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Record W2588383513 · doi:10.1109/sasg.2016.7849660

Quantifying the value of pumped storage hydro (PSH) in the Saudi electric grid

2016· article· en· W2588383513 on OpenAlexaff
Mohamed Hassan Ahmed, Mohammed Arif, T.K. Abdel-Galil, Luai M. Alhems, Willy W. Kotiuga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsGridPumped-storage hydroelectricityValue (mathematics)Computer scienceEnvironmental scienceElectrical engineeringDistributed generationEngineeringRenewable energyGeologyGeodesy

Abstract

fetched live from OpenAlex

The potential increase of the connected capacity of renewable energy sources in Saudi Arabia will open the doors for more investment in energy storage especially pumped storage hydro (PSH). New PSH, particularly in areas with increased wind and solar capacity, would significantly improve system reliability while reducing the need to construct new fossil-fueled generation. PSH is proving to be an established technology for renewable power because it can absorb excess generation and release it during peak demand times. PSH can also provide many ancillary services to the power system that should be added to the benefits of PSH projects, such as increased flexibility, primary frequency response, following reserves, and fast-acting regulation reserves. PSH is typically not adequately represented during the optimization of the commitment and dispatch formulations in which reduces their perceived benefits. The paper discusses how to quantify PSH benefits to the power system operators so that the PSH projects would be more economically viable to the Kingdom and more appealing for private investment. Potential market changes that can help PSH in today's restructured markets are presented and discussed in this paper.

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 score0.233
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.217
Teacher spread0.200 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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