%0 Journal Article %A Marco Wirtz %A Miguel Heleno %A Hannah Romberg %A Thomas Schreiber %A Dirk Muller %B Energy and Buildings %D 2023 %G eng %P 112858 %R 10.1016/j.enbuild.2023.112858 %T Multi-period design optimization for a 5th generation district heating and cooling network %U https://linkinghub.elsevier.com/retrieve/pii/S0378778823000889 %V 284 %8 02/2023 %! Energy and Buildings %X

In the planning phase of district energy systems, optimization models based on mathematical programming are a widely used approach. Most optimization models determine the optimal energy system design based on a single representative year. However, this approach is unable to consider changing economic and technological boundary conditions over the system’s lifetime. As a result, these one-period models fall short for districts with 5th generation district heating and cooling (5GDHC) networks, which are typically developed over long time periods and are built in multiple construction phases. In this paper, we present two multi-period optimization approaches for designing 5GDHC districts: The first approach is a forward-looking model which determines the optimal investment pathway by using a perfect foresight of future parameter developments. In the second approach, a one-period model is solved repeatedly for every investment period without knowledge of future parameter developments. Both approaches are compared to a one-period model for a real-world 5GDHC district in Germany. In comparison with a one-period design approach, the forward-looking method leads to total cost savings of up to 17 % and the sequential method of up to 11 %. By using a forward-looking model, gas-fired technologies are sized smaller while the capacity of electricity-driven technologies in the energy hub as well as photovoltaic modules and thermal energy storages in buildings increases compared to a one-period model. The case study shows that a multi-period modeling approach is an important addition to design optimization models for 5GDHC networks and can have a significant impact on the optimal design.