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Modeling control strategies for prosumers in a Python-based modular simulation tool

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The planned massive increase of producers and consumers such as electric vehicles, heat pumps and photovoltaic systems in distribution grids will lead to new challenges in the electrical power system. These can include grid congestions at the low voltage level but also at higher voltage levels. Control strategies can enable the efficient use of flexibilities and therefore help mitigate upcoming problems. However, they need to be evaluated carefully before their application in the energy system to avoid any unwanted effects and to choose the most fitting strategy for each application. In this publication, a Python-based modular simulation tool for developing and analysing control strategies for prosumers, which uses pandapower (Thurner et al. 2018), is presented. It is intended for sequential simulations and enables detailed operational analyses, which include evaluating the influence on grid situations, the necessary behavior of energy system components, required measurements and communications. This publication also gives an overview of control strategies, existing simulation tools, how the modular simulation tool fits in and illustrates its functionalities in an application example, which further highlights its versatility and efficiency. Time series simulations with the tool allow analyses regarding the effect of control strategies on power flow results. Moreover, the simulation tool also facilitates evaluating the behavior of energy system components (e.g. distribution substations), necessary communications and measurements as well as any faults that might occur.
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RESEARCH
Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
https://doi.org/10.1186/s42162-023-00275-2
Energy Informatics
Modeling control strategies forprosumers
inaPython-based modular simulation tool
Andrea Schoen1,2*, Jan Ringelstein1, Denis Mende1,2 and Martin Braun1,2
From The 12th DACH+ Conference on Energy Informatics 2023
Vienna, Austria. 4-6 October 2023. https://www.energy-informatics2023.org/
Abstract
The planned massive increase of producers and consumers such as electric vehicles,
heat pumps and photovoltaic systems in distribution grids will lead to new challenges
in the electrical power system. These can include grid congestions at the low volt-
age level but also at higher voltage levels. Control strategies can enable the efficient
use of flexibilities and therefore help mitigate upcoming problems. However, they
need to be evaluated carefully before their application in the energy system to avoid
any unwanted effects and to choose the most fitting strategy for each application. In
this publication, a Python-based modular simulation tool for developing and analys-
ing control strategies for prosumers, which uses pandapower (Thurner et al. 2018),
is presented. It is intended for sequential simulations and enables detailed operational
analyses, which include evaluating the influence on grid situations, the necessary
behavior of energy system components, required measurements and communications.
This publication also gives an overview of control strategies, existing simulation tools,
how the modular simulation tool fits in and illustrates its functionalities in an applica-
tion example, which further highlights its versatility and efficiency. Time series simula-
tions with the tool allow analyses regarding the effect of control strategies on power
flow results. Moreover, the simulation tool also facilitates evaluating the behavior
of energy system components (e.g. distribution substations), necessary communica-
tions and measurements as well as any faults that might occur.
Keywords: Distribution grids, Power system modeling, Distribution system operation,
Prosumers, Control strategies, pandapower
Introduction
Due to Germany’s ambitious goals to increase renewable generation to at least 80 % by
2030 and reducing the dependency on fossil fuels, the number of new loads and gen-
erators, such as electric vehicle (EV) charging stations, heat pumps (HPs) and photo-
voltaic (PV) systems, is rapidly increasing (German Federal Government 2023). Control
strategies can be useful to help mitigate negative impacts on the grid situation, e.g. by
reducing grid congestions. is is especially relevant for loads like EV charging stations
*Correspondence:
andrea.schoen@iee.fraunhofer.de
1 Fraunhofer Institute for Energy
Economics and Energy
System Technology IEE,
Joseph-Beuys-Strasse 8,
34117 Kassel, Germany
2 Department of Energy
Management and Power System
Operation, University of Kassel,
Wilhemshoeher Allee 73,
34121 Kassel, Germany
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
and HPs. Before implementing any control strategy into the real energy system, it is
vital to analyse its behavior and expected influence on the grid. As described in (Schoen
etal. 2021a) and shown in Fig.1, this is a multi-level process starting with a theoretical
analysis, followed by a simulation-based analysis and generally completed by lab-based
analyses and/or field tests. In this paper, a Python-based modular simulation tool for
modeling control strategies for prosumers in distribution grids is presented. It closes the
gap in the analysis process by offering a connecting link between the analysis of grid
planning aspects and lab-based analysis using co-simulation tools and later field tests.
is simulation tool makes use of pandapower power flow controllers, which are
originally intended for simulating grid assets that are controlled based on power flows
in electrical grids (urner etal. 2018; Fraunhofer IEE and University of Kassel2023a,
2023b). An innovate way of using these power flow controllers in a coordinated, flex-
ible and modular simulation tool in an architecture motivated by co-simulation tools,
such as OpSim(Fraunhofer IEE 2023a), is applied and presented in this paper. is ena-
bles modeling relevant power system components, such as distribution system opera-
tor (DSO), distribution substations and home energy management systems (HEMS),
and communications between them. By making use of the pandapower controllers,
non-concurrent operations and communications with each other can also be modelled.
is allows modeling control strategies and performing detailed operational analyses by
means of versatile simulations within the Python-based pandapower framework.
is paper presents the modular simulation tool and is structured as follows: First, an
overview of control strategies and their role in grid operation is given in sectionGrid
Operation and Control Strategies for Prosumers, followed by a summary of require-
ments for simulation tools for operational analyses of control strategies and existing
tools insectionSimulation Tools for Operational Analyses of Control Strategies. Sub-
sequently, sectionModular Simulation Tool for Control Strategies presents an in-depth
description of the modular simulation tool including an application example. Finally, the
conclusion and outlook are given.
