Power System Operator in Mexico Reveals Millions in Savings by Updating Its Short-Term Thermal Unit Commitment Model
Bibliographic record
Abstract
In 2013, Centro Nacional de Control de Energía (CENACE), which is Mexico’s power system operator, updated its short-term hydrothermal coordination planning (STHTCP) tool. CENACE utilized commercial software to solve mixed-integer programming models for the unit commitment and economic dispatch of thermal units, such as gas, coal-fired, and combined-cycle plants. In an earlier paper that we reference, authors of this paper describe the mathematical model for the thermal unit commitment (TUC) problem, which is a sub-problem in the STHTCP process. The new STHTCP tool, which uses a mixed-integer programming-based TUC approach, has enhanced the modeling and solution quality compared to the Lagrangian relaxation-based TUC approach. The new tool has improved CENACE’s operations for managing its existing infrastructure, including power stations and transmission lines, and establishing the marginal prices needed to make energy trades. From the beginning of 2013 to the end of 2014, CENACE saved $2.2 million annually, which it attributes to better management of its thermal units. Over 10 years, it anticipates that these savings will represent more than $20 million in total savings.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".