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Record W2028481738 · doi:10.1109/pesgm.2012.6344948

Power grid planning and operation with higher penetration of intermittent

2012· article· en· W2028481738 on OpenAlexaboutno aff
T. Hillman, Li Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsWind powerEnvironmental economicsElectric power systemElectricityGridBusinessElectricity marketReliability (semiconductor)Renewable energyElectricity generationReliability engineeringPower (physics)EngineeringElectrical engineeringEconomics

Abstract

fetched live from OpenAlex

MISO is responsible for running electricity markets and maintaining the reliable transmission of power in 12 States and the Canadian Province of Manitoba. It is one of the largest power grid system operators in the world, with a peak load of 104, 508 MW (July 20, 2011) in its Reliability Coordination footprint. MISO runs day-ahead and realtime co-optimized energy and ancillary service markets, as well as markets for financial transmission rights and resource adequacy. With regulatory mandates and the administration's energy policy, MISO has seen significant increases in the number of intermittent interconnection requests, mainly from wind generation. As of fall of 2011, the wind operational capacity has exceeded 11,000MW, and is still growing. The variability of these intermittent resources introduces new operational challenges. At the same time, new proposed environmental regulations for power plants could accelerate retirement of the conventional power plants, mainly coal and oil-fired resources, thus adversely affecting grid reliability and increasing electricity costs for consumers. The combination of the high penetration of the intermittent resources and the Environmental Protection Agency (EPA) rules brought challenges to planning, reliability and market operation.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.185

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)0.0000.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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