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Record W4235578714 · doi:10.32920/ryerson.14660970

AC-DC Microgrid Optimal Power Flow

2021· preprint· en· W4235578714 on OpenAlexaff
Inderjeet Duggal

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicrogridReliability (semiconductor)Electricity generationElectricityGridBattery (electricity)Peaking power plantPower (physics)Dual (grammatical number)Mathematical optimizationConvertersDeregulationComputer scienceReliability engineeringElectrical engineeringDistributed generationRenewable energyEngineeringEconomicsMathematicsVoltage

Abstract

fetched live from OpenAlex

Electricity market deregulation has opened the door for novel electricity production schemes within the existing central production paradigm that dominates the electricity power industry. The Microgrid concept allows generation and load located in close vicinity to be organized so that the local load is served as far as possible with local generation. The Microgrid examined in this thesis consists of both AC and DC network components connected using power converters. The varied Microgrid power sources (microturbine generators, PV, battery, power imports from the grid etc.) are scheduled over 24 hours to satisfy load demand at minimum cost and maximum reliability. Towards this end, a dual objective problem is formulated using Fuzzy sets and the final problem takes the form of a Mixed-Integer Nonlinear optimization problem. The interplay between the disparate objectives of minimum cost and maximum reliability and the battery is then examined.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.004
GPT teacher head0.180
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2021
Admission routes1
Has abstractyes

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