Renewable energy quality trilemma and coincident wind and solar droughts
Bibliographic record
Abstract
Renewable energy is essential for power system decarbonization, but extended and unexpected periods of extremely low wind and solar resources (i.e., wind and solar droughts) pose a threat to reliability. The challenge is further exacerbated if shortages of the two occur simultaneously or if they affect neighboring grids simultaneously. Here we present a framework to characterize these events and propose three metrics to comprehensively assess renewable energy quality: resource availability, variability, and extremeness. An examination of long-term data across a vast geographical region shows a strong spatial correlation and temporal coincidence of renewable energy droughts. It also finds a lack of sites that excel in all three quality attributes, which presents a trilemma to investors, system planners, and policymakers. These findings underscore the significance of considering factors beyond mere resource availability and contribute to developing informed strategies for the reliable and sustainable deployment of variable energy resources. The introduction of indicators such as availability, variability, and extremeness significantly alter our understanding of renewable energy quality, posing a challenge for investors, system planners, and policymakers, according to results from a statistical framework and time-series data analysis.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".