A Framework for Volt-VAR Optimization in Distribution Systems
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
- Genre
- Candidate signal: MethodsConsensus signal: none
- Teacher disagreement score
- 0.987
- Threshold uncertainty score
- 0.835
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.205 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
The possibility of leveraging the data provided by smart meters to understand the load characteristics is studied in this paper. The loads are modeled as voltage-dependent elements to increase the accuracy of volt-VAR optimization (VVO) techniques for distribution systems. VVO techniques are part of the distribution management system and may be used for purposes such as loss reduction, voltage profile improvement, and conservation voltage reduction. A deterministic framework is proposed that formulates the VVO problem as a mixed-integer quadratically constrained programming problem, which is solved efficiently using advanced branch-and-cut techniques. The proposed framework is capable of optimally controlling capacitor banks, voltage regulators, and under-load tap changers (ULTCs) for day-ahead operation planning. The results indicate that loss reductions of up to 40% and a total demand reduction of up to 4.8% are achievable under some loading conditions in a radial test system. The effect of the load voltage dependence is also demonstrated through analytical simulations.
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.
The record
- Venue
- IEEE Transactions on Smart Grid
- Topic
- Smart Grid Energy Management
- Field
- Engineering
- Canadian institutions
- University of British Columbia
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Keywords
- Voltage reductionReduction (mathematics)VoltVoltageCapacitorControl theory (sociology)AC powerMathematical optimizationVoltage regulatorInteger programmingDemand responseOptimization problemLinear programmingEngineeringLoad managementVoltage regulationComputer scienceMathematicsElectrical engineeringElectricity
- Has abstract in OpenAlex
- yes