IEEE Task Force on Data Sharing in Energy Systems

Officers

Chair: Dr. Yi Wang, The University of Hong Kong, SAR China
Vice-Chair: Dr. Ricardo Bessa, INESC TEC, Portugal

Background

The PES Subcommittee on Big Data & Analytics for Power Systems brings together leaders from different disciplines and domains to promote the research and application of big data analytics in future digitalized power and energy systems. Aggregating a massive number of data from different resources is the basis for big data analytics. However, these kinds of big data may belong to different owners. Privacy has become an emerging social concern. Governments or organizations worldwide are increasingly committed to data privacy protection. For example, different countries have different consumer data privacy regulations for energy consumption. The UK launched two policies in 2018, Smart Meter Bill and The Data Protection Act, respectively authorizing half-hourly electricity consumption data collection and implementing General Data Protection Regulation (GDPR) to utilize consumers’ data and protect data privacy at the same time. To address privacy concerns with smart grid technology, the Office of Electricity Delivery and Energy Reliability and the Federal Smart Grid Task Force has published a Voluntary Code of Conduct (VCC) for utilities and third parties in the United States. Data Security Law of the Peoples’ Republic of China was passed on June 10, 2021, which strictly regularizes data collection, storage, use, processing, transport, provision, and disclosure.
Data barrier becomes a fundamental concern for big data analytics for power systems. Data barrier among data owners exists because they cannot or are not willing to directly share their data with others because of data privacy regulation, business competition, etc. Thus, it is of vital importance to figure out how to preserve the privacy of consumers as well as promote secure data sharing among each other in power and energy systems.

Objectives

There are two main reasons for the formation of data barriers: privacy issues because of legal risks and business competition. Thus, to break the data barrier and promote data sharing, efforts should be devoted to two aspects: 1) privacy-preserving data analytical methods; 2) data pricing or valuation approaches. Both are emerging topics in power and energy systems. This task force aims to:

• Identify data barriers in the process of power and energy generation, transmission, distribution, and consumption;

• Investigate recent advances in privacy-preserving machine learning methods (e.g., federated learning, differential privacy, etc.) and their applications in power and energy systems;

• Summarize and compare data trading mechanisms and data value quantification methods in different industries, including the power and energy industry;

• Summarize practical implementations in data privacy and pricing;

• Provide recommendations for typical application scenarios for data sharing in power and energy systems, including energy forecasting, behavior modeling, etc.

Activities

IEEE SmartGridComm 2022 Workshop on Data Sharing in Smart Grids

Chairs

Yi Wang, Assistant Professor, The University of Hong Kong, yiwang@eee.hku.hk
Ricardo Bessa, Senior Researcher, INESC TEC, ricardo.j.bessa@inesctec.pt

Schedule

Half-day (three hours), Day of the workshop: 27 October, 2022 (Singapore time)

15:00-15:40 Keynote 1:

• Prof. Pierre Pinson, Imperial College London, “Data Markets in Energy Forecasting”

15:40-16:55 Hot topic session:

• Dr. Paul Cuffe, University College Dublin, “Prediction Markets as a Data Aggregation Mechanism”

• Dr. Jianxiao Wang, Peking University, “Market-oriented Data Valuation in Smart Grids”

• Dr. Jean-Francois Toubeau, KU Leuven, “Privacy-Preserving Probabilistic Forecasting in Smart Grids”

16:55-17:35 Keynote 2:

• Prof. Qinglai Guo, Tsinghua University, “Data Sharing: Value, Method and Mechanism”

17:35-18:00 Panel:

• Discussion between the expert research panel and the audience about the current status quo of data sharing in smart grids and the challenges to their implementation, as well as a realistic assessment of their potential going forward.