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Sizing Transmission-Scale Battery Energy Storage System with Dynamic Thermal Line Rating

2022· article· en· W4312549653 on OpenAlexafffund
Vadim Avkhimenia, Petr Musı́lek, Tim Weis

Bibliographic record

Venue2022 IEEE Power & Energy Society General Meeting (PESGM) · 2022
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBattery (electricity)SizingEnergy storageComputer scienceReliability (semiconductor)Computer data storageTransmission lineElectric power transmissionAutomotive engineeringReliability engineeringElectric power systemTransmission (telecommunications)Electrical engineeringEngineeringPower (physics)TelecommunicationsComputer hardware

Abstract

fetched live from OpenAlex

Incorporating battery energy storage system together with dynamic thermal line rating has the potential to defer the construction of new transmission lines in multibus systems. This paper presents a linear programming methodology for calculating the optimal battery energy storage system capacity sizing together with the power rating for a multibus system that takes into account battery energy storage system degradation, transmission line outages, and dynamic thermal line rating of transmission facilities. The battery energy storage system degradation equations are linearized using a multilayer perceptron. The proposed method is tested on the IEEE 24-bus reliability test system using the Merseyside weather data from the British Atmospheric Data Center. The results show that this methodology produces an accurate estimate of BESS capacity values.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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.

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

Citations0
Published2022
Admission routes2
Has abstractyes

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