Quantum-Enhanced Deep Deterministic Policy Gradient for Voltage Regulation in Smart Grids With High Electromobility Penetration
Bibliographic record
Abstract
The increasing adoption of electric vehicles and integration of distributed renewable energy resources are reshaping power distribution systems. Although these developments promote decarbonization and enhance energy accessibility, they also present challenges related to nodal voltage regulation and power flow management. Quantum-enhanced learning can address modern power system control challenges. We introduce a Quantum-Enhanced Deep Deterministic Policy Gradient algorithm (Q-DDPG) for voltage regulation in smart grid networks with high electric vehicle integration. By incorporating parameterized quantum circuits into the actor network, our approach efficiently searches for high-dimensional control policies using entanglement and superposition. We model the smart grid as a continuous Markov decision process, with nodal voltages, electric vehicle demands, and renewable fluctuations as states, and voltage setpoints and reactive power dispatch as actions. Simulations on a 33-bus feeder with varying electric vehicle penetration levels show that Q-DDPG achieves faster convergence, maintains voltage profiles within ±1% of nominal values, and significantly reduces voltage deviation compared to standard classical reinforcement learning models. Robustness analysis demonstrates that quantum-enhanced exploration yields more resilient control strategies, suggesting that quantum-inspired reinforcement learning can significantly enhance voltage stability and power quality in smart grids.
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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) | 0.000 | 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".