Measurement-based Optimal Power Flow with Linear Power-flow Constraint for DER Dispatch
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Bibliographic record
Abstract
This paper proposes a measurement-based method to obtain optimal power-flow (OPF) solutions that optimize distribution-system operations by dispatching active- and reactive-power outputs of distributed energy resources (DERs). Central to the proposed method is the estimation of a linear power-flow model from synchronized voltage and power-injection data collected from distribution-level phasor measurement units (D-PMUs). The estimated model is then incorporated into an OPF problem as an equality constraint. We formulate a quadratic cost function that enables co-optimization of DER active- and reactive-power costs, voltage deviations away from prescribed reference levels, as well as active- and reactive-power deviations from desired setpoints. Via numerical simulations of the IEEE 33-bus distribution test system, we demonstrate that the proposed measurement-based method yields sufficiently accurate solutions compared to model-based OPF solutions. Furthermore, we highlight the adaptability of the proposed method in case an accurate network model is not available.
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Full frame distilled prediction
Teacher imitationNot 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.
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) | 0.001 | 0.000 |
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.
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