Optimal Sizing and Scheduling of Mobile Energy Storage Toward High Penetration Levels of Renewable Energy and Fast Charging Stations
Bibliographic record
Abstract
This paper presents a planning model that utilizes mobile energy storage systems (MESSs) for increasing the connectivity of renewable energy sources (RESs) and fast charging stations (FCSs) in distribution systems (DSs). The proposed planning model aims at enabling high penetration levels of green technologies while minimizing the total DS cost that includes investment, operating, and emission costs. The proposed model determines the optimal MESS sizes and transportation schedules as well as the optimal sizes and locations of wind-based distributed generators (DGs), photovoltaic (PV) DGs, and FCSs. The model takes into account techno-economic and environmental factors in addition to the power variations of RESs, FCSs, and load demands. The proposed planning model is applied on two benchmark test systems (i.e., 33-bus and 69-bus DSs). To evaluate the efficacy of the proposed model, the results obtained from the model are compared to those obtained from a traditional planning technique. The comparison of the results demonstrates that the proposed model with MESSs successfully achieves a significant cost saving and increases the penetration levels of RESs and FCSs.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".