Synergizing Smart Electric Railway Networks with Integrated Wind Generation: An Optimal Energy Management Approach Considering Stochastic and Probabilistic Analysis
Bibliographic record
Abstract
The electric railway system (ERS) plays a notable role in contributing to air pollution and consumes a substantial amount of electrical energy. The Railway Energy Management System (REMS) is a green and innovative solution to reduce pollution and electricity consumption in ERSs. This paper specifically focuses on improving the efficiency of the Bovisa railway station in Milan, Italy. We consider the existence of the electrical grid, the use of Energy Storage Systems (ESSs), and harnessing Regenerative Braking Energy (RBE). Moreover, integrating wind farm generation into the system is also explored, offering a more sustainable and beneficial approach to railway energy management. To achieve greater cost reduction and more accurate results, the study delves into the stochastic and probabilistic behaviors of wind generation, RBE, ESSs, and the electrical grid. This enhanced focus allows the REMS model to account for the unpredictable nature of these factors. The model is implemented using MATLAB, and the results underscore its effectiveness and practical applicability in real-world scenarios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".