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Record W2922181834 · doi:10.1002/ep.13172

Techno‐economic feasibility analysis of stand‐alone hybrid wind/photovoltaic/diesel/battery system for the electrification of remote rural areas: Case study Persian Gulf Coast‐Iran

2019· article· en· W2922181834 on OpenAlexaff
Mohammadreza Dehghan Abnavi, Niyousha Mohammadshafie, Marc A. Rosen, Amir Dabbaghian, Farivar Fazelpour

Bibliographic record

VenueEnvironmental Progress & Sustainable Energy · 2019
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsRenewable energyPhotovoltaic systemDiesel generatorEnvironmental scienceDiesel fuelElectrificationWind powerHybrid systemGreenhouse gasEnvironmental engineeringAutomotive engineeringEngineeringElectricityElectrical engineeringComputer science

Abstract

fetched live from OpenAlex

In this study, a techno‐economic feasibility study of off‐grid hybrid power systems is performed for a rural area, which is located at a significant distance from the grid connection in Bushehr province of Iran, beside the Persian Gulf. HOMER simulation software is used to determine the economic feasibility of the systems. The simulations were focused on net present cost which includes cost of energy (COE) and renewable fraction of the hybrid configurations. The results indicate that the wind/PV/diesel/battery hybrid renewable system configuration is the optimum system, primarily based on COE and achieving a 61.4% renewable energy fraction. The proposed hybrid power system not only exhibits better performance in fuel consumption but also reduces carbon dioxide emissions. It could mitigate the emission of 664 tons of greenhouse gases to the local atmosphere of the village, as well as other air pollution emissions, which could offer substantial benefit to both residence and environment. Furthermore, a sensitivity analysis shows that a rise in fuel prices will derive more demand for alternative energy sources. For instance, when the fuel price is more than $0.5/L, the optimal energy option would be the wind/PV/battery system. In that case, it is no longer economical to use the diesel generator and renewable energy penetration becomes 100%. © 2019 American Institute of Chemical Engineers Environ Prog, 38:e13146, 2019

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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
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

Citations35
Published2019
Admission routes1
Has abstractyes

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