Renewables, storage, intelligent control: how to address complexity and dynamics in smart district heating systems?

O’Donovan, Keith; Falay, Basak; Leusbrock, Ingo

doi:10.1016/j.egypro.2018.08.217

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Neirotti et al [17] evaluate the potential for lowering the temperatures in a small existing DHN using Modelica. Using test cases with about 100 HCs, Schweiger et al [18] and O'Donovan et al [3] demonstrate that Modelica is well-suited for the simulation of mediumsized DHNs.…”

Section: Simulation Of District Heating Network: Tools and Applicationsmentioning

confidence: 99%

“…The transformation towards fourth-generation district heating requires the development and successful implementation of novel concepts for heat supply and distribution, which raises numerous challenges: The integration of distributed renewable heat sources leads to a more decentralized feed-in and requires reduced temperatures in the district heating network (DHN) [3]. This may entail new hydraulic bottlenecks [4], (more frequent) flow reversals, formation of cold plugs, increased thermo-mechanical stress on the pipes, frequent changes of the hydraulically critical path and temperature undersupply of heat consumers (HC) that are far from heat supply units or require high supply temperatures.…”

Section: Introductionmentioning

confidence: 99%

“…These challenges require targeted reinforcement of the DHN or even novel network layouts [4], as well as novel concepts for design and placement of new heat supply and heat storage units, and innovative operation strategies for all components. To develop these concepts, simulations of the DHNs are required [3,5] using dynamic thermo-hydraulic models able to handle bi-directional flows [6]. Furthermore, the models need to be suitable for long-term simulations, even annual, if heat sources show seasonal fluctuations or if long-term heat storage is to be considered.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Heat Consumer Model for Robust and Fast Simulations of District Heating Networks Using Modelica

Zipplies,

Orozaliev,

Jordan

et al. 2024

Electronics

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Dynamic thermo-hydraulic simulations of district heating networks (DHN) are essential to investigate novel concepts for their sustainable design and operation. To develop solutions for a particular case study, numerous long-term simulations are required. Therefore, computational effort for simulation is critical. Heat consumers (HC) are numerous and determine the dynamics of mass flows and return temperatures in the DHN. Thus, the way in which HCs are modeled has significant impact on the computational effort and the results of the simulation. This article presents a novel Modelica-based model for HCs that builds on an existing simplified modeling approach (open-loop design). The calculation of mass flow and return temperature is improved in terms of robustness, plausible behavior and low computational effort. In particular, the model reacts to limited differential pressure and supply temperatures to ensure plausible behavior across all operating conditions, including undersupply situations. The model is successfully tested using an exemplary DHN. The analysis proves that the HC model itself requires little time to simulate. Nevertheless, it significantly influences the simulation time for the entire DHN, which varies by a factor of five for the investigated system depending on the HC model. Fast dynamics, including a bypass in the model and correction of deviations between set point and actual heat load increase the simulation time, so users should sensibly choose how to use these options. HC models triggering many state events result in high computational effort. Compared to other simple HC models, the proposed model produces more plausible results while maintaining at least equal simulation performance (for models without bypass) or even improving it (for models with bypass, CPU time is reduced by at least 35%).

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Section: Simulation Of District Heating Network: Tools and Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%