Pandapipes: An Open-Source Piping Grid Calculation Package for Multi-Energy Grid Simulations
- Assessment of supply options with different energy infrastructures: With increasing interconnection of infrastructures, how energy is supplied to end consumers can vary greatly. For instance, Then et al.  gave an overview of studies on the controversial role of the natural gas infrastructure and elaborated on the effects that a decline in energy demand can have on grid charges. In , they put forward that increasing grid charges could accelerate gas grid defection as a self-induced effect. Kisse et al.  investigated the effects that changes in heating technologies for residential buildings have on investments into electrical and gas grid expansion as well as on CO2-emissions. To improve the prediction of suitable supply scenarios for network operators in a multi-energy system (MES), methods and models have to consider all energy infrastructures in a combined optimization approach.
- Grid integration and expansion in coupled energy infrastructures: The progressive electrification and renewable generation increases both load and generation in distribution grids. All new devices and power plants must be integrated on the respective voltage level which often spurs network operators to expand or re-design their power grids. In the future, this planning process needs to address the increasing interconnection to other infrastructures and new opportunities for trading flexibility or storing energy . Approaches should consider the possibility to rededicate parts of the infrastructure to other carriers or even dismantle infrastructure that is no longer required [2,5]. It is crucial to identify suitable investment paths toward a future supply infrastructure taking into account the longevity and high capital expenditure of infrastructure assets. These challenges also have to be reflected by large-scale grid integration and expansion studies (e.g., [9,10]).
- Analysis and optimization of operation strategies: Sector coupling facilities, such as combined heat and power (CHP) plants, heat pumps or power-to-gas (P2G) devices, are expected to deliver ancillary services for power system operation (e.g., frequency support, voltage support, congestion management, etc.). Although such services are mainly required in the power grid, operation strategies should be optimized by respecting the operational constraints of all connected infrastructures. For example, Liu et al.  analyzed different operation modes of CHP plants and how they influence power and gas grid constraints. In , an optimal power flow (OPF) implementation is presented that includes constraints from the gas grid as side conditions.
- Urban planning: The planning phase of new districts offers great potential for low-cost decarbonized energy supply by implementing small MESs with optimized grid infrastructure . To address arising challenges of urban planning, the spatial and energy infrastructure planning need to be interconnected . Forming so-called energy communities with high shares of renewable energy sources (RES) and local trading capabilities can play an important role in increasing energy efficiency and is therefore incentivized by the European Union . In such small systems, choosing and optimizing the operation strategy is crucial in order to use energy locally or to offer ancillary services to the external power grid .
- Market design: If sector coupling facilities deliver flexibilities or other ancillary services, they also need to be remunerated. Mancarella et al.  suggested an approach to determine the profitability of MES, which deliver ancillary services, by considering the revenue and the cost of energy shifting. With an increased interest in local energy markets  comes a need to analyze approaches with respect to the technical and economic performance. For such analyses, the complex state of the whole MES and market mechanisms need to be modeled.
2. Multi-Energy Grid Simulation: Overview of Approaches and Requirements
- Clear Data Structure: Mastering the complexity of MEG simulations is only possible if the data is contained in a clearly structured database that allows storing large amounts of data. This is true for the model data of the coupled grids as well as the output data, especially from time series simulations. Many tool descriptions highlight the way data are stored and handled due to convenience and efficiency [20,32,35].
- Adaptable MEG Model Setup: The construction and adaptation of a full simulation model should be simple and efficient. This requires pre-defined, but adaptable models for grid components and sector coupling facilities with their physical properties and respective control strategies. Extensive component libraries are important parts of tools in the area of MEG simulation [38,41]. A permissive open source license is a good precondition to encourage model development . The coupling of simulations for different infrastructures should be as simple as choosing which grids to couple and defining the models for coupling facilities. Different simulation types should be available and combinable, e.g., steady-state, transient or OPF, in order to address different levels of detail of specific use cases.
