Evaluation of various large-scale energy storage technologies for flexible operation of existing pressurized water reactors
Bibliographic record
Abstract
The lack of plant-side energy storage analysis to support nuclear power plants (NPP), has setup this research endeavor to understand the characteristics and role of specific storage technologies and the integration to an NPP. The paper provides a qualitative review of a wide range of configurations for integrating the energy storage system (ESS) to an operating NPP with pressurized water reactor (PWR). The role of ESS technologies most suitable for large-scale storage are evaluated, including thermal energy storage, compressed gas energy storage, and liquid air energy storage. The methods of integration to the NPP steam cycle are introduced and categorized as electrical, mechanical, and thermal, with a review on developments in the integration of ESS with an operating PWR. By adopting simplified off-design modeling for the steam turbines and heat exchangers, the results show the performance of the PWR steam cycle changes with respect to steam bypass rate for thermal and mechanical storage integration options. Analysis of the integrated system characteristics of proposed concepts for three different ESS suggests that certain storage technologies could support steady operation of an NPP. After having reviewed what have been accomplished through the years, the research team presents a list of possible future works.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".