Secondary Voltage Collaborative Control of Distributed Energy System via Multi-Agent Reinforcement Learning

Abstract

In this paper, a new voltage cooperative control strategy for a distributed power generation system is proposed based on the multi-agent advantage actor-critic (MA2C) algorithm, which realizes flexible management and effective control of distributed energy. The attentional actor-critic message processor (AACMP) is extended into the MA2C method to select the important messages from all communication messages adaptively and process important messages efficiently. The cooperative control strategy trained by centralized training and decentralized execution frame will take over the responsibility of the secondary control level for voltage restoration in a distributed manner. The introduction of the attention mechanism reduces the amount of information exchanged and the requirements of the communication network. Finally, a distributed system with six energy nodes is used to verify the effectiveness of the proposed control strategy.

Keywords

• distributed energy
• deep reinforcement learning
• attentional mechanism
• nodal voltage
• coordination optimization