Pipeline to Reliability: Unraveling Gas and Electric Interdependencies Across the Eastern Interconnection
Bibliographic record
Abstract
Uncertainties surrounding the continued operation of older coal generation units, the increased penetration of renewable resources, and the aging or retirement of certain nuclear units have exposed vulnerabilities in the natural gas supply chain. The increased availability and low price of natural gas for electric generation in many parts of the United States compounds them by increasing the economic pressure on various non-gas-fired base-load generation plants. Our growing dependence on natural gas as a primary fuel for electricity generation offers environmental and efficiency benefits, but it also presents operational challenges for independent system operators (ISOs) and regional transmission organizations (RTOs) that depend on the natural gas pipeline and storage network to facilitate reliability objectives. During the peak heating season, pipeline congestion can result in interruptions of gas deliveries to those gas-fired generators lacking primary firm entitlements. Scheduling restrictions associated with the provision of nonfirm transportation for gas-fired generators stress the capability of the electric system to meet demand and maintain operating reserves, as RTOs must quickly replace output from more efficient natural gas-fueled combined-cycle plants and quick-start peakers to maintain electric reliability. In this article, we address the gas-electric interdependencies across the Eastern Interconnection that are the subject of a multitarget research project sponsored by the U.S. Department of Energy (DOE) with the participation of PJM Interconnection, Midcontinent Independent System Operator (MISO), New York Independent System Operator (NYISO), ISO New England (ISO-NE), TVA, and the Independent Electricity System Operator of Ontario (IESO), collectively known as the participating planning authorities (PPAs).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".