A Techno-Economic Study of an Entirely Renewable Energy-Based Power Supply for North America for 2030 Conditions
Bibliographic record
Abstract
In this study power generation and demand are matched through a least-cost mix of renewable energy (RE) resources and storage technologies for North America by 2030. The study is performed using an hourly resolved model based on a linear optimization algorithm. The geographical, technical and economic potentials of different forms of RE resources enable the option of building a super grid between different North American regions. North America (including the U.S., Canada and Mexico in this paper), is divided into 20 sub-regions based on their population, demand, area and electricity grid structure. Four scenarios have been evaluated: region-wide, country-wide, area-wide and an integrated scenario. The levelised cost of electricity is found to be quite attractive in such a system, with the range from 63 €/MWhel in a decentralized case and 42 €/MWhel in a more centralized and integrated scenario. Electrical grid interconnections significantly reduce the storage requirement and overall cost of the energy system. Among all RE resources, wind and solar PV are found to be the least-cost options and hence the main contributors to fossil fuel substitution. The results clearly show that a 100% RE-based system is feasible and a real policy option at a modest cost. However, such a tremendous transition will not be possible in a short time if policy-makers, energy investors and other relevant organizations do not support the proposed system.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".