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Enregistrement W6981302036

Economic, Environmental, and Health Impact Analysis of Developing Hydrogen Economy in Canada

2022· dissertation· en· W6981302036 sur OpenAlexaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2022
Typedissertation
Langueen
DomaineEnergy
ThématiqueHybrid Renewable Energy Systems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRenewable energyDamagesElectricityEnergy engineeringWork (physics)Energy policyEnergy conservationEnergy (signal processing)Externality
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The greatest challenge to the development of a cleaner energy system is economic issues. However, if the environmental and health externalities of the current energy system is considered, other energy alternatives become economically competitive. Therefore, hydrogen can become an option in different energy sectors. As an energy vector, hydrogen can be represented as the missing link between clean energy sources and energy consumers. The real cost of an energy system includes environmental and health-related hidden costs. The current energy system imposes lots of critical damages to the environment and human lives. All these damages are avoidable if governments follow the prevention policy instead of the cure policy. In other words, governments can support developing clean energy solutions by incentivizing them. In this regard, the government should be aware of the hidden costs of energy for both fossil-fuel-based and hydrogen-based energy systems. Therefore, in this work, a comprehensive cost calculation is conducted for using hydrogen in different energy sectors in this work. The result from this work shows that the idea of Hydrogen Economy is economically competitive with the current energy system, if the hidden costs of environmental and health effects are taken into account. The first study is focused on developing a five-year mathematical model for finding the optimal sizing of renewable energy technologies for achieving specific CO2 emission reduction targets. An industrial manufacturing facility that uses CHP for electricity generation and natural gas for heating is considered the base case in this work. The CHP capacity is 4500 kW and the furnace is operated 8 AM to 4 PM with a natural gas consumption of 4000 m3/h. Different renewable energy technologies are assumed to be developed each year to achieve a 4.53% annual CO2 emission reduction target. The results of this study show that wind power is the most cost-effective technology for reducing emissions in the first and second years, with a cost of 44 and 69 CAD per tonne of CO2, respectively. On the other hand, hydrogen is more cost-effective than wind power in reducing CO2 emissions from the third year onward. The cost of CO2 emission reduction with hydrogen doesn't change drastically from the first year to the fifth year (107 and 130 CAD per tonne of CO2). Solar power is a more expensive technology than wind power for reducing CO2 emissions in all years due to lower capacity factor (in Ontario), more intermittency (requiring mores storage capacity), and higher investment cost. A hybrid wind/battery/hydrogen energy system has the lowest emission reduction cost over five years. The emission reduction cost of such a hybrid system increases from 44 CAD per tonne of CO2 in the first year to 156 CAD per tonne of CO2 in the fifth year. The developed model can be used for long-term planning of energy systems to achieve GHG emission targets in regions/countries with fossil fuel-based electricity and heat generation infrastructure. The second study develops a multi-objective model to determine the optimal sizes and locations of the hydrogen infrastructure needed to generate and distribute hydrogen for the critical Highway Corridor (HWY 401) in Ontario. The model is used to aid the early-stage transition plan for converting conventional vehicles to FCEVs in Ontario by proposing a feasible solution to the infrastructure dilemma posed by the initial adoption of hydrogen as fuel in the general market. The health benefit from the pollution reduction is also determined to show the potential social and economic incentives of using FCEVs. The results show that hydrogen production and delivery cost can reduce from $22.7/kg H2 in a 0.1% market share scenario to $14.7/kg H2 in a 1% market share scenario. The environmental and health benefit of developing hydrogen refueling infrastructure for heavy-duty vehicles is 1.63 million dollar per year and 1.45 million dollars per year, respectively. Also, every kilogram of H2 can avoid 11.09 kg CO2 from entering the atmosphere. In a 1% market share scenario, the proposed hydrogen network avoids more than 37,000 tonnes of CO2 per year. The third study aims to determine the economic burden of environmental and health impacts caused by Highway 401 traffic. Due to the high volume of vehicles driving on the Toronto Highway 401 corridor, there is an annual release of 3771 tonnes of carbon dioxide equivalent (CO2e). These emissions are mainly emitted onsite through the combustion of gasoline and diesel fuel. The integration of electric and hydrogen vehicles shows maximum reductions of 405–476 g CO2e per vehicle kilometer. Besides these carbon dioxide emissions, there is also a large number of hazardous air pollutants. The mass and concentrations of criteria pollutants of PM2.5 and NOx emitted by passenger vehicles and commercial trucks on Highway 401 were determined using the MOVES2014b software to examine the impact of air pollution on human health. Then, an air dispersion model (AERMOD) was used to find the concentration of different pollutants at the receptor’s location. The increased risk of health issues was calculated using hazard ratios from literature. Finally, the health cost of air pollution from Highway 401 traffic was estimated to be CAD 416 million per year using the value of statistical life, which is significantly higher than the climate change costs of CAD 55 million per year due to air pollution. The fourth study discovers the health benefit of reducing fossil-fuel vehicle market share and utilizing more Zero-Emission Vehicles (ZEVs). A historical dataset from 2015-2017 is used to learn a Long Short-Term Memory (LSTM) model that can predict future NOx concentration based on traffic volume, weather condition, time, and past NOx concentration. The developed model is used in a modified manner to predict NOx concentration in the long term. Then, the developed model is utilized to predict annual average NOx reduction in four different scenarios. Interpolation methods are used to predict pollution reduction in all Dissemination Areas (DA) of Toronto. Finally, a health cost assessment is conducted to estimate the health benefit from different scenarios. The results show that the western areas of Toronto experience more NOx concentration reduction in all scenarios, which is the result of a stronger correlation between traffic volume and pollution in those areas. Also, by 10% reduction in fossil-fuel traffic volume, 70 deaths can be prevented annually, equivalent to CAD 560 million health benefit per year. There are plenty of opportunities for future work in this area to make more robust energy models which can take all aspects of implementing the idea of Hydrogen Economy. First, the impact of using different types of hydrogen storage can be investigated in terms of cost. Also, a comprehensive hydrogen-based energy model can be optimized if the cost-benefit analysis is conducted in all energy sectors. Finally, different objective functions such as energy, environmental, health, and social costs can be optimized to reach an optimal sustainable energy system for Ontario.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,064
Score d'incertitude au seuil0,462

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0020,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,200
Écart entre enseignants0,192 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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