A feasibility analysis of transactive energy systems in Ontario
Bibliographic record
Abstract
This research paper explores the potential for transactive energy systems (TESs) and blockchainenabled microgrids (BEMs) to be integrated into Ontario’s existing electrical grid as a sustainable energy solution for climate change, while also delivering economic and reliability benefits to consumers and other stakeholders. The multi-layer perspective (MLP) framework is applied to assess whether or not a socio-technical transition is possible and/or likely in Ontario, and how this transition might occur. These questions are answered by relying primarily on industry and academic literature in the form of technical whitepapers, academic journal articles and theses. Several case studies are also presented to show how TESs and BEMs have been integrated into existing grids in a variety of jurisdictions around the world. Areas of future research are presented following the case studies to highlight important yet unexplored topics concerning TESs in Ontario. The paper concludes that the blockchain component of BEMs is unnecessary, given Ontario’s incompatible cultural and political context with the technology’s value proposition. However, the paper finds that TESs are likely to be adopted in Ontario, and in some cases, they already have been to a limited extent, as can be seen in the cases of Alectra Utilities and Opus One Solutions. This adoption of TESs in the province is considered to be the beginning of the reconfiguration path transitional pathway, as identified in the MLP literature.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".