District heating modeling tools: A review based on design and simulation of renewable district heating networks

• Key R-DH abilities include TES, demand response, and advanced control strategies. • Twelve DH tools were evaluated for simulating near-100% renewable R-DH systems. • User-friendly DH-tools often lack flexibility for advanced R-DH strategies. • Non-DH tools offer flexibility but face challenges in scalability and DH-specific use. • This work guides tool selection and supports future R-DH tool development. To achieve net-zero greenhouse gas (GHG) emissions, the EU aims to fully decarbonize district heating (DH) by 2050. This work evaluates DH simulation tools based on their ability to support the operational requirements of renewable district heating (R-DH) systems, including thermal energy storage (TES), direct demand response (DR), and advanced control strategies such as model predictive control (MPC). Twelve tools were assessed using criteria derived from real-life R-DH operational needs. The methodology involved identifying the key operational requirements of R-DH systems through a comprehensive literature review and defining modeling criteria to assess the tools. These criteria include the ability to model TES across various timescales, simulate building thermal dynamics for DR scenarios, and integrate advanced control strategies. The tools were then evaluated and compared to provide insights for researchers and network operators, as well as recommendations for tool developers to address existing gaps. The results show that DH-dedicated tools are generally well-suited for network-specific studies and practical applications due to their user-friendly interfaces and built-in components. Non-DH-dedicated tools, offer greater flexibility and customization, excelling in TES modeling, building thermal dynamics, and secondary side configurations, but often require significant user expertise and additional effort to adapt to DH-specific applications. Notably, DH-dedicated tools like SIR3S and Fluidit Heat are bridging the gap between DH-dedicated and Non-DH-dedicated tools by incorporating features such as custom-scripting and co-simulation, offering enhanced flexibility for advanced research and practical applications. In conclusion, improving both DH-dedicated tools, and Non-DH-dedicated tools, will better support researchers and developers in driving the transition to efficient and sustainable R-DH systems.