Stochastic Multi-Energy Optimization of a Smart University Campus with Integrated Demand Response and Renewable Energy

The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stage stochastic mixed-integer linear programming (MILP) framework for the optimal day-ahead energy management of a smart university campus. The proposed model jointly coordinates photovoltaic generation, battery energy storage systems, electric vehicle charging, HVAC operation, and demand response under uncertainties associated with solar generation, electricity demand, energy prices, and ambient temperature. Unlike previous campus energy management approaches, the proposed framework explicitly distinguishes first-stage scheduling decisions from second-stage recourse actions, enabling adaptive operation while preserving decision consistency across uncertainty scenarios. A realistic case study based on the operational characteristics of the Universidad Politécnica Salesiana campus in Ecuador is used to evaluate the proposed methodology. The results demonstrate that the coordinated stochastic scheduling strategy reduces daily operating costs by 36.37%, decreases CO2 emissions by 42.81%, and lowers peak grid demand by 37.99% compared with conventional operation. In addition, photovoltaic self-consumption reaches 91.7%, while renewable energy utilization increases to 93.4% without compromising occupant thermal comfort. The proposed framework provides a scalable pathway toward low-carbon, resilient, and energy-efficient smart campus operation.