Driving Sustainability Through Fleet Electrification, Smart Routing, and Peer-to-Peer Energy Trading: Energy Operations Can Become a Source of Profit
Bibliographic record
Abstract
Freight transportation remains a significant source of carbon emissions, making the electrification of commercial vehicle fleets a crucial strategy for sustainability. Beyond simply replacing diesel trucks with zero-emission vehicles, there is an opportunity to optimize how electrified fleets interact with the power grid. This study proposes an integrated framework that combines the daily vehicle routing problem (VRP) of an electric fleet with participation in a decentralized peer-to-peer (P2P) energy market. By allowing fleet vehicles to trade energy through fast charging stations (FCSs) with other local energy users, the model enables temporal arbitrage (shifting energy use across time). We evaluate the approach using case studies under multiple scenarios, from a baseline with no grid interaction up to full P2P energy trading. The results show that, under the P2P trading scenario, the fleet can utilize nearly 90% of the renewable energy surplus in the local grid. Besides, it can supply a significant portion of local power shortfalls, drastically reducing reliance on backup fossil-fueled generators. Economically, this decentralized strategy reduces the fleet’s net energy cost so dramatically that energy operations become a source of profit: the fleet buys electricity cheaply during off-peak periods and sells power back at higher prices during peak demand. These findings demonstrate that intelligently integrating P2P energy trading with electric fleet operations can simultaneously cut operating costs, reduce carbon emissions, and enhance overall grid resilience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".