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Coordinating the Frequency-Droop Controls of Inverter-Based Resources and Diesel Generators in an Isolated Microgrid

2023· article· en· W4390188537 on OpenAlexaff
Mohan Du, Nayeem Ninad, Dave Turcotte

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsVoltage droopMicrogridDistributed generationAutomatic frequency controlDiesel generatorInverterRenewable energyComputer scienceEngineeringControl theory (sociology)Automotive engineeringVoltageControl (management)Diesel fuelElectrical engineeringVoltage source

Abstract

fetched live from OpenAlex

With the increasing penetration level of renewable energy sources (RESs), microgrids (MGs) implemented with inverter-based resources (IBRs) and diesel generators (DGs) are considered to be new solutions to supply power in remote areas and islands. Despite the introduction of power and voltage balancing services for distributed energy resources (DERs) and DGs in recent standards and codes, the coordination between the services provided by DERs and DG is not thoroughly investigated. This paper assesses the frequency-droop control defined in IEEE 1547–2018 std. for a DER, investigates the coordination between the DER's and DGs' droop controls, and reveals a risk of malfunction—exaggerating frequency deviation and power waste—if their droop controls do not coordinate well. Furthermore, this paper presents a frequency-restoration function to resolve the observed malfunction. The coordination of DER's and DG's control parameters is also examined. Moreover, the power sharing between multiple DGs and DERs during significant load changes is investigated. Simulation results show that the presented frequency-restoration function, suggested control parameters, and developed power sharing method can significantly benefit the coordination of multiple DERs and DGs in an isolated MG.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.324

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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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