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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 OpenAlexaff
Ebrahim Hasan, F.D. Galiana

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.

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)0.001
metaresearch head score (Gemma)0.009
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
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 head0.005
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

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

Citations21
Published2010
Admission routes1
Has abstractyes

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