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Record W2148537030 · doi:10.2202/1553-779x.1000

Transmission Congestion Management and Pricing in Simple Auction Electricity Markets

2004· article· en· W2148537030 on OpenAlexaff
Claudio A. Cañizares, Chen Hong, Federico Milano, Ajit Kumar Singh

Bibliographic record

VenueInternational Journal of Emerging Electric Power Systems · 2004
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBiddingComputer scienceElectricityTransmission (telecommunications)Electricity marketMathematical optimizationVoltageDemand responseDemand sideTransmission systemEconomicsMicroeconomicsTelecommunicationsElectrical engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

This paper presents a novel technique to analyze, manage and price transmission congestion in electricity markets based on a simple auction mechanism. The proposed technique is basically an iterative rescheduling algorithm, relying on an ``on-line'' evaluation of the transmission system congestion, as defined by a System Security Index (SSI), and associated sensitivities, which are all computed based on formulas that account for voltage stability constraints as well as thermal and bus voltage limits. The methodology is tested using a 3-area test system, a 6-bus test system with both demand-side bidding and inelastic demand, as well as a 129-bus model of the Italian High Voltage transmission system with demand-side bidding. The results obtained for these test systems with the proposed technique are compared with similar results obtained from an optimization-based method.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
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 head0.217
Teacher spread0.212 · 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

Citations23
Published2004
Admission routes1
Has abstractyes

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