Central Situational Awareness System for Resiliency Enhancement of Integrated Energy Systems
Bibliographic record
Abstract
In integrated gas and electricity energy systems, a catastrophic outage in one system could propagate to other, resulting in severe service interruption like what happened in 2021 Texas Blackout. To alleviate detrimental effects of these events, a coordinated effort must be adopted between integrated energy systems. In this paper, a central situational awareness system (CSAS) is developed to improve the coordination of operational resiliency measures by facilitating information sharing between power distribution systems (PDSs) and natural gas networks (NGNs) during emergency conditions. The CSAS collects operational data of the PDS and the NGN as well as data of upcoming weather condition, extracts the most vulnerable lines and pipelines, and accordingly obtains emergency actions. The emergency actions, i.e., optimal multi-microgrid formation, scheduling of distribution energy resources (DERs), and optimal electrical and gas load shedding plan, are optimized through a coupled graph-based approach with stochastic mixed integer linear programming (MILP) model. In the proposed model, uncertainties of renewable energy resources (RESs) is also considered. Numerical results on an integrated IEEE 33-bus and 30-node NGNs demonstrate the effectiveness of proposed CSAS.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".