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Record W2783223033 · doi:10.37798/2015641-4157

Phase shifting autotransformer, transmission switching and battery energy storage systems to ensure n-1 criterion of stability

2022· article· en· W2783223033 on OpenAlexaff
Zoran Zbunjak, Hrvoje Bašić, Hrvoje Pandžić, Igor Kuzle

Bibliographic record

VenueJournal of Energy - Energija · 2022
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsIndependent Electricity System Operator
Fundersnot available
KeywordsAutotransformerDispatchable generationElectric power systemTransmission systemEnergy storageBattery (electricity)Computer scienceTransmission (telecommunications)Energy management systemRenewable energySoftwareElectric power transmissionPower (physics)Automotive engineeringEngineeringElectrical engineeringEnergy (signal processing)Energy managementVoltageDistributed generationTransformer

Abstract

fetched live from OpenAlex

Since the portion of non-dispatchable renewable generators in the system is increasing, several challenges to the safety and stability of the power system have arisen. The focus of this paper is analyzing local congestion effects in the system by using three distinct methods: phase shifting autotransformer, transmission switching and battery energy storage system. This work includes a review of the congestion management techniques and the results of simulations that utilize phase shifting autotransformer to reduce power flows in the network, and transmission switching and battery energy storage system in order to ensure N-1 stability criterion in case of malfunction of the integrated autotransformer. Power system is modelled and simulated using Power Transmission System Planning Software, a software tool for electric transmission system analysis and planning. Results of simulations are presented, a thorough analysis of the results is performed and justification of investments in proposed methods is elaborated.

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 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: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.223
Teacher spread0.213 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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