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Design and Analysis of a Stand-alone DC Hybrid Microgrid for a Rural Base Transceiver Station in Nigeria

2020· article· en· W3126257330 on OpenAlexaff
Cyprian Oton, M. Tariq Iqbal

Bibliographic record

Venue2020 IEEE Electric Power and Energy Conference (EPEC) · 2020
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMicrogridRenewable energyBase stationTransceiverEnvironmental economicsAutomotive engineeringEngineeringElectrical engineeringTelecommunicationsWirelessEconomics

Abstract

fetched live from OpenAlex

The proliferation of mobile base transceiver station sites in Nigeria comes with a growing need to address those sites' source of power. Sustainability and mitigating harmful environmental impact caused by the diesel-only method of power generation is of great concern. This article examines the optimal sizing of a small DC microgrid to power an outdoor base station site in Agbaja, a rural settlement in Kogi State, Nigeria. A DC system has better performance because multiple energy conversion experienced in the AC system is eliminated. HOMER pro is used to size the system based on the measured load and available renewable resources. The PV/Diesel/Battery configuration resulted in the least Net Present Cost (NPC), Cost of Energy (COE), and unmet energy. A comparison between this proposed system and the current system shows a reduction in operating expenditure (OPEX) by 75% with zero unmet energy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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.

Study designBench or experimental
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

Citations3
Published2020
Admission routes1
Has abstractyes

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