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Enregistrement W3016667827

Optimal Design and Operation of Community Energy Systems

2020· dissertation· en· W3016667827 sur OpenAlexfundno aff
Sayyed Faridoddin Afzali

Notice bibliographique

RevueMacSphere (McMaster University) · 2020
Typedissertation
Langueen
DomaineEngineering
ThématiqueIntegrated Energy Systems Optimization
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of Canada
Mots-clésEnergy (signal processing)Computer scienceMathematicsStatistics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Energy demand for buildings has been rising during recent years. Increasing building energy consumption has caused many energy-related problems and environmental issues. The on-site community energy system application is a promising way of providing energy for buildings. Community energy system usage reduces the primary energy consumption and environmental effects of greenhouse gas (GHG) emissions compared to the implementation of the stand-alone energy systems. Furthermore, due to the increase in electricity price and shortage of fossil fuel resources, renewable energies and energy storage technologies could be great alternative solutions to solve energy-related problems. Generally, the energy system might include various technologies such as internal combustion engine, heat recovery system, boiler, thermal storage tank, battery, absorption chiller, ground source heat pump, heating coil, electric chiller, solar photovoltaics (PV) and solar thermal collectors, and seasonal thermal energy storage. The economic, technical and environmental impacts of energy systems depend on the system design and operational strategy. The focus of this thesis is to propose unified frameworks, including the mathematical formulation of all of the components to determine the optimal energy system configuration, the optimal size of each component, and optimal operating strategy. The proposed methodologies address the problems related to the optimal design of the energy system for both deterministic and stochastic cases. By the use of the proposed frameworks, the design of the energy system is investigated for different specified levels of GHG emissions ratio, and the purpose is to minimize the annual total cost. To account for uncertainties and to reduce the computational times and maintain accuracy, a novel strategy is developed to produce scenarios for the stochastic problem. System design is carried out to minimize the annual total cost and conditional value at risk (CVaR) of emissions for the confidence level of 95%. The results demonstrate how the system size changes due to uncertainty and as a function of the operational GHG emissions ratio. It is shown that with the present-day technology (without solar technologies and seasonal storage), the lowest amount of GHG emissions ratio is 37%. This indicates the need for significant technological development to overcome that ratio to be 10% of stand-alone systems. This thesis introduces novel performance curves (NPC) for determining the optimal operation of the energy system. By the use of this approach, it is possible to identify the optimal operation of the energy system without solving complex optimization procedures. The application of the proposed NPC strategy is investigated for various case studies in different locations. The usage of the proposed strategy leads to the best-operating cost-saving and operational GHG savings when compared to other published approaches. It has shown that other strategies are special (not always optimal) cases of the NPC strategy. Based on the extensive literature review, it is found that it is exceptionally complicated to apply the previously proposed models of seasonal thermal energy storage in optimization software. Besides, the high computational time is required to obtain an optimum size and operation of storage from an optimization software. This thesis also proposes a new flexible semi-analytical, semi-numerical methodology to model the heat transfer process of the borehole thermal energy storage to solve the above challenges. The model increases the flexibility of the storage operation since the model can control the process of the storage by also deciding the appropriate storage zone for charging and discharging.

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), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,718
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,012
Tête enseignante GPT0,177
Écart entre enseignants0,165 · 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'étudeSimulation ou modélisation
Domainenon disponible
GenreAutre

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
Publié2020
Routes d'admission1
Résumé présentoui

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