The event was held virtually in June 2021. The award includes a certificate and a 300 Euros prize.
In the award-winning paper, titled “Graph-Based Modeling and Decomposition of Energy Infrastructures,” Shin presented nonlinear optimization problems as graph-structured optimization problems and shows how that structure can be exploited at both the modeling and the solver level. The approach, which he implemented as a general-purpose nonlinear programming solver called MadNLP.jl, was evaluated in tests on problems arising in transient gas network optimization and multiperiod AC optimal power flow. Compared with off-the-shelf tools, MadNLP.jl reduced solution times by 300%.
Shin received his Ph.D. degree in chemical engineering from the University of Wisconsin - Madison. As a graduate student he had spent the summer of 2018 at Argonne, and in May 2021 he joined Argonne as a postdoctoral appointee. His research, which includes model predictive control, optimization algorithms, and their applications to large-scale energy infrastructure, is funded under the Argonne-led MACSER (Multifaceted Mathematics for Rare, High Impact Events in Complex Energy and Environmental Systems) project
For information about the conference and the award, click here. For the full paper, see Sungho Shin, Carleton Coffrin, Kaarthik Sundar, and Victor M. Zavala, “Graph-Based Modeling and Decomposition of Energy Infrastructures,”.