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A Comprehensive Review on Microgrid Architectures for Distributed Generation

2019· review· en· W3018352928 on OpenAlexaff
Haytham Abdelgawad, Vijay K. Sood

Bibliographic record

Venuenot available
Typereview
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMicrogridDistributed generationPhotovoltaic systemReliability (semiconductor)GridComputer scienceWind powerDistributed computingReliability engineeringElectricity generationRenewable energyEngineeringElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

In the recent years, there has been a growing interest in the concept of microgrids to integrate distributed generation systems like solar photovoltaic (PV) and wind to reduce greenhouse gas (GHG) emissions, to provide higher reliability for critical loads and to supply electricity in areas not served by existing grid infrastructure. A microgrid (MG) is a portion of the electrical system which views generation and associated loads as a subsystem, with the ability to operate both grid connected or islanded from grid, thus maintaining a high level of service and reliability. The existing grid infrastructure, the distributed energy resources to be integrated, as well as specific customer-oriented requirements will determine the best fitting architecture to constitute a microgrid. In this review, most common microgrids architectures based on AC, DC and hybrid AC/DC buses are analyzed. Furthermore, comparisons are made between different microgrid architectures. Positive and negative features of different architectures are given as a guide for further microgrid system studies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.004

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 head0.056
GPT teacher head0.297
Teacher spread0.241 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations24
Published2019
Admission routes1
Has abstractyes

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