Power Congestion Management in Integrated Electricity and Gas Distribution Grids
Bibliographic record
Abstract
The proliferation of gas-fired generation (GfG) units and emerging power-to-gas (PtG) technology can set the stage for an integrated natural gas and power distribution system. PtG and gas-fired units have the potential to mitigate several existing and imminent issues of power distribution systems. This paper investigates how PtG and GfG facilities can be added to the portfolio of conventional resolutions when the motivation is (partly) congestion management in power distribution systems. It demonstrates how PtG and GfG units as merchant investments can change the operation philosophy from the traditional preventive to the novel corrective mode. To that end, a new real-time optimal scheduling algorithm is proposed to enable a PtG-GfG unit to optimally contribute to the congestion management. Merchandise operators profit, in addition to the arbitrage benefit, by relieving the distribution grid's congestion, thereby achieving a more stable return on investments. Slack variables are incorporated into the optimization problem to measure the contribution of the PtG-GfG unit to the congestion management. A new mechanism is proposed through which the merchandise operator is financially compensated by the power system operator, due to its contribution to the congestion management. Numerical studies using real-world data on a test system validate the efficacy and feasibility of the algorithm.
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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 | 0.001 |
| 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".