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Record W1937774581 · doi:10.1109/icget.2015.7315111

Efficient hybrid renewable energy system for industrial sector with on-grid time management

2015· article· en· W1937774581 on OpenAlexaff
Mehedi Hasan, Aman A. Tanvir, S.M. Shahnewaz Siddiquee, Ahmed Zubair

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsRenewable energyIntermittent energy sourceDiesel generatorPhotovoltaic systemHybrid systemGridEnvironmental economicsEnergy managementElectricity generationComputer scienceEngineeringAutomotive engineeringDistributed generationElectrical engineeringEnergy (signal processing)Power (physics)Diesel fuelEconomics

Abstract

fetched live from OpenAlex

Hybrid renewable energy systems shows a great potential for electricity generation in Bangladesh. Hybrid renewable energy system can be set up such a way that the electricity will add to national grid connection and it will eventually reduce the pressure of electricity demand from national grid. Abundance of renewable energy sources in the form of solar energy provides opportunities of renewable energy based hybrid energy system in the industrial areas of Bangladesh. This work is an in-depth scenario and analysis of the renewable hybrid energy system in Tongi industrial areas of Bangladesh. This study is also includes co-production of diesel generator, solar PV and grid system. Optimization of hybrid renewable energy systems looks into the process of selecting the best components and its sizing with appropriate operation strategy to provide cheap, efficient, reliable and cost effective alternative energy. This paper analyzes all the conditions and constraints of the renewable energy integrated grid connection system with compensation and proposes an optimal combination of energy components for compensating regular grid failure in industrial area with minimizing the pressure of electricity demand from national grid and minimizing the life cycle cost. The final optimization result from HOMER shows that the cost of energy (COE) for 0 hour, 1 hour, 2 hour, 3 hour, 4 hour, 5 hour, 6 hour, 7 hour, 8 hour and 9 hour compensation is respectively $0.092, $0.098, $0.106, $0.113, $0.123, $0.133, $0.144, $0.152, $0.159 and $0.163.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.206
Teacher spread0.179 · 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 designSimulation or modeling
Domainnot available
GenreOther

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

Citations10
Published2015
Admission routes1
Has abstractyes

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