactive power support, renewable energy integration, and transmission deferral. Unlike stationary energy storage units, an MESS can move between different buses by a truck to provide different local services within the distribution feeder. This paper proposes a day-ahead energy management system (EMS) for an MESS that aims to minimize the cost of the power imported from the grid. The MESS does not only shift renewable energy power to load peak-hours but also can provide localized reactive power support. Given the day-ahead predictions, the EMS decided the optimal MESS stations in the feeder and the operating power. Next, a particle swarm optimization-based algorithm is developed to tune the moving time of the MESS according to a transit delay model. The applicability of the proposed scheduling and operation algorithms is tested on a typical 41-bus radial feeder.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".