Grid operation andcontrol strategies forprosumers
e large number of low voltage (LV) grids (
500,000 LV grids in Germany (Mende
2022)), which are often operated decoupled from each other, in conjunction with dispro-
portionate expenses for connection/accessibility and remote controllability, has made
active grid operation in the lowest voltage levels virtually impossible. First steps to miti-
gate the influences of a large number of distributed generation systems are implemented
by means of distributed and similar plant behavior based on characteristic curves. ese
Fig. 1 Modular simulation tool in the multi-level analysis of control strategies (based on (Schoen et al.
2021a))
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
have also found their way into the corresponding connection guidelines for distributed
generation systems (e.g. VDE-AR-N 4105 for the connection to low voltage grids (VDE
2023) or the guidelines for feed-in management in Germany (Bundesnetzagentur 2023)).
Due to expected increase in system loading and decreasing distances to operational lim-
its, an active management of generation plants and consumers becomes increasingly rel-
evant, which is why control strategies for decentralized prosumers gain importance.
Control strategies forprosumers
In this paper, the term control strategy refers to controlling consumers, producers or
prosumers on the LV level of the electrical power grid according to a pre-defined con-
trol behavior. is section focuses on control strategies for EV charging, which are cur-
rently of highest interest due to the fast increase in the share of EVs (ADAC 2023) and
the potential for shifting and adapting charging processes as a result of the long parking
times of an average EV in comparison to its active usage. Additionally, there are also sev-
eral approaches for controlling PV systems, HPs, battery storage systems and strategies
for combinations thereof (Gomes etal. 2022). ere is a large variety of control strategies
for electric vehicle charging such as control-signal-based strategies and specification-
based strategies (Schoen etal. 2020). e latter can be further divided into power-based
and price-based strategies (Schoen et al. 2021a). e DSO often defines the specifica-
tions in power-based strategies based on the knowledge about the grid and its opera-
tional limits. An example of a power-based strategy is a time-dependent Power Limit,
where the charging power limit is determined based on the average annual load profiles
(Zhang etal. 2014; Schoen etal. 2023). e power limit is lower in times of high values in
the annual load profile and vice-versa. e goal of price-based strategies is often related
to minimizing costs for the energy provider and/or for the customer by shifting charg-
ing processes (Hildermeier etal. 2023; Birk Jones etal. 2022). e DSO Access intro-
duced in (Schoen etal. 2020) is an example for a control-signal-based strategy, where the
DSO can actively curtail the charging powers in case of a critical grid situation (here: red
BDEW signal phase (BDEW 2023)).
Classication ofcontrol strategies
Control strategies for prosumers can be further classified using criteria such as:
objective(s), operation architecture, input, control variables, method and communication
(Wenderoth etal. 2019). is is helpful for their clear description and an important pre-
paratory step for their implementation into simulation tools. An objective in this context
refers to the goal of a control strategy. Table1 gives an overview of relevant operation
architectures. Operation unit refers to a component, which makes decisions for the oper-
ation based on the processed information. A power system component that is controlled
Table 1 Overview of operation architectures (from (Wenderoth et al. 2019))
Local Decentralized Distributed Centralized
Number of operation units 1
>1
>1
1
Number of controlled elements 1
>1
>1
>1
Coordination between operation units N/A No Yes N/A
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
locally or by another entity is referred to as controlled element. ere is also a hierar-
chical architecture, which refers to the organisation of operation units in several levels
that depend on each other and have a clear hierarchy. Input refers to the values that are
needed to realize a control strategy, which can be measurements or set point values.
Control variables can also be set point values or physical values. e method describes
the algorithm or approach that is used within a component to reach the objective. Evalu-
ating the communication of a control strategy and comparing it with the status quo in
the energy system is vital for judging the current practicability of a strategy and deter-
mining necessary developments to enable its implementation (Wenderoth etal. 2019).
Simulation tools foroperational analyses ofcontrol strategies
As shown in Fig.1, the analysis of control strategies is a multi-level process. Operational
analyses regarding the influence of control strategies on selected grid situations and sce-
narios are an important part of this. ey can be used to evaluate the necessary behavior
of energy system components, the communication between them, potential variations
and faults. is can either be done as part of the simulation-based or lab-based analy-
sis or a combination of both. is section summarizes requirements for tools for such
operational analyses, presents existing ones and highlights the relevance and novelty of
the modular simulation tool.
Tool requirements formodeling control strategies foroperational analyses
e classifiers introduced in section Classification of Control Strategies need to be
integrated into a simulation tool to perform effective operational analyses with control
strategies. Moreover, such tools also need to be able to consider the hierarchies of the
relevant energy system components as mentioned in the Introduction. Figure2 shows
the relevant hierarchy levels for modeling control strategies as introduced in (Schoen
etal. 2020): DSO, distributionsubstation, HEMS and prosumer. e figure represents
one element each in a system with one DSO, where several components can be on levels
2, 3 and 4. ese hierarchy levels can work together in the different operation architec-
tures shown in Table1. Generally, the HEMS and prosumer(s) on the lower hierarchy
levels are either controlled by the distributionsubstation or DSO at the upper hierarchy
levels. Figure2 represents the main communication streams of the control strategies,
but additional communication streams are also possible (e.g. directly between DSO and
Fig. 2 Relevant hierarchy levels for modeling control strategies (adapted from (Schoen et al. 2020))
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
HEMS). Moreover, the figure also represents goals of the levels and possible interac-
tion with upper voltage levels. In addition to that, simulation tools also need to be able
to correctly take into account the various relevant input parameters, control variables,
objectives and necessary communications of the control strategies.
