The effect of dynamic security constraints on the locational marginal prices
Bibliographic record
Abstract
In a restructured power system, locational marginal prices (LMP) are important pricing signals to the participants. LMP at a given node of a power system is the incremental cost of supplying power at that node. In a lossless system with no active constraints, the LMPs at all the nodes are equal. However, due to the losses in the power system, the LMPs at different nodes is different. Any operating constraint such as line flow limits also contribute to the LMP at a node. This paper investigates the effect of a dynamic security constraint on the LMPs. The transient stability margin expressed as a function of nodal voltages and phase angles, is used as a constraint in an optimal power flow (OPF) program to determine the LMPs at all the nodes of a power system. The Lagrange multiplier associated with the transient stability constraint gives the marginal cost of the transient stability constraint. A case study on the New England 39 bus system is presented to demonstrate the effect of the dynamic security constraint on the LMPs. In a nodal pricing scheme, any active constraint results in an additional revenue to the system operator. This revenue, known as the network rental, is also investigated in the paper.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".