Leveraging mandatory and enforceable Electric Reliability Standards for non-registered rural electric utilities
Bibliographic record
Abstract
The August 2003 blackout impacted over 50 million people and resulted in the loss of 61,800 megawatts (MW) of load in eight states and one Canadian Province. Following the blackout, the U.S. - Canada Power System Outage Task Force made several recommendations, the number one being to “[m]ake reliability standards mandatory and enforceable, with penalties for noncompliance”. The Energy Policy Act of 2005 was the first major energy law enacted by Congress in more than a decade and among other things, it gave the Federal Energy Regulatory Commission (FERC) responsibility for overseeing the reliability of the nation's electricity transmission grid. Subsequently, the FERC issued Order 672 which created the Electric Reliability Organization, which eventually was certified to be the North American Electric Reliability Corporation (NERC), and set forth Procedures for the Establishment, Approval, and Enforcement of Electric Reliability Standards. The Electric Reliability Standards are enforceable only upon those electric utilities which are registered entities in accordance with established registration criteria. Many rural utilities, connected to the bulk electric system typically through radial transmission lines, do not meet the criteria for registration and thus are not subject to enforcement of the Reliability Standards. However, due consideration should be given to the level of effort expended by NERC and industry participants to continue to develop new, and improve existing, reliability standards. In this paper, an examination of a recent draft of a revised reliability standard currently under development addressing the maintenance and testing of protection systems will yield some best practices worthy of consideration by rural electric utilities in an effort to continually improve the reliability of their systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".