Existing tools foroperational analyses
ere are already several tools that enable operational analyses at the LV level and can
therefore also model control strategies for prosumers. In this section, the tools shown in
Table2 are considered.
A common approach is using multi-agent systems, where the agents can model any
type of component of the energy system and therefore allow the implementation of a
wide variety of control strategies (Mahela etal. 2022). Reference (Papadopoulos et al.
2013) introduces an example of this, where control strategies for EV charging are imple-
mented and analysed using a multi-agent system (MAS), which models relevant system
components as agents, such as the DSO or the EVs. e agent-based tool Simona allows
detailed modelling of grid system participants, including the modelling of technical grid
equipment, regulation, monitoring and control algorithms (TU Dortmund University
2023; Römer etal. 2019). Moreover, the power distribution system simulation and analy-
sis tool GridLAB-D (Pacific Northwest National Laboratory 2023) also provides capa-
bilities for modeling an agent-based system with a focus on power system modeling.
ere are also several other approaches for evaluating the influence of control strat-
egies on electrical grids, based on modeling the electrical grid and adding control-
lable elements to it. Such modeling and the corresponding execution of time series
simulations can be done with tools such as pandapower (urner etal. 2018), Grid-
Sim (Forschungsstelle fur Energiewirtschaft e.V 2023) or MATLAB(e MathWorks,
Inc.2023). Additionally, control behaviour can also be integrated into such grid mod-
eling approaches by integrating time series data, where the control behavior is already
considered. For this, tools such as datafev can be used (Gümrükcü etal. 2023).
Another common modeling approach, which can combine the strengths of the afore-
mentioned ones is the use of co-simulations. Reference (Vogt et al. 2018) gives an
Table 2 Simulation tool overview
Tool name Tool type Reference
MAS for EV
control strate-
gies
Multi-agent system Papadopoulos et al. (2013)
Simona Agent-based simulation environment TU Dortmund University (2023) and Römer
et al. (2019)
GridLAB-D Power system simulation and analysis tool Pacific Northwest National Laboratory (2023)
pandapower Power system modeling tool Thurner et al. (2018)
GridSim Power system modeling tool Forschungsstelle für Energiewirtschaft e.V.
(2023)
MATLAB Programming and numeric computing platform The MathWorks, Inc. (2023)
datafev Python package for modeling EV charging Gümrükcü et al. (2023)
OpSim Co-simulation tool Fraunhofer IEE (2023a)
helics Co-simulation tool NREL (2023b2023)
beeDIP Data integration tool Requardt (2021)
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
overview of existing co-simulation tools for modeling smart grid solutions. One of these
tools is OpSim, which is a test and simulation environment for the development and
testing of smart grid solutions, where components of the energy system as well as the
electrical grid can be modeled and combined in a co-simulation (Fraunhofer IEE 2023a).
Moreover, the project ReCharge (NREL 2023a) highlights the use of the co-simulation
tool helics (NREL 2023b) in a project for evaluating EV control strategies. An impor-
tant part of modeling control strategies is the preparation for their integration into the
real energy system. For this, data integration tools such as beeDIP (Requardt 2021) are
needed. ese tools can also be used to prepare theenergy system integration of control
strategies, which can also be tested beforehand by emulating components such as the
electrical grid or DSO.
Relevance andnovelty ofdeveloped methodology
e aforementioned tools offer a large variety of possibilities for modeling control strat-
egies for prosumers. Many of them are also able to fulfill the introduced tool require-
ments. Co-simulation tools such as OpSim also enable the coupling of different software
tools within one simulation enabled by its flexible message bus architecture, which
allows a wide array of investigations regarding grid operation and any type of control
behavior. Modules such as pandapower controllers offer many possibilities regarding
the implementation of control algorithms for grid assets in power flow simulations. With
the goal of developing a lightweight pandapower-based tool that allows for research-
ing and developing control strategies including agent-like controller interaction, we
developed the modular simulation tool, which uses pandapower controllers in a new
way that enables an integrated implementation of relevant energy system components,
communications between them and a coordination of relevant control behaviors in a
pandapower-based Python environment without the need for additional libraries next
to pandapower, e.g. to handle the messaging. is provides a tool that enables detailed
analyses of control strategies regarding their impact on the electrical grid and aids in the
development and testing of control strategies, which can be further integrated into tools,
such as co-simulation frameworks or data integration tools.
Modular simulation tool forcontrol strategies
is section presents the modular simulation tool and how the pandapower (urner
etal. 2018) control module is used to build it. First the pandapower control module
and the system architecture are presented. Subsequently, the system components are
described, including how they can communicate with each other and how the correct
simulation sequence is ensured. Additionally, insights into how control strategies can be
modeled and analyzed with the system are given. Finally, an application example is pre-
sented to highlight the functionalities and some possible applications of the tool.
pandapower control module
e modular simulation tool is implemented in Python and uses pandapower to model
the relevant energy system behavior (urner etal. 2018; FraunhoferIEE and Univer-
sity of Kassel 2023a). pandapower is a Python-based tool for modeling, analysing and
optimizing power systems. pandapower can be used to model electric grids and all
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
relevant components, such as lines, transformers, switches, loads, generators and others.
Additionally, it also allows for time series simulations consisting of subsequent power
flow calculations. By making use of the control module, any control behavior can also
be integrated into these time series simulations through the use of so-called control-
lers. Generally, the objective of one controller is adapting the behavior of a singular grid
asset. e modular simulation tool uses these pandapower controllers in an innovative
way allowing the coordination of different components, which do not necessarily need
to be a grid asset, and enables the communication between them in an architecture that
is motivated by architectures such as OpSim. Controllers are Python classes, where the
control behavior is realized in the method control_step of the controller, which can adapt
any grid parameters based on the implemented control behavior. Before the control_
step can be executed, it needs to be prepared appropriately. e preparatory steps vary
depending on the desired control behavior but can include performing an initial power
flow, updating input and control variables for the current time step or modifying any
grid parameters. ereafter, the control_step can be executed. Its input always includes
the current state of the pandapower grid model it is applied to and the current time
step. Other relevant parameters can be processed in the control_step by making use of
class variables that need to be defined and/or adapted before the control_step is applied.
