A power optimization model for the long‐term planning scenarios: Case study of <scp>Mexico's</scp> power system decarbonization
Bibliographic record
Abstract
Abstract Mexico is committed to reducing its CO2 emissions according to the Paris Agreement. A relevant effort must be made for the analysis of Mexico's electric energy system towards a progressive decarbonization with a larger participation of intermittent renewable energies. The analysis of power planning scenarios, with different assumptions on costs, emissions, and intermittent performance of the power generating technologies, is needed to make sustainable decisions in the transition toward a cleaner power sector. Tools for energy modelling are required to develop and analyze scenarios with minimum costs subject to environmental constraints. The purpose of the article is to explain the modelling approach of a novel and flexible power planning tool, which is based on a well‐known linear programming optimization method combined with a computing strategy to optimize time consumed for reading, processing calculations, and writing the huge number of economic and technical parameters required for the hourly power dispatch in complex interconnected electric systems. The time consumed has been optimized by means of a binary matrix that activates the input and use of only the data needed for the solution of the problem. The paper describes the MC model and demonstrates some of its analytical capabilities through a Mexican case study with a least cost scenario and two decarbonization scenarios of the power interconnected system for the period from 2020‐2050.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".