联邦学习在新型电力系统中的应用与展望

doi:10.3969/j.issn.2097-0706.2024.11.007

综合智慧能源 ›› 2024, Vol. 46 ›› Issue (11): 54-64.doi: 10.3969/j.issn.2097-0706.2024.11.007

• 综合能源系统优化运行与控制 • 上一篇下一篇

联邦学习在新型电力系统中的应用与展望

吕永升¹^,²(), 张啸宇¹^,²^,^*(), 王榕夕³, 郭佩乾⁴

1.安徽大学人工智能学院，合肥 230601
2.自主无人系统技术教育部工程研究中心，合肥 230601
3.浙江万里学院信息与智能工程学院，浙江宁波 315100
4.清华大学电机工程与应用电子技术系，北京 100084

收稿日期:2024-06-17 修回日期:2024-08-09 出版日期:2024-11-25
通讯作者: * 张啸宇（1993），男，助理教授，博士，从事智能电网与综合能源智能决策、智能电网数据的信息与隐私安全等方面的研究，zhangxiaoyu@ahu.edu.cn。
作者简介:吕永升（2001），男，科研助理，硕士，从事联邦学习研究，372316825@qq.com。
基金资助:
国家自然科学基金项目(62303005)

Application and prospect of federated learning in new power systems

LYU Yongsheng¹^,²(), ZHANG Xiaoyu¹^,²^,^*(), WANG Xirong³, GUO Peiqian⁴

1. School of Artificial Intelligence， Anhui University，Hefei 230601，China
2. Engineering Research Center of Autonomous Unmanned System Technology， Ministry of Education， Hefei 230601，China
3. School of Information and Intelligent Engineering， Zhejiang Wanli University，Ningbo 315100，China
4. Department of Electrical Engineering， Tsinghua University， Beijing 100084，China

Received:2024-06-17 Revised:2024-08-09 Published:2024-11-25
Supported by:
National Natural Science Foundation of China(62303005)

摘要/Abstract

摘要：

新型电力系统的核心目标是实现清洁、低碳、安全、灵活和高效的电力供应，同时是推动“双碳”目标的关键举措。然而，面对新能源的广泛接入、人工智能技术的深度融合以及智能电网和电动汽车等分布式能源的快速发展，传统的集中式数据处理模式在确保数据隐私和实现智能化管理方面显得力不从心。联邦学习（FL）作为一种创新的分布式机器学习技术，以其在数据隐私保护和智能化方面的潜力，为新型电力系统智能化管理、数据隐私保护和效率优化提供了新的解决方案。系统回顾了FL在新型电力系统中的应用，介绍了FL的基本原理和主要算法。重点探讨了FL在隐私保护下的负荷预测与异常数据检测、分布式电源控制与能源管理等方面的应用实例，分析了当前面临的技术挑战。最后，对FL在新型电力系统中的应用前景进行了展望。

关键词: “双碳”目标, 新型电力系统, 联邦学习, 智能电表, 能源管理, 微电网, 数据隐私保护, 效率优化, 深度学习

Abstract:

New power systems aiming to make clean， low-carbon， safe， flexible and efficient power supply is a key measure to achive the "dual carbon" target. However， with the widespread access of renewable energy， the fusion of artificial intelligence technologies and the rapid development of smart microgrids and distributed energy sources， such as electric vehicles， traditional centralized data processing methods fall short in protecting data privacy and enabling intelligent management. Federated learning （FL）， an innovative distributed machine learning technology， offers an effective efficiency optimization solution for new power systems due to its data privacy protection capability and intelligence. In reviews on the applications of FL in new power systems， basic principles and main algorithms of FL are expounded， practical cases of FL applied in load forecasting， anomaly detection， distributed power control and energy management under data privacy protection are analysed. Then the current technical challenges encounter by FL are also discussed. Finally， the prospects of FL in new power systems are made.

Key words: "dual carbon" target, new power system, federated learning, smart meter, energy management, microgrid, data privacy protection, efficiency optimization, deep learning

中图分类号:

TK01

吕永升, 张啸宇, 王榕夕, 郭佩乾. 联邦学习在新型电力系统中的应用与展望[J]. 综合智慧能源, 2024, 46(11): 54-64.

LYU Yongsheng, ZHANG Xiaoyu, WANG Xirong, GUO Peiqian. Application and prospect of federated learning in new power systems[J]. Integrated Intelligent Energy, 2024, 46(11): 54-64.

图/表 4

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联邦学习在新型电力系统中的应用与展望

Application and prospect of federated learning in new power systems

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