Integrated planning of Natural Gas and electricity distribution networks with the presence of distributed natural gas fired generators
Bibliographic record
Abstract
A sequential recourse stochastic optimization approach to solving the long-term integrated planning problem of a Natural Gas (NG) distribution system and Natural Gasfired Distributed Generators (NGDGs) is presented. The NGDG location and sizing problem under uncertain demand is solved in the first stage. The computed location and size of NGDG is employed to compute the NG demand at each node. The deterministic mixed integer non-linear programming NG optimal pipeline route selection problem is solved in the second stage. The solution methodology is illustrated using a simple case study over a long term planning period of 20 years. This work extends previous heuristic based approaches dependent on consideration of limited candidate solutions by employing a two stage recourse stochastic optimization technique and an analytical solution technique for solving the integrated problem. The proposed model allows for planning the future integration of NGDG and NG-pipelines without having to populate a set of expansion options.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".