S5 Data Management and Grid Analytics

Symposium Co-Chairs

  • George Kesidis, Penn State University, USA
  • Husheng Li, University of Tennessee, USA
  • Jianhui Wang, Argonne National Laboratory, USA

Scope and Motivation

This symposium is focused on large-scale data management and analytics to operate large-scale electricity transmission and distribution networks and to manage the associated electricity marketplaces which also span generation and demand, or large and distributed installations such as multi-homed data centers. In power engineering, big-data analytics are required to process data from numerous and spatially diffuse PMUs and/or smart-meters. Such analytics require communication, storage and computational systems that are suitable for speedy, secure and reliable processing. 

Topics of Particular Interest

This symposium focuses on, but it is not limited to, the following aspects:
  • Data management strategies:
​- hierarchical strategies for wide-area monitoring and visualization
- system-state estimation
- innovative use of cloud architectures
- reliable and privacy-preserving storage and communication of large data quantities from diffuse sources
  • Analytics:
​- "big data" issues
- machine learning 
- privacy-preserving analytics
- semantic techniques
- real-time data analysis and decision making
  • Application of data management and analytics to:
- power-grid transmission and distribution management
- electricity marketplace
- exploitation of intermittently available renewables in economically sensible fashion (considering ramp-up/down costs and dynamics of legacy supply)
- demand aggregation 
- managing future electric vehicle demand at scale
- managing smart buildings/houses at scale
- advanced metering infrastructure/smart meters
- phasor measurement units (PMUs)
- data centers
- load forecasting and price forecasting
- weather forecasting
- asset management
- demand response
- social media data integration for utility applications
- case studies

Technical Program Committee (TPC) Members

Ehab Al-Shaer, University of North Carolina at Charlotte, USA
Mahnoosh Alizadeh, University of California at Davis, USA
Rakesh Bobba, University of Illinois at Urbana-Champaign, USA
Richard Brooks, Clemson University, USA
Chen Chen, Argonne National Laboratory, USA
Michael Devetsikiotis, North Carolina State University, USA
Cangbing Li, Hunan University, China
Fangxing (Fran) Li, University of Tennessee at Knoxville, USA
Zuyi Li, Illinois Institute of Technology, USA
Xiaojun Lin, Purdue University, USA
Ning Lu, North Carolina State University, USA
George Michailidis, University of Michigan at Ann Arbor, USA
Hairong Qi, University of Tennessee at Knoxville, USA
Jeff Rowe, University of California at Davis, USA
Walid Saad, University of Miami, USA
Uday Shanbhang, Pennsylvania State University, USA
Wei Tian, Illinois Institute of Technology, USA
Qianfan Wang, Alstom Grid, USA
Zhaoyu Wang, Georgia Institute of Technology, USA
Le Xie, Texas A&M University, USA
Lin Zhang, Tsinghua University, China
Hao Zhu, University of Illinois at Urbana-Champaign, USA