The persistence of flexible coal in a deeply decarbonizing energy system
Bibliographic record
Abstract
Abstract Extensive literature has highlighted the difficulty in operating baseload power plants—especially coal-fired units—in a decarbonized electric power system with a high share of variable renewable energy, with some of it recommending immediate coal phaseouts. However, the coal fleet across China is large and young, making its imminent phaseout unrealistic. Moreover, power system operators and policy makers face other constraints in their pursuit of energy system decarbonization—chief among them the need to achieve high levels of reliability—something coal units could provide. We assess the persistence of coal in a decarbonizing power system under various retrofit scenarios that seek to enhance the flexibility of coal units: after all, energy transitions do not occur in a vacuum and owners of coal power plants will likely pursue innovations to extend the lifetimes and profits of their assets, even as the wider energy transition unfolds. We evaluate the economic and environmental impacts of improving coal power unit flexibility in Jiangsu’s power system under four levels of renewable energy penetration and three scopes of coal flexibility retrofits. Our results show that coal units persist even at very high renewable penetrations, and retrofits help them reduce power system costs, enable renewable energy integration, and marginally cut emissions. Smaller coal units become peaker rather than baseload units, providing the power system with flexibility rather than just energy. Our results show how challenging the low-carbon transition is likely to be without outright phaseouts of coal generation.
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 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".