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Record W4414908751 · doi:10.1109/tia.2025.3618816

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

2025· review· en· W4414908751 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2025
Typereview
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsVoltage reductionEnergy conservationRenewable energyReduction (mathematics)VoltageGridLow voltageDistribution gridLoad management

Abstract

fetched live from OpenAlex

Conservation voltage reduction (CVR) is a grid management technique that utilities can use to improve energy efficiency, and achieve energy savings and peak load shaving. CVR controls voltage-dependent loads by intentionally reducing bus voltages to values near their lower operational limit. Integration of renewable distributed generation (DG) units in distribution grids attracts renewed interests of CVR. In this paper, CVR techniques in modern renewable-rich distribution grids in the literature are extensively reviewed. CVR can be realized through two steps: 1) CVR assessment, through which the effects of CVR on different system sections/feeders are evaluated to determine feeders with high CVR potential; 2) CVR implementation, including modeling the system components, such as voltage regulation devices and loads, and implementing CVR through control, optimization, or the combination of both. 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.003
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 head0.025
GPT teacher head0.301
Teacher spread0.276 · 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