摘要
针对现有多主体综合能源微网群协同运行中,集中式优化面临的主体隐私保护、参数难以共享问题,以及分布式优化面临的优化模型须大量简化近似、全局最优性难以保障的问题,提出了一种基于联邦学习的多主体综合能源微网群协调优化运行方法,以兼顾主体隐私性与全局最优性。首先,基于循环门控单元(gated recurrent unit,GRU)深度学习网络构建各综合能源微网的等值互动特性封装模型并上传至云端;其次,在不侵入各微网内部隐私数据的基础上,将各微网等效模型加密后于云端汇总并进行联邦学习;然后,依据云端联邦学习的结果对边端各综合能源微网互动特性封装模型进行修正和更新,迭代直至损失函数收敛,进而实现隐私保护下综合能源微网群的全局协同优化运行;最后,通过典型的综合能源微网群仿真算例验证了所提方法的可行性和有效性,结果表明,所提方法能实现综合能源微网群的快速高效优化运行,并有效保护各参与方的数据隐私。
In the current cooperative operation of multi-agent integrated energy microgrids,the centralized optimization strategy has been experiencing the contradiction of agent privacy protection and parameter sharing,while in the distributed optimization,the optimization model needs to be simplified and approximated extensively such that the global optimal solution is not guaranteed.With regard to these challenges,this paper proposes a coordinated and optimized operation method for multi-agent integrated energy microgrids based on federated learning to achieve global optimum without compromising the agent privacy.Firstly,the equivalent interactive characteristic packaging model of each integrated energy microgrid is built based on the gated recurrent unit deep learning network and then uploaded to the cloud.Secondly,on condition of no invasion into the internal privacy data of each microgrid,the equivalent model of each individual microgrid is encrypted,and then consolidated in the cloud for federated learning.Thirdly,according to the results of cloud federation learning,the packaging model of interaction characteristics of each integrated energy microgrid at the edge is modified and updated iteratively until the loss function converges.In this way the global collaborative optimization operation of the integrated energy microgrids can be achieved under privacy protection.Finally,the feasibility and effectiveness of the proposed method are verified through case studies simulating a typical integrated energy microgrids.The results show that this method can realize the fast and efficient optimization operation of the integrated energy micro-group and effectively protect the data privacy of all participants.This work is supported by Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(No.J2021058).
作者
薛溟枫
毛晓波
肖浩
周毅斌
浦骁威
裴玮
XUE Mingfeng;MAO Xiaobo;XIAO Hao;ZHOU Yibin;PU Xiaowei;PEI Wei(Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co.,Ltd.,Wuxi 214000,China;Institute of Electrical Engineering,Chinese Academy of Sciences,Beijing 100190,China)
出处
《中国电力》
CSCD
北大核心
2023年第12期164-173,共10页
Electric Power
基金
国网江苏省电力有限公司科技项目(J2021058)。
关键词
综合能源系统
微网
联邦学习
优化运行
人工智能
integrated energy system
microgrid
federated learning
optimization operation
artificial intelligence
