scholarly journals Coordinated Complex-Valued Encoding Dragonfly Algorithm and Artificial Emotional Reinforcement Learning for Coordinated Secondary Voltage Control and Automatic Voltage Regulation in Multi-Generator Power Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 180520-180533
Author(s):  
Linfei Yin ◽  
Shikui Luo ◽  
Yaoxiong Wang ◽  
Fang Gao ◽  
Jun Yu
Keyword(s):  
Reinforcement Learning ◽  
Power Systems ◽  
Voltage Control ◽  
Voltage Regulation ◽  
Dragonfly Algorithm ◽  
Secondary Voltage ◽  
Complex Valued

scholarly journals Secondary voltage regulation based on average voltage control

TecnoLógicas ◽  
2018 ◽  
Vol 21 (42) ◽  
pp. 63-78
Author(s):  
Edwin H. Lopera-Mazo ◽  
Jairo Espinosa
Keyword(s):  
Control System ◽  
Power Systems ◽  
Reactive Power ◽  
Voltage Control ◽  
Voltage Regulation ◽  
Voltage Profile ◽  
Real Power ◽  
Power Resources ◽  
Control Actions ◽  
Secondary Voltage

This paper compares a conventional Secondary Voltage Regulation (SVR) scheme based on pilot nodes with a proposed SVR that takes into account average voltages of control zones. Voltage control significance for the operation of power systems has promoted several strategies in order to deal with this problem. However, the Hierarchical Voltage Control System (HVCS) is the only scheme effectively implemented with some relevant applications into real power systems.The HVCS divides the voltage control problem into three recognized stages. Among them, the SVR is responsible for managing reactive power resources to improve network voltage profile. Conventional SVR is based on dividing the system into some electrically distant zones and controlling the voltage levels of some specific nodes in the system named pilot nodes, whose voltage levels are accepted as appropriate indicators of network voltage profile.The SVR approach proposed in this work does not only consider the voltage on pilot nodes, but it also takes the average voltages of the defined zones to carry out their respective control actions. Additionally, this innovative approach allows to integrate more reactive power resources into each zone according to some previously defined participation factors.The comparison between these strategies shows that the proposed SVR achieves a better allocation of reactive power in the system than conventional SVR, and it is able to keep the desired voltage profile, which has been expressed in terms of network average voltage.

Recent results in secondary voltage control of power systems

1991 ◽  
Vol 11 (2) ◽  
pp. 49
Author(s):  
A. Stankovic ◽  
M. Ilic ◽  
D. Maratukulam
Keyword(s):  
Power Systems ◽  
Voltage Control ◽  
Secondary Voltage

Recent results in secondary voltage control of power systems

1991 ◽  
Vol 6 (1) ◽  
pp. 94-101 ◽  
Author(s):  
A. Stankovic ◽  
M. Ilic ◽  
D. Maratukulam
Keyword(s):  
Power Systems ◽  
Voltage Control ◽  
Secondary Voltage

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 Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8270
Author(s):  
Nikita Tomin ◽  
Nikolai Voropai ◽  
Victor Kurbatsky ◽  
Christian Rehtanz
Keyword(s):  
Reinforcement Learning ◽  
Voltage Control ◽  
Power Grids ◽  
Droop Control ◽  
Electrical Networks ◽  
Heavy Load ◽  
Market Interaction ◽  
Secondary Voltage ◽  
Multi Agent ◽  
Nodal Voltage

The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction.

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