Frequency Dynamics-aware Real-time Marginal Pricing of Electricity Under Uncertainty
Bibliographic record
Abstract
This paper presents a real-time electricity marginal pricing method that captures the impact of frequency dynamics in the presence of i) aggressive net-load variability and ii) substantial net-load forecast uncertainty, both made more likely by extensive renewable integration. The proposed method augments a traditional economic dispatch with constraints pertaining to system frequency dynamics and explicit models for the net-load forecast uncertainty, while minimizing the sum of expected operation cost and expected frequency deviations from the synchronous speed across the scheduling horizon of interest. Chance constraints in the modified problem dictate the tolerable probability of violating lower and upper limits of dynamic system frequency and generator outputs. To facilitate solution efficiency, the chance-constrained economic dispatch transforms into a deterministic optimization problem under the assumption that the uncertainty in the net-load forecast is Gaussian. The effectiveness of the proposed model is demonstrated through case studies based on the Western System Coordinating Council test system. The results show that the proposed pricing method can reflect the impact of transient frequency deviations, yield greater profits for generators, and hedge against net-load uncertainty.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".