Any control behavior can be realized here, such as a transformer tap position optimiza-
tion, PV power plant curtailment or adapting the active powers of any load or generator
according to a specified control strategy. e latter is most relevant for the application in
the modular simulation tool. Generally, controllers are used to adapt the behavior of sin-
gle grid assets, but it is also possible to add other types of functionalities, e.g. modeling
entities like a DSO acting as a central controller, which the modular tool makes use of.
In many cases, there are several controllers in a pandapower grid model that need
to be executed in a certain sequence. is can be realized by the Cascade Control func-
tionality that is integrated in the pandapower control module. With this functionality,
levels and orders can be assigned to controllers. is allows executing controllers and
applying their behavior in a specified sequence. Figure3 represents how the level and
order parameters can be used to define the sequence of the controllers within one time
step. At first, all controllers of the first level (here: level 0) are executed in the specified
order (here: starting with order 0 up to m), afterwards all controllers on the next level
(here: level 1) are executed in the specified order and so on. Moreover, before a new level
is started, all controllers of the previous level have to indicate that they have converged.
Fig. 3 Cascade control (adapted from (Fraunhofer IEE and University of Kassel 2023b))
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
As long as this is not the case, they are re-executed starting with the lowest-order con-
troller again. is allows for multiple executions of the same controller within a single
time step. Once all controllers have been executed, the next time step can be calculated.
is allows a variety of different applications of the controllers. e modular simula-
tion tool makes use of these Cascade Control functionalities to model hierarchy levels
as illustrated in Fig.2 in its tool architecture and for ensuring the correct simulation
sequence. Additionally, the modular simulation tool also enables the communication
between controllers and a detailed initialization process for all modeled components,
which is uncoupled from their control functionalities.
e pandapower functionalities provide the foundation for the modular simula-
tion tool. It uses the existing capabilities for modeling electrical grids and performing
power flow simulations, and the presented functionalities of the control module. While
the tool makes use of these functionalities, its tool architecture, components, and espe-
cially the communication capabilities, are additional implementations that are done
within the modular simulation tool. To this, the tool provides additional implementa-
tions: (i) a standardized class for general controllers that do not represent grid assets
and are equipped with a mailbox, (ii) a messaging scheme for exchanging information
between such controllers, (iii) message send and receive methods and (iv) a scheme that
organizes control strategy simulation with seamless integration into the existing pan-
dapower controller architecture. ese new implementations are needed to create a
tool, where relevant energy system components and their interaction can be modeled
with a level of detail that reproduces the real energy system as closely as possible while
enabling agent-like controller interaction without the need for additional packages other
than pandapower. e following sections present these new implementations.
Tool architecture
e goal of the tool is modeling the components shown in Fig.2 and also enabling the
representation of the architectures presented in Table1. e tool architecture is moti-
vated by the architecture of OpSim (Fraunhofer IEE 2023a) with the difference that it
directly coordinates pandapower controllers. e system consists of different compo-
nents, which include the DSO, the Substation(s) and the HEMS/Prosumers (Schoen etal.
2021a). Prosumers are modeled as part of the HEMS and can be any type of load or
generator that is related to the household. ere is also the component Grid Calcula-
tion, which is needed to model the electrical grid that the other components are part of.
Additionally, the Simulation Tool Core is needed to enable all communications between
the components by feeding a public message list in a mailbox-like structure. Moreover, it
ensures the efficient initialization of the simulation by providing the relevant parameters
to the corresponding components. e Simulation Control is required for setting the
parameters of the simulation. Figure4 gives an overview of the components and their
interaction.
Figure 5 shows how the components work together during the simulation. It also
highlights that the Simulation Control and Simulation Tool Core are responsible for
ensuring the correct simulation structure (including initialization, communication and
sequence). e components Grid Calculation, DSO, Substation(s) and HEMS / Prosum-
ers are part of the simulation process and are called successively in order to perform
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
power flow calculations and implement control strategy behavior. erefore, the terms
structure components and process components will be used in the following. Each of the
process components is modeled by a Python class with a wide variety of functionalities.
To enable an efficient implementation of these components, they are structured into
three layers as shown in Fig.6. e light blue ellipse represents a process component as
shown in Figs.4 and 5. e upper layer Initialization is used to initialize the components
and their variables. e second layer is the Controller itself, which is created in the layer
Initialization. Depending on the component, several controllers can be created. As the
control functionalities can be quite complex, they are allocated to the third level Control
Functionality. Messages are processed and sent during the initialization of the simula-
tion and in each time step. is is why the Initialization and Control Functionality layers
are equipped with a message box each. e integration of the messaging functionality
Fig. 4 Simulation tool components (adapted from (Schoen et al. 2021a))
Fig. 5 Simulation sequence
Fig. 6 Three-layered structure of a simulation tool process component
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
using pandapower controllers is a key feature of the modular simulation tool. Here,
a method is implemented for each control strategy, which is called when the strategy is
active in a simulation to execute the corresponding behavior of the component. Several
control strategies can be active in one simulation by combining the different methods
appropriately. ere are two approaches for realizing the combination of control strate-
gies: A new method can be implemented, which combines the desired functionalities or
existing methods are executed after one another. Here, the method of the control strat-
egy with the highest priority is executed last, e.g. when a Power Limit and DSO Access
are active at the same time, the DSO Access is executed last to ensure that no additional
congestions are caused by the application of the Power Limit.
