| Summary: | There is currently an ongoing urban transformation in a small Swedish town named Kiruna, it is located in the very north of Sweden well above the Arctic Circle in a sub-arctic climate. Large part of the town will be relocated due to the ground deformation that is caused by the progressing iron ore mining activity and it is affecting all infrastructures of the town. This thesis aims to accomplish a holistic analysis on the district heating (DH) system for the town of Kiruna and its future challenges. Energy companies with a DH system recognize the importance in having a good understanding about the network characteristics, for obtaining an efficient and stabile heat delivery to the end-users. In this thesis, a method for modeling and simulation of meshed DH networks is described, that makes it possible to study and analyze the flow pattern in order to locate non-obvious paths, bottlenecks and overloaded pipes. For carrying out the DH simulations a fundamental input is to set the thermal losses for each pipe segment in the model, a fictitious series with all pipe diameters is created which corresponds to the annual losses in the real network. In comparison with the pipes series manufactured today the created one is best described by the series with least insulation and highest thermal losses. The studied network has its origin in the 60th and is the sum of the different piping technics that has been valid over time, this mixture is positioning the thermal performance as a close to a worst-case scenario. To the meshed DH network a number of heat production sites are connected for delivering the thermal requested by the end-users, each site consisting of several boilers and using different resources. A hybrid evolutionary-Mixed Integer Linear Programing (MILP) optimization approach is developed and applied for finding the cost-optimal heat production for three scenarios in combination of two heating demand levels. It is stated that no matter the geographically location of the site the cheapest resource should always ...
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