MétaCan
Menu
Back to cohort
Record W2499015274 · doi:10.1109/tste.2016.2598265

Estimating the Price Impact of Proposed Wind Farms in Competitive Electricity Markets

2016· article· en· W2499015274 on OpenAlexaffabout
Payam Zamani-Dehkordi, Logan Rakai, Hamidreza Zareipour

Bibliographic record

VenueIEEE Transactions on Sustainable Energy · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectricityWind powerElectricity marketElectricity priceElectricity retailingElectricity generationGridElectricity price forecastingStand-alone power systemEnvironmental economicsOffshore wind powerRenewable energyBusinessEnvironmental scienceNatural resource economicsEconomicsEconometricsEngineeringDistributed generationPower (physics)Electrical engineeringMathematics

Abstract

fetched live from OpenAlex

It has been proven extensively in the literature that higher availability of wind generation reduces electricity prices. However, the impact on electricity prices of an individual wind farm under different conditions has yet to be explored. In this paper, a data-driven approach is proposed for analyzing the effect on wholesale electricity prices of integrating a new wind farm into the grid. First, the price impact of previously integrated wind farms into the system is modeled. Then, an algorithm is proposed to estimate the future impact of proposed wind farms on electricity prices. Numerical results based on Alberta's electricity system are provided.

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)0.008
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.205
Teacher spread0.202 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Sustainable EnergySame topicElectric Power System OptimizationFrench-language works237,207