Title: Privacy-Preserving Energy Management in Smart Grid: Tradeoffs between Data Privacy and Electricity Cost
Traditional power grids are being transformed into smart grids using advanced information control and communication technologies to offer higher reliability, security and efficiency in power systems. As a vital component of smart grid, demand-side management (DSM) plays a key role in reducing the peak load and incorporating renewables into the grid. Effective DSM depends on data analytics of fine granularity power consumption data, with which, however, it is possible to identify consumers’ specific activities or behavior patterns, thereby giving rise to serious privacy concerns. Therefore, there is an urgent need to develop a novel privacy-preserving energy management in smart grid. On one hand, effective methods to protect consumers’ privacy need to be designed. On the other hand, novel DSM programs need to be developed to take into account the privacy protecting behaviors at the consumer’s side. In this talk, we will discuss privacy-preserving energy management in smart grid at both the consumer’s and the utility’s sides. Intuitively, to protect data privacy, consumers can provide noisy energy usage data to the utility, which inevitably would cause higher energy cost and make it challenging to design DSM programs. Clearly, there is a tradeoff between the data privacy and the energy cost. We will discuss this tradeoff and propose a reinforcement learning based pricing scheme for DSM.
Lei Yang received the B.S. and M.S. degrees in electrical engineering from Southeast University, Nanjing, China, in 2005 and 2008, respectively, and the Ph.D. degree from the School of Electrical Computer and Energy Engineering at Arizona State University, Tempe, in 2012. He is currently an Assistant Professor in the Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA. He was a postdoctoral scholar at Princeton University, Princeton, NJ, USA, and an Assistant Research Professor with the School of Electrical Computer and Energy Engineering at Arizona State University, Tempe. He has received the Best Paper Award Runner-up of IEEE INFOCOM 2014.