Multi-Agent Safe Reinforcement Learning Based Real-Time Volt/Var Optimization for Modern Distribution Networks
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Bibliographic record
Abstract
Increasing photovoltaic (PV) power generation may lead to fast voltage fluctuations, and the Volt/Var optimization (VVO) technique can be used to regulate the voltage profile. In this paper, a real-time data-driven safe VVO framework is proposed for modern distribution networks (MDNs), where a MDN is divided into multiple regions, and a decentralized deep reinforcement learning (DRL) is used to achieve the coordinated control of reactive power within each region for the voltage profile regulation and power loss minimization. The VVO is formulated as a safe partially observable Markov decision process. To provide a safe operational zone, a penalty-based reward function is formulated. A modified transition probability is then integrated into the proposed safe DRL-based VVO framework. This proposed framework can be used to regulate PV power generation within a 3-minute time resolution, and is validated through the IEEE 33-bus test system by using actual load and PV power generation data, showing superior performance comparing to three state-of-the-art DRL-based VVO frameworks.
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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) | 0.000 | 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