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Record W2957072925 · doi:10.3390/en12142675

Allocation of Ontario’s Surplus Electricity to Different Power-to-Gas Applications

2019· article· en· W2957072925 on OpenAlexaffabout
Suaad Al-Zakwani, Azadeh Maroufmashat, Abdelkader Mazouz, Michael Fowler, Ali Elkamel

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPower to gasNatural gasRenewable energyNatural gas pricesRenewable natural gasEnvironmental scienceElectricityElectricity generationBase load power plantWaste managementEnvironmental economicsPower (physics)Fuel gasEngineeringEconomicsDistributed generationChemistryElectrical engineering

Abstract

fetched live from OpenAlex

Power-to-Gas (PtG) is a potential means of managing intermittent and weather-dependent renewable energies to create a storable chemical energy form. Power-to-Gas is not only a storage technology; its role can be extended to many other applications including energy distribution, transportation, and industrial use. This study quantifies the hydrogen volumes upon utilizing Ontario, Canada’s surplus electricity baseload and explores the allocation of the hydrogen produced to four Power-to-Gas pathways in terms of economic and environmental benefits, focusing on the following Power-to-Gas pathways: Power-to-Gas to mobility fuel, Power-to-Gas to industry, Power-to-Gas to natural gas pipelines for use as hydrogen-enriched natural gas, and Power-to-Gas to renewable natural gas (i.e., Methanation). The study shows that the Power-to-Gas to mobility fuel pathway has the potential to be implemented. Utilization of hydrogen for refueling light-duty vehicles is a profitable business case with an average positive net present value of $4.5 billions, five years payback time, and 20% internal rate of return. Moreover, this PtG pathway promises a potential 2,215,916 tonnes of CO2 reduction from road travel.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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.

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

Citations14
Published2019
Admission routes2
Has abstractyes

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