Application and prospect of federated learning in new power systems

doi:10.3969/j.issn.2097-0706.2024.11.007

Abstract

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

CLC Number:

TK01

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.

Figures/Tables 4

References 67

[1]	IPCC. Synthesis report:Contribution of working groups Ⅰ, Ⅱ and Ⅲ to the sixth assessment report of the intergovernmental panel on climate change[R]. IPCC, 2023.
[2]	张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8):2806-2819.
	ZHANG Zhigang, KANG Chongqing. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2819.
[3]	惠红勋, 漆淘懿, 孙雨乐, 等. 路网耦合下计及电动汽车V2G潜力的充电站选址定容研究[J]. 综合智慧能源, 2024, 46(1):1-10. doi: 10.3969/j.issn.2097-0706.2024.01.001
	HUI Hongxun, QI Taoyi, SUN Yule, et al. Siting and sizing of electric vehicle charging stations under the coupling of transport and power networks considering V2G potential[J]. Integrated Intelligent Energy, 2024, 46(1):1-10. doi: 10.3969/j.issn.2097-0706.2024.01.001
[4]	龚钢军, 刘雪松, 王浩淼, 等. 电力数据共享安全防护与隐私保护综述[J]. 综合智慧能源, 2024, 46(5): 30-40.
	GONG Gangjun, LIU Xuesong, WANG Haomiao, et al. Research on security and privacy protection of electric power data sharing[J]. Integrated Intelligent Energy, 2024, 46(5):30-40. doi: 10.3969/j.issn.2097-0706.2024.05.004
[5]	KONEČNÝ J, MCMAHAN H B, YU F X, et al. Federated learning: Strategies for improving communication efficiency[J]. arXiv preprint arXiv, 2016:1610.05492.
[6]	YANG T, ANDREW G, EICHNER H, et al. Applied federated learning: Improving google keyboard query suggestions[J]. arXiv preprint arXiv, 2018:1812.02903.
[7]	周传鑫, 孙奕, 汪德刚, 等. 联邦学习研究综述[J]. 网络与信息安全学报, 2021, 7(5):77-92.
	ZHOU Chuanxin, SUN Yi, WANG Degang, et al. Survey of federated learning research[J]. Chinese Journal of Network and Information Security, 2021, 7(5):77-92.
[8]	MCMAHAN B, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data[C]// Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). JMLR: W&CP, 2017.
[9]	YANG Q, LIU Y, CHEN T J, et al. Federated machine learning: Concept and applications[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(2): 1-19.
[10]	IEEE guide for architectural framework and application of federated machine learning: IEEE 3652.1—2020[S].
[11]	LIU Y, KANG Y, XING C P, et al. A secure federated transfer learning framework[J]. IEEE Intelligent Systems, 2020, 35(4):70-82.
[12]	QI J J, ZHOU Q H, LEI L, et al. Federated reinforcement learning: Techniques, applications, and open challenges[J]. arXiv preprint arXiv, 2021:2108.11887.
[13]	BRIGGS C, FAN Z, ANDRAS P. Federated learning for short-term residential load forecasting[J]. IEEE Open Access Journal of Power and Energy, 2022(9): 573-583.
[14]	焦润海, 褚佳杰, 李俊良, 等. 基于数据分解的多区域个性化联邦负荷预测方法[J/OL]. 中国电机工程学报: 1-13(2024-02-01)[2024-07-21]. http://kns.cnki.net/kcms/detail/11.2107.TM.20240131.1525.007.html.
	JIAO Runhai, CHU Jiajie, LI Junliang, et al. Personalized federated multi-region load forecasting method based on data decomposition[J/OL]. Proceedings of the CSEE:1-13(2024-02-01)[2024-07-21]. .
[15]	QIN D A W, WEN C X, CHEN Q S, et al. Personalized federated DARTS for electricity load forecasting of individual buildings[J]. IEEE Transactions on Smart Grid, 2023, 14:4888-4901.
[16]	WANG Y, GAO N, HUG G. Personalized federated learning for individual consumer load forecasting[J]. CSEE Journal of Power and Energy Systems, 2022, 9(1): 326-330.
[17]	HUSNOO M A, ANWAR A, HOSSEINZADEH N, et al. A secure federated learning framework for residential short term load forecasting[J]. IEEE Transactions on Smart Grid, 2024, 15(2):2044-2055.
