UC Berkeley

The increased electrification of society and calls for improvements in electrical grid reliability lend renewed attention to the domain of power systems optimization. For the future smart grid, it will be crucial both to find better solutions to classical power grid optimization problems such as optimal power flow (OPF) and state estimation (SE) as well as to develop new optimization methods for the updated physical systems. Due to the nonlinearity of alternating current (AC), many optimization problems in power systems are nonlinear and nonconvex and thus are hard to solve with existing fast convex methods. In this dissertation, we propose some efficient methods related to the OPF, post-contingency OPF, SE, and power flow (PF) problems. These methods provide new optimization and machine learning frameworks for ensuring the reliable and efficient operation of the power grid.