Automated computational design method for energy systems in buildings using capacity and operation optimization

Research has been conducted previously to reduce the energy consumption and costs of energy systems by focusing on the optimization of building designs. However, the operation method significantly affects the results of the design optimization. Therefore, the aim of this study was to optimize the operation of thermal energy storage, which is particularly difficult, and the load dispatch to the heat source equipment. The optimization was achieved using a hybrid strategy involving three methods: epsilon differential evolution with random jumping (εDE-RJ) for design optimization, dynamic programming for heat storage tank optimization, and Lagrange's multiplier method for load dispatch optimization. Based on this method, 28% and 8% reductions were achieved in the life cycle costs associated with operation and design optimization, as compared to those incurred using the conventional method and the design optimization approach without operation optimization, respectively. The results show that optimal systems can be realized by simultaneously optimizing the operation and design of energy systems.