Evaluating the economic and carbon emission reduction potential of fuel cell electric vehicle-to-grid
Bibliographic record
Abstract
As part of the effort to achieve net zero, hydrogen will become significantly used in transportation and energy generation by 2050. Hydrogen is fit for long-haul vehicles because of the short refueling time and long range of using hydrogen as onboard storage instead of batteries. Meanwhile, hydrogen can also be used for long-time grid energy storage because of the low material cost and low self-discharge. By using fuel cell electric vehicles (FCEVs) for energy generation, the fuel cells (FCs) in idle FCEVs can be connected to the grid (FCEV2G) and supply electricity to the grid by consuming hydrogen stored in a station. In this way, the hydrogen usage in the transportation and energy storage sectors can be synergically integrated. A mixed integer linear programming (MILP) model is established to simulate and evaluate the economic and environmental potential of the operation of a FCEV2G station. The station’s profit and carbon emission reduction potential depend on the traffic and electricity profiles. It was estimated that a net profit of 233,976 USD can be generated and simultaneously 210 tonnes carbon emissions can be reduced, using the historic traffic and electricity data of Alberta. Furthermore, considering the Canadian carbon tax in the optimization increases the net profit and carbon reduction to 246,704 USD and 377 tonnes, respectively. Meanwhile, using electricity data with lower carbon intensity and less fluctuation, e.g., that in Ontario, significant technological improvements are needed to make the FCEV2G station operation viable. These results demonstrate the potential of FCEV2G in generating monetary incentives and environmental benefits by integrated the transportation and energy storage sectors.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".