A Multiagent Quantum Deep Reinforcement Learning Method for Distributed Frequency Control of Islanded Microgrids

This article proposes a data-driven method for distributed frequency control of islanded microgrids based on multiagent quantum deep reinforcement learning (DRL). The proposed method combines the conventional DRL framework with quantum machine learning, and can adaptively obtain the optimal cooperative control strategy. The microgrid secondary frequency control is organized in a distributed manner in which each agent performs the control action only based on the local and neighboring information. To solve the DRL problem, the deep deterministic policy gradient algorithm is derived to tune the agents' parameters. Simulation tests are performed on an islanded microgrid with four distributed generators and a 13-bus microgrid. The results demonstrate that the proposed method can effectively regulate the frequency with better time-delay tolerance, and displays the quantum advantage in parameter reduction.

Automated summary

This article proposes a data-driven method for distributed frequency control of islanded microgrids based on multiagent quantum deep reinforcement learning (DRL). The proposed method combines the conventional DRL framework with quantum machine learning, and can adaptively obtain the optimal cooperative control strategy.