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Smart Energy Network Management: From Model-based to Data-Driven

创建时间:  2020-11-19  王智渊    浏览次数:


报告时间:2020年11月20日16:00

报告方式:线上:Zoom会议ID:291 500 0758;密码:974282     线下:机自大楼322伯时会堂

报告题目:Smart Energy Network Management: From Model-based to Data-Driven


报告摘要:UK is facing the biggest energy revolutions in decades, which is driven by 4D, Digitalisation, decarbonisation, democratisation and decentralisation. These energy transition poses a serious challenge to our current energy operation systems. This talk is mainly focusing on how to manage the operation of smart local energy systems, including electricity, heating and gas networks using both the model based methods and data-driven methods (deep reinforcement learning). The research is based on theEurope’s largest ‘at scale’ multi-vector smart energy demonstrator (SEND project) at Keele University.


报告人简介:Jun Cao, Keele University, Staffordshire, UK. He received a master's degree from Zhejiang University in 2009, a doctorate from Queen's University of Belfast in 2013, and then served as an associate professor at North China Electric Power University. He is a member of the IEEE power & Energy Society Smart Buildings - Loads - Customer Systems Committee member. He has published many valuable papers in smart grid journal, power system journal and IEEE Power System journal. Now he is working on the SEND project: creating a campus-scale laboratory for low carbon multi-vector energy research.



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