Teaching an Old Dog New Tricks: Identifying Policy and Regulatory Barriers in Microgrid Adoption
Bibliographic record
Abstract
The electricity sector is poised to be a powerful enabler towards creating a low carbon economy.The Ontario government began instituting policies to achieve emissions reductions through programs like the Feed in Tariff.The initial policy framework advocated for a specific tool (renewable generation) to achieve its inferred mandate of reducing carbon emissions and codified these programs in legislation.As more technologies evolved additional policy prescriptions were created to integrate them; these include smart grid integration and energy storage procurement.The fundamental environmental objectives of reducing emissions were not mentioned in the creation of these policies.Ontario is now faced with a patchwork of policy tools that cannot value disruptive technologies like microgrids because the current electricity markets that could leverage them were designed to be compartmentalized and do not value emissions reductions.This thesis uses two examples of integrating microgrids in Ontario to highlight the challenges created by the current policy framework.This thesis argues that while the Ontario government has invested in creating tools to deploy some new technologies like renewable generation, without thoughtful reform it will be unable to effectively realize an environmental return on its investments.
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 | 0.030 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".