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Record W2014256944 · doi:10.1109/epec.2012.6474938

Optimal break-even distance for design of microgrids

2012· article· en· W2014256944 on OpenAlexaff
Omar Hafez, Kankar Bhattacharya

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrogridRenewable energyGridElectricityComputer scienceEnvironmental economicsWind powerCost of electricity by sourceElectric power transmissionElectricity generationAutomotive engineeringReliability engineeringEngineeringElectrical engineeringEconomicsPower (physics)

Abstract

fetched live from OpenAlex

Around the world there are many rural areas need access to electricity, extend the transmission line is one option. However, the long distance between the nearest main grid and the rural system as well as the costs of transmission line expansion rapidly increasing makes grid extension difficult, costly and economically unviable. Therefore, using microgrid technology with renewable energy options to meet electricity demand in remote locations becomes more attractive. In this paper the break-even distance which makes electricity from microgrid cost effective over the one form main grid is calculated. Moreover, the Net Present Costs (NPC) of both providing electricity through the microgrid and the main grid are calculated and compared. Five different cases including a diesel-only, a fully renewable-based, a diesel-renewable mixed, a solar-only, and a wind-only microgrid configurations are designed, to compare and evaluate their economics with the main grid. The well known energy modeling software for hybrid renewable energy systems, HOMER is used in the studies reported in this paper.

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: Methods · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.252

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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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
GenreMethods

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

Citations7
Published2012
Admission routes1
Has abstractyes

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