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Record W186571071

Impacts of Wind and Solar on Fossil-Fueled Generators: Preprint

2012· article· en· W186571071 on OpenAlexaff
D. Lew, Greg Brinkman, Nikhil Kumar, P. M. Besuner, D. Agan, S. Lefton

Bibliographic record

VenueUniversity of North Texas Digital Library (University of North Texas) · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsIntertek (Canada)
FundersOffice of Energy EfficiencyOffice of ElectricityOffice of Energy Efficiency and Renewable EnergyU.S. Department of Energy
KeywordsEnvironmental scienceWind powerFossil fuelRenewable energySolar powerAutomotive engineeringMeteorologyEngineeringPower (physics)Electrical engineeringWaste management
DOInot available

Abstract

fetched live from OpenAlex

High penetrations of wind and solar power will impact the operations of the remaining generators on the power system. Regional integration studies have shown that wind and solar may cause fossil-fueled generators to cycle on and off and ramp down to part load more frequently and potentially more rapidly. Increased cycling, deeper load following, and rapid ramping may result in wear-and-tearimpacts on fossil-fueled generators that lead to increased capital and maintenance costs, increased equivalent forced outage rates, and degraded performance over time. Heat rates and emissions from fossil-fueled generators may be higher during cycling and ramping than during steady-state operation. Many wind and solar integration studies have not taken these increased cost and emissions impactsinto account because data have not been available. This analysis considers the cost and emissions impacts of cycling and ramping of fossil-fueled generation to refine assessments of wind and solar impacts on the power 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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0340.005

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 head0.007
GPT teacher head0.164
Teacher spread0.157 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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