[18]	ZHANG X Y, CÓRDOBA-PACHÓN J R, GUO P, et al. Privacy-preserving federated learning for value-added service model in advanced metering infrastructure[J]. IEEE Transactions on Computational Social Systems, 2024, 11(1):117-131.
[19]	WANG Y, BENNANI I L, LIU X F, et al. Electricity consumer characteristics identification:A federated learning approach[J]. IEEE Transactions on Smart Grid, 2021, 12(4):3637-3647.
[20]	LIN J, MA J, ZHU J G. Privacy-preserving household characteristic identification with federated learning method[J]. IEEE Transactions on Smart Grid, 2021, 13(2): 1088-1099.
[21]	WANG Y, JIA M S, GAO N, et al. Federated clustering for electricity consumption pattern extraction[J] IEEE Transactions on Smart Grid, 2022, 13(3):2425-2439.
[22]	WEN M, XIE R, LU K, et al. Feddetect: A novel privacy-preserving federated learning framework for energy theft detection in smart grid[J]. IEEE Internet of Things Journal, 2021, 9(8):6069-6080.
[23]	XIE R. An energy theft detection framework with privacy protection for smart grid[C]// International Joint Conference on Neural Networks. IJCNN, 2023: 1-7.
[24]	LI X, WANG N Y, ZHU L H, et al. FUSE: A federated learning and U-shape split learning-based electricity theft detection framework[J]. Science China Information Sciences, 2024, 67(4):149302.
[25]	WANG J, SI Y F, ZHU Y H, et al. Cyber attack detection for electricity theft in smart grids via stacking ensemble GRU optimization algorithm using federated learning framework[J]. International Journal of Electrical Power & Energy Systems, 2024, 157: 109848.
[26]	JITHISH J, ALANGOT B, MAHALINGAM N, et al. Distributed anomaly detection in smart grids: a federated learning-based approach[J]. IEEE Access, 2023, 11:7157-7179.
[27]	BONDOK A H, MAHMOUD M, BADR M M, et al. Novel evasion attacks against adversarial training defense for smart grid federated learning[J]. IEEE Access, 2023, 11: 112953-112972.
[28]	WANG X Y, XIE H P, TANG L F, et al. Decentralized Privacy-preserving electricity theft detection for distribution system operators[J]. IEEE Transactions on Smart Grid, 2024, 15(2): 2179-2190.
[29]	LI Y, WEI X H, LI Y Z, et al. Detection of false data injection attacks in smart grid: A secure federated deep learning approach[J]. IEEE Transactions on Smart Grid, 2022, 13(6): 4862-4872.
[30]	IBRAHEM M I, MAHMOUD M, FOUDA M M, et al. Privacy-preserving and efficient decentralized federated learning-based energy theft detector[C]// Proceedings of the IEEE Global Communications Conference. GlobeCOM, 2022 :287-292.
[31]	WU L Q, SHI H Y, FU S J, et al. p2Detect: Electricity theft detection with privacy preservation for both data and model in smart grid[J]. IEEE Transactions on Smart Grid, 2022, 14(3): 2301-2312.
[32]	蒲天骄, 杜帅, 李烨, 等. 面向隐私保护基于联邦强化学习的分布式电源协同优化策略[J]. 电力系统自动化, 2023, 47(8): 62-70.
	PU Tianjiao, DU Shuai, LI Ye, et al. Collaborative optimization strategy of distributed generators based on federated reinforcement learning for privacy preservation[J]. Automation of Electric Power Systems, 2023, 47(8): 62-70.
[33]	周毅斌, 肖浩, 裴玮, 等. 基于纵向联邦学习的微电网群协同优化运行与策略进化[J]. 电力系统自动化, 2023, 47(11):121-132.
	ZHOU Yibin, XIAO Hao, PEI Wei, et al. Collaborative optimization operation and strategy evolution of microgrid cluster based on vertical federated learning[J]. Automation of Electric Power Systems, 2023, 47(11):121-132.
[34]	黎海涛, 刘伊然, 杨艳红, 等. 基于改进联邦竞争深度Q网络的多微网能量管理策略[J]. 电力系统自动化, 2024, 48(8):174-184.
