At UC Davis, we are constantly looking for ways to reduce energy, save the campus money, and meet our 2025 carbon neutrality goal. An improved understanding of our electricity rate structure and demand response advantages presented an opportunity to dispatch our 5-million-gallon thermal energy storage tank more effectively. We developed an optimization algorithm which, combined with a PI Vision dashboard, empowers the central plant operators to run our campus chillers at the best times of the day. Cumulative dollars saved and other KPIs incentivize operators to support the optimization effort, which is expected to reduce campus chilled water production costs by 25%. Our optimization algorithm, which uses historical, real-time, and future data also pushed us to improve data quality. We have developed an array of data science tools using Python and machine learning to detect bad data and anomalies across all 1,100 utility meters to provide higher quality information to data consumers.