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Record W4377099604 · doi:10.1149/11106.0785ecst

Energy Transition Scenarios in Off-grid Communities using SOFC-CHP/Battery Hybrid Systems

2023· article· en· W4377099604 on OpenAlexaff
Laura Nousch, Marie-Lise Tremblay, Simon Besner, Guillaume Jeanmonod, Mathias Hartmann, Daniela Herold, Martin Simoneau, A. Michaelis

Bibliographic record

VenueECS Transactions · 2023
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsElectricityBattery (electricity)Automotive engineeringEnvironmental scienceWork (physics)Stand-alone power systemElectricity generationDiesel fuelWaste heatProcess engineeringHeat generationDistributed generationWaste managementPower (physics)EngineeringElectrical engineeringRenewable energyMechanical engineeringHeat exchangerThermodynamics

Abstract

fetched live from OpenAlex

Today, off-grid communities rely mainly on diesel gensets with limited electrical efficiency and diesel furnaces for electricity and heat generation, respectively. The CO 2 emissions associated with this electricity production can be reduced by improving the electrical efficiency of the system thus reducing the fuel consumption. Additionally, waste heat from the electricity production can be recovered for heating thus creating a combined heat and power system. In this work, a solid oxide fuel cell combined heat and power system hybridized to a battery was proposed for this purpose. Simulation of the annual electricity and heat production were performed using real temporal electrical demand data. Results showed that a reduction of up to 36 % in primary energy consumption can be reached compared with the current situation.

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 score0.058
Threshold uncertainty score0.665

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 head0.017
GPT teacher head0.200
Teacher spread0.183 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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