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Conservation Voltage Reduction in Renewable-Rich Distribution Networks: A Review

2024· review· en· W4411271262 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

Venuenot available
Typereview
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsReduction (mathematics)Voltage reductionRenewable energyVoltageComputer scienceEnvironmental scienceElectrical engineeringEngineeringMathematics

Abstract

fetched live from OpenAlex

Increasing energy efficiency and reducing energy consumption are of interest to electric utilities. Conservation voltage reduction (CVR) is a conventional grid management technique that utilities use to achieve peak load shaving and energy consumption reductions. CVR controls voltage-dependent loads by reducing bus voltages intentionally to values near their lower operational limit. Integration of renewable distributed generation (DG) units in distribution grids attracts renewed interests for CVR. In this paper, CVR techniques in modern renewable-rich distribution grids is extensively reviewed. CVR can be realized through two steps: 1) CVR assessment, which evaluates effects of CVR on different system feeders to determine the feeders with high CVR potentials; 2) CVR implementation, which consists of the system modeling for components of distribution grids, such as voltage regulation devices and loads, and CVR implementation through control-, optimization-, or the coordination of control and optimization-based methods. Future research directions for CVR are recommended in the paper.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.449
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.288
Teacher spread0.264 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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