Adaptive Distribution Network Topology Reconfiguration via Potential Games
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".