Integrated planning of Natural Gas and electricity distribution networks with the presence of distributed natural gas fired generators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it