Maintaining the Frequency of AI-based Power System Model using Twin Delayed DDPG(TD3) Implementation
- Resource Type
- Conference
- Authors
- Dubey, Rohan; Loka, Renuka; Parimi, Alivelu Manga
- Source
- 2022 2nd International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC) Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2022 2nd International Conference on. :1-4 Jan, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Renewable energy sources
Stochastic processes
Optimal control
Reinforcement learning
Hybrid power systems
Data models
Numerical models
Load frequency control
multi-agent deep reinforcement learning
multi-agent Twin Delayed DDPG(MA-TD3)
single-area power systems
hybrid power systems
- Language
This paper proposes a multi-agent deep reinforcement learning (MA-DRL) method for load frequency control of a renewable energy single-area power system in a continuous action-space domain. This method can non-linearly adapt the control strategies for cooperative LFC control through off-policy learning. Multi-agent twin delayed deep deterministic policy gradient (TD3) is proposed to adjust and refine the control system parameters considering variational load and source behaviour. Implementation of the model requires only local information for each control area to achieve an optimal control state. Comparison between TD3 and DDPG model proves the edge of TD3 model. Simulations and numerical data comparison on a renewable energy single-area power system demonstrate that the proposed model can successfully reduce control errors and stochastic frequency deviations caused by load and renewable power fluctuations.