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Record W2743854355 · doi:10.1109/tie.2017.2682779

Mobile Energy Storage Scheduling and Operation in Active Distribution Systems

2017· article· en· W2743854355 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

VenueIEEE Transactions on Industrial Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEnergy storagePeaking power plantRenewable energyAC powerScheduling (production processes)Energy managementAutomotive engineeringGridEnergy management systemEngineeringComputer scienceElectric power systemParticle swarm optimizationDistributed generationPower (physics)Electrical engineeringEnergy (signal processing)Voltage

Abstract

fetched live from OpenAlex

A mobile (transportable) energy storage system (MESS) can provide various services in distribution systems including load leveling, peak shaving, reactive power support, renewable energy integration, and transmission deferral. Unlike stationary energy storage units, an MESS can move between different buses by a truck to provide different local services within the distribution feeder. This paper proposes a day-ahead energy management system (EMS) for an MESS that aims to minimize the cost of the power imported from the grid. The MESS does not only shift renewable energy power to load peak-hours but also can provide localized reactive power support. Given the day-ahead predictions, the EMS decided the optimal MESS stations in the feeder and the operating power. Next, a particle swarm optimization-based algorithm is developed to tune the moving time of the MESS according to a transit delay model. The applicability of the proposed scheduling and operation algorithms is tested on a typical 41-bus radial feeder.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score0.614

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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