Vieth, JonathanJonathanViethWestphal, JanJanWestphalSpeerforck, ArneArneSpeerforck2026-06-032026-06-032026-05-20Energy 358: 141350 (2026)https://hdl.handle.net/11420/63316The use of carbon neutral heat sources such as industrial waste heat or large-scale heat pumps requires district heating networks. While automated planning of new district heating networks is well-studied, many metropolitan areas in Central Europe already have existing networks. The main challenge in metropolitan areas is to find the optimal district heating network expansion to connect new consumers to the already existing district heating network. The expansion may lead to overloading of existing pipes or generation units, which has to be considered in the planning process. For larger networks with multiple generation units, ensuring proper operation even during the failure of one generation unit is essential, making resilience a key factor in planning. In this work, we present an algorithm for automated and optimal expansion planning of district heating networks. The algorithm prevents overloads in existing and new pipes and incorporates potential outages of generation units to achieve a resilient network design. The approach was tested on a case study involving more than 3000 consumers and ten generation units with different price assumptions. Results show that the algorithm can compute an optimal expansion within 20s, without considering resiliency. When considering resiliency, the computation time increases to 81s or 876s, depending on the price scenario. Furthermore, the peak demand added by the optimization decreases by up to 97%. Therefore, considering resiliency has not only a major influence on expansion planning regarding the resulting topology, but also regarding computation time.en1873-67852026Elsevierhttps://creativecommons.org/licenses/by/4.0/District heating networkDistrict heating network planningEnergy system planningExpansion planningOptimizationResilienceSocial Sciences::333: Economics of Land and Energy::333.7: Natural Resources, Energy and EnvironmentTechnology::620: EngineeringNatural Sciences and Mathematics::519: Applied Mathematics, ProbabilitiesJournal Articlehttps://doi.org/10.15480/882.1724110.1016/j.energy.2026.14135010.15480/882.17241