Resilient Operation Strategies for Integrated Power-Gas Systems
Bibliographic record
Abstract
This article presents a novel methodology for analyzing the resilience of an active distribution system (ADS) integrated with an urban gas network (UGN). It demonstrates that the secure adoption of gas turbines with optimal capacity and allocation can enhance the resilience of the ADS during high-impact, low-probability (HILP) events. A two-level tri-layer resilience problem is formulated to minimize load shedding as the resilience index during post-event outages. The challenge of unpredictability is addressed by an adaptive distributionally robust optimization strategy based on multi-cut Benders decomposition. The uncertainties of HILP events are modeled by different moment-based probability distributions. In this regard, considering the nature of each uncertain variable, a different probabilistic method is utilized. For instance, to account for the influence of power generated from renewable energy sources on the decision-making process, a diurnal version of the long-term short-term memory network is developed to forecast day-ahead weather. In comparison with standard LSTM, the proposed approach reduces the mean absolute error and root mean squared error by approximately 47% and 71% for wind speed, as well as 76% and 77% for solar irradiance network. Finally, the optimal operating framework for improving power grid resilience is validated using the IEEE 33-bus ADS and 7-node UGN.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".