	LI Haitao, LI Yiran, YANG Yanhong, et al. Energy management strategy for multi-microgrid based on improved federated dueling deep Q network[J]. Automation of Electric Power Systems, 2024, 48(8):174-184.
[35]	VEERASAMY V, SAMPATH L P M I, SINGH S, et al. Blockchain-based decentralized frequency control of microgrids using federated learning fractional-order recurrent neural network[J]. IEEE Transactions on Smart Grid, 2023, 15(1): 1089-1102.
[36]	孙智卿, 陈益芳, 樊立波, 等. 基于联邦强化学习的综合能源系统优化调度方法及系统:202311412348[P] [2024-07-17].
[37]	李彬, 白雪峰, 李志超, 等. 基于联邦学习的分布式电采暖互动模式设计与展望[J]. 综合智慧能源, 2024, 46(1):56-64. doi: 10.3969/j.issn.2097-0706.2024.01.007
	LI Bin, BAI Xuefeng, LI Zhichao, et al. Design and prospect of distributed electric heating interactive mode based on federated learning[J]. Integrated Intelligent Energy, 2024, 46(1):56-64. doi: 10.3969/j.issn.2097-0706.2024.01.007
[38]	BALINT A, RAJA H, DRIESEN J, et al. Using domain-augmented federated learning to model thermostatically controlled loads[J]. IEEE Transactions on Smart Grid, 2023, 14(5):4116-4124.
[39]	LEE S, CHOI D H. Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources[J]. IEEE Transactions on Industrial Informatics, 2020, 18(1): 488-497.
[40]	TOUBEAU J F, TENG F, MORSTYN T, et al. Privacy-preserving probabilistic voltage forecasting in local energy communities[J]. IEEE Transactions on Smart Grid, 2022, 14(1):798-809.
[41]	LIN J, MA J, ZHU J G. A privacy-preserving federated learning method for probabilistic community-level behind-the-meter solar generation disaggregation[J]. IEEE Transactions on Smart Grid, 2021, 13(1):268-279.
[42]	瞿祥谋, 吴映波, 蒋晓玲. 一种非独立同分布问题下的联邦数据增强算法[J]. 计算机科学, 2022, 49(12):33-39. doi: 10.11896/jsjkx.220300031
	QU Xiangmou, WU Yingbo, JIANG Xiaoling. Federated data augmentation algorithm for non-independent and identical distributed data[J]. Computer Science, 2022, 49(12):33-39. doi: 10.11896/jsjkx.220300031
[43]	王峰. 变电设备数据质量评价模型研究[J]. 广东电力, 2020, 33(3):17-25.
	WANG Feng. Research on data quality evaluation model of substation equipment[J]. Guangdong Electric Power, 2020, 33(3):17-25.
[44]	CAO X J, SUN G, YU H F, et al. PerFED-GAN: Personalized federated learning via generative adversarial networks[J]. IEEE Internet Things Journey, 2023, 10(5): 3749-3762.
[45]	WANG Y S, XU H, ALI W, et al. FedFTHA: A fine-tuning and head aggregation method in federated learning[J]. IEEE Internet Things Journal, 2023, 10(14):12749-12762.
[46]	王波, 代晓蕊, 王伟, 等. 面向联邦学习的对抗样本投毒攻击[J]. 中国科学:信息科学, 2023, 53(3):470-484.
	WANG Bo, DAI Xiaorui, WANG Wei, et al. Adversarial examples for poisoning attacks against federated learning[J]. Science China Information Sciences, 2023, 53(3):470-484.
[47]	汤奕, 陈倩, 李梦雅, 等. 电力信息物理融合系统环境中的网络攻击研究综述[J]. 电力系统自动化, 2016, 40(17):59-69.
	TANG Yi, CHEN Qian, LI Mengya, et al. Overview on cyber-attacks against cyber physical power system[J]. Automation of Electric Power Systems, 2016, 40(17):59-69.
[48]	肖雄, 唐卓, 肖斌, 等. 联邦学习的隐私保护与安全防御研究综述[J]. 计算机学报, 2023, 46(5):1019-1044.
	XIAO Xiong, TANG Zhuo, XIAO Bin, et al. A survey on privacy and security issues in federated learning[J]. Computer Science, 2023, 46(5):1019-1044.
[49]	XIA Q, TAO Z Y, LI Q, et al. Byzantine Tolerant algorithms for federated learning[J]. IEEE Transactions on Network Science and Engineering, 2023, 10(6):3172-3183.
[50]	MIAO Y B, YAN X R, LI X H, et al. RFed: Robustness-enhanced privacy-preserving federated learning against poisoning attack[J]. IEEE Transactions on Information Forensics and Security, 2024, 19: 5814-5827.
