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Enregistrement W2737960286

Investigation of energy storage options for sustainable energy systems.

2013· dissertation· en· W2737960286 sur OpenAlexaboutno aff
Mehdi Hosseini

Notice bibliographique

Revuee-scholar@UOIT (University of Ontario Institute of Technology) · 2013
Typedissertation
Langueen
DomaineEnergy
ThématiqueHybrid Renewable Energy Systems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSustainable energyEnergy storageEnergy (signal processing)Environmental scienceEnvironmental economicsEngineeringRenewable energyPhysicsEconomicsElectrical engineeringThermodynamics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Determination of the possible energy storage options for a specific source of\nenergy requires a thorough analysis from the points of energy, exergy, and\nexergoeconomics. The main objective of this thesis is to investigate energy storage\noptions for sustainable energy systems. A technology description and illustration of\nconcerns regarding each system is presented. Moreover, the possibility of\nimplementing each option into different sources of energy is investigated. Thus,\nintegrated energy systems are developed, utilizing energy storage options with the aim\nof achieving more efficient systems.\nEnergy and exergy analyses are performed for three novel, integrated renewable\nenergy-based systems. Energy storage methods investigated here include hydrogen\nstorage, thermal energy storage, compressed air energy storage, and battery. Solar,\nwind, and biomass are the energy sources considered for the integrated systems. In\nthis research, a discussion on various energy storage systems is presented, and the\npotential of each storage option in the current and future energy market is studied.\nEach of the integrated systems is described and its operating strategy is presented.\nThe components of the integrated systems are first modeled to obtain their operating\ncharacteristics. The energy, exergy, and exergoeconomic equations are applied to the\ncomponents to calculate the rates of energy and exergy flows. The efficiencies are\nsubsequently calculated. The results of energy and exergy analyses are combined with\nexergoeconomic equations to report the unit exergy cost of flows in the components.\nSystem 1 consists of a PV system, a water electrolyser and a fuel cell to generate\nelectricity for a house. Hydrogen and thermal energy storage are considered as the\nstorage options. The results show that the capacities of the components depend on\nweather data and electric power demand. In System 1, the PV electric power output\nexceeds demand during months with high-solar irradiance. The results of a case study\nbased on the weather data in Toronto, Canada, and the electricity demand pattern of a\nCanadian house (5.74 kW maximum demand) are presented. The photovoltaic system\ncapacity and the electrolyser nominal hydrogen production rate are 37.17 kW and 4.5\nkg/day, respectively. The economic investigation of the hybrid system reports an\naverage cost of electricity of 0.84 $/kWh based on 25 years of operation. The optimal nominal capacity of the fuel cell is found to be 1.5 kW, according to the optimization\nresults. The optimal exergy efficiency varies from 9.91 to 9.94%.\nSystem 2 consists of a wind park, a PV-fuel cell and a biomass-fuel cell-gas\nturbine system. This integrated renewable energy-based system is developed for\nbaseload power generation and utilizes wind, solar and biomass energy resources. For\na 64 bar compressed air storage system, and a 36 bar gas turbine inlet air pressure,\n356 wind turbines are required. The lower the pressure difference between the\ncompressed air in the cavern and the gas turbine inlet air pressure, the fewer the\nnumber of wind turbines required in the Wind-CAES system. The results also show\nthat 5.4??105 PV modules (covering 0.66 Mm2 of land) are required to generate 5 MW\nof baseload electric power. Optimization of System 2 provides a range of optimal\npoints at which the exergy efficiency and the total purchase cost of the system are\noptimum. At an optimal point, the overall exergy efficiency of the integrated system is\nreported as 36.85%. At this point, the optimal values of compression ratio, gas turbine\nexpansion ratio, and CAES storage capacity are 8, 6.5, and 240 h, respectively.\nSystem 3 consists of a biomass gasifier integrated with a gas turbine cycle\n(biomass-GT). As another sub-part of System 3, a PV-electrolyser module is\nintegrated with a compressed air energy storage system. The overall hybrid system\nsupplies 10 MW baseload electric power, and 7730 MWh thermal energy. The PV is\naccountable for 56% of the annual exergy destruction in the hybrid system, and 38%\nof the annual exergy destruction occurs in the biomass-GT system. The overall energy\nand exergy efficiencies of System 3 are 34.8 and 34.1%, respectively. The hybrid PVbiomass\nsystem is sensitive to some parameters such as the steam-to-carbon ratio of\nthe biomass gasifier, and the gas turbine inlet temperature and expansion ratio. A 29%\nincrease in energy and exergy efficiencies is reported with the increase in SC from 1\nto 3 mol/mol. The related specific carbon dioxide emission reduction is from 1441 to\n583 g/kWh.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,959
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,011
Tête enseignante GPT0,191
Écart entre enseignants0,180 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
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

Citations4
Publié2013
Routes d'admission1
Résumé présentoui

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