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Dynamic Security Constrained Pool Dispatch in Competitive Electricity Market

2005· article· en· W2010285714 on OpenAlexvenueno aff
Satnesh Singh

Bibliographic record

VenueInternational Journal of Power and Energy Systems · 2005
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic dispatchElectricity marketMargin (machine learning)Electric power systemMarket clearingTransient (computer programming)ElectricityCompetition (biology)Point (geometry)ClearingComputer scienceFault (geology)Function (biology)EconomicsPower (physics)MicroeconomicsEngineeringFinanceMathematics

Abstract

fetched live from OpenAlex

This article studies the impact of incorporating dynamic security considerations on dispatch in the operation of a power pool in an open-market environment. Making a system dynamically secure may require the re-dispatch of generators in an unbundled system and have an effect on pool prices and on commercial competition between generators. Achieving a commercially transparent and technically justifiable approach, therefore, is very important. The authors use a transient energy function approach to calculate the stability margin of the system after clearing a typical fault. A hybrid method is applied to calculate the approximate unstable equilibrium point that is used to locate the exact unstable equilibrium point that is required for establishing an energy margin. Re-dispatch is undertaken if this margin is inadequate. The apportionment of dispatch changes among generators is made sensitive to price signals so as to allow competition among generators. This article considers pool dispatch only.

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)0.004
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.002
GPT teacher head0.204
Teacher spread0.201 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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