Research on load frequency control of multi-microgrids in an isolated system based on the multi-agent soft actor-critic algorithm

Load variation, distributed power output uncertainty and multi-microgrids network complexity have brought great difficulties to the frequency stability of the whole microgrid. To address this problem, this paper uses a multi-agent deep reinforcement learning(DRL) algorithm to design the controllers to control the frequency of the multi-microgrids. Firstly, a load frequency control (LFC) model for multi-microgrids was built. Secondly, based on the centralized training and decentralized execution (CTDE) multi-agent reinforcement learning (RL) framework, the multi-agent soft actor-critic (MASAC) algorithm was designed and applied to the multi-microgrids model. The state space and action space of multi-agent were established according to the frequency deviation of every sub-microgrid and the output of each distributed power source. The reward function was then established according to the frequency deviation. The appropriate neural network and training parameters were selected to generate the interconnected microgrid controllers through multiple training of pre-learning. Finally, the simulation study shows that the MASAC controller proposed in this paper can quickly maintain frequency stability when the system is disturbed. Sensitivity analysis shows that the MASAC controller can effectively cope with the uncertainty of the system parameters.

Automated summary

To address the difficulties caused by load variation, distributed power output uncertainty, and multi-microgrid network complexity, this paper proposes a multi-agent deep reinforcement learning (DRL) algorithm. The algorithm is used to design controllers that can control the frequency of the multi-microgrids. The proposed MASAC controller has been shown to quickly maintain frequency stability and effectively cope with the uncertainty of system parameters.