Dynamic Distribution Network Reconfiguration With Generation and Load Uncertainty
Bibliographic record
Abstract
Given the uncertainty in load demand and renewable energy sources, the distribution network reconfiguration (DNR) problem is a stochastic mixed-integer nonlinear optimization program with a running time that scales exponentially with the number of sectional and tie line switches. Stochastic optimization techniques require knowledge of the stochastic processes of the uncertain parameters, which may not be available in practice. This paper addresses both the scalability and uncertainty issues in solving the DNR problem by developing a deep reinforcement learning (DRL) algorithm that determines the optimal topology using a transformer deep neural network (DNN) architecture, and subsequently solves an AC optimal power flow (OPF) problem to satisfy the operation constraints. A neural combinatorial optimization algorithm is applied to train the DNN, which penalizes infeasible solutions. Simulations on a 119-bus test system show that our proposed algorithm can obtain a near-optimal solution to the stochastic DNR problem with a small gap (i.e., 4.7% on average) from the objective value of the deterministic DNR problem. When compared with existing learning-based DNR algorithms in the literature, our proposed algorithm can obtain at least 11% lower objective value. We demonstrate the scalability of our proposed algorithm in larger systems with 595, 1190, and 3570 buses.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".