Service Restoration Using Deep Reinforcement Learning and Dynamic Microgrid Formation in Distribution Networks
Why this work is in the frame
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Bibliographic record
Abstract
A resilient power distribution network can reduce length and impact of power outages, maintain continuous services, and improve reliability. One effective way to enhance the system's resilience is to form microgrids during outages. In this article, a novel dynamic microgrid formation-based service restoration method using deep reinforcement learning is proposed, and it is treated as a Markov decision process (MDP) while taking operational and structural limitations of microgrids into account. The deep Q-network is employed to obtain optimal control strategies for microgrid formation. We have introduced a new way for the agent to choose actions when building a microgrid using the deep Q-learning method, which ensures that the microgrid has a feasible radial structure. The proposed service restoration method enables real-time computing to facilitate online formation of dynamic microgrids and adapts to changing conditions. The influence of optimal switch placement on service restoration using proposed method is also investigated. The effectiveness of proposed service restoration method is validated by case studies using the modified IEEE 33-node test system and a real 404-node distribution system operated by Saskatoon Light and Power in Saskatoon, Canada.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it