- Performance: In research, the evaluation of many different setups and use cases is of interest. When performing large-scale studies (e.g., ), the simulation time plays an important role to be considered in the design of the tool. From the user’s point of view, it should also be considered that spending a lot of time waiting for calculations to finish can be very inconvenient and inefficient. Model efficiency and simulation speed is therefore also addressed by many tools [20,31,38,40].
3. Overview of Pandapipes
3.1. Illustrating Clear Data Structure: Architecture of Pandapipes
3.1.1. The Pandapipes Grid Structure
3.1.2. The Pipeflow Procedure
3.1.3. The Controller Architecture and Time Series Simulations
3.2. Illustrating Adaptable MEG Model Setup: Introduction to Pandapipes Multi-Energy
3.3. Illustrating Performance: Comparison between Pandapipes and STANET®
3.3.1. Simulation Setup for the Performance Comparison between Pandapipes and STANET®
- The district heating grid (Figure A1) consists of four heat exchangers and is operated at 6 bar and 43 °C. The demand of each heat exchanger is constant over time. No fluid supply is required, as the grid is considered to be a closed system. Solely the pump ensures a circulation of the fluid and provides the required heat supply.
- The water grid (Figure A2) comprises 105 sinks and is operated at 6 bar as well. To increase the complexity, each sink follows an individual time series.
3.3.2. Comparison of Calculation Time
4. Solving Problems of Coupled Multi-Energy Grid Simulation with Pandapipes Multi-Energy
4.1. Use Case 1: Local Peak Shaving Strategies to Support the Supply of District Heating Grids
4.1.1. Introduction to the Problem
4.1.2. Use Case Implementation
- The simulation is performed in 15 min increments with the temperature being evaluated every 50 m. The heat pump supplies a pressure which is high enough to make sure that the fluid arrives at the consumers within one time step.
- If inertial effects were not neglected, depending on the ambient temperature and pipe insulation, the fluid temperature would continuously adopt to the steady state. By neglecting inertia, we observe a worst case where the fluid temperature instantaneously mixes with ambient temperature levels. As the primary intention in this use case is to show how the state of one grid influences the state of a coupled grid, this approach is feasible without a detailed analysis of the heating grid.
4.1.3. Result Evaluation
4.2. Use Case 2: Flexibility Provision of Power-To-Gas Devices
4.2.1. State-of-the-Art Review
4.2.2. Use Case Implementation
4.2.3. Study of the Power-To-Gas Flexibility Potential
5. Summary and Conclusions
- With its internal architecture, pandapipes addresses the criterion of a clear data structure. Using pandas tables for input parameters and results enables an easy setup and adaptation of grid states and post-processing with the help of convenient pandas functions. Large time series simulations can be performed and results stored efficiently. The use of numpy arrays with a unified design makes the internal structure clear for developers, and parts of the calculation logic can be adapted without putting much effort into its restructuring.
- The specialized multi-energy module addresses the criterion of an adaptable MEG model setup. Models for coupling components can be chosen from the existing implementations or defined by the user himself. In the use cases, we showed the flexibility of these models, as controllers were defined for heat pumps and P2G devices that relied on results of heating and gas grid simulations. This information can be exchanged at run time of the control loop. By connecting different control strategies and types of simulations, a wide range of research questions can be addressed.
- The comparison to STANET® illustrated the performance of pandapipes, which we identified as another criterion. Comparing run times of time series simulations for different grid models between the two tools revealed that pandapipes is faster in each simulation setup. One possible reason is the handling of time series data, which is especially important in MEG simulations. The performant core makes pandapipes a perfect tool for extensive investigations, including probabilistic grid planning, time series, Monte Carlo and placement studies. Unlike with co-simulation environments, the coupling of simulations hardly adds any overhead to the simulation time. The demonstrated use cases underline how crucial performance is, as a large number of simulations had to be performed for each of the studies. Comparisons to other tools, especially open source tools such as EPANET , should be considered in the future.
