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Record W2099975670 · doi:10.1109/tpwrs.2009.2037153

Fast Computation of Pure Strategy Nash Equilibria in Electricity Markets Cleared by Merit Order

2010· article· en· W2099975670 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

VenueIEEE Transactions on Power Systems · 2010
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsClearanceNash equilibriumOligopolyElectricity marketMathematical optimizationConstant (computer programming)Block (permutation group theory)ComputationMarket powerOrder (exchange)Integer programmingElectricityLinear programmingComputer scienceMathematical economicsEconomicsMathematicsMicroeconomicsEngineeringAlgorithmMonopolyElectrical engineeringCombinatorics

Abstract

fetched live from OpenAlex

We consider an electricity market cleared by merit- order in which generating companies (Gencos) own any number of units and submit offers consisting of multiple blocks of finite generating capacity and constant incremental cost (IC). It has been shown that if the IC block offers can vary continuously, the market outcomes supported by pure strategy Nash equilibria (NE) are fewer than or equal to the number of Gencos and can all be computed through a mixed-integer linear programming (MILP) scheme. Knowledge of these NE then serves to study how an oligopolistic market of this type behaves under a variety of demand and market power conditions.

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 categoriesMeta-epidemiology (narrow)
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 score0.789
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.206
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