Simulation tool structure components
e components Simulation Tool Core and Simulation Control are key elements for
the simulation tool structure and ensure the initialization, communication and cor-
rect simulation sequence.
Simulation tool core
e Simulation Tool Core fulfills the following main tasks:
Starting the simulation: triggering the initialization of all components and the
start of the time series simulation once all components are initialized,
Handling communication between the components by: adding all incoming mes-
sages to the intended inboxes (process) and sending all outgoing messages to the
intended receiving components (send).
Simulation control
Different grids, future scenarios, various control strategies and any combinations thereof
can be considered with the modular simulation tool. Additional parameters are also
defined, e.g.: the time frame that is to be simulated, where the input data is to be col-
lected from and which power flow results are to be saved and where. All relevant simula-
tion parameters are set in the Simulation Control, where they can be easily adapted by
the user. When a simulation is started by the Simulation Tool Core, the aforementioned
parameters are distributed from the Simulation Control to the relevant components to
initialize the simulation. Once all components are initialized, the Simulation Tool Core
starts the time series simulation. It is also possible to parameterize the simulation using
a Graphical User Interface (GUI), which is implemented using Plotly Dash (Plotly
2023). It can be used to define all of the aforementioned simulation parameters, starting
and stopping the simulation and also displaying the results of the simulation.
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Simulation tool process components
e process components Grid Calculation, DSO, Substation(s) and HEMS / Prosum-
ers all work according to the same principle, where they need to go through an ini-
tialization process first before the start of the time series simulation. e properties
and behavior of these components will be described in two steps: initialization and
time step behavior. During the initialization, the components process the incoming
messages and get prepared accordingly. If needed, they also send messages during
the initialization to update other components on their status. e initialization var-
ies depending on the components, but the following actions need to be performed
for all components in the process-method of the Initialization layer as shown in
Algorithm1:
Handle strategy specifications: All parameters related to the active strategy or
strategies are processed and handled accordingly. Generally, this means updat-
ing the corresponding class variables or adding new ones so the specifications can
easily be accessed or updated during the simulation. Some examples for variables
that could be set initially (and updated later if applicable) are: operating limits,
power limits, price curves, etc.
• Create Controllers: e defined number of Controllers is created:
GridCalculation: Two Controllers GridControl1 and GridControl2 are created.
DSO: One Controller is created as it is assumed that one DSO is responsible
for the modeled grid.
Distribution Substation(s): One Controller per distribution substation in the
modeled grid is created.
HEMS / Prosumers: One Controller per household is created.
e Controllers are connected to the Simulation Tool Core and the component itself
and the Control Functionality is added to it. If any other actions need to be performed
during the initialization, it is specified explicitly in the following. Otherwise, only the
time step behavior of the components is described. Within one time step, each com-
ponent exhibits the behaviour as shown in Algorithm2, which is realized in the layer
Control Functionality. e if-query can be extended depending on the number of
implemented strategies. Any variables that were previously initialized can be updated
here.
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Grid calculation
An important task of the Grid Calculation is setting up the following input data:
A base grid model (grid topology, existing consumers and producers, line/trans-
former standard types, measurement data (transformer drag pointer)),
Placement and specifications of additional consumers and producers according to
the desired simulation scenario (e.g. high PV penetration),
Time series data for all consumers and producers (with a fitting resolution, e.g. 15
min, and at least for the time frame that is to be simulated).
Within the initialization phase of the simulation, this component performs the following
tasks in addition to the general initialization tasks:
Set up the grid model: e grid model is set up according to the specified grid param-
eters. It can be directly loaded with all scenario and time series information already
added to it if it has been previously configured and saved. Alternatively, a new grid
model can be configured according to the specified grid parameters. is means that
future consumers and producers (incl. the corresponding time series information)
are added to the base grid model.
Handle which results are to be saved where: In detail, this means that the output
writer of the pandapower time series module is configured.
Start the time series simulation: Once all components are configured, the time series
simulation is started as stated above. e actual corresponding pandapower func-
tion is located in the Grid Calculation component. After this call, pandapower
takes over the control flow and calls the controllers according to the fixed cascade
sequence as shown in Fig.3.
In each time step, the Grid Calculation controllers perform the following tasks:
GridControl1: is Controller is called at the very beginning of a time step. It per-
forms a power flow calculation and sends the required results to the receiving com-
ponents. In most cases, this is the DSO, which takes care of any further handling of
this information.
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
GridControl2: is is the last Controller that is called in a time step. It updates
the control variables in the grid model (mostly active and/or reactive power set
points of consumers and/or producers) so that they can be considered for the ini-
tial power flow in the following time step.
DSO
In each time step, the DSO handles incoming information from the Grid Calcula-
tion. Depending on the active strategy or strategies, the DSO makes decisions regard-
ing the behavior of other components, i.e. Distribution Substation(s) and/or HEMS /
Prosumers. e results of these are then sent to the corresponding recipients. Algo-
rithm3 shows an example of a potential strategy behavior after handling the incom-
ing information for the DSO Access strategy. Here, it is assumed that the DSO can
determine the elements criticalGridElements in the grid which violate the operat-
ing limits. In the simulation, this information is sent from the Grid Calculation to
the DSO and added to the timestepVariables at the beginning of the time step when
incoming messages are interpreted as shown in Algorithm2.
