Big Data for Enhanced Grid Performance with Considerations of Data Barriers and Privacy

Recent developments in monitoring systems, communication and sensor networks dramatically increase the variety, volume, and velocity of big data in the modern power grids. The increased complexity and uncertainty of big data brings new challenges and opportunities for fast, intelligent, and reliable data analytics. Meanwhile, data barrier becomes a fundamental concern for big data analytics for power systems, where the data owners 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. This panel will discuss some big data analytic techniques, such as cutting-edge artificial intelligence and quantum computing, for enhanced grid security, reliability, and resilience. We will also discuss about the efforts that help break the data barrier and promote data sharing in two aspects: 1) privacy-preserving data analytical methods;2) data pricing or valuation approaches. This panel session will invite experts from academia and industry to discuss key techniques in the above areas. Presentations in this panel session: - Big Data Analytics for Enhancing Distribution Grid Modeling and Resilience (23PESGM0936) - Federated Learning for Predictive Management of Low Voltage Grids (23PESGM0940)