Analysis of the Economic and Carbon Emission Reduction Potential of Fuel Cell Electric Vehicle-to-Grid in Alberta and Ontario
Notice bibliographique
Résumé
Connecting battery electric vehicle (BEV) to the grid is a way of utilizing existing BEV fleet to cut the cost on energy storage and provide monetary incentives to vehicle owners. By coordinating the charging and discharging of the growing BEV fleet, the grid load can be shifted. Meanwhile, fuel cell electric vehicles (FCEVs) are gaining popularity, especially in heavy-duty vehicle market because of the advantages of hydrogen over battery such as the higher gravimetric density and faster refueling time. Similarly, FCEV fleet can also be connected with the grid (FCEV2G) and become moving energy generators that generate electricity and supply it to the grid using hydrogen. The hydrogen used can be produced locally with cheap and excess electricity or in a centralized production site at lower cost. A profit could be made to benefit from the high electricity price during peak hours, which can be shared among FCEV owners and the FCEV2G coordinator. \nThis study analyzes an FCEV2G station that can connect a few FCEVs to the grid to generate electricity. The operation, including local hydrogen production and storage, hydrogen purchased from a centralized plant, and schedules of FCEV2G, is modeled as a mixed integer linear programming problem. Using historical data of electricity price and generation mix in Alberta and Ontario, in 2019 and 2022, The profits of this FCEV2G station with different configurations are optimized and compared. Parameters including component efficiency, onsite electrolyzer are studied to investigate their impacts on the optimization result. The carbon emission potential of FCEV2G is also evaluated. \nThe results in Alberta show that an annual net revenue as high as 66k USD could be made in 2022 via FCEV2G, as the high and volatile electricity prices amplify the load-balancing function of FCEV2G. In addition, 185 t CO2 emission could also be avoided by using clean hydrogen to generate electricity and supply it to the carbon-intensive grid in Alberta. However, under the base case assumption, such a FCEV2G station could not make profit in 2019 in Alberta because of the efficiency losses of the electrolyzer and fuel cells as well as the relatively stable electricity price. This means, high and unstable electricity prices through a year are the key factors for FCEV2G to be profitable. \nOn the other hand, Ontario has abundant nuclear and hydro power supply and hence maintain a stable electricity price profile. A parametric study is conducted to study how the profitability will depend on technological improvements in the future, and it finds that, by using the 2022 data, the FCEV2G station becomes profitable after market hydrogen cost divided by fuel cell efficiency is below 86 USD/MWh. Meanwhile, the carbon intensity of electricity varies largely in Ontario because natural gas is primarily used to meet peak demands. This allows a FCEV2G pathway to reduce the carbon emissions during peak hours, and the result shows as high as 213 t CO2 emissions could be reduced in the 2022 base case.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».