MétaCan
Menu
Back to cohort
Record W4411072474 · doi:10.1051/e3sconf/202562905006

Review and analysis of wind and solar PV farms power outputs to meet Ontario hourly electricity demand with optimal sizing of PV farms, wind farms, and energy storage systems

2025· article· en· W4411072474 on OpenAlexafffundabout
Alan S. Fung

Bibliographic record

VenueE3S Web of Conferences · 2025
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Toronto
FundersIndependent Electricity System OperatorCanada First Research Excellence Fund
KeywordsSizingPhotovoltaic systemWind powerElectricityEnvironmental scienceGrid parityPumped-storage hydroelectricityEnergy storageStand-alone power systemMeteorologyEngineeringEnvironmental economicsRenewable energyPower (physics)Electrical engineeringDistributed generationPhotovoltaicsEconomicsGeography

Abstract

fetched live from OpenAlex

This study explores the feasibility of eliminating natural gas-based power generation from Ontario’s power grid by focusing on integrating renewable energy sources such as solar and wind energy. Due to the weather dependent nature of these energy sources, the integration of energy storage systems (ESS) into the power grid was also examined to ensure grid stability. Two types of ESS examined were the battery energy storage system (BESS) and pumped hydroelectric storage (PH). The analyses founds that the most cost-effective power generation configuration is to expand the current wind energy generation to 4 times of its current size, solar energy to 5.67 times, nuclear energy to 1.1 times and utilize 289 BESS units. Moreover, a 3-year long continuous analysis was performed to assess the configuration’s long-term stability and its adaptability to change in demand, it was found that BESS is better suited for wind energy due to its faster response time while solar energy favors PH as the energy storage solution due to having larger storage capacity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.226
Teacher spread0.215 · 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 teacher head, not a consensus.

Study designObservational
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

Citations1
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueE3S Web of ConferencesSame topicHybrid Renewable Energy SystemsFrench-language works237,207