期刊
APPLIED ENERGY
卷 310, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.118494
关键词
District heating network; Transformation; Low-temperature; Design; Optimal phasing; Mixed-integer linear programming
In order to meet decarbonization targets, contemporary district heating networks need to transform into more efficient low-temperature networks. Economic challenges can be addressed by implementing appropriate transformation strategies, reducing excessive network expansion and decommissioning. Furthermore, generalized claims for lowest supply and return temperatures may not be economically viable.
In order to meet the decarbonization targets of the heating sector, contemporary district heating networks (DHN) are facing the need for a transformation towards more efficient low-temperature DHN, enabling the integration of regenerative heat generation technologies. Moreover, advancing efficiency measures on the consumer-side will lead to decreasing heat demand densities posing economic challenges on the operation of DHN. To sustain economic viability in the future, network operators are therefore confronted with the decision to either adopt post-densification, expansion or decommissioning measures. In order to assess the economic impact of different transformation targets on the DHN topology, an existing mixed-integer linear programming (MILP) model was enhanced to identifying the most cost-effective network design and long-term development of DHN for various transformation strategies. The results of the underlying use case suggest that both excessive network expansion and economically driven decommissioning can be significantly reduced by appropriate transformation strategies which consist of temperature and post-densification measures of varying degrees of intensity. In addition, we were able to show that generalized claims for lowest supply and return temperatures in future DHN, although mostly technically feasible, must not be economically viable intrinsically.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据