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Record W1989993131 · doi:10.1109/pesmg.2013.6672879

Photovoltaics in distribution systems — Integration issues and simulation challenges

2013· article· en· W1989993131 on OpenAlexfundno aff
Jens Schoene, Vadim Zheglov, Douglas Houseman, J. Charles Smith, Abraham Ellis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsnot available
FundersCentre for Energy Advancement through Technological InnovationColorado State University
KeywordsComputer sciencePhotovoltaicsPhotovoltaic systemContext (archaeology)Risk analysis (engineering)Task (project management)Distribution (mathematics)Operations researchReliability engineeringSystems engineeringEngineeringElectrical engineeringBusinessMathematics

Abstract

fetched live from OpenAlex

High penetration of PVs in distribution systems can causes a number of issues on the system. Determining whether or not an issue exists for a given distribution circuit with PV is a non-trivial task. Accurate knowledge regarding the conditions under which issues can be positively ruled out would be immensely beneficial for solar projects as this would eliminate the need for a costly and time-consuming system impact study. In this paper, we focus on the challenges associated with evaluating the types of systems for which potential PV related problems turn into actual problems. In this context, we summarize utility experiences with PV on their system as discussed during UVIG meetings and reported in the pertinent literature. Furthermore, we present simulation results of a PV integration study we conducted for CEATI and discuss the implications of simulation challenges and simplifying assumption often associated with these types of studies.

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)0.002
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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