Distribution substation(s)
If applicable, the Distribution Substation(s) component refines the decisions made by
the DSO after processing the incoming information. is is mostly done based on the
additional information about the grid section corresponding to the substation. An
example of this could be the fair distribution of charging contingents to individual
charging stations in a strategy where the DSO allocates charging contingents to sub-
station areas based on the grid situation (Schoen etal. 2020). At the end of a time
step, the decisions are sent to the corresponding recipients, e.g. HEMS/Prosumers in
the grid section. Continuing the DSO Access example, Algorithm4 shows a potential
behavior of the Distribution Substation(s) component, where it forwards the informa-
tion loadsToBeCurtailed from the DSO to the HEMS.
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
HEMS/Prosumers
First, all incoming information is processed. Depending on the active strategy or strat-
egies and available information, the control variables are then updated here, e.g. the
charging powers are curtailed based on an active charging power limit in the time step.
Finally, the updated control variables are sent back to the Grid Calculation for their use
in the next time step. In the DSO Access example, the HEMS/Prosumers component can
exhibit the strategy behavior as shown in Algorithm5, where the newChargingPower is
determined.
In addition to the shown functionality, the HEMS/Prosumers component also takes
care of handling any energy that was curtailed and gradually increasing the charging
power after a curtailment. is avoids starting a cycle of mitigating a violation in one
time step and causing one again in the subsequent one by increasing the charging power
too quickly, while the overall grid situation is still close to operating limits. As one con-
troller per household in the considered grid is created, this behavior takes place for each
household individually. Each controller is referenced to a household, where the uncon-
trolled behavior is portrayed by the corresponding load/generation profile. Depend-
ing on the desired behavior in the controlled case, the HEMS/Prosumers component is
defined accordingly.
Sequence andcommunication
is section provides a closer look at the simulation sequence as shown in Fig.5 and
how the components communicate with each other. e Cascade Control functionality
is used within this simulation tool to ensure the correct sequence of the process com-
ponents within each time step of the time series simulations that are performed with it.
Table3 presents the hierarchy of the simulation tool process components.
e levels and orders are used to ensure the correct sequence in a time step. e
Grid Calculation component has two main tasks and is therefore called twice: (i) per-
forming the initial power flow at the very beginning of a time step and (ii) updat-
ing any grid parameters (e.g. charging powers) if they were changed during the time
Table 3 Hierarchy of the simulation tool process components (adapted from (Schoen et al. 2021a))
Component Level Order
Grid Calculation 0 and 5 1 and 1
DSO 1 1
Distribution Substation(s) 2 1, 2, ..., m
1
Profile Update 3 1
HEMS/Prosumers 4 1, 2, ... m
2
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
step because of the control strategy behavior. In addition to all components shown in
Figs.4 and 5, there is one more functionality needed for the time series simulations:
updating all loads and generators with the corresponding profile data (active power,
and— if applicable—reactive power) in every time step. is is done internally in the
time series simulation and is referred to as Profile Update here. is happens after the
DSO and Distribution Substation(s) are called as their behavior is to be based on the
results of the previous time step to model the behavior of the real energy system as
closely as possible, where decisions of the DSO or other actors can generally only be
made based on what has already happened in the electrical grid. By the time any deci-
sions are made, some time has generally passed and the grid situation has changed.
e sequence of the components, especially Profile Update, is supposed to model this
behavior.
Each of the components in Table3 (except for Profile Update) is equipped with a
message box as shown in Figs.4 and 6 to enable receiving messages from other com-
ponents. Each component can also send messages to any other component. Both of
these functionalities are realized by making use of the Simulation Tool Core, which
can distribute messages between the components. While communication between all
components is theoretically possible, any type of communication behavior (e.g. which
components communicate with each other in which frequency) can be implemented
to model different existing and future communication infrastructures in the energy
system. As one of the applications of this system is developing and testing control
algorithms with the pandapower framework before their integration into co-sim-
ulation platforms such as OpSim (Fraunhofer IEE 2023a), the modular simulation
tool aims to mimic message-bus-based communication. Each component is equipped
with a pipe-like structure that is implemented as a public message list which is fed
by the Simulation Tool Core. ere is no need for synchronisation since the control-
ler components are executed subsequently during the time series simulation by pan-
dapower callbacks, with the order fixed by the level/order setup. e data model
for the messages is kept simple to ensure an efficient implementation process and to
keep the focus of the system on evaluating the grid impact of control strategies. e
versatility and convenient interpretability of Python Strings is used for this purpose.
e sender and receiver need to be specified as well as the content itself. e mes-
sage should contain a keyword describing its content, e.g. “Critical loads”, and and
the corresponding information in a format that can be integrated into a String and
interpreted easily later on. e receiver then splits these messages into its elements
(sender, receiver, content), checks if it has been delivered correctly, and if so, inter-
prets the message accordingly. e OpSim framework uses a similar format, but there
it is augmented with a time stamp to allow for conservative synchronization. Figure7
shows a communication example with four components taking place within one time
step. In this example Component A sends two messages, one intended for Component
B and one for Component C. e Simulation Tool Core distributes the messages to the
intended recipients. ese handle the messages internally and both send one message
intended for Component D. e handling of all messages is done by the Simulation
Tool Core and the correct component sequence is ensured by the defined level and
order of the components.
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Implementation ofcontrol strategies
As the correct simulation sequence and communication are ensured as described before,
the implementation can focus on: behavior of the components, the question which
components need to communicate and specification of information that needs to be
exchanged between them. A theoretical evaluation of the control strategies according to
the classifiers described in sectionClassification of Control Strategies (Wenderoth etal.
