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PV Integration Study in a Canadian Northern Remote Community

2020· article· en· W3120475164 on OpenAlexafffundabout
Sanjayan Srikumar, Nayeem Ninad, Dave Turcotte

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsNatural Resources Canada
FundersNatural Resources Canada
KeywordsMicrogridPhotovoltaic systemRenewable energyGridEnvironmental economicsComputer scienceElectricityAutomotive engineeringDiesel generatorEnvironmental scienceEngineeringDiesel fuelElectrical engineeringEconomics

Abstract

fetched live from OpenAlex

The vast majority of the 300 Canadian off-grid communities rely heavily on diesel-fuel generators as their source of electricity. In northern communities, the annual photovoltaic (PV) potential ranges from 850 to 1150 kWh/kWp. Therefore, there is an opportunity for these communities to decrease their dependency on diesel fuel by integrating more PV to supply a portion of their electricity demand. Currently, remote Canadian microgrid models are not available to conduct studies that involves integrating PV as alternative solution. The availability of a simulation model with field data will allow for the opportunity to study renewable energy integration as well as hosting capacity with smart grid technologies such as advanced inverter functions and demand-side management. This paper addresses the need to reduce the diesel consumption of Jean-Marie River First Nation; a remote community in the Northwest Territories of Canada. A digital microgrid model of the community's power grid was developed using OpenDSS and Matlab. The model was then validated with field data measured using an advanced metering infrastructure. The validated model was used to conduct renewable energy (RE) integration study and establish a range of maximum allowable PV capacity on the power grid based on voltage limits. Later, advanced inverter functions were applied to mitigate voltage violations resulting from the RE integration thus further increasing the PV integration limit or hosting capacity of the microgrid.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.499

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 head0.014
GPT teacher head0.196
Teacher spread0.182 · 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

Citations1
Published2020
Admission routes3
Has abstractyes

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