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Record W3209151174 · doi:10.1109/tsg.2021.3123467

Finite Horizon Optimal Control With Voltage Regulation

2021· article· en· W3209151174 on OpenAlexafffund
F. A. Sabbir Ahamed, Pirathayini Srikantha

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceGridConvertersTime horizonVoltage regulationVoltageControl theory (sociology)Distributed generationTransient (computer programming)Regular polygonOptimal controlConvex optimizationMathematical optimizationControl (management)Control engineeringEngineeringMathematicsElectrical engineeringRenewable energy

Abstract

fetched live from OpenAlex

The modern grid is undergoing unprecedented transformations with the proliferation of highly variable loads and generation systems. In order to ensure that the grid operates efficiently in the presence of these diverse entities while preserving operational limits, strategies that combine traditionally disparate transient control and steady-state coordination are necessary. However, these introduce fundamental challenges such as inscalability and non-convexities. In this paper, we formulate a finite horizon constrained optimal control problem that allows for the actuation of voltage source converters of distributed energy resources to minimize thermal losses while accounting for voltage regulation and balancing power demand with available supply. We overcome the afore-mentioned challenges by introducing strategic decompositions of the original problem over spatial and temporal domains that allow for exact convex relaxations. The efficacy of our proposal is demonstrated via comprehensive theoretical, comparative and practical studies conducted on realistic medium-voltage distribution network settings.

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 score0.984
Threshold uncertainty score0.687

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 head0.005
GPT teacher head0.173
Teacher spread0.168 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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