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Record W4386758449 · doi:10.18280/ijdne.180407

Economic Feasibility of a PV-Wind Hybrid Microgrid System for Off-Grid Electrification in Papua, Indonesia

2023· article· en· W4386758449 on OpenAlexvenueno aff
Dominicus Danardono Dwi Prija Tjahjana, Suyitno Suyitno, Rendy Adhi Rachmanto, Wibawa Endra Juwana, Yudin Joko Prasojo, Singgih Dwi Prasetyo, Zainal Arifin

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2023
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
FundersUniversitas Sebelas Maret
KeywordsMicrogridRenewable energyElectrificationPhotovoltaic systemWind powerHybrid systemRural electrificationTurbineEnvironmental economicsCost of electricity by sourceElectricityDistributed generationEngineeringEnvironmental scienceElectricity generationElectrical engineeringComputer scienceEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

Electricity is fundamental to both urban and rural livelihoods. However, with the impending depletion of fossil fuels, an urgent transition towards sustainable and environmentally friendly renewable energy sources is necessary for electricity generation. The Hybrid Renewable Energy System (HRES), which amalgamates various renewable energy sources, offers a promising solution. This study investigates the economic viability of a photovoltaic (PV)-wind turbine hybrid microgrid system for off-grid electrification in five distinct cities in Papua, Indonesia. A simulation of the hybrid system was conducted using the Hybrid Optimization Model for Electric Renewable (HOMER) application, leveraging local solar and wind resource data. The simulation results indicated that Waropen city yielded the most optimal PV-wind turbine hybrid system, exhibiting the lowest Net Present Cost (NPC) of Rp397,591,000.00 and Cost of Energy (COE) of Rp 5,513.37/kWh. This system was capable of generating 9,098 kWh/year of electricity. Consequently, the proposed PV-wind turbine hybrid microgrid system emerges as an economically feasible solution for off-grid electrification in Papua, underscoring the potential of renewable energy sources in addressing the global energy crisis.

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 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 score0.039
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.260
Teacher spread0.246 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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