Logenthiran, ThillainathanSheng, JieHockman, Michael2023-04-172023-04-172023-04-172023Hockman_washington_0250O_25007.pdfhttp://hdl.handle.net/1773/49895Thesis (Master's)--University of Washington, 2023This work focuses on optimizing the performance of power networks bymaximizing and optimizing the utilization of renewable energy sources (RESs). In order to accomplish this, a cooperative distributed model predictive control scheme is used in which each microgrid subsystem consists of a controllable load, an energy storage system (ESS), and a non-renewable controllable generator. This thesis will also be looking at methods of increasing the computational efficiency of previously established algorithms. The result is better utilization of available RESs while also keeping supply-demand balance satisfied all in a more computationally efficient manner than would be otherwise possible. Simulated results are promising, showing that the utilization of RESs in the network as a whole is increased while also preventing deep discharging of the ESSs. This demonstrates the feasibility of the project as a whole.application/pdfen-USCC BYControlElectrical PowerMicrogridModel Predictive ControlPower SharingRenewable PowerElectrical engineeringComputer engineeringElectrical engineeringThesis