Optimal dispatch of a mobile storage unit to support electric vehicles charging stations
Bibliographic record
Abstract
Abstract As transportation electrification increases globally, new technologies emerged in the past few years to meet the growth of the electricity demand. Mobile Energy Storage Systems (MESS) offer versatile solutions, aiding distribution systems with reactive power, renewables integration, and peak shaving. An MESS can be utilized to serve electric vehicles (EVs) in different parking lots (PLs), in addition to supplying power to the grid during overloads. The task of multiple stationary units can be achieved using MESS at a relatively lower cost. This paper proposes an MESS owned by multiple PLs sharing the same geographical area and sharing its capital and operational cost. The main objective of the proposed approach is to dispatch the MESS in conjunction with optimal EVs’ charging coordination to minimize operational costs and address the extra demand of PLs. A mixed‐integer nonlinear programming (MINLP) problem is formulated and solved. Considering electricity price variations and EVs uncertainties, three different case studies are performed to highlight the efficiency and success of the proposed approach. The simulation results in a huge reduction in the total operation cost and the savings reach up to 27.51% in comparison with the base case.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".