This project develops a model predictive control to optimally respond to a dynamic utility signal for a district cooling system with thermal energy storage. Using the central chiller plant at UC Merced as a demonstration site, the new control system aims to promote the self-consumption of the on-site renewable while responding to the grid signal. Using sophisticated predictive controls that consider weather, occupancy, electricity prices, and greenhouse gas emissions, this project will maximize clean energy use and support the grid while reducing campus demand charges and utility costs.