Two-Critic Deep Reinforcement Learning for Inverter-Based Volt-Var Control in Active Distribution Networks-Reference-Cited by-同舟云学术

Two-Critic Deep Reinforcement Learning for Inverter-Based Volt-Var Control in Active Distribution Networks

Author:

Liu Qiong¹^ORCID,Guo Ye¹^ORCID,Deng Lirong²^ORCID,Liu Haotian³^ORCID,Li Dongyu⁴^ORCID,Sun Hongbin³^ORCID,Huang Wenqi⁵^ORCID

Affiliation:

1. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China

2. Department of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China

3. State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing, China

4. School of Cyber Science and Technology, Beihang University, Beijing, China

5. Digital Grid Research Institute, China Southern Power Grid, Guangzhou, China

Funder

National Key R&D Program of China

Shanghai Chenguang Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

Reference48 articles.

1. Reducing learning difficulties: One-step two-critic deep reinforcement learning for inverter-based volt-var control;Liu,2022

5. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor;Haarnoja,2018

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