Status of energy storage options for electricity from nuclear power plants
Bibliographic record
Abstract
Existing nuclear power plants benefit from high efficiency by operating at full capacity for generating electricity. However, the demand for electricity is an hourly variable and thus excess electricity is available at off-peak times on a given day. The price of this off-peak electricity is very low compared to the average price. Storing or utilizing this off-peak electricity for various processes will provide additional value to the electricity and will improve the overall economics of the nuclear power plant. This work looks at a few energy storage technologies suitable for large-scale electricity storage from base-load power plants such as nuclear power plants. A preliminary assessment of these technologies has been completed through a literature review. These technologies are categorized into three forms of energy: chemical, mechanical and hydrogen. Batteries are found to be too impractical for large-scale electricity storage until vast amounts of distributed battery storage is available through plugin electric vehicles. Pumped hydro storage is suitable in terms of energy storage capacity but not in terms of cost, construction time and location. Gaseous hydrogen storage in underground reservoirs is suitable at scale but not enough clean hydrogen production processes are ready for large-scale demonstration. Storing hydrogen in natural gas pipeline networks is promising in the short-term while deployment capabilities are being assessed by commercial companies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".