Evaluation of EHV and AC/DC technologies for integration of large‐scale renewable generation in Saudi Arabian network
Bibliographic record
Abstract
Renewable energy is a rapidly growing environmental‐friendly alternative for electricity generation, which will supersede using fossil fuels in the near future. Renewable‐based generation is usually located at remote areas, and the large‐scale generated power is required to be transmitted to the main load centres; thus, the main challenge facing the bulk power transmission is the precise determination of the most appropriate transmission option. This study presents a comprehensive techno‐economic study for the selection of the adequate transmission option for large‐scale power transmission. The proposed work aims to study different transmission alternatives to transfer 10,000 MW of renewable generated power to the load centres in the central region of Saudi Arabia. Different high‐voltage AC (HVAC) voltage levels such as 725 and 500 kV, and high‐voltage DC (HVDC) technologies are considered as alternatives, and a techno‐economic evaluation of each option is presented. Moreover, detailed comparisons between different HVAC and HVDC technologies are introduced from technical, economic, and environmental perspective. The presented study, comparisons, and the subsequent recommendations are helpful for the network planner to evaluate different extra high‐voltage (EHV) and AC/DC transmission options in terms of accessibility, load‐carrying capability, efficiency, reliability, stability, environmental impact, and economics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".