Conflicts of Interest
|l||pipe length coordinate||m|
|heat transfer coefficient|
|pressure loss coefficient||1|
|Darcy friction factor||1|
|b||referring to a branch|
|n||referring to a node|
|referring to node 1 (entering a branch)|
|referring to node 2 (leaving a branch)|
|N||referring to the reference state|
|referring to the network|
|at reference node|
|at slack node|
|referring to the surface|
|CHP||Combined heat and power|
|ESMO||Energy system modeling and optimization|
|GUI||Graphical user interface|
|OPF||Optimal power flow|
|PGR||Power and gas grid restricted|
|PR||Power grid restricted|
|RCR||Relative curtailment reduction|
|RES||Renewable energy sources|
Appendix A. Mathematical Model Formulation in Pandapipes
- Junctions are the node representations. Their model implementation corresponds to the mass and energy flow balance as formulated in Equations (A1) and (A2). However, since there are only formulations for pressure and temperature drops in the system of equations, for at least one junction, the pressure and temperature need to be preset as boundary conditions.
- External grids represent connections to other systems. This node element fixes pressure or temperature for the connected junction to form the above stated boundary condition, thus turning it into a reference node.
- Sinks and sources are node elements which insert a mass flow entering or leaving the connected junction.
- Pipes are the main branch components, i.e., they connect two junctions, respectively. Their physical representation is explained in the following paragraphs.
- Valves are branch components that can also disconnect the two connected junctions from each other, depending on an “opened” flag. They are modeled as branches with length 0, but can still introduce a lumped pressure loss.
- Pumps are branch components that introduce a pressure lift. The pressure lift is calculated according to a characteristic that sets the operating point in dependency of the calculated flow rate.
- Circulation pumps are useful for district heating grid calculations. At the outgoing junction, the pressure is fixed and at the incoming junction the mass flow is set. This behavior corresponds to two node elements: an external grid and a sink. It is a simplified component neglecting operation limitations of a real pump.
|External grid||fixed pressure||bar||mass flow to grid|
|Sink/Source||set mass flow||mass flow to / from node|
|k||roughness||mm||temperature at junction 1||K|
|loss coefficient||-||temperature at junction 2||K|
|heat transfer coefficient||mass flow through pipe|
|external heat flow||W||norm volume flow|
|external temperature||K||Re||Reynolds number||-|
|s||number of sections||-||Darcy friction factor||-|
|flag for flow||-||pressure difference||bar|
|loss coefficient||-||mass flow through valve|
|norm volume flow|
|Pump||pump standard type (for characteristic)||-||pressure difference||bar|
|Circulation Pump||pressure set point||bar||pressure difference||bar|
|pressure lift||bar||mass flow through pump|
|temperature set point||K|
Appendix B. System Specification of the Laptop Used for the Performance Comparison between Pandapipes and STANET®
|Operating System||Microsoft Windows 10 (64 bit)|
|CPU||Intel Core i7-6700HQ|
|system memory||16 GB|
|solid-state drive||Samsung MZ7TY256HDHP-000L7|
|graphic cards||Intel HD Graphics 530,|
NVIDIA GeForce 940MX
Appendix C. Plots Displaying Grids Used for Performance Comparison
