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Record W2186661904 · doi:10.5555/2615731.2615861

Efficient coordinated power distribution on private infrastructure

2014· article· en· W2186661904 on OpenAlexaff
Andrew Perrault, Craig Boutilier

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrivate networkPower (physics)Electric power transmissionDistribution (mathematics)Construct (python library)Computer scienceGridControl (management)Distributed computingTopology (electrical circuits)Computer networkElectrical engineeringMathematicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Current power distribution network design makes it attractive for agents to generate their own power (distributed generation) and to construct private infrastructure (e.g., distribution lines) to exchange power without using the main public grid. We show that such private transactions may increase overall network load because of increased transmission distances, thus increasing resis-tive losses. We present a coordination scheme for the centralized control of private infrastructure that satisfies participation con-straints and budget balance. Experiments show that our scheme reduces distribution losses by 4-5 % when there are only a con-stant number of private lines and by 55%-60 % when the number of private lines is proportional to the number of agents.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.019
GPT teacher head0.321
Teacher spread0.302 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations7
Published2014
Admission routes1
Has abstractyes

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