on the morning of january 5, 2018, dr. yuting ji of stanford university was invited to the department of electrical engineering for an academic visit and gave an academic report entitled "optimizing and learning in the smart grid operation under uncertainty". the seminar was chaired by prof. zhong haiwang of the department of electrical engineering. associate prof. hu wei, associate prof. guo qinglai and assistant prof. wei wei of the department of electrical engineering, assistant professors wu chenye and yu yang of the institute for interdisciplinary information sciences, and more than 20 students of the department of electrical engineering and the institute for interdisciplinary information sciences, attended the seminar
dr. ji's report is divided into two parts. in the first part, dr. ji proposed a real-time dispatching method for power system considering the uncertainty of new energy sources. dr. ji verified the method with the ieee-118 polish-3120 node systems by combining the conventional optimization model with the online dictionary learning. compared with the conventional solution and monte carlo simulation method, this method can solve the uncertainties in a shorter time, such as probabilistic real-time market node electricity-price forecasting.
in the second part, aiming at the problem of uncertain power exchange multi-area interconnected power grids considering, dr. ji expounded the reasons for the inefficiency of the current tie-line power exchange planning, including the uncertainty of new energy, the error of electricity price prediction and network loss, she proposed the corresponding stochastic solution, and verifies its effectiveness, optimality and convergence. finally, the participants held questions and heated discussions on the contents of the report.
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dr. ji yuting is a postdoctor at stanford university. she received her ph. d. degree from the school of electrical and computer science, cornell university, and her bachelor’s degree from the department of computer science, tsinghua university. her research interests include data analysis, optimization theory, machine learning, and their applications in smart grids and energy systems.
