Andrea Schoen’s research while affiliated with University of Kassel and other places

Die vorliegende Arbeit untersucht den Einfluss verschiedener Detailgrade auf die Ergebnisqualität von Netzstudien. Dabei werden die Herausforderungen der Netzplanung im Zusammenhang mit dem raschen Ausbau erneuerbarer Energien und der Elektrifizierung von Wärme- und Mobilitätssektoren angesprochen. Es wird eine zweistufige Methodik angewendet, die zunächst die klassische, vereinfachte Modellierung erweitert, um verschiedene Aspekte wie detaillierte Szenarien, Regionalisierung, Netzmodellierung und innovative Netzplanungsmaßnahmen zu integrieren. Im zweiten Schritt erfolgt eine Analyse zur Komplexitätsreduktion, um eine angepasste Modelltiefe zu bestimmen, die ein Optimum zwischen Detailgrad, Datenaufbereitung, Rechenaufwand und Ergebnisqualität ermöglicht. Die Ergebnisse unterstreichen die Bedeutung einer detaillierten Netzmodellierung für präzise Prognosen und Planung von Verteilnetzen im Kontext der Energiewende. Anhand verschiedener Untersuchungsperspektiven wird gezeigt, dass eine angepasste Modelltiefe, die den spezifischen Anforderungen der Netzstudien entspricht, entscheidend für die Effizienz und Ergebnisse verschiedener Netzstudien ist. Durch die Anwendung der entwickelten Methodik kann die Modelltiefe bei einer Netzstudie mit Fokus auf Netzausbauplanung flexibel und effektiv angepasst werden.

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.

In this publication, we introduce a methodology for power system planning that enables large-scale analyses with the consideration of control strategies for electric vehicle charging. This methodology is developed within the research project Ladeinfrastruktur 2.0. A part of the scope of this project is deriving recommendations for a cost-optimized integration of charging infrastructure into the electric distribution system [1]. In [2] we introduced a time-series-based planning approach which can consider different types of control strategies and allows detailed analyses of their influence on selected grids as well as required grid reinforcement and extension measures. We further extended this planning approach by incorporating a simultaneity-factor-based method for determining worst case grid situations without the need for time series simulations, which enables large-scale grid analyses and grid planning studies. This facilitates more general conclusions regarding the effect of control strategies for electric vehicle charging on the need for grid reinforcement and extension measures. To illustrate the functionality of this new methodology, a case study with a large number of real German low voltage grids is performed. The case study highlights the feasibility of large-scale studies with the presented methodology. It also shows how the modeled grid-friendly control strategy for electric vehicle charging contributes to the mitigation of grid violations and therefore the reduction of required grid reinforcement and extension measures, while the considered market-oriented approaches have the opposite effect. 1 Introduction The number of electric vehicles (EVs) has been increasing rapidly over the last years [3] leading to challenges in the electric distribution system caused by the increasing power demand [4]. The research project Ladeinfrastruktur 2.0 tackles these challenges by providing a holistic investigation of the integration of electromobility, while focus-ing on optimizing the operation and rollout of charging infrastructure in distribution grids [1]. Control strategies for EV charging can contribute to reducing or delaying the need for costly grid reinforcement or extension measures. Therefore, simulation tools for the evaluation of control strategies and their effect on these grid reinforcement and extension demands are being developed within this project. In [2] we introduced a time-series-based approach for considering control strategies in grid planning. In this approach the worst cases needed as a basis for grid planning are determined using time series simulations. Control strategies can be integrated into those time series simulations by means of pandapower power flow controllers , which allow the modeling of any type of control behavior [5]. The big strength of this methodology is enabling the evaluation of the effects of any control strategy on the grid and the required reinforcement and extension measures. Its drawback is the amount of power flow calculations that are needed to determine the relevant worst case situations, which limits the simulation scope to case studies with a manageable number of grids and scenarios. Such small-scale case studies are a suitable way to analyze the effects of control strategies on grid reinforcement cost but a bigger simulation scope is needed for more general conclusions. Therefore, the existing methodology was enhanced to enable large-scale studies by determining worst case situations in a more efficient manner, which significantly reduces the number of required power flow calculations. This methodology will be described in section 3 after an overview of the state of the art regarding control strategies and grid planning with EVs in section 2. Afterwards, a case study will be presented in section 4 to highlight the behavior of the methodology. Finally, the conclusions are presented in section 5 and an outlook is given in section 6. 2 State of the Art In this section we give a short overview of the state of the art regarding control strategies and how EVs can be considered in grid planning. Based on that, the need for further developments regarding the integration of control strategies in grid planning is highlighted. 2.1 Control Strategies for EV Charging Currently, there is a spectrum of approaches regarding control strategies for EV charging with varying goals. On the one side of the spectrum, there are strategies that focus on grid-friendly EV charging based on power limits, which are expected to reduce the impact on the grid. This can be done by shifting charging processes to times when the overall grid load is expected to be low. In order to achieve this, a time-dependent power limit can be set by the grid operator as illustrated in [2] and [6]. This power limit is usually determined based on historic data regarding the load situation in the grid. On the other side of the spectrum, market-oriented approaches focus on economic goals and are usually based on tariff systems, which aim at incentivizing users to shift their loads, e.g. electric vehicle charging, to times when electricity prices are low. There