2019) immensely supports the preparation of the implementation as it inherently tackles
the aforementioned points. An important additional part of the preparation is defining
individual component functionalities according to the expected behavior of the real sys-
tem. Moreover, when planning the communication between the components, the data
availability in the real system should be taken into account. Different data availabilities
and qualities can also be considered and compared in this process. After this prepara-
tory process, the derived functionalities can be efficiently implemented into the simula-
tion tool components, tested and improved. Furthermore, it is also possible to extend
the system with additional components if the control behavior cannot be realized with
the existing ones alone. is can be done by implementing a new component with the
structure as described in sectionTool Architecture and adapting the levels and orders
of all components accordingly. It is possible to flexibly do this for one strategy but keep-
ing the participating components and their levels and orders unchanged for any other
strategy.
Application example
is section presents an application example to illustrate the functionalities and pos-
sible applications of the modular simulation tool. For this purpose a distribution grid
model with nine substations, 1062 customers and a total line length of 59km is used. It
is configured for a possible future scenario with randomly distributed 182 PV systems,
580 HPs and 651 EV charging stations at the LV level. e grid model and the time series
data are based on the LV3 data of the benchmark data set SimBench (Fraunhofer IEE
and University of Kassel 2023c; Meinecke etal. 2020) and the strategies Power Limit
and DSO Access described in Control Strategies for Prosumers are used. Table4 shows
the result of the application of selected classifiers from sectionClassification of Control
Strategies.
e properties identified in Table4 are implemented within the modular simula-
tion tool to enable the analysis of these strategies. e main difference between both
approaches in terms of objective and method is the preventative nature of the Power
Fig. 7 Communication example with four components
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
Limit, which aims at avoiding critical grid situations by reducing the charging powers
during times when a critical grid situation, such as line overloadings, is expected. e
DSO Access is of a reactive nature as it intervenes to mitigate congestions that already
occurred. Additionally, the communications between components is also modeled and
evaluated in the system. For the Power Limit, this means that the DSO needs to deter-
mine the time-dependent power limit curve based on its knowledge about the average
load profile in the grid area and communicate the power limit curve to the HEMS. is
can be done once for an entire year or updated more regularly, e.g. based on large devia-
tions from the standard load profile. e latter functionality might lead to more pre-
cise results but significantly increases the need for measurements and communication
in the real system. As far as the DSO Access is concerned, continuous measurements
of the grid state, e.g. bus voltages, and communication between the DSO and HEMS
are needed for the strategy. Table5 gives an overview of the resulting communications,
excluding any communication to and from the Grid Calculation component that are
needed for simulation purposes only. In addition to helping evaluate how the real sys-
tem needs to operate to enable the integration of these strategies, the table also allows
conclusions regarding the effect of any communication faults. Here, it is assumed that
all communication between DSO and HEMS is done with the support of the Distribu-
tion Substation(s) but a direct communication is also possible. Generally, the Power
Limit can be operated by updating the power limit curve only once a year. e advan-
tage of this yearly update is that it does not require much additional communication
or measurement infrastructure and is therefore also resilient to any fault occurring in
that infrastructure. As opposed to that, constant communication and a large amount of
Table 4 Application of selected classifiers to Power Limit and DSO Access
Classier Power Limit DSO Access
Objective(s) Avoidance of critical grid situations Congestion management
Operation Architecture Centralized Centralized
Input Average load profile based on historic data Current state of the grid
Control Variables Charging powers Charging powers
Method Preventative reduction of charging powers accord-
ing to power limit Reactive curtailment of
charging stations
Table 5 Evaluation of communications between DSO, Substation(s) and HEMS
Power Limit DSOAccess
Information Possible frequency Information Possible frequency
DSO to
Substation(s) Power limit curve Min. 1 × per year Curtailment control signal When a critical grid situ-
ation occurs
Substation(s)
to HEMS Power limit curve Min. 1 × per year Curtailment control signal When a critical grid situ-
ation occurs
HEMS to
Substation(s) Power values Min. 1 × per year Bus voltages, power
values In every time step
Substation(s)
to DSO Power val-
ues summary
per feeder
Min. 1 × per year Bus voltages, power val-
ues, line and transformer
loadings
In every time step
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
measurements are needed for the implementation of the DSO Access. is enables the
active curtailment but requires additional implementation effort. It also makes the sys-
tem prone to communication or measurement faults, so functionalities for dealing with
such faults need to be implemented.
e modular simulation tool also enables evaluating the effect of control strategies on
power flow results. Figure8 shows the LV level of the considered grid on the left and the
time series results of a critical time frame on an evening in February of the simulated
scenario year on the right. e orange markers on the left indicate the location of the
corresponding grid elements (transformer: diamond, line: line, bus: circle), where the
highest loadings or lowest voltage occurred in that time frame. e results of the appli-
cation example demonstrate how the simulation tool can aid in the analysis of control
strategies on grid elements by providing the possibility to analyse the influence on all
grid assets and relevant parameters, either for a summary of all assets or with a more
detailed view. e figure also shows that both strategies can improve the grid situation,
with the combination of both of them having the largest possible impact. e bus volt-
age and line loading violations can be completely mitigated by the strategies and even
though the transformer loading violation cannot be completely mitigated at all times,
it is still reduced notably by at most 30 % in the simulated time frame. As the consid-
ered variant of the DSO Access only intervenes after a critical grid situation has already
occurred and only curtails the charging powers to 50 % of their initial value, the Power
Limit manages to achieve more favorable effects here. Since the focus of this paper is the
methodological description, a further detailing of the concrete simulation results is not
shown here.
