An innovative centralized voltage control method for MV distribution systems based on deep reinforcement learning: application on a real test case in Benin

2021 ◽  
Author(s):  
B. B. Zad ◽  
J. F. Toubeau ◽  
O. Acclassato ◽  
O. Durieux ◽  
F. Vallée
Keyword(s):  
Reinforcement Learning ◽  
Distribution Systems ◽  
Control Method ◽  
Voltage Control ◽  
Test Case ◽  
Real Test

A Target Voltage Control Method for Distribution Systems based on Nodal P&Q Prices for PV Distributed Generators

2020 ◽  
Vol 140 (6) ◽  
pp. 456-464
Author(s):  
Naoto Yorino ◽  
Tsubasa Watakabe ◽  
Ahmed Bedawy Khalifa ◽  
Yutaka Sasaki ◽  
Yoshifumi Zoka
Keyword(s):  
Distribution Systems ◽  
Control Method ◽  
Voltage Control ◽  
Distributed Generators

scholarly journals Deep-Reinforcement-Learning-Based Two-Timescale Voltage Control for Distribution Systems

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3540
Author(s):  
Jing Zhang ◽  
Yiqi Li ◽  
Zhi Wu ◽  
Chunyan Rong ◽  
Tao Wang ◽  
...  
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Distribution Systems ◽  
Rapid Development ◽  
Voltage Control ◽  
Distribution Networks ◽  
Voltage Regulation ◽  
Continuous Variables ◽  
Online Application ◽  
Optimal Control Scheme

Because of the high penetration of renewable energies and the installation of new control devices, modern distribution networks are faced with voltage regulation challenges. Recently, the rapid development of artificial intelligence technology has introduced new solutions for optimal control problems with high dimensions and dynamics. In this paper, a deep reinforcement learning method is proposed to solve the two-timescale optimal voltage control problem. All control variables are assigned to different agents, and discrete variables are solved by a deep Q network (DQN) agent while the continuous variables are solved by a deep deterministic policy gradient (DDPG) agent. All agents are trained simultaneously with specially designed reward aiming at minimizing long-term average voltage deviation. Case study is executed on a modified IEEE-123 bus system, and the results demonstrate that the proposed algorithm has similar or even better performance than the model-based optimal control scheme and has high computational efficiency and competitive potential for online application.

scholarly journals Distributed Imitation-Orientated Deep Reinforcement Learning Method for Optimal PEMFC Output Voltage Control

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiawen Li ◽  
Yaping Li ◽  
Tao Yu
Keyword(s):  
Reinforcement Learning ◽  
Control Strategy ◽  
Output Voltage ◽  
Proton Exchange Membrane ◽  
Control Method ◽  
Voltage Control ◽  
Proton Exchange ◽  
Gradient Algorithm ◽  
Voltage Control Strategy ◽  
The Stability

In order to improve the stability of proton exchange membrane fuel cell (PEMFC) output voltage, a data-driven output voltage control strategy based on regulation of the duty cycle of the DC-DC converter is proposed in this paper. In detail, an imitation-oriented twin delay deep deterministic (IO-TD3) policy gradient algorithm which offers a more robust voltage control strategy is demonstrated. This proposed output voltage control method is a distributed deep reinforcement learning training framework, the design of which is guided by the pedagogic concept of imitation learning. The effectiveness of the proposed control strategy is experimentally demonstrated.

A Novel Voltage Control Method for Distribution Systems based on P&Q Nodal Prices for Distributed Generations

2019 ◽  
Vol 139 (3) ◽  
pp. 178-185 ◽  
Author(s):  
Naoto Yorino ◽  
Tsubasa Watakabe ◽  
Yuki Nakamura ◽  
Yutaka Sasaki ◽  
Yoshifumi Zoka ◽  
...  
Keyword(s):  
Distribution Systems ◽  
Control Method ◽  
Voltage Control ◽  
Distributed Generations ◽  
Nodal Prices

Research on Signal Control Method of Single Intersection Based on Reinforcement Learning

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Yilong Ren ◽  
Le Zhang ◽  
Han Jiang ◽  
Chengsheng Liu
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Signal Control
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