Modeling Emerging Uncertainties in Bulk Power System Reliability Assessment
Bibliographic record
Abstract
Environmental concerns have led to increased deployment of clean and sustainable energy resources. Growing mix of emerging generation and transmission technologies, and increased uncertainty in load profiles at the various nodes of a bulk electric system network pose structural and operational complexities. Maintaining acceptable grid reliability has become increasingly challenging during system planning and operation. There is growing uncertainty in power supply due to rapid replacement of firm capacity by widely distributed intermittent renewable generation that are correlated by varying degrees. Moreover, emerging factors such as electric vehicles cause growing uncertainties in demand characteristics at the dispersed load points and create significant challenges in load modeling for composite system reliability (CSR) assessment. The adverse effects introduced by these emerging uncertainties can be mitigated using smart initiatives, such as demand response, energy storage, power electronic devices, and other smart grid technologies. This work investigates the development in modeling the characteristics of these new technologies and analyzes their utility in probabilistic CSR evaluation. The ongoing research in this direction is analyzed, and the research gaps are reported with a critical review in this paper.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".