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Record W4388068048 · doi:10.6036/10882

IMPACT OF THE IMPLEMENTATION OF THE NEW QUARTER-HOURLY MODEL ON A WIND FARM IN THE PENINSULAR ELECTRICITY SYSTEM

2023· article· en· W4388068048 on OpenAlexaboutno aff
Raquel Caro Carretero, FERNANDO GARCIA JIMENEZ

Bibliographic record

VenueDYNA · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergy Load and Power Forecasting
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)ElectricityEnvironmental economicsWind powerCompetition (biology)Electricity marketTransparency (behavior)BusinessDemand responseIndustrial organizationEconomicsComputer scienceEngineeringComputer security

Abstract

fetched live from OpenAlex

Electricity sector, in global terms, has undergone significant and severe structural changes, with the aim of allowing free choice for energy consumers and achieving greater competition between markets. In this regard, quarter-hourly deviations are promoted by European regulations for the purpose of energy market management. The EU requires electricity suppliers to provide data on their energy production and consumption every 15 minutes. This requirement has been designed to promote transparency and competition in the energy market. Until now, Spain has used an hourly deviation settlement system, so the implementation of this new model will entail a complex and gradual change. In fact, the introduction of a quarter hourly market would enable consumers to access more frequent pricing information, which would help them make more informed decisions about their energy usage. This would also create opportunities for more sophisticated demand-side management strategies, such as real-time pricing and automated demand response. Although the integration of the electrical sector is an important aspect of the EU's broader efforts to create a single energy market and reduce carbon emissions, there are still significant challenges to be overcome, such as the need to improve grid infrastructure, ensure the security of supply, and harmonize regulatory frameworks across member states. The main objective of this article is to study the impact that the implementation of the new quarter-hourly model will have in a wind farm, given the intrinsic variability of this energy source and the current reliability of prediction systems. With continued advancements in technology and improved forecasting techniques that allow suppliers to adjust their operations in real-time based on changing conditions, it is likely that the uncertainty of unmanageable units will continue to decrease over time, making them an even more reliable and efficient source of clean energy in the future. Keywords: Quarter-hourly model, imbalances, electricity market, wind energy, hourly deviation settlement system

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes1
Has abstractyes

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