MétaCan
Menu
Back to cohort
Record W2552652620 · doi:10.1109/epec.2010.5697212

Analysis of Energy Storage sizing and technologies

2010· article· en· W2552652620 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsSizingMicrogridRenewable energyEnergy storageDiesel fuelWind powerAutomotive engineeringComputer scienceElectric power systemController (irrigation)Reliability engineeringProcess engineeringEngineeringControl engineeringElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

This paper performs a sensitivity analysis of varying Energy Storage System (ESS) sizes and technologies in an isolated wind-diesel power system. Energy Storage Systems have been proposed in order to improve the penetration of Renewable Energy (RE) sources to microgrids. The main goal of this paper is to identify, from an economic point of view, the optimal ratings and technologies for reducing the total cost of the operation of the system. A Knowledge Based Expert System Controller, which has been shown to yield minimized energy costs for a wind-diesel microgrid, is implemented to control the charging/discharging of the ESS, the diesel generation, and the dump load.

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.

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 score0.635
Threshold uncertainty score0.106

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 head0.002
GPT teacher head0.158
Teacher spread0.155 · 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

Quick stats

Citations62
Published2010
Admission routes1
Has abstractyes

Explore more

Same topicMicrogrid Control and OptimizationFrench-language works237,207