Two-Stage Optimal Economic Scheduling for Commercial Building Multi-Energy System Through Internet of Things

In this study, an advanced Internet of Things (IoT) technology is applied to the energy management of an intelligent combined cooling, heating, and power (CCHP) commercial building system. Based on the framework of a smart energy management system (SEMS) using IoT technology, a two-stage optimal scheduling model is proposed to determine the most economic CCHP commercial building system integrated with a three-way valve. In the day-ahead scheduling stage, the schedule is planned using the lowest operating costs of the system. In the real-time correction stage, the correction strategy employs minimum adjustment of the output of each unit. Moreover, the schedule plan is corrected to smooth out fluctuations in the loads and renewable energy resources (RES) in a timely manner to better absorb green energy. The day-ahead scheduling model is a large-scale mixed integer nonlinear programming (MINLP) problem solved through a linearization method proposed in this study and the mixed integer linear programming method. The real-time correction optimization model is a nonlinear programming problem solved by the quantum genetic algorithm (QGA). A case study is employed to demonstrate that the IoT-based SEMS improves the system automation energy management level and user comfort. Furthermore, the proposed system structure can significantly reduce system operating costs and improve the utilization of waste heat from the internal combustion engine. In conclusion, the economic and environmental superiority of the two-stage optimal dispatch model is verified.