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PV Utilization Analysis for a Canadian Small Arctic PV-Diesel Hybrid Microgrid

2020· article· en· W3113560502 on OpenAlexafffundabout
Nayeem Ninad, Dave Turcotte

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsNatural Resources Canada
FundersNatural Resources Canada
KeywordsMicrogridPhotovoltaic systemEnvironmental scienceDiesel fuelElectricityGreenhouse gasArcticStand-alone power systemAutomotive engineeringEnvironmental economicsEnvironmental engineeringRenewable energyEngineeringDistributed generationElectrical engineeringEconomicsEcology

Abstract

fetched live from OpenAlex

Canadian remote arctic communities are mostly supplied electricity by diesel generators. The electricity price in these communities is high due mostly to the transportation cost of the diesel fuel to these remote locations. A large portion of the financial budget from the government or local community is allocated to cover the electricity cost. Diesel power plants are also major emitters of greenhouse gases (GHGs). The annual solar photovoltaic (PV) potential in the Canadian arctic region ranges from 850 to 1150 kWh/kWp. Therefore, a significant portion of the community energy requirement can be supplied by PV systems thus reducing diesel fuel consumption and associated GHG emissions. This paper presents the impact of PV integration on the system annual energy performance at various levels of PV penetration. The modelling of a typical small arctic PV-Diesel hybrid microgrid is addressed with the specifications of a small representative community. The PV utilization analysis establishes the low PV penetration regime up to 40% of peak load. Then two technical alternatives are presented that allow medium PV penetration up to 70% of peak load with associated PV energy contribution up to 23% of the community energy requirement.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.240
Teacher spread0.178 · 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 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

Citations5
Published2020
Admission routes3
Has abstractyes

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