Conclusion andevaluation
e modular simulation tool uses pandapower controllers in an innovative way, which
ensures the correct simulation sequence and enables the modeling of hierarchies and
communication between components without the need for additional message bus
packages. e layered component scheme also allows for a structurized implementation
of control functionalities. Combining these capabilities creates a versatile and powerful
Fig. 8 Grid topology with critical elements (orange) and corresponding time series results
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
tool for modeling control strategies for prosumers and for performing simulations with
them. In addition to allowing analyses regarding the effect of control strategies on power
flow results, it also facilitates evaluating the behavior of energy system components,
necessary communications and measurements and any faults that might occur. Moreo-
ver, its system architecture that inherently takes care of the correct simulation sequence
and communications, enables an efficient implementation process that can also be used
for pre-development and testing of control approaches prior to their use in other envi-
ronments (e.g. lab tests). Despite allowing a straightforward implementation of control
strategies and efficient time series simulations, the simulation scope is still fairly lim-
ited in terms of the amount of different scenario configurations that can be considered.
Different approaches are needed to determine critical grid situations with and without
control strategies for a large number of different grid configurations when this informa-
tion is to be used as the basis for robust grid planning results. Grid planning approaches
that enable the consideration of control strategies, either in time-series-based (Schoen
et al. 2021b) planning or simultaneity-factor-based (Schoen et al. 2023) planning are
well-suited for this purpose. e operational analyses with the modular system and both
grid planning approaches together enable a holistic simulation-based analysis. is is
vital for developing and analysing control strategies but should be followed by lab and/or
field tests before their real-life application. is enables the consideration of additional
aspects such as communication protocols and data models, measurements and real user
interaction. As this is out of the scope for the modular simulation tool, non-sequential
simulations cannot be performed with the modular simulation tool and results cannot
be directly implemented into lab testing equipment or used in field tests. However, it can
immensely aid in the development of control algorithms and other relevant function-
alities that can then be efficiently integrated into systems such as OpSim or beeDIP,
which can fulfill the aforementioned tasks. Despite these limitations, the modular sim-
ulation tool is a powerful tool for developing and analysing control strategies for pro-
sumers in distribution grids. It builds on the functionalities of the pandapower control
module and applies it in a way that allows for the efficient implementation and testing
of control strategies regarding various aspects. ese developments can then be further
used in other environments as well. Additionally, it enables detailed operational analyses
in the system itself. e influence on grid parameters can be studied in simulations and
different evaluations regarding resilience in the power system, available communication
infrastructure and measurements are also possible.
Outlook
In the project Ladeinfrastruktur 2.0, the modular simulation tool is used in the
multi-level analysis of control strategies for electric vehicles (Fraunhofer IEE 2023b). e
modular simulation tool can be extended by any additional Python-based components
or functionalities. Neural-network-based state estimation can already be included in
some strategies (Liu etal. 2022). e tool could also further be supplemented by also
integrating forecasting functionalities. is could include the process of forecasting data
delivery, interpretation and the resulting determination of control signals to be com-
municated to different prosumers. While the system is notdirectly related to the Smart
Grid Architecture Model (SGAM), it can be used to model and analyze use cases related
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Schoenetal. Energy Informatics 2023, 6(Suppl 1):39
to it, especially concerning the function, information and component layer (CEN-
CENELEC-ETSI Smart Grid Coordination Group 2023). If other types of tools are to
be used together with the implemented control strategies, the pre-developed and tested
control behavior can be integrated into co-simulation tools like OpSim, where further
investigations are possible, e.g. the interaction with real-time power simulators, event-
based simulations or distributed simulation across several locations (Fraunhofer IEE
2023a). Especially in time-critical applications, the possibility to also include the effect
of communication times and delays might be an interesting field for further applications
of the modular simulation tool, similar to the project EriGrid 2.0 (Montoya etal.
2018). ese aspects will also be investigated in the new project ReCoDE, in which co-
simulation frameworks are used to couple electric grid simulations with communication
network simulators. In terms of grid operation and operational planning, more time-
critical applications such as curative congestion management currently are discussed
and under investigation. Similarly, the coordinated control and flexibility provision for
upstream networks might be a research field in which the modular simulation tool could
be applied in. Additionally, the definitely needed level of detail in modelling and sim-
ulation (e.g. communication delays etc.) in order to get a certain result in operational
planning or long term planning studies has to be evaluated, which is the main research
question of the project MotiV (Fraunhofer IEE 2023c; Mende etal. 2022).
Acknowledgements
The authors would like to thank their colleagues at the Fraunhofer IEE and the University of Kassel, especially Frank
Marten, for the fruitful discussions regarding the modular simulation tool and their helpful insights.
About this supplement
This article has been published as part of Energy Informatics Volume 6 Supplement 1, 2023: Proceedings of the 12th
DACH+ Conference on Energy Informatics 2023. The full contents of the supplement are available online at https://
energ yinfo rmati cs. sprin gerop en. com/ artic les/ suppl ements/ volume- 6- suppl ement-1.
Author contributions
AS and JR worked on the implementation of the modular simulation tool. AS wrote the manuscript with the help of JR,
DM and MB.
Funding
This work presents results of the project Ladeinfrastruktur 2.0 which is funded by the German Federal Ministry
for Economic Affairs and Climate Action (formerly known as the German Federal Ministry for Economic Affairs and
Energy) under grant no. “0350048A”, based on a decision of the Parliament of the Federal Republic of Germany. It also
presents selected developments of the project MotiV, which is funded under grant no. “03EI1023A/B”.
Availability of data and materials
The application example is based on grid and profile data from the SimBench project. More information on the project
and the data can be found here: https:// simbe nch. de/ en/. The simulation tool implementation uses pandapower,
which is available as an open source Python package here: https:// www. panda power. org/. The documentation will be
updated with information on how users can create their own modular simulation tool using pandapower.
Declarations
Competing interests
The authors declare that they have no competing interests.
Published: 19 October 2023
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