Wednesday, October 27, 2021 - 4:00pm to 4:30pm
Event Calendar Category
LIDS & Stats Tea
Building and Room Number
In recent years extreme weather conditions such as serious floods in China and Europe, long-lasting wild fires in America and Australia, extreme winter cyclones in the southern part of the U.S., and extreme heat in the Arctic circle occurred more frequently than often, suggesting a prelude of the potential global climate change. To prevent, or at least alleviate, the devastating consequences of abrupt climate change, the world has reached a consensus of reducing the carbon emission from human activities. Efforts include re-electrifying traditional industry, promoting electric vehicles in transportation systems, and replacing the fossil fuels with the renewable energy in the electric power generation. These revolutions depend heavily on the reliability and resiliency of the electric energy systems. However, due to the intermittent feature and the large spatial dispersion of the renewable energy, the near future electric energy systems are expected to work at a huge number of different modes with frequent changes. This situation brings a great complexity challenge to the traditional operational methods which heavily rely on good predictions and a few typical system changing directions.
In this talk, we will discuss how to apply differential geometry methods to dealing with the complexity challenge introduced by the distributed renewable energy resources. Specifically, we will focus on the long-term voltage stability problem. An introduction to the traditional voltage stability analysis methods will be summarized at first. Then, we will demonstrate and visualize the drawbacks of the traditional methods based on the Euclidean metric distance formulation. To cope with these drawbacks, we re-establish the voltage stability analysis and computations on the manifold through an optimal control framework and differential geometry methods, and illustrate its accuracy in predicting the shortest voltage collapse path. A computationally efficient approximation will be further discussed to further validate the applicability of our proposed framework in large-scale implementations. Finally, some new research observations based on curvature tensors will be presented and compared.
Dr. Dan Wu received the B.S. degree in electrical engineering and automation from the Huazhong University of Science and Technology, Wuhan, China, in 2012, and the M.S. and Ph.D. degrees from the University of Wisconsin-Madison, Madison, WI, USA, in 2014 and 2017, respectively. From 2017 to 2019, he was a Postdoctoral Associate with the Mechanical Engineering Department, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. Now he is working in the Laboratory for Information and Decision Systems at MIT. His research focuses on improving reliability, efficiency, and resiliency of complex energy systems under climate change and extreme events, including electric power grids, natural gas networks, and interdependent energy systems, by inventing new mathematical modellings, advanced analysis techniques, and efficient numerical solvers.