MétaCan
Menu
Back to cohort
Record W2149177425 · doi:10.1109/tpwrs.2008.920718

Models for Quantifying the Economic Benefits of Distributed Generation

2008· article· en· W2149177425 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 · 2008
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsMcGill University
Fundersnot available
KeywordsDistributed generationEconomic dispatchDistributed power generationCost–benefit analysisEconomic impact analysisIndustrial organizationBusinessEnvironmental economicsComputer scienceEconomicsElectric power systemRisk analysis (engineering)Power (physics)MicroeconomicsEngineeringRenewable energy

Abstract

fetched live from OpenAlex

We examine some of the most important economic benefits brought about by distributed generation technologies to the distribution utility and the power system. Models are developed that allow the quantification of those benefits in economic terms. In some cases, industry regulators or utilities charge connection fees to the owners of distributed generators, even if they are saving the local utility considerable amounts of money every year in deferred network upgrades, reduced losses, avoided wholesale market purchases and others. Efficient economic systems dictate that a proper share of the indirect benefits created by a given economic activity leads to overall optimal independent decision-making by its participants. Quantifying and allocating the benefits of distributed generation to the owners improves the economic performance of their investments and encourages the implementation of those distributed generation applications most valuable to the system.

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 score0.849
Threshold uncertainty score0.549

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 head0.057
GPT teacher head0.239
Teacher spread0.182 · 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