Measurement-based Locational Marginal Pricing in Active Distribution Systems
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yields similar DLMPs as solving the OPF problem with an up-to-date model and greatly outperforms it when the model is out of date.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".