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Design and sizing of a microgrid system for a University community in Nigeria

2022· article· en· W4214895510 on OpenAlexaff
Stephen Ogbikaya, M. Tariq Iqbal

Bibliographic record

Venue2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) · 2022
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMicrogridDiesel generatorAutomotive engineeringGridPhotovoltaic systemElectricitySizingLoad profileElectric power systemElectrical engineeringRenewable energyComputer scienceEngineeringPower (physics)Diesel fuelGeography

Abstract

fetched live from OpenAlex

Due to the epileptic power experienced in Nigerian national grid system, an on-grid microgrid system consisting of PV panels, inverter, grid system and diesel generator set is designed and sized for a university community in Nigeria. In this paper, the load profile (kWh) of the campus was determined based on the electric load of the campus. “HOMER” Pro software was then used to design and size a microgrid system for the selected campus based on its electric load and PVWATT software was used to determine the dimension of the area required for the installation of the PV panels. Results from simulation indicates that the dimension of the area required for the PV installation is 17,696m2 and the daily power generated from the microgrid system is always above the electric load of the system. Further analysis shows that 88.0% of the annual energy generated to supply the electric load of the campus can be produced by the PV panels and 12.0% by the grid system. This in turn reduces the amount being spent on electricity bill by the university campus by 88.0%. Economically, the cost of installation of the microgrid system is N295M with a simple payback of 3 years 5 months.

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)0.002
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 score0.313
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.029
GPT teacher head0.242
Teacher spread0.214 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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