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Record W2909010331 · doi:10.5539/mas.v13n2p116

Candidate Sites for Pumped Hydroelectric Energy Storage System in Jordan

2019· article· en· W2909010331 on OpenAlexvenueno aff
Salih N. Akour, Anas Aref Al-Garalleh

Bibliographic record

VenueModern Applied Science · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsHydroelectricityWind powerPumped-storage hydroelectricityRenewable energyEnvironmental scienceIntermittent energy sourceEnergy storageGridStructural basinEnergy sourceComputer scienceDistributed generationPower (physics)EngineeringGeologyElectrical engineering

Abstract

fetched live from OpenAlex

Renewable energy sources particularly wind energy is becoming immensely popular throughout the world. Jordan is one of the countries that are interested in increasing the integration level of the wind energy on the national electrical grid. The main drawback of wind power is its inherent variability and uncertainty of source making wind energy a difficult resource to dispatch. A Pumped Hydroelectric Energy Storage (PHES) system is considered to be an attractive alternative solution for load balancing and energy storage mainly with wind farms. The current research utilizes the existing dams in Jordan as lower basin and provides candidate locations for upper pumped storage basins in the vicinity of these dams without affecting their functionality. These upper basin are semi-natural basins with least amount of construction, i.e. relatively least construction cost. A location survey of the candidate sites in Jordan is conducted where the PHES can be implemented and operated in an efficient manner. Ten locations have been analyzed deeply in the location survey. The results show that six of them are successful candidates and appropriate locations to implement PHES system since they pass all PHES design requirements.

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: none
Teacher disagreement score0.530
Threshold uncertainty score0.403

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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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