Optimizing pipe network design and central plant positioning of district heating and cooling System: A Graph-Based Multi-Objective genetic algorithm approach. (1st November 2022) - Su, Lingqi; Nie, Ting; On Ho, Chi; Yang, Zheng; Calvez, Philippe; Jain, Rishee K.; Schwegler, Ben
Highlights: A graph-based multi-objective genetic algorithm for district energy system design. Simultaneously optimize DHC pipe network layout and central plant positioning. Improve valid offspring design generation and enable plant positioning optimization. Six real-world case study to demonstrate outperformance of proposed work. Abstract: District heating and cooling systems are one of the most promising technologies to support rapid decarbonization of our energy system as they enable significant energy and emissions savings. However, realizing such savings is highly dependent on the pipe network design and the central plant positioning. Today, the pipe network design and the central plant positioning still rely on expert experience and most of them are sub-optimal in terms of cost and efficiency performance. To tackle this issue, we propose a graph-based multi-objective genetic algorithm for simultaneous optimization of the pipe network design and the position of a central plant in a district heating and cooling system. Differing from a traditional genetic algorithm, our proposed algorithm uses a custom mutation and crossover technique to ensure the generation of valid offspring. The extra relocation step in the algorithm enables the optimization of the central plant location. Finally, we used the proposed algorithm to design the pipe network layout and decide the central plant location for six city districts in a metropolitan area in China. To assess and benchmark the performance of our proposed method, we utilized an expert-based approach in which several experienced engineers produced reference designs. Results demonstrate that our proposed algorithm outperforms the reference designs for all six cases. The proposed method was able to achieve a maximal pressure drop reduction of 37% and a maximal cost saving of 10% compared to the reference design.