Evaluation of low-exergy heating and cooling systems and topology optimization for deep energy savings at the urban district level

被引:44
作者
Allen, Amy [1 ,2 ]
Henze, Gregor [1 ,2 ,3 ]
Baker, Kyri [1 ,2 ,3 ]
Pavlak, Gregory [4 ]
机构
[1] Univ Colorado, UCB 428, Boulder, CO 80309 USA
[2] Natl Renewable Energy Lab, 15013 Denver West Pkwy, Golden, CO 80401 USA
[3] Renewable & Sustainable Energy Inst, 027 UCB Suite N321, Boulder, CO 80309 USA
[4] Penn State Univ, 104 Engn Unit A, University Pk, PA 16802 USA
关键词
Fifth-generation district heating and cooling systems; Topology analysis; Hydraulic network modeling; RADIANT; PERFORMANCE; SIMULATION; DESIGN; MODEL;
D O I
10.1016/j.enconman.2020.113106
中图分类号
O414.1 [热力学];
学科分类号
摘要
District energy systems have the potential to achieve deep energy savings by leveraging the density and diversity of loads in urban districts. However, planning and adoption of district thermal energy systems is hindered by the analytical burden and high infrastructure costs. It is hypothesized that network topology optimization would enable wider adoption of advanced (ambient temperature) district thermal energy systems, resulting in energy savings. In this study, energy modeling is used to compare the energy performance of "conventional" and "advanced" district thermal energy systems at the urban district level, and a partial exhaustive search is used to evaluate a heuristic for the topology optimization problem. For the prototypical district considered, advanced district thermal energy systems mated with low-exergy building heating and cooling systems achieved a source energy use intensity that was 49% lower than that of conventional systems. The minimal spanning tree heuristic was demonstrated to be effective for the network topology optimization problem in the context of a prototypical district, and contributes to mitigating the problem's computational complexity. The work presented in this paper demonstrates the potential of advanced district thermal energy systems to achieve deep energy savings, and advances to addressing barriers to their adoption through topology optimization.
引用
收藏
页数:16
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