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Record W2528126479 · doi:10.1109/sege.2016.7589520

Resilient interconnected microgrids (IMGs) with energy storage as integrated with local distribution networks for railway infrastructures

2016· article· en· W2528126479 on OpenAlexaff
Mohamed I. A. Othman, Hossam A. Gabbar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsEnergy storageTrainComputer scienceDistributed generationEnergy (signal processing)Work (physics)Distributed computingPower (physics)HeuristicEfficient energy useReliability engineeringAutomotive engineeringRenewable energyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper is aiming to development and design of Interconnected Microgrids (IMGs) with effective strategies for integrated energy storage and hybrid Distributed Energy Resources (DERs) with the distribution lines so that it can store energy in the off peak for re-use during the day. One potential application is the integration with the railway infrastructures as a new green technology. This goal will be achieved by proposing heuristic technique to enable interconnected MGs to work transparently with the recent energy storage. Railway transportation MG model is proposed to balance energy flows between trains moving and braking energy, energy storage system and a main power utility network. The paper proposes an energy optimization tool for the interconnected railway-MG system. Artificial Bee Colony Algorithm (ABC) is applied for achieving the economical cost during the operation. Digital simulation scenario has been validated by real data; the achieved results show the impact and effectiveness of the proposed strategies.

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: none
Teacher disagreement score0.964
Threshold uncertainty score0.540

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 head0.156
Teacher spread0.154 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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