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Record W3214024635 · doi:10.5455/jjee.204-1616695846

Optimum Sizing of Stand-Alone Hybrid Photovoltaic Systems Equipped with Reverse Osmosis Desalination System for a Rural House in Iran.

2021· article· en· W3214024635 on OpenAlexaff
Mohammad Mousavi, Muhammad Babar Iqbal

Bibliographic record

VenueJordan Journal of Electrical Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsReverse osmosisSizingDesalinationPhotovoltaic systemEnvironmental scienceReverse osmosis plantEnvironmental engineeringWaste managementEngineeringElectrical engineeringMembraneChemistry

Abstract

fetched live from OpenAlex

Energy and water crisis affects every aspect of modern human life, and thus, addressing both in one solution is a growing trend. Merging hybrid renewable energy systems (HRES) and water desalination system in one system is a promising solution. In this paper, optimization of stand-alone hybrid photovoltaiv (PV) systems for powering a house equipped with a reverse osmosis (RO) water desalination system in Sinak village, Tehran, Iran, is discussed. RO system configuration, regular house load and RO deferrable load, solar radiation capability and HRES components have been analyzed in the first part of this paper. The second part deals with optimization, cost analysis and sensitivity analysis - using HOMER Pro software - of two HRES scenarios equipped with RO: i) a PV system with battery storage and ii) a PV system with battery storage and a gas generator. Moreover, different dispatch strategies for controlling the investigated systems - namely cycle charging (CC) and load following (LF) - are described. In the last part, a comparison between the two scenarios is performed. The obtained results show that the hybrid PV-battery-RO system is more energy-effective, has less control complexity and has a capability of meeting the load demand with with zero carbon emissions. Results of the conducted economic analysis reveal that the system has a net present cost (NPC) and a cost of electricity (COE) of 10,245 US$ and 0.31 US$/kWh, respectively, while fewer sensitivity variables affect the system’s cost.

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 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 score0.197
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.194
Teacher spread0.185 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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