"GridSim" is a simulation model for the detailed analysis of distribution grids based on load flow calculations, conceived by the FfE. The impact of various decentral generation and consumption systems on the distribution grid can be determined through a comprehensive depiction of the electric energy system at the distribution grid level. The name derives from the words "grid" and "sim", abbreviated for simulation.
The FfE developed the simulation model to analyze and evaluate emerging changes and challenges for distribution grids within the energy transition. Since 2012, various projects continuously enhanced and applied the model (essential projects see Figure 1). GridSim served as a tool to answer research questions in these projects. In this context, 2 dissertations and more than 20 final theses were generated.
Figure 1: The History of the FfE Model GridSim
The eGAP projects laid the foundation for GridSim. These projects aimed to assess the effects of high penetration of electric vehicles (SmartGrid @GAP) and prosumers with PV installations, home storage systems, and electric vehicles (Sun2Car @GAP), on the distribution grid. Equally, grid optimization possibilities were investigated using controllable local network transformers (rONT) and linear voltage regulators (Smart Grid Controller @GAP).
The project Merit Order Grid Expansion 2030 (MONA 2030) supplemented the model with the heat sector and its related components, heat pumps, and electric storage heating (also known as night-storage heating). Considering these extensions, MONA implemented various network optimization measures (NoM) in the model and systematically compared representative type networks chosen by a clustering procedure within the project.
The subsequent project C/sells applied the model for determining the need for flexibility in the demonstration cell of Altdorf, near Landshut. Real network data from the medium and low voltage grid level as well as associated network loads were integrated into GridSim. The to be developed Flex platform “Alf” receives the calculated need for flexibility.
In the project eXtremOS, available flexibility from the distribution grid, considering its grid restrictions, is determined through simulations and transferred to the energy system model – ISAaR – to be able to retrieve flexibility from the distribution grid.
The project Munich electrified (Me) deploys GridSim to assess the imposed grid load of electric mobility in Munich. Different options for charging infrastructure usage (e.g., at home, at work, or in public) were integrated into GridSim and analyzed in case studies with regards to their current and future grid load in the distribution grids of selected study areas and their effects on the total residual load in Munich.
The model comparison within the project MODEX MEO compares GridSim methodically with other research institutes’ models by calculating different future scenarios, like the expansion of renewable energies or the electrification of heat and mobility. The comparison identifies suitable modeling structures for specific questions and develops concrete optimization potentials of the different models, thereby creating more transparency in the operative system analysis.
The project Bidirectional Charging Management (BCM) researches the intelligent interaction of bidirectional electric vehicles with the energy system using GridSim. Different applications are modeled, e.g., for integrating renewable energy, and their impact on the distribution grids is analyzed. Equally, the project examines the usefulness of electric vehicles for the grid.
What is the simulation model GridSim suitable for?
GridSim provides answers to the following questions:
- What grid loads do high penetrations of, e.g., electric vehicles or power-to-heat systems, cause in the distribution grid?
- Is the decentral generation able to supply future consumers?
- To what extent do (functional) electricity storages increase the absorption capacity of power grids for renewable energies?
- How high is the maximum integrable generation power into the grid, or starting from which renewable energy extension does the grid require upgrades?
- Which simultaneity factors evolve from different operating modes, depending on the number of consumer categories, like electric vehicles?
- What are the consequences and potential of increasing the commercialization of decentral small-scale plants?
- Which grid optimization measures are the most effective and inexpensive in which grid and scenario?
- To what extent can distribution grids provide system service for higher voltage levels?
The combination of energy system model for distribution grids, enabling a detailed energetic consideration (focus: electricity) of the systems and components connected to the distribution grid including different operating modes or charge controls, and the three-phase load flow calculation of the corresponding grid area allows a comprehensive analysis of the mentioned questions. Complex analyses and evaluations can be generated with daily or yearly simulations and time steps from one minute to one hour. The broad parameter selection, preset with standard values, ensures a simple and efficient scenario generation as well as an intuitive usage of GridSim.
How does the simulation model GridSim work?
Initially, a network topology is chosen according to the case of the application. GridSim enables investigating real and synthetic network topologies, e.g., typecasted through clustering (see MONA base network topologies). In the next step, buildings with residential and commercial units are assigned to the low voltage grid connection nodes. Figure 2 depicts an exemplary low voltage grid area. Units receive not only a three-phase electric load curve but also a heat demand.
Figure 2: Schematic Grid Area Including the Modeled Components
Besides, components like electric vehicles, PV systems, power stores, or heat pumps can be assigned to each building. At the simulation start, the components are distributed randomly (but reproducibly) or from defined tables according to specific criteria and are linked to a generation curve or driving profile each. Coupling with the FfE region model enables using numerous real, regionally high dissolved, energy-economic data to allocate the components.
All distributed components offer various operating modes during the configuration, which affect the active and reactive power. For example, this includes the increase in self-consumption with electricity storage devices or a voltage-guided control to avoid voltage band violations at the grid connection point.
Subsequently, the simulation run calculates the residual load per building for each time step, considering the individual components’ controls. A load flow calculation helps to determine the current grid status based on this residual load matrix. Thereby, all voltages, currents, and equipment utilizations are calculated. Depending on the selected control mode, these results are directly incorporated into the component control (e.g., rONT) or are saved.
In addition to the grid states, the load curves of all components, and for storages the states of charge, are calculated and saved. After calculating load flows, energy balances of the total grid area, the utilization of grid components, or equivalent full cycles of storages can be generated or calculated based on this data. Equally, CO2 balances of the grid area and typical, statistical charging load curves, depending on the selected charge control, can be calculated.
Since the component distribution in the grid areas, e.g., all generators at the string’s end in an extreme case, strongly impacts the simulation results, all scenarios with different distributions are computed several times. Starting from a certain number of distributions, statistically sound evaluations of grid effects can be made for a particular component penetration. Figure 3 summarizes the schematic simulation process.
An automated, multi-stage evaluation of the simulation results follows the simulation by calculating statistics and creating illustrations. These evaluations are created in the first stage of each random distribution and summarized in a second evaluation stage to calculate statistically significant results for the considered scenario.
Figure 3: Schematic Procedure of the Simulation in GridSim
The following figures demonstrate exemplary evaluations:
- Sun2Car@GAP – On-site Consumption of Photovoltaic Energy with Electric Vehicles
- Merit Order Grid Expansion 2030 (MONA 2030)
- C/sells - The energy system of the future in the "solar arch" in southern Germany
- Value of flexibility in the context of European electricity market coupling with extreme technological, regulatory and social developments
- Munich electrifies - Expansion of Munich's Charging Infrastructure
- Bidirectional Charging Management (BCM) – intelligent interaction of electric vehicles, charging infrastructure and energy system
- Development of an integrated simulation model for load and mobility profiles of private households