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Record W1992605262 · doi:10.1109/tsg.2013.2286623

Equilibrium in Wholesale Energy Markets: Perturbation Analysis in the Presence of Renewables

2014· article· en· W1992605262 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 Smart Grid · 2014
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsAlberta Energy
Fundersnot available
KeywordsIntermittencyRenewable energySmart gridKarush–Kuhn–Tucker conditionsWind powerDemand responseGridEnergy marketMarket integrationMathematical optimizationComputer scienceEconomicsEconometricsMicroeconomicsElectricityMathematicsEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

One of the main challenges in the emerging smart grid is the integration of renewable energy resources (RER). The latter introduces both intermittency and uncertainty into the grid, both of which can affect the underlying energy market. An interesting concept that is being explored for mitigating the integration cost of RERs is demand response (DR). Beginning with an overall model of the major market participants with RER and DR, together with the constraints of transmission and generation, we analyze the energy market in this paper and derive conditions for existence and uniqueness of the competitive market equilibrium using standard Karush-Kuhn-Tucker (KKT) criteria. The effect of wind uncertainty on the competitive market equilibrium is then quantified. Perturbation analysis methods are used to compare the equilibria in the nominal and perturbed markets. This analysis is used to quantify the effect of RERs uncertainty and its possible mitigation using DR. Finally numerical studies are reported using an IEEE 30-bus to validate the theoretical results.

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 score0.941
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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