MétaCan
Menu
Back to cohort
Record W2919610672 · doi:10.1109/repe.2018.8657480

Resilient Interconnected Microgrids for Subway Networks

2018· article· en· W2919610672 on OpenAlexaff
Hossam A. Gabbar, Mohamed I. A. Othman, Jonathan Chang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMicrogridFlywheelResilience (materials science)Energy storageEnergy managementComputer scienceArchitectureControl (management)Reliability engineeringEnergy (signal processing)Automotive engineeringControl engineeringEngineeringPower (physics)

Abstract

fetched live from OpenAlex

This paper presents the design of resilient interconnected microgrids to provide clean and cheap energy to subway infrastructures during normal and emergency operational scenarios based on different mobility models. The proposed microgrid will include hybrid energy storage of flywheel and battery systems, which will provide flexible and adaptive energy supply in view of subway loads. The interconnected microgrids will ensure profitable operation with consideration of fluctuation of energy prices and the interactions among different utilities around the subways. In addition, the proposed solution will ensure sustained and high performance energy supply in different operational and mobility scenarios. Integrated control architecture is proposed to ensure local and supervisory control and management of the interconnected microgrids with best utilization of energy storage systems through braking moments. The proposed solution has been demonstrated using simulation and real data, where the achieved results confirm the effectiveness of the proposed control architecture in terms of cost, performance and resilience.

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 score0.965
Threshold uncertainty score0.304

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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicMicrogrid Control and OptimizationFrench-language works237,207