Measurement-based Locational Marginal Pricing in Active Distribution Systems
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a measurement-based method for calculating real-time distribution locational marginal prices (DLMPs) without the use of an offline network model. Instead, the proposed method relies only on online measurements collected at a subset of distribution system buses to estimate a linear sensitivity model mapping bus voltages to injections, which in turn is embedded in an optimal power flow (OPF) problem as an equality constraint. The proposed method completely obviates the need for an accurate distribution network model that may not be available, especially for active distribution networks with faster variations in operating point. Also, the proposed method renders the original OPF problem with nonlinear constraints a computationally efficient quadratic programming problem (with linear constraints) and provides sufficiently accurate DLMPs at buses where measurements are collected. Via numerical simulations involving a 33-bus test system, we demonstrate that the proposed method yields similar DLMPs as solving the OPF problem with an up-to-date model and greatly outperforms it when the model is out of date.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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