Appendix D. Calculation Results of Performance Comparison between Pandapipes and STANET®
|Heat Grid||Water Grid||Gas Grid|
|total time||pandapipes||mean [s]|
standard deviation [s]
standard deviation [s]
|simulation time||pandapipes||mean [s]|
standard deviation [s]
standard deviation [s]
Appendix E. Deviation between Pandapipes and STANET® Results
|Measurand in the Corresponding Grid||Average Result||Mean Deviation||Relative Deviation|
|pressure [bar]||heat grid||6.00||0.00065||0.011%|
|gas grid||0.94||2.41 ×||0.0025%|
|velocity||heat grid||0.015||3.76 ×||0.25%|
|temperature [℃]||heat grid||47.31||0.42||0.89%|
Appendix F. Use Case 1: Additional Information on the Peak Shaving Algorithm
Appendix G. Use Case 2: Details of the Analyzed Grids
Appendix H. Use Case 2: Implementation of the Feed-In Restriction
Appendix I. Use Case 2: Results for Ten Exemplary Hours
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|Tool||Type||OS||Power||Gas||District Heating||Detailed Grid Model||Coupled Simulation||Additional Features|
|SINCAL ||GS||√||√||√||√||GUI, OPF, (TOP) ***|
|STANET ||GS||√||√||√||√||GUI, TC (DH), (TOP) ***|
|TRNSYS [29,30]||GS||√||√||GUI, TC (DH), CTRL|
|MATPOWER ||GS||√||√||√||OPF, (TOP) ***, LIB|
|PyPSA ||GS||√||√||**||**||√||OPF, (TOP) ***, LIB|
|pandapower ||GS||√||√||√||OPF, CTRL, TOP, LIB|
|pandapipes||GS||√||√||√||√||CTRL, TOP, LIB|
|Switch 2.0 ||ESMO||√||√||√||LIB|
|oemof ||ESMO||√||√||√||√||√||TOP, LIB|
|SAInt ||MEGS||√||√||√||√||GUI, OPF, TC (G)|
|MYNTS ||MEGS||√||√||√||√||GUI, TC (all), CTRL|
|TransiEnt [41,42]||MEGS||*||√||√||√||√||√||TC (all), CTRL|
|pandapipes multi-energy||MEGS||√||√||√||√||√||√||OPF, CTRL, LIB|
|# junctions||20||151||2634 (1128 in service)|
|# pipes||18||149||2634 (1128 in service)|
|# heat exchangers||4||-||-|
|# external grids||-||1||1|
|# profiles||- **||105||132|
|meshing degree *||1.21||1.29||1.00|
|RES (PV)||RES (wind)||RES (mix)||Electric Loads||Gas Loads|
|maximum power [MW]||54.6||68.5||47.2||17.2||15.8|
|total energy over year [GWh]||77.8||110.0||94.0||80.7||38.2|
|Power Grid||Gas Grid|
|preset voltage at slack node||preset pressure at reference node|
|maximum voltage||maximum pressure P2G node|
|maximum line loading||maximum feed-in with methane|
|maximum hydrogen fraction|
|P2G Devices||RES Scenario||P2G Power [MW]||Fluid||Positions||Case|
|0||PV, mix, wind||-||-||-||base, PR|
|1||PV, mix, wind||1–10||H2, CH4||1||PR, PGR|
|1||PV, mix, wind||1, 2, 5, 10||CH4||2, 3, 4, 5, 6||PR, PGR|
|2||PV, mix, wind||1, 2, 5, 10||CH4||1 + (2, 3, 4, 5, 6)||PR, PGR|
|2||PV, mix, wind||1, 5, 10||CH4||(2, 3, 4) + (5, 6)||PR, PGR|
|6||PV, mix, wind||1, 2, 5||CH4||1 + 2 + 3 + 4 + 5 + 6||PR, PGR|
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Lohmeier, D.; Cronbach, D.; Drauz, S.R.; Braun, M.; Kneiske, T.M. Pandapipes: An Open-Source Piping Grid Calculation Package for Multi-Energy Grid Simulations. Sustainability 2020, 12, 9899. https://doi.org/10.3390/su12239899
Lohmeier D, Cronbach D, Drauz SR, Braun M, Kneiske TM. Pandapipes: An Open-Source Piping Grid Calculation Package for Multi-Energy Grid Simulations. Sustainability. 2020; 12(23):9899. https://doi.org/10.3390/su12239899Chicago/Turabian Style
Lohmeier, Daniel, Dennis Cronbach, Simon Ruben Drauz, Martin Braun, and Tanja Manuela Kneiske. 2020. "Pandapipes: An Open-Source Piping Grid Calculation Package for Multi-Energy Grid Simulations" Sustainability 12, no. 23: 9899. https://doi.org/10.3390/su12239899