Using Dynamic Thermal Rating systems to reduce power generation emissions
Bibliographic record
Abstract
Globally, consumption of electricity has increased substantially in recent years, resulting in high pressure on existing power infrastructure. In addition, in most jurisdictions, transmission networks have not seen any significant upgrades nor investment. This problem has been compounded by the increased interest in green energy production, partly as a result of greater climate change awareness and the resulting push for more sustainable energy systems. However, green power needs to be harnessed where it is available and it is often quite far from load centers. Unfortunately, existing power transmission lines were not constructed to incorporate distributed energy sources, and thus are often inadequate to transmit the total amount of power that could potentially be generated. One modern cost-effective approach to minimize the cost of transmission expansion is to utilize Dynamic Thermal Rating (DTR) systems to identify and harness underutilized capacity of existing conductors. This approach would allow the industry to transmit more electricity over power lines by assessing the actual operating conditions, rather than using the currently assumed conservative estimates. This study presents the reduction in power generation emissions that could be achieved by using DTR technology to incorporate more green energy onto the existing power grid. Using a model scenario, it also illustrates the optimal capacity sizing of green generation sources that could be constructed to maximize the amount of clean electricity that could be put onto the existing grid.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".