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Record W2006840149 · doi:10.1109/isgt-la.2011.6083195

Integrating distributed generation with Smart Grid enabling technologies

2011· article· en· W2006840149 on OpenAlex
Rodrigo Hidalgo, Chad Abbey, G. Joós

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcGill UniversityHydro-Québec
Fundersnot available
KeywordsSmart gridDistributed generationComputer scienceSoftware deploymentKey (lock)GridSystems engineeringRisk analysis (engineering)Process managementDistributed computingComputer securityRenewable energyEngineeringBusinessSoftware engineering

Abstract

fetched live from OpenAlex

The integration of distributed generation and Smart Grid enabling technologies and concepts to power systems has been widely accepted by the industry and academia as the key to achieve a more reliable, efficient, and secure grid, with an active participation from customers, and environmentally sustainable. However, there is a lack of information about the costs and economic benefits of research and development projects about distributed generation and Smart Grids. This paper proposes a methodology for the technical and economic analysis of the implementation of a distributed generator into a distribution network, combined with an automatic voltage control and a dynamic line rating tool. The methodology is applied to a case study to show the technical impacts and performance of the network, and the economic feasibility of the projects. A better understanding of cost - benefit analyses of such initiatives will foster de deployment of distributed generation and Smart Grid projects.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.246

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 head0.013
GPT teacher head0.163
Teacher spread0.150 · 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

Quick stats

Citations13
Published2011
Admission routes1
Has abstractyes

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