A grand challenge for power grid management lies in how to plan and operate with increasing penetration of distributed energy resources (DERs), such as electric vehicles (EV) and solar photovoltaics. In this talk, we show how to find the loadability point with a large amount of EVs when the system parameters are known. For such a loadability analysis, we convert the power system's Jacobian matrix into a linear form and employ the concept of Pareto Front for an optimization. When the system parameters are unknown, we show how the support vector machine can be used to reproduce the power flow equation. Such a representation is subsequently used to schedule capacitor bank for preventing voltage issue caused by electrical vehicles. Finally, as existing grid has limited EV numbers, the grid data cannot be directly used for testing tools above towards deep electric vehicle penetration. Therefore, we demonstrate an EV robot-based testbed for validating the first two ideas in this talk.
Yang Weng is an assistant professor at the School of Electrical, Computer and Energy Engineering (ECEE) of ASU. He received his Ph.D. in Electrical and Computer Engineering (ECE) from Carnegie Mellon University, where he also obtained his M.S. degree in Machine Learning from the School of Computer Science. Before joining ASU, Yang was a TomKat postdoctoral scholar at Stanford University, where he is one of the leaders in a Department of Energy (DOE) sponsored project on visualization and machine learning for distribution systems with deep renewable penetration. Yang was the Best Paper Award winner of the 2012 International Conference on Smart Grid Communication. In 2013, his paper was ranked first in the same conference. In 2014, his paper was among the Best Papers at the IEEE Power and Energy Society General Meeting. In 2016, his paper won the Best of Best Paper Award at the International Conference on Probabilistic Methods Applied to Power Systems. In 2017, his paper won the Best Paper Award for designing Electric Vehicle Robot-based TestBed for Future Grid's Evaluation.