Assessing the complementarity of future hybrid wind and solar photovoltaic energy resources for North America
Bibliographic record
Abstract
Renewable energy plays a key role into achieving the international targets for reducing global greenhouse gas emissions. Considering that these forms of energy are dependent on climate conditions and that their variability occurs at different time scales, it is important to analyze the complementarity to ensure a stable power supply to the grid in the context of climate change. A multi-model ensemble of 10 global climate models from the CMIP6 project was used to analyze the complementarity between wind and solar photovoltaic power in North America from 2025 to 2054 under the SSP2-4.5 scenario. This complementarity was evaluated using two indices that account for the similarity between the two resources (Similarity index, Si) and the temporal complementarity (Concurrency index, Ci). The combination of the two resources reduced spatial heterogeneity in terms of annual mean power in North America. The highest values of Si were detected west of California and in the Caribbean Sea, and the lowest were found in Mexico. Regarding Ci, the highest values were detected in ocean areas north of 30°N. Both indices were divided into four categories to assess the most suitable areas for combining wind and solar photovoltaic power. Coastal areas in the Gulf of Mexico and substantial areas in the Caribbean Sea are considered optimal in terms of complementarity. Inland, good complementarity was observed on the US-Canada border (e.g., the Great Lakes) and in northern areas such as Alaska or the Labrador Peninsula. The lowest values of complementarity were detected in Mexico.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".