An Economic Based Approach for Optimal and Secure Reactive Power Provision in Deregulated Environments
Bibliographic record
Abstract
In deregulated electricity markets, reactive power provision is one of the most important ancillary services which is vital for reliable and secure operation of the system. In all electricity markets, economic issues play an important role in market scheduling. This paper presents an algorithm to find optimal reactive power market schedule. The proposed algorithm seeks to minimize a novel three-component payment function which precisely considers economic issues in the market. Two first parts of this function are reactive power provision cost and transmission loss payment. Moreover, another important part of this payment function, which has not considered in the time of reactive power market clearing in literature, is transmission charge payment. As it is shown in simulation results, this term of the function can importantly impact on final reactive power market schedule and consequently on total market payment and final market schedule. Furthermore, due to significant impact of reactive power on system voltage stability, the algorithm tries to schedule reactive power market with an adequate voltage security margin. Sequential quadratic programming is employed to clear the algorithm which is a nonlinear constrained optimization problem. The proposed algorithm is applied on IEEE-24 bus test system with satisfactory results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".