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Record W4242035895 · doi:10.1504/ijpse.2017.084741

Supervisory control of a resilient DC microgrid for commercial buildings

2017· article· en· W4242035895 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Process Systems Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMicrogridPhotovoltaic systemRenewable energyEnergy storageBattery (electricity)Automotive engineeringInverterGridEngineeringSupercapacitorController (irrigation)Distributed generationEnergy managementElectrical engineeringComputer sciencePower (physics)Energy (signal processing)VoltageCapacitance

Abstract

fetched live from OpenAlex

This paper presents a supervisory controller for DC microgrid consists of a solar photovoltaic (PV) system, fuel cell, a supercapacitor and battery bank. The DC microgrid is proposed for more efficient resilient electricity distribution in a commercial building. The operation strategy of energy storage systems (ESS) is proposed to solve the power changes from PV array and building loads fluctuations locally, instead of reflecting those disturbances into the utility grid. Furthermore, the ESS energy management scheme will help to achieve the peak reduction of the building daily electrical load demand. The DC microgrid studied in this paper is interfaced with the battery bank by using a bidirectional DC-DC converter, whilst the building electrical AC load is interfaced to grid using DC-AC inverter. The control of the studied microgrid is designed as a method to improve microgrid resilience and incorporate renewable power generation and storage into the grid.

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.

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 score0.665
Threshold uncertainty score0.528

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 science0.0010.000
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 head0.011
GPT teacher head0.237
Teacher spread0.227 · 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