Bacterial Foraging Algorithm & Demand Response Programs for a Probabilistic Transmission Expansion Planning With the Consideration of Uncertainties and Voltage Stability Index
Bibliographic record
Abstract
The rapid growth of the power system with respect to many uncertainties has made the transmission expansion planning (TEP) problem more tedious. This article proposes a novel holistic method to solve the TEP problem in the deregulated market environment considering uncertainties at the load side. A mixed-integer nonlinear programming model has been considered in solving the raised issue. A bacterial foraging algorithm has been employed to optimize the problem. Considering the nature of the power system, applying the ac power flow model is a necessity in optimizing the problem in order to obtain applicable results. Distributed generations (DGs) have been included at the load side to satisfy the variation in the demand. Demand response programs (DRPs) have been considered to reduce the cost and increase the closeness between customers and the utility side. Furthermore, this work uses the Monte Carlo simulation (MCS) to handle the uncertainties associated with DGs and DRPs at the load side. A voltage stability index study through PQVSI is carried out to ensure the applicability and stability of the considered plan. A comprehensive planning framework is obtained by testing the proposed method on the Colombian 93-bus test system. The use of DGs and DRPs has a significant impact on reducing the overall expansion plan of the network.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".