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Enregistrement W7106032407 · doi:10.7939/83355

Study On Electricity Market Dynamics, Cycling And Emissions In Decarbonized Scenarios Of The Alberta Electricity Market

2025· dissertation· en· W7106032407 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2025
Typedissertation
Langueen
DomaineEngineering
ThématiqueIntegrated Energy Systems Optimization
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPhotovoltaic systemElectricityRenewable energyGreenhouse gasElectricity generationElectricity marketTonneNatural gasNatural gas prices

Résumé

récupéré en direct d'OpenAlex

This research explored electricity decarbonization through two approaches: simulating scenarios with increased variable renewable energy (VRE) integration using historical market data and employing an energy market simulation model to identify generator fleet mixes that maximize system value. Focusing on Alberta’s electricity market, the study analyzed photovoltaic (PV) energy’s impact on emissions, load and pricing during the province’s transition period from coal to natural gas. Simulations from 2010 to 2019 found that increasing levels of PV capacity (from 15 MW to 3000 MW in a system with an average load of around 9000 MW) significantly reduced greenhouse gas emissions. As the carbon price increased from 15 to 30 Canadian dollars per tonne of carbon dioxide equivalent (CAD/tCO₂e), it significantly impacted the merit order by prioritizing the displacement of higher-emitting sources like coal, enhancing the environmental benefits of PV energy. Consequently, the displacement of coal-fired generation emissions by PV energy rose from 6.1% in 2010 to 10.6% in 2019, while the displacement of natural gas (NG)-fueled generation decreased from 30.2% to 6.9% over the same period. However, inserting modelled PV output into existing market merit orders resulted in steep midday price declines. At low installed capacities, PV plants achieved high market values because their midday production aligned with peak electricity prices. However, as PV capacity increased, oversupply during high solar production hours drove electricity prices down, drastically reducing PV's market value. With 1 GW of installed PV capacity, market value decreased by 30%, 66%, and 78% under low, medium, and high price regimes, respectively. At 3 GW of capacity, the declines were even more pronounced, reaching 51%, 75%, and 95%, respectively. Furthermore, the research explored optimizing PV system orientations (panel’s tilt and azimuth angles) to maximize revenue while addressing aforementioned price cannibalization. Simulations of PV capacities found that, at low capacities, energy-maximizing and revenue-maximizing orientations aligned. At higher PV capacities, revenue-optimal orientations shifted to times of higher-value energy but lower total generation due to intensified price cannibalization. Incorporating carbon credits further aligned revenue and energy-maximizing configurations. Lastly, the effects of carbon pricing on the cycling behavior of fleet mixes with substantial shares of net-zero resources in Alberta by 2035 were studied. The research simulated electricity market operations using commitment and dispatch modeling. Cycling events, such as ramps and startups, were influenced not only by the variability of wind and solar (W&S) energy but also by variable-cost competition between NG-fueled generation without CCS and net-zero resources. Emission costs, driven by the carbon price, introduce a premium that can alter the dispatch order of generators, prioritizing lower-emission resources and reshaping the operational dynamics of the electricity market. Carbon pricing incentivized NG-fueled generation with carbon capture and sequestration (CCS) over NG without CCS, reducing emissions and cycling events. W&S-dominated fleet mixes achieved emissions intensities of 67 kilograms of carbon dioxide equivalent per megawatt-hour (kgCO₂e/MWh), outperforming non-W&S NG-dominated mixes of ~100 kgCO₂e/MWh. Blue hydrogen competed in the merit order against NG without CCS at an estimated carbon price of 170 CAD/tCO₂e, while nuclear facilities would have dispatch priority over thermal units with hydrogen or CCS due to its low variable operating costs. The electricity sector faces uncertainty about how fleet mixes will evolve to reduce emissions, raising concerns among society and stakeholders about costs, revenues, and operational impacts. This dissertation examined key variables, including the effects of PV energy on market prices, how market price dynamics impact PV revenues, and how PV facilities can adapt to changes in fleet composition. It also explored future fleet mixes with net-zero resources, analyzing scenarios with and without W&S and the operation of hydrogen, natural gas with CCS, and nuclear energy. Additionally, it evaluated the impact of carbon pricing on PV revenues and operation patterns of net-zero resources such as dispatch and cycling (ramps and starts). By presenting various net-zero integration scenarios, this research serves as a valuable reference for policymakers and society, aiding informed decision-making in the energy transition.

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)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,548
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)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
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 Opus0,003
Tête enseignante GPT0,171
Écart entre enseignants0,168 · 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
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
Publié2025
Routes d'admission1
Résumé présentoui

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