云边智能: 电力系统运行控制的边缘计算方法及其应用现状与展望

白昱阳; 黄彦浩; 陈思远; 张俊; 李柏青; 王飞跃

doi:10.16383/j.aas.2020.y000001

云边智能: 电力系统运行控制的边缘计算方法及其应用现状与展望

doi: 10.16383/j.aas.2020.y000001

白昱阳^1,,
黄彦浩^2,,
陈思远^1,,
张俊^{1, 3,},
李柏青^2,,
王飞跃^3,

1.
武汉大学电气与自动化学院武汉 430072
2.
中国电力科学研究院有限公司北京 100192
3.
中国科学院自动化研究所复杂系统管理与控制国家重点实验室北京 100190

基金项目: 国家电网公司科技项目(XT71-19-032)资助

详细信息

作者简介:
白昱阳：武汉大学电气与自动化学院硕士研究生. 2019 年获得武汉大学电气工程学院学士学位. 主要研究方向为边缘计算在电力系统运行和控制中的应用. E-mail: baiyuyang@whu.edu.cn

黄彦浩：中国电力科学研究院有限公司博士, 高级工程师. 主要研究方向为电力系统仿真分析. E-mail: hyhao@epri.sgcc.com.cn

陈思远：武汉大学电气与自动化学院博士研究生. 2018 年获得武汉大学电气工程学院硕士学位. 主要研究方向为边缘计算在电力系统运行和控制中的应用. E-mail: wddqcsy@whu.edu.cn

张俊：武汉大学电气与自动化学院教授. 2003 年和 2005 年分别获得华中科技大学电子信息与通信工程系学士与硕士学位. 2008 年获得亚利桑那州立大学电气工程博士学位. 主要研究方向为智能系统, 人工智能, 知识自动化, 及其在智能电力和能源系统中的应用. 本文通信作者. E-mail: jun.zhang@qaii.ac.cn

李柏青：中国电力科学研究院有限公司教授级高级工程师, 主要研究方向为电力系统分析与运行控制技术.E-mail: libq@epri.sgcc.com.cn

王飞跃：中国科学院自动化研究所复杂系统管理与控制国家重点实验室主任, 国防科技大学军事计算实验与平行系统技术研究中心主任, 中国科学院大学中国经济与社会安全研究中心主任, 青岛智能产业技术研究院院长. 主要研究方向为平行系统的方法与应用, 社会计算, 平行智能以及知识自动化. E-mail: feiyue.wang@ia.ac.cn

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出版历程
- 收稿日期: 2019-11-08
- 录用日期: 2019-12-01
- 网络出版日期: 2020-03-30
- 刊出日期: 2020-03-30

Cloud-edge Intelligence: Status Quo and Future Prospective of Edge Computing Approaches and Applications in Power System Operation and Control

1.
School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072
2.
China Electric Power Research Institute, Beijing 100192
3.
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

Funds: Supported by Science and Technology Project of State Grid Corporation of China (XT71-19-032)

摘要

摘要: 本文分析了当前我国电力系统的运行与控制面临的挑战, 对边缘计算的发展背景和关键技术进行了介绍, 阐述了云边协同和边边协同的功能与特征, 并对边缘协同技术下的边缘智能技术进行了探讨. 结合电力系统的层级式构架, 讨论了在电网部署边缘计算层的方法, 提出利用云边协同、边边协同、边缘智能等技术解决电力系统面临的实时性高、数据周期短、任务复杂等难题, 在减轻边缘节点与云中心通信压力的同时, 提高业务服务质量, 保障边缘节点的数据隐私. 通过对边缘计算在“源 − 网 − 荷”各环节的应用前景进行分析与讨论, 阐述了边缘计算在电网中的可行性与实用性. 最后, 对边缘计算的应用范式与方案进行了总结, 并对其在未来电力系统中的发展方向进行了展望.
- 边缘计算 /
- 云计算 /
- 云边协同技术 /
- 边缘智能 /
- 电力系统运行控制
Abstract: In this paper, the current challenges faced by China′ s power grid are analyzed, and the corresponding developmental background and key techniques of edge computing are introduced, including the functionalities and features of cloud-edge coordination and edge-edge coordination. Then, edge intelligence resulted from edge coordination is discussed. Considering the hierarchical architecture of power grids, the deployment of edge computing layer for power grid operation and control is illustrated in details. Through reducing communication data volume among edge nodes and the cloud center, the edge computing architecture and corresponding coordination mechanism aims to improve real-time performance of complex grid tasks, bring distributed intelligence to the system, while protecting data privacy of the edge nodes. Finally, the application paradigms of edge computing are summarized, and its future developmental directions in the power system operation and control are prospected.
- Edge computing /
- cloud computing /
- cloud-edge coordination /
- edge intelligence /
- power system operation and control

HTML全文

图 1 边缘智能架构

Fig. 1 The architecture of edge intelligence

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图 2 云边协同和边边联邦协同的联合训练框架

Fig. 2 Joint training framework for cloud-edge collaboration and edge-edge federation collaboration

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图 3 边缘节点在电力系统中的部署示意图

Fig. 3 Schematic diagram of edge node deployment in power system

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图 4 边缘计算平台在电力系统中的应用框架

Fig. 4 Application framework of edge computing platform in power system

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图 5 变电站综自系统结构图

Fig. 5 Structure of integrated automation system of substation

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图 6 边缘计算在变电站中的数据交互示意图

Fig. 6 Data interaction diagram of edge computing in substation

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图 7 负荷建模边缘计算构架图

Fig. 7 The architecture diagram of edge computing for load modelling

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