Optimal Operation of GENCOs in Competitive Electricity Markets
Bibliographic record
Abstract
In a deregulated power system, power generators submit offers to sell energy and operating reserve in the electricity market.The market can be described as an oligopoly due to certain characteristics such as a restricted number of producers.A sealed bid auction is the usual practice with competing generators having no information on rivals' bids.This paper presents a technique for power producers to make security-constrained offers in different electricity markets considering incomplete market information and uncertainty in load forecast.The methodology employed is based on forecasting and optimization.Electricity market clearing price at each interval is predicted using the double seasonal Holt-Winters method and used in the optimization problem of profit maximization to estimate maximum benefit at the interval.Economic dispatch of committed generating units is also evaluated using a dynamic programming procedure to minimize production cost.A numerical example serves to illustrate the proposed approach as it is applied to a practical system.Results indicate that a generator can make adequate short-term analysis on market behavior and maximize its benefits for the period based on available historical data on market operation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".