Probabilistic generation and transmission planning with renewable energy integration
Bibliographic record
Abstract
Renewable energy sources are playing a vital role with increasing influences in modern power grid. Among various forms of renewables, wind turbine generators and solar photovoltaic (PV) systems have drawn much attention because of their relatively mature technologies and large-scale deployment worldwide. However, wind and solar power is intermittent in nature, which poses significant uncertainties to power grid operation. Excessive curtailments have occurred for wind power in the field and caused financial losses. Facing such challenges, the conventional power system planning methods must be changed in order to accommodate grid-connected renewable energy sources reliably and economically. Traditionally, deterministic approaches for power system planning have been used, but with increasing penetration of renewable energy sources, probabilistic methods appear to be more suitable to address stochastic features and uncertainties associated with the overall system. In this paper, an extensive literature review is conducted on probabilistic methods for generation and transmission planning incorporating wind power. The state-of-art techniques in the field are summarized, and future research directions are recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".