Despite advanced campus energy systems, Princeton spends $10-11 million/year on energy to heat, cool, and power campus buildings, in addition to emitting 75,000-80,000 metric tons of CO2 (equivalent to the emissions from over 16,300 average US cars). This begs the question of whether these numbers can be improved through advanced optimization techniques. While models for optimizing campus energy plants exist in literature, they exhibit two key limitations: (1) Oversimplifications of complex plant components and/or (2) Extremely long runtimes that make literature models unsuitable for many uses. However, a novel mechanical approach to optimization can overcome both of these limitations by modeling Princeton’s plant as a series of energy flows, rather than a large number of abstract equations. The resulting optimization model achieves exceptional results. Its immense efficiency relative to traditional mathematical approaches enables runtimes over 1,000x faster than many literature examples, while simultaneously modeling plant dynamics far more accurately. Using only Princeton’s existing energy infrastructure, the optimization model yields an average 6.5% reduction in CO2 emissions (equivalent to taking 1,084 cars off the road) while saving $1.70M/year in operating costs. To achieve these savings in real life, collaboration with Princeton’s campus energy team is ongoing to help implement the model’s recommendations.