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Special Section Paper | Open Access

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

Shixiong Fan1( ), Jianbo Guo1, Shicong Ma1, Lixin Li1, Guozheng Wang2, Haotian Xu1, Jin Yang3, Zening Zhao1
China Electric Power Research Institute, Beijing 100192, China
Beijing Huairou Laboratory, Beijing 101400, China
James Watt School of Engineering, the University of Glasgow, Glasgow G12 8QQ, UK
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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.

Keywords

Artificial intelligencehuman-machine collaborative controlhuman-machine hybrid intelligenceoptimization and evolutionpower grid dispatching and control

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 1,
January 2024
Pages 1-12
DOI: 10.17775/CSEEJPES.2023.00940
Cite this article:
Fan S, Guo J, Ma S, et al. Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control. CSEE Journal of Power and Energy Systems, 2024, 10(1): 1-12. https://doi.org/10.17775/CSEEJPES.2023.00940

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Received: 09 February 2023
Revised: 08 June 2023
Accepted: 06 July 2023
Published: 28 December 2023
© 2023 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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