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Record W2987293247 · doi:10.1109/psce.2006.296350

Sensitivity-Based Security-Constrained OPF Market Clearing Model

2006· article· en· W2987293247 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMarket clearingSensitivity (control systems)BiddingBenchmark (surveying)Electricity marketPower flowComputer scienceMathematical optimizationElectric power systemVoltageStability (learning theory)ContingencyClearingPower (physics)ElectricityControl theory (sociology)EngineeringMathematicsEconomics

Abstract

fetched live from OpenAlex

This paper proposes a novel technique for representing system security constraints that properly include voltage stability limits in the operation of competitive electricity markets. The market-clearing algorithm is modeled as a voltage stability constrained optimal power flow (OPF) problem, while the distance to the closest critical power flow solution is represented by means of a loading parameter and evaluated using a continuation power flow (CPF) technique. Sensitivities obtained at the OPF step are used to estimate power directions for the CPF method, while the CPF analysis provides the loading parameter to be used in the OPF problem based on an N-1 contingency criterion. The OPF and the CPF steps are repeated until the maximum loading parameter is found, thus providing optimal solutions considering both proper market conditions and security margins. Two benchmark systems with both supply and demand bidding are used to illustrate and test the proposed technique

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.

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: none
Teacher disagreement score0.712
Threshold uncertainty score0.714

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 head0.004
GPT teacher head0.184
Teacher spread0.179 · 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

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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