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Record W4410082744 · doi:10.3390/en18092355

Techno-Economic Comparison of a Large-Scale Nuclear Power Plant, Small Modular Reactors, and Wind and Solar Power Plant Deployment

2025· article· en· W4410082744 on OpenAlexaff
Sanja Milivojevic, Milan M. Petrović, Vladimir D. Stevanović, Jovica Riznic, Milos Lazarevic

Bibliographic record

VenueEnergies · 2025
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsOntario Tech University
FundersScience Fund of the Republic of Serbia
KeywordsCost of electricity by sourceWind powerRenewable energyConcentrated solar powerGrid parityPower stationModular designPayback periodSolar powerElectricityPumped-storage hydroelectricityElectricity generationEnergy storageStand-alone power systemNuclear powerEnvironmental scienceEnvironmental economicsEngineeringDistributed generationProduction (economics)Power (physics)Electrical engineeringComputer scienceEconomicsPhysics

Abstract

fetched live from OpenAlex

A comparison of the net present value, the payback period, and the levelized cost of electricity for three different projects of construction and exploitation of plants for electricity production with the aim of decarbonizing the energy sector is conducted. The first project is the building of a large-scale nuclear power plant with a light-water reactor, the second one is the deployment of several identical small modular reactors, and the third project is based on solar and wind power plants. Given that the sun and wind are intermittent renewable energy sources, it is inevitable to take into account the construction of an energy storage facility in the last project. The results show that the most profitable are the small modular reactors, while the investment into solar and wind power plants is burdened with the necessary electricity storage plant costs. Another drawback of an investment in solar and wind power plants is their shorter exploitation lifetime of 25 years compared to the long-term operation of nuclear power plants of 60 years or even more.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
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
Bibliometrics0.0000.000
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 head0.221
Teacher spread0.213 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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