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Record W3173658285 · doi:10.1109/access.2021.3091738

Techno-Economic Design of a Combined Heat and Power Microgrid for a Remote Community in Newfoundland Canada

2021· article· en· W3173658285 on OpenAlexaffabout
Hashem Elsaraf, Mohsin Jamil, Bishwajeet Pandey

Bibliographic record

VenueIEEE Access · 2021
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMicrogridRenewable energyEnvironmental scienceCost of electricity by sourceWind powerEnvironmental economicsDiesel fuelDistributed generationAutomotive engineeringComputer scienceElectricity generationProcess engineeringElectrical engineeringEngineeringPower (physics)

Abstract

fetched live from OpenAlex

Around 300 remote communities in Canada rely on diesel for their energy needs, a situation associated with high costs, high emissions, and accessibility problems. Various studies have addressed this problem by proposing renewable energy microgrids, which have a lot of potential due to the abundance and availability of renewable sources. However, there is a lack of studies regarding remote communities in Newfoundland and combined heat and power microgrids. This study chose Cartwright remote community based on consumption and available resources. Both distributed and centralized microgrid components were designed using MS Excel, Polysun, HOMER, and BEOPT. The final system included solar thermal, PV, wind energy, hydroelectric energy and fuel cells for energy generation, and hydrogen as an energy carrier for storage. The solar thermal distributed system reduced the thermal load by 35%. The microgrid reduced diesel consumption by 71% and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions by 9000 tons. Renewable sources provided 100% of the electric load and 63.5% of the thermal load. The microgrid achieved a Levelized cost of -0.0245 $ \$ $ /kWh, which is only possible for combined heat and power systems.

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 score0.684
Threshold uncertainty score0.673

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 head0.027
GPT teacher head0.263
Teacher spread0.236 · 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

Citations52
Published2021
Admission routes2
Has abstractyes

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