Decentralized Nodal-Price Self-Dispatch and Unit Commitment
Bibliographic record
Abstract
This chapter sets forth a scheme for self-scheduling independent market participants in a power pool. The approach, named DNSA for Decentralized Nodal-Price Self-Scheduling Auction, is proposed as an alternative to centralized Pool auctions and operation. DNSA exploits the intrinsic parallelism of the dual unit commitment problem to decentralize the various scheduling and dispatch functions. Each competing participant (GENCO, DISTCO) maximizes its profit for any set of nodal prices by choosing its level of production or consumption. Similarly, the TRANSCO independently maximizes its merchandising surplus within the network security constraints. The price caller, a centralized entity without access to proprietary cost information, updates prices through an effective Newton algorithm until the power balance at each bus is satisfied. DNSA does not assume a perfect market and accounts for the AC load flow model including transmission losses and line congestion, in addition to integer variables, ramping rates, start-up costs, and minimum up and down times. The convergence of DNSA hinges on the notions of profit optimality and the convexifying market rule . We present several study cases to illustrate the characteristics of DNSA. We conclude that to achieve fairness of treatment for all competing participants, they should be allowed to optimize their profit by self-scheduling. Therefore, to the extent possible, the next generation of unit commitment models should include profit optimality. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".