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An Inverter-Based Resource Hosting Capacity Method for Microgrids

2021· article· en· W3198699027 on OpenAlexaff
Alexandre B. Nassif, Hesam Yazdanmpanahi, Matthew B. Wright

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsATCO (Canada)
Fundersnot available
KeywordsMicrogridBlackoutRenewable energyReliability engineeringInverterReliability (semiconductor)Computer scienceEnergy storageDistributed generationResource (disambiguation)Renewable resourceRisk analysis (engineering)EngineeringAutomotive engineeringEnvironmental economicsElectrical engineeringElectric power systemBusinessPower (physics)Computer networkEconomics

Abstract

fetched live from OpenAlex

There is no question microgrids have increasingly gained attention from customers, utilities, and academia. The conceptual premise is to increase reliability, enable greater renewable resource integration, and advance technology. This also results in a range of technical challenges that often stem from a mix of rotational base generation (often fossil-fuel-based) and inverter-based generators. Not always a battery energy storage system can be introduced immediately or eventually to stabilize the configuration, which can expose the system to instability in case the inverter-based generators experience a disturbance. If not managed appropriately, disturbances that affect the renewable generation plant can lead to an unintentional microgrid-wide blackout. This paper presents a general study addressing the amount of renewable generation that can be integrated in a microgrid configuration that does not include batteries (i.e., its hosting capacity) prior to experiencing severe frequency stability degradation.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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
GenreMethods

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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