Power Loss Alleviation in Integrated Power and Natural Gas Distribution Grids
Bibliographic record
Abstract
The existing distributed gas-fired generation (GfG) units have (partially) connected the power and gas distribution grids. In addition, as the emerging technology for conversion of renewable/surplus power to synthetic natural gas, i.e., power-to-gas (PtG) materializes, the foundation for a fully integrated power and gas distribution grid is more likely to be set. This could, in turn, bring about new opportunities for exploiting the natural gas distribution grid for mitigation of the existing and imminent issues in power distribution systems. To that end, this paper unveils a new model for optimal joint scheduling of PtG and GfG units in a power-gas embedded grid. The PtG-GfG facility is operated for arbitrage and loss alleviation as a regulation service to the power distribution system. The mathematical formulation of a new method for estimation of the loss reduction in the integrated grid is developed and embedded into the optimization problem. The efficacy and feasibility of the model is numerically validated on a test system. The results indicate that the proposed model can reliably estimate the loss reduction percentage and accordingly determine the scheduling setpoints to achieve the determined loss reduction. It is demonstrated that the model increases the profitability of investment in PtG-GfG facilities via extra financial settlements for the facility operator.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".