Geographically informed automated non-linear topology optimization of district heating networks

Abstract

District heating networks are becoming an important component of future sustainable energy systems, providing space heating and hot water to buildings. The design of these networks can be complex and requires accurate consideration of various factors including geographical location, building characteristics, and energy demand. Here we propose a methodology to base automated non-linear optimization of district heating networks (DHNs) on geographical data. By integrating Geographic Information Systems (GIS) data with a non-linear optimization algorithm, an optimal network design and topology are determined that minimize the total cost of the system while ensuring heat demand satisfaction of the served buildings. The potential pipe routing is based on the existing street network and the building heat demands are derived from actual metered demand data. The approach is applied to a case study, hereby demonstrating its effectiveness in finding an optimal design for a challenging problem with producers at different temperature levels and varying consumer characteristics. The study shows that taking into account the geographical constraints and characteristics of buildings, the approach allows for more accurate and efficient system design. Partial automation on the input of optimization process also has the potential to significantly reduce the time and effort required for early stage planning of DHNs.