[51]	CHEN Z K, YU S X, FAN M Y, et al. Privacy-enhancing and robust backdoor defense for federated learning on heterogeneous data[J]. IEEE Transactions on Information Forensics and Security, 2024, 19: 693-707.
[52]	ZHANG J L, ZHU C C, SUN X B, et al. FLPurifier: Backdoor defense in federated learning via decoupled contrastive training[J]. IEEE Transactions on Information Forensics and Security, 2024, 19: 4752-4766.
[53]	邱鑫源, 叶泽聪, 崔翛龙, 等. 联邦学习通信开销研究综述[J]. 计算机应用, 2022, 42(2):333-342. doi: 10.11772/j.issn.1001-9081.2021020232
	QIU Xinyuan, YE Zecong, CUI Xiaolong, et al. Survey of communication overhead of federated learning[J]. Journal of Computer Applications, 2022, 42(2):333-342. doi: 10.11772/j.issn.1001-9081.2021020232
[54]	CHEN C, JIANG B H, LIU S L, et al. Efficient federated learning in resource-constrained edge intelligence networks using model compression[J]. IEEE Transactions on Vehicular Technology, 2024, 73(2):2643-2655.
[55]	XIA C Y, TSANG D H K, LAU V K N. Structured Bayesian federated learning for green AI: A decentralized model compression using turbo-VBI-based approach[J]. IEEE Internet Things Journal, 2024, 11(7): 12783-12798.
[56]	MALINOVSKY G, KOVALEV D, GASANOV E, et al. From local SGD to local fixed-point methods for federated learning[C]// Proceedings of the 37th International Conference on Machine Learning. ICML, 2020:13-18.
[57]	REN C E, AN R Q, XUAN Z H. FBL-ET: A federated broad learning framework based on event trigger[J]. Knowledge-Based Systems, 2023, 265: 110366.
[58]	SUN J, CHEN T Y, GIANNAKIS G B, et al. Lazily aggregated quantized gradient innovation for communication-efficient federated learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(4): 2031-2044.
[59]	GU B, XU A, HUO Z Y, et al. Privacy-preserving asynchronous vertical federated learning algorithms for multiparty collaborative learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(11): 6103-6115.
[60]	LI H X, CAI Z Y, WANG J Y, et al. FedTP: Federated learning by transformer personalization[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023 :1-15.
[61]	VETTORUZZO A, BOUGUELIA M R, VANSCHOREN J, et al. Advances and challenges in meta-learning: A technical review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(7):4763-4779.
[62]	WANG X, FAN W B, HU X Z, et al. Differential privacy-preserving of multi-party collaboration under federated learning in data center networks[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(2):1223-1237.
[63]	SCHNEIDER T, SURESH A, YALAME H. Comments on privacy-enhanced federated learning against poisoning adversaries[J]. IEEE Transactions on Information Forensics and Security, 2023, 18: 1407-1409.
[64]	HALLAJI E, RAZAVI-FAR R, SAIF M, et al. Decentralized federated learning: A survey on security and privacy[J]. IEEE Transactions on Big Data, 2024, 10(2) :194-213.
[65]	HASSAN N U, YUEN C, NIYATO D. Blockchain technologies for smart energy systems fundamentals, challenges, and solutions[J]. IEEE Industrial Electronics Magazine, 2019, 13(4):106-118.
[66]	MHAISEN N, ABDELLATIF A A, MOHAMED A, et al. Optimal user-edge assignment in hierarchical federated learning based on statistical properties and network topology constraints[J]. IEEE Transactions on Network Science and Engineering, 2022, 9(1): 55-66.
[67]	李彬, 白雪峰, 李志超, 等. 基于联邦学习的分布式电采暖互动模式设计与展望[J]. 综合智慧能源, 2024, 46(1): 56-64. doi: 10.3969/j.issn.2097-0706.2024.01.007
	LI Bin, BAI Xuefeng, LI Zhichao, et al. Design and prospect of distributed electric heating interactive mode based on federated learning[J]. Integrated Intelligent Energy, 2024, 46(1): 56-64. doi: 10.3969/j.issn.2097-0706.2024.01.007