CSEE Journal of
Power & Energy Systems

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

2024-12-06

Cite This:

S. Fan et al., "Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control," in CSEE Journal of Power and Energy Systems, vol. 10, no. 1, pp. 1-12, January 2024, doi: 10.17775/CSEEJPES.2023.00940.

Author: Shixiong Fan; Jianbo Guo; Shicong Ma; Lixin Li; Guozheng Wang; Haotian Xu;Jin Yang; Zening Zhao

Affiliation: China Electric Power Research Institute, China; Beijing Huairou Laboratory, China; School of Engineering the University of Glasgow, UK

Abstract:

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.

Read full article: https://ieeexplore.ieee.org/document/10375976