In this publication, we introduce a methodology for power system planning that considers grid-friendly electric vehicle (EV) charging, which is developed within the research project "Ladeinfrastruktur 2.0" [1]. This publication shows how control strategies for EV charging can be integrated into probabilistic, time-series-based grid planning approaches to determine necessary grid reinforcement and extension measures. Since this method can be computationally expensive, the efficient integration of control strategies into this process is crucial. In order to compare practical and simulated findings, control strategies based on the field test project "E-Mobility-Allee" by the German distribution system operator (DSO) Netze BW [2] are applied in simulations. The methodology presented in this paper is developed for this purpose and applied in a case study with a real low voltage (LV) grid. Finally, conclusions regarding the field test and real-life applications are drawn based on the results of the case study, which indicate that the selected control strategies can lead to a reduction of necessary grid reinforcement and extension measures. 1 Introduction The increasing power demand caused by a growing number of EVs in the energy system can lead to grid congestions that require grid reinforcement, which may have a significant impact on the cost of EV integration. Determining and optimizing the overall cost of the integration of EVs into distribution grids is one of the main goals of the research project "Ladeinfrastruktur 2.0" [1]. In this process , it is important to consider the influence of control strategies for EVs, since they can have a significant impact on critical grid situations and the need for grid extension measures. The influence of such control strategies on real grid situations was demonstrated in the field test project "E-Mobility-Allee" by the German DSO Netze BW, where the effect of a large share of EVs in one street was investigated [2]. This practical analysis of preventative and cura-tive control approaches showed that critical grid situations caused by EV charging can be mitigated notably when applying such approaches [3], leading to a reduction of grid reinforcement measures. Additionally, grid operators can achieve an improved planning reliability regarding the power demand of EVs and their grid impact, when charging processes are controlled. To achieve universally valid statements regarding the effect of control strategies on grid extension measures, analyses involving a high number of probabilistic time-series-based simulations are needed. Therefore, a new grid planning methodology is developed within the project "Ladeinfrastruktur 2.0" and introduced in this publication. This methodology can take the seasonal behavior and dependency on the time of day for any consumer and/or producer into account. Thus, it also enables the consideration of control strategies for EV charging. This publication focuses on presenting the methodology regarding its ability to consider such control strategies. It is applied in a case study on a real LV grid provided by the Stadtwerke Wiesbaden Netz GmbH and the results are contrasted to the aforementioned field test. This publication is structured as follows: First, the state of the art regarding grid planning with EVs, control strategies and field tests as well as the derived research gap is introduced in section 2. Secondly, the developed methodology for considering control approaches for EVs in grid planning is presented in section 3. Subsequently, it is applied and evaluated in a case study in section 4. Finally, conclusions are drawn and an outlook is presented. 2 State of the Art In this section, the state of the art regarding the consideration of EVs in grid planning, control strategies and a related , recent field test is introduced. Based on that, the need for a methodology for considering control strategies for EV charging in grid planning is highlighted.