Towards Resilient Self-Proactive Distribution Grids Against Wildfires: A Dual Rolling Horizon-Based Framework
Bibliographic record
Abstract
Wildfires have recently posed a significant danger to the security and reliability of electrical power systems, prompting the development of innovative frameworks for enhanced resilience. Existing literature has primarily focused on service restoration using stochastic optimization models that lack the necessary dynamism to tackle uncertainties effectively. To address this problem, a Dual Rolling horizon optimization (DRHO) is utilized to devise a smart resilience controller (SRC) that mitigates distribution network outages caused by wildfire disruptions. The proposed SRC can dynamically monitor and analyze the spatiotemporal behaviors of wildfires, such as their intensity, arrival time, and binding pathways from their ignition sources to electrical equipment. In doing so, it can proactively take real-time corrective measures before the wildfire reaches power distribution lines. The primary objective is to minimize load shedding and operational costs through re-configuring the network and employing a mix of stationary and mobile distributed energy resources (MDERs) that operate under a master-slave control scheme. The effectiveness of the proposed DRHO-based SRC is validated through diverse case studies and is compared against scenario-based approaches. Considering real-world data of Alberta wildfires, simulation results demonstrate the proposed solution's robustness to uncertainties, significantly reducing power outages and ensuring enhanced resilience.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".