mlsanityData to 14 Oct 2026

Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networks

arXiv 2005.11142 ↗First posted 20 May 2020eess.SY cs.LG cs.SYcited by 12

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Abstract (arXiv)

Model-based Vol/VAR optimization method is widely used to eliminate voltage violations and reduce network losses. However, the parameters of active distribution networks(ADNs) are not onsite identified, so significant errors may be involved in the model and make the model-based method infeasible. To cope with this critical issue, we propose a novel two-stage deep reinforcement learning (DRL) method to improve the voltage profile by regulating inverter-based energy resources, which consists of offline stage and online stage. In the offline stage, a highly efficient adversarial reinforcement learning algorithm is developed to train an offline agent robust to the model mismatch. In the sequential online stage, we transfer the offline agent safely as the online agent to perform continuous learning and controlling online with significantly improved safety and efficiency. Numerical simulations on IEEE test cases not only demonstrate that the proposed adversarial reinforcement learning algorithm outperforms the state-of-art algorithm, but also show that our proposed two-stage method achieves much better performance than the existing DRL based methods in the online application.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 May 2020.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 5; 2025: 0; 2026: 1.

12 papers.

  1. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 50
  2. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systemscited by 11
  3. 2021Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Trainingcited by 8
  4. 2023Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Networkcited by 4
  5. 2021Bi-level Off-policy Reinforcement Learning for Volt/VAR Control Involving Continuous and Discrete Devicescited by 3
  6. 2022Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 1
  7. 2024Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Informationcited by 1
  8. 2024Robust Deep Reinforcement Learning for Inverter-based Volt-Var Control in Partially Observable Distribution Networkscited by 1
  9. 2024Residual Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 1
  10. 2026Two-Stage Active Distribution Network Voltage Control via LLM-RL Collaboration: A Hybrid Knowledge-Data-Driven Approachcited by 1
  11. 2022Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 0
  12. 2024Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Controlcited by 0