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Record W2086983430 · doi:10.1109/icsai.2014.7009286

Renewable energy sources in a transactive energy market

2014· article· en· W2086983430 on OpenAlexaff
Tugcan Sahin, Daniel Shereck

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsSiemens (Canada)
Fundersnot available
KeywordsRenewable energyEnvironmental economicsRevenueFossil fuelTransactive memoryWind powerRenewable energy creditIntermittent energy sourceBusinessGrid parityNatural resource economicsEnergy sourceGridEnergy developmentFeed-in tariffDistributed generationEconomicsEnergy policyComputer scienceEngineeringWaste managementFinanceElectrical engineering

Abstract

fetched live from OpenAlex

Due to government policies, the declining cost of renewable energy technology and the increased costs of fossil fuels, energy sources such as wind and solar are becoming a larger part of the power producing mix. As renewable generators start to replace conventional sources such as coal, oil, gas and nuclear there is a growing concern by utility operators that the power system reliability may be compromised. Additionally, as more and more customers begin to produce their own energy there is a growing concern of market parity. As consumers become prosumers the utility is left with the role of maintaining the grid despite declining revenues. Those who cannot afford their own renewables sources are also left to foot the bill as rates rise. Therefore the current market mechanisms do not properly distribute the costs as well as ensure grid reliability. This paper seeks to classify the costs and benefits of renewables for all market participants using the Transactive Energy Framework proposed by the GridWise Architecture Council in 2013.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.988

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)0.0010.000

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 head0.169
Teacher spread0.165 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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