Addressing Future Power Demand Through Optimized Transmission Planning Using High-Power Conductors and Partial Dynamic Line Loading
Bibliographic record
Abstract
India's power demand is increasing at a compound annual growth rate (CAGR) of 7.18% due to rapid urbanization.The paper examines the planning for the extension of transmission to suit the increased load development.Before deregulation due to the power monopoly, optimization techniques were undeveloped, contingency analyses were not conducted, resulting in inefficient transmission investments, increased transmission charges, and diminished reliability, which resulted in inferior quality and increased electricity prices for consumers.The Cost/Benefit index is formulated based on the revenue generated by the line and employed for the optimization of transmission lines.Urbanization will result in Right of Way (RoW) issues for new line installation, necessitating the usage of high-power conductors (HPCs) in these regions.Dynamic line loading (DLL) may or may not yield a "return on investment" in countries facing extreme heat conditions.Consequently, the partial DLL using ambient temperature was employed as a test model in this paper instead of explicitly implementing the complete DLL.This technique is applied to the IEEE 14 test bus system, based on the assumption of a 60% uniform load growth and non-uniform load growth rates of 18%, 21%, and 24% at various buses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".