With integration of large-scale renewable energy

new controllable devices

and required reinforcement of power grids

modern power systems have typical characteristics such as uncertainty

vulnerability and openness

which makes operation and control of power grids face severe security challenges. Application of artificial intelligence (AI) technologies represented by machine learning in power grid regulation is limited by reliability

interpretability and generalization ability of complex modeling. Mode of hybrid-augmented intelligence (HAI) based on human-machine collaboration (HMC) is a pivotal direction for future development of AI technology in this field. Based on characteristics of applications in power grid regulation

this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence (HHI) system for large-scale power grid dispatching and control (PGDC). First

theory and application scenarios of HHI are introduced and analyzed; then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed. Key technologies are discussed to achieve a thorough integration of human/machine intelligence. Finally

state-of-the-art and future development of HHI in power grid regulation are summarized

aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.