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Record W4391993816 · doi:10.32920/25260829.v1

Hybrid Solar Plant Comparisons in Ontario: Implications for Canadian and Global Development and Investment Strategies

2024· preprint· en· W4391993816 on OpenAlexaffabout
Troy Bell

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsInvestment (military)Natural resource economicsEconomicsEnvironmental scienceBusinessPolitical science

Abstract

fetched live from OpenAlex

<p>Anthropogenic climate change is driven by the emissions of greenhouse gases (GHG). To limit global warming to 1.5°C above pre-industrial levels and satisfy growing global energy demand, new sources of energy with low GHG emission levels must be developed. Furthermore, at a national level, Canada is not close to meeting its GHG emission reduction targets under the 2015 Paris Agreement.</p> <p>Hybrid solar power plants have been identified as a potential source of reliable, abundant, and environmentally friendly energy. By combining energy sources, hybrid power plants are able to overcome issues such as intermittency and excess electricity that are typically associated with renewable energy sources. However, significant logistical and contextual questions remain concerning the best strategies for hybrid energy development at the national and international levels.</p> <p>This research used hybrid modelling software (HOMER Pro) to perform a direct comparative analysis of the performance and efficacy of a series of different hybrid solar power plant designs. The study compared a variety of hybrid solar power plant designs (using solar, wind, batteries, and fossil fuel generators) on the basis of three equal factors (GHG emission reductions, power generation and reliability, and cost-effectiveness), under the same environmental conditions and location (Peterborough, Ontario).</p> <p>The modelling results showed that the hybrid solar power plant designs H5 (PV-BAT-DG) and H10 (PV-BAT-NG) performed best across all three primary factors of analysis. H7 (PV-Wind-BAT-DG) and H11 (PV-Wind-BAT-NG) produced the lowest quantities of GHG emissions among all of the hybrid solar power plant designs.</p> <p>Canada and countries with similar pollution pricing measures should invest in H7 (PV-Wind-BAT-DG) and H11 (PV-Wind-BAT-NG) hybrid designs in order to maximize GHG emission reductions and develop, diversify energy resources, and service increasing long-term energy demands. Countries that are more concerned with short-term cost-effectiveness as well as reducing GHG emissions should focus on the H5 (PV-BAT-DG) and H10 (PV-BAT-NG) hybrid</p> <p>designs.</p>

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.989

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 head0.078
GPT teacher head0.333
Teacher spread0.255 · 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 designTheoretical or conceptual
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
Published2024
Admission routes2
Has abstractyes

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