The increasing complexity of the design and operation evaluation process of multi-energy grids (MEGs) requires tools for the coupled simulation of power, gas and district heating grids. In this work, we analyze a number of applicable tools and find that most of them do not allow coupling of infrastructures, oversimplify the grid model or are based on inaccessible source code. We introduce the open source piping grid simulation tool pandapipes that—in interaction with pandapower—addresses three crucial criteria: clear data structure, adaptable MEG model setup and performance. In an introduction to pandapipes, we illustrate how it fulfills these criteria through its internal structure and demonstrate how it performs in comparison to STANET®. Then, we show two case studies that have been performed with pandapipes already. The first case study demonstrates a peak shaving strategy as an interaction of a local electricity and district heating grid in a small neighborhood. The second case study analyzes the potential of a power-to-gas device to provide flexibility in a power grid while considering gas grid constraints. These cases show the importance of performing coupled simulations for the design and analysis of future energy infrastructures, as well as why the software should fulfill the three criteria.

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The increasing complexity of the design and operation evaluation process of multi-energy grids (MEGs) requires tools for the coupled simulation of power, gas and district heating grids. Most tools analyzed in this paper either do not allow coupling of infrastructures, simplify the grid model or are not publicly available. We introduce the open source piping grid simulation tool pandapipes that – in interaction with pandapower - fulfills three crucial criteria: clear data structure, adaptable MEG model setup and performance. In an introduction to pandapipes we illustrate how it fulfills these criteria through its internal structure and demonstrate how it performs in comparison to STANET®. Then we show two case studies that have been performed with pandapipes already. The first case study demonstrates a peak shaving strategy as interaction of a local electricity and district heating grid in a small settlement. The second case study analyzes the potential of a power-to-gas device to serve as flexibility in a power grid under consideration of gas grid constraints. They both show the importance of a clear database, a simple simulation setup and good performance to set up different large and complex studies on grid infrastructure design and operation.