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Record W4285165370 · doi:10.5455/jjee.204-1644676329

Dynamic Modeling of an Optimal Hybrid Power System for a Captive Power Plant in Pakistan

2022· article· en· W4285165370 on OpenAlexafffund
Luqman Ahsan, M. Tariq Iqbal

Bibliographic record

VenueJordan Journal of Electrical Engineering · 2022
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSizingRenewable energyContext (archaeology)Power stationPhotovoltaic systemGridMATLABComputer scienceSoftwareVirtual power plantAutomotive engineeringReliability engineeringControl engineeringEngineeringDistributed generationElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents the optimized design, economic feasibility and dynamic modeling of a grid-tied captive hybrid renewable energy power plant for a Pakistani industrial area. Since the proposed plant, encompasses a photovoltaic (PV) array - as its main component - and for an efficient and reliable operation many issues - including industrial load variations and expected dynamics - should be investigated before its implementation. In this context, Homer Pro software is utilized in the design and economic optimized sizing of the PV array, and the PVWatts is used in land requirement analysis. The designed grid-tied plant is modeled in the MATLAB/Simulink using Simscape blocksets to investigate the plant’s dynamic behavior due to typical practical disturbances. The obtained results reveal that the plant has a low per-unit energy cost and provides significant savings. Results of dynamic simulation show that the plant can respond to the ramp-up and ramp-down load variations in industrial settings. Moreover, the plant has a fast response to step changes in irradiance; proving that the proposed plant is reliable and suitable candidate for fulfilling the designated load.

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)0.001
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: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.222
Teacher spread0.217 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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