Multi-Agent Safe Reinforcement Learning Based Real-Time Volt/Var Optimization for Modern Distribution Networks
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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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 | 0.002 |
| 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) | 0.001 | 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".