Market Design for the 21st Century: Recommendations for Alberta’s Power Market
Bibliographic record
Abstract
Alberta’s electricity system is in transition. From a system dominated by coal generation less than a decade ago, Alberta’s electricity is now largely supplied by natural gas generation with an increasing share of variable wind and solar energy. Falling clean technology costs, federal clean electricity regulations and a rising carbon price will further shift Alberta’s supply mix away from unabated fossil fuels into one more reliant on a mix of renewables and new technologies, such as carbon-captured natural gas, small modular nuclear reactors and hydrogen generation. In this report, we consider future market designs fit for purpose for this changing electricity mix. We assess market design options based on the criteria of reliability, affordability, investor confidence and complexity. At the heart of the matter is the question of which approach is best able to deliver reliable and low emission supply at low cost to Alberta consumers. Importantly, we consider market designs suitable for the changing nature of 21st century grids with more variability on the supply side and more flexibility on the demand side. If the grid of the past involved forecasting demand and dispatching supply, increasingly grids of the future will flip this upside down by forecasting supply and dispatching demand. Alberta’s market design needs to both reflect and enable this new reality.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".