Physics-Shielded Multi-Agent Deep Reinforcement Learning for Safe Active Voltage Control With Photovoltaic/Battery Energy Storage Systems
Bibliographic record
Abstract
While many multi-agent deep reinforcement learning (MADRL) algorithms have been implemented for active voltage control (AVC) in power distribution systems, the safety of electrical components involved in the operation of these algorithms are mostly ignored. In this work, a safe MADRL control scheme is proposed to regulate the reactive and active power control of photovoltaics (PVs) to alleviate power congestion and improve voltage quality by coordinating battery energy storage systems (BESSs) and static var compensators (SVCs). Uniquely, the learning algorithm designed in this paper can limit the action of the agent when approaching a dangerous state to ensure the safety of BESSs during the training process, which is realized by developing a multi-agent twin delayed deep deterministic (MATD3) policy gradient algorithm with a physics-based shielding mechanism. Specifically, actions that lead to dangerous states, the state-of-charge (SoC) of BESSs is fully loaded or drained, are replaced by the shielding mechanism with safe actions while maintaining system stability. Furthermore, each PVs node in the power distribution network is treated as an agent under the fact of reactive and active power sensitivities to voltage in the MATD3 algorithm, which is beneficial for improving scalability. Training, testing and comparative results on IEEE 33-bus and 141-bus with real-world data are provided to demonstrate the effectiveness and superiority of the proposed algorithm.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".