Canadian Energy Storage Report: case study for Atlantic Canada
Bibliographic record
Abstract
Canada is in the enviable position of being relatively rich in natural resources and having one of the cleanest, least expensive, and most reliable electricity grids in the world. However, a decrease in infrastructure investments in the 1990’s along with an increase in the integration of renewables, a rise in smart grid technologies, and changes in demand and policies at a national and provincial level have created an increased awareness that fundamental changes in the way we build, own, and operate our electricity systems may berequired. Many studies, organizations and experts worldwide have concluded that these changes provide a perfect opportunity for energy storage (ES) technologies to demonstrate their value in supporting energy security and climate change goals, as well as creating a more integrated and optimized energy system. However,few comprehensive studies exist at a national or provincial level that comprehensively address the marketpotential and costs and benefits, as well as economic and environmental impacts of significant ES utilizationgiven the complexities of the analysis and the marketplace. Understanding the potential value of ES may help provide cost effective solutions for secure and reliable electric grids, and may also provide opportunities as an economic engine to drive the global competitiveness of Canadian energy products and home-grown expertise. However, most studies undertaken to date have reviewed ES on a project-by-project basis, which makes it difficult to ascertain the full value and costs of implementing the technology. It is within this context that the NRC, through its Energy Storage for Grid Security and Modernization Program (more recently the Advanced Clean Energy Program), has undertaken the development of a Canadian Energy Storage Study with support and input from NRCan’s Office of Energy Research and Development (OERD), strategic partners and consultants, stakeholders across the value chain, andan expert advisory board. This study, comprised of three pillars of analysis, is intended to provide a neutral andindependent analysis jurisdiction by jurisdiction across Canada that outlines the potential costs and benefits of the adoption of ES technologies.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.013 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".