A multi-period optimization model for energy planning with CO2 emission considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A multi-period optimal energy planning program for Ontario has been developed in mixed-integer non-linear programming using General Algebraic Modeling System, GAMS. The program applies both time-dependent and time-independent constraints. These include, but not limited to, construction time, fluctuation of fuel prices, and CO2 emission reduction target. It also offer flexibility of fuel balancing and fuel switching of the existing boilers and option purchasing of Carbon credit if the reduction target is not achievable. The objective function incorporates all these constraints as well as minimizes over all the cost of electricity and meets the projected electricity demand over a span of 14 years. Originally it was used for only two study cases which are the base case scenario for Ontario where no CO2 emission reduction target is applied and the 6% reduction case to meet the Kyoto Protocol; to reduce its CO2 emission to 6% below that of 1990. This project utilizes the program for various similar study cases and beyond. The Ontario’s study cases include different CO2 emission reduction targets ranging from 6% to 75% below 1990 levels by 2012. The overall cost of the electricity for different CO2 emission reduction targets increases linearly with slope of 1.3. Carbon capture and sequestration, retrofitting of the carbon capture and storage, and fuel switching are the main strategy in order to keep the cost of electricity relative low and satisfy the CO2 emission constraints. These results help us better understand the factors affecting the fleet’s structure. It may also help plan the energy direction of Ontario and perhaps serve as an example for other provinces, territories, states, and even countries.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it