reXplan: A Novel Tool for the Analysis of Climate Resilience in Power Systems
Bibliographic record
Abstract
The increase in extreme weather events induced by climate change, and their impact on power systems, has created a need for tools that can assess and improve system resilience. To meet this need, the R&D team of the Energy System Consulting segment of Tractebel Engineering GmbH has developed a novel tool called reXplan. ReXplan is a Python library for resilient electrical system planning under extreme hazard events, such as windstorms, earthquakes, floods, wildfire, etc. It is designed to help power system operators and planners make better-informed decisions to create more resilient and secure power grids. This paper provides an overview of reXplan’s main features, architecture, methodology of analysis and metrics. ReXplan is capable of modeling both spatiotemporal extreme events and electrical power systems. It leverages technologies and techniques such as Julia/JuMP package and sequential Monte Carlo analysis with multivariate stratified sampling to achieve high accuracy in the results while reducing computational load. By quantifying resiliency metrics, comparing different planning strategies, and validating technical solutions, reXplan can help reduce the risks of severe outages in the grid. The software can be easily integrated into common data science environments and is available as a Python library. For computationally intensive tasks, such as optimal power flow, reXplan is exploiting the fast speed of Julia programming language, using PowerModels.jl as backend.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".