Calibration of Grid Models for Analyzing Energy Policies
Bibliographic record
Abstract
Intermittent forms of renewable energy destabilize electricity grids unless adequate reliable generating capacity and storage are available, while instability of hybrid electricity grids and cost fluctuations in fossil fuel prices pose further challenges for policymakers. We examine the interaction between renewable and traditional fossil-fuel energy sources in the context of the Alberta electricity grid, where policymakers seek to eliminate coal and reduce reliance on natural gas. We develop a policy model of the Alberta grid and, unlike earlier models, calibrate the cost functions of thermal generation using positive mathematical programming. Rather than employing constant average and marginal costs, calibration determines upward sloping supply (marginal cost) functions. The calibrated model is then used to determine an optimal generation mix under different assumptions regarding carbon prices and policies to eliminate coal-fired capacity. Results indicate that significant wind capacity can enter the Alberta grid if carbon prices are high, but that it remains difficult to eliminate reliable baseload capacity. Adequate baseload coal and/or natural gas capacity is required, which is the case even if battery storage is allowed into the system. Further, significant peak-load gas capacity will also be required to backstop intermittent renewables.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".