Adaptive Distribution Network Topology Reconfiguration via Potential Games
Why this work is in the frame
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Bibliographic record
Abstract
The rapid proliferation of diverse loads such as electric vehicles and storage systems in active distribution networks (DNs) has increased risks of line congestions and violations of physical electrical limits that can amalgamate in cascading outages. As such, effective coordination amongst cyber-enabled power nodes that are prevalent in today's grid is essential for maintaining the secure and stable operations in these changing conditions. In this paper, we present a novel decentralized DN topology reconfiguration algorithm based on potential game theoretic constructs. This algorithm allows active cyber agents residing in DN buses to infer the global state of the system by way of peer-to-peer data exchanges. This knowledge is then utilized by these entities to make local line switching decisions that iteratively improve load balance and voltage profile across the feeder while adhering to physical system limits. We show that the algorithm is guaranteed to converge to the Nash Equilibrium by evoking potential and finite game theoretic constructs. The proposed algorithm is then compared with recent literature based on genetic algorithm via practical simulation studies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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