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Record W2755316406 · doi:10.1109/tii.2017.2752464

Optimal Design of Community Battery Energy Storage Systems With Prosumers Owning Electric Vehicles

2017· article· en· W2755316406 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Industrial Informatics · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnergy storageComputer scienceAutomotive engineeringReliability engineeringDistributed generationArbitrageRenewable energyEngineeringElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

This paper presents a novel approach that aims to assist a distribution system operator to intelligently design the community battery energy storage systems considering high penetration of prosumers equipped with rooftop solar photovoltaics and electric vehicles. The design problem is mathematically formulated after incorporating the battery storage system's cost model. The results show the capability of the proposed approach to optimally design the community battery energy storage systems. This results in improving the voltage profile, reducing the power loss, and mitigating the distribution transformer aging while attaining a profit from the energy arbitrage. Also, the obtained results show a positive net present value of using the community battery energy storage systems, which demonstrates the cost-effectiveness of the proposed optimal design approach. The proposed approach may represent an effective tool for future disturbed energy resources management systems.

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 score0.934
Threshold uncertainty score0.620

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.211
Teacher spread0.175 · 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