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Record W7065961836

Flexibility assessment of Canada’s electricity system for deep decarbonization

2023· dissertation· en· W7065961836 on OpenAlexaboutno aff

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Software deploymentElectricityRenewable energyVariable renewable energyElectric power systemLeverage (statistics)Greenhouse gasDemand response
DOInot available

Abstract

fetched live from OpenAlex

In accordance with the Paris Climate Agreement, Canada has committed to reaching net-zero greenhouse gas (GHG) emissions by 2050, necessitating decarbonization across various sectors, including the electrical grid. The widespread deployment of variable renewable energy (VRE) sources holds the potential for a carbon-free electrical grid. However, the variable nature of VRE can result in technical challenges such as network frequency fluctuations, requiring a highly flexible power network to address these issues. Both the demand side and generation side can contribute to network flexibility. Generation-side flexibility can be provided by high ramping generators, such as hydro units, while demand response programs can incentivize customers to adjust their consumption patterns, offering demand-side flexibility. This PhD dissertation seeks to investigate the decarbonization of Canada's electricity system through VRE integration, with a focus on flexibility assessment on both the generation and demand sides. The investigation of VRE integration in this study centers on employing the operation (optimal dispatch) model. The present study undertakes an examination of Canada's existing electrical system with a view to exploring its capability for the integration of VRE. The study commences by evaluating the generation-side flexibility and transmission network adequacy of the system. Thereafter, it examines two strategies aimed at enhancing the integration of VRE. The first strategy considers integrated operation of neighboring networks to leverage flexibility and enhance VRE integration in less flexible networks. The second strategy assesses the impact of demand-side flexibility in enhancing VRE integration. The findings demonstrate that Canada's hydro-dominated electrical network possesses significant potential for integrating VRE, which leads to a significant reduction in GHG emissions. Nonetheless, the current potential falls short of achieving net zero emissions, implying the need for further actions to reach this goal. The integration of neighboring electrical networks through integrated operation can enhance flexibility and VRE integration in networks that are less flexible. However, high flexible networks in Canada, dominated by hydropower, may have their flexibility provision capacity impacted by climate change. This implies that a range of flexibility resources must be taken into consideration to ensure secure and reliable VRE integration. Demand side flexibility can aid in facilitating VRE integration, however, its efficacy is contingent on consumer behavior and preferences, as well as incentives offered by the system operator. Additionally, the findings indicate that the transmission network holds a crucial role in achieving maximum flexibility on both the generation and demand sides. An inadequate transmission capacity can serve as a hindrance to achieving maximum VRE integration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.286
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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