Human–Machine Collaborative Reinforcement Learning for Power Line Flow Regulation

The complexity and uncertainty in power systems leads to a great challenge for controlling the power grid using traditional manual adjustment methods. Reinforcement learning is a promising data-driven paradigm to address control issues in power grids. This article presents a novel human–machine collaborative (HMC) framework for line flow control. We formulate the collaboration between humans and machines as an extended Markov decision process (MDP) and introduce a human–machine collaborative reinforcement learning (HMC-RL) approach, which comprises a routing module, a machine dispatching module and an HMC dispatching module. The routing module determines whether the power system should be operated by the machine or through human–machine collaboration. The machine dispatching module predicts a machine dispatching action for regulating line flow, while the HMC module predicts an HMC dispatching action with human assistance. Experimental results conducted on the IEEE 39-bus and IEEE 118-bus systems demonstrate that our HMC-RL approach can significantly improve the performance of regulation compared to the machine dispatching policy. Specifically, HMC-RL achieves a 40.03% performance improvement on the IEEE 118-bus system, with 25.8% of human participation.