Design and optimal siting of regional heat-gas-renewable energy system based on building clusters

Distributed energy systems (DESs) are considered to be the future of energy systems because of their inherent high efficiency, cost-effectiveness, energy-saving capability, and environmentally friendly characteristics. Although there are many reports on the design optimization of DESs, few of them focus on the classification and siting optimization of building clusters, which are of critical importance to district-scale DESs. This study proposes a cost-effective hybrid method to optimize the design and siting of multiple DESs for district building clusters. First, k-means clustering is performed to initially divide the district into building clusters. A mixed-integer linear programming (MILP) model is then established to optimize the DES configuration and operation strategies for building clusters in consideration of the design and operation constraints. Genetic algorithm is subsequently implemented to optimize the cluster configurations for the district such that the annual total cost is minimized. The proposed method was applied to a twenty-building district-scale energy system located in Guangzhou, China as a case study. The results show that the optimized design and siting scheme obtained via the hybrid method can reduce the annual total cost and carbon emissions by 23.65% and 75.32%, respectively, relative to the conventional energy system; the stability and convergence rate of the hybrid method were also tested and analyzed. In addition, a sensitivity analysis was carried out to analyze the effect of energy price on the economic and environmental performance of DESs.