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Abbildung 4-45: Spannungsabhängiger Leistungsfaktor (cos φ (U)) und daraus maximal resultierende, normierte Blindleistung Wirkleistungsunabhängiges, spannungsabhängiges Blindleistungsmanagement -Q(U) Sämtliche bisher vorgestellten Methoden besitzen eine lineare Wirkleistungsabhängigkeit, welche die Blindleistung begrenzt. Die Q(U)-Regelung ist dagegen wirkleistungsunabhängig, kann also auch die maximale Blindleistung beziehen oder liefern wenn die Wirkleistung gering oder sogar Null ist, wie beispielsweise nachts bei PV-Anlagen. Die Spannungs-Stützpunkte der beispielhaften Q(U)-Kennlinie in Abbildung 4-46 wurden entsprechend denen der cos φ (U)-Methode in Abbildung 4-45 gewählt. Es ergibt sich ein stückweise linearer Zusammenhang zwischen Spannung und Blindleistung. Für die Kennlinie sind in der Simulation beliebig viele Stützpunkte definierbar.
Citations
... Most studies applied k-means or hierarchical clustering algorithms to characterize and summarize LV grids. Hierarchical clustering was applied in [22] as well as [23], and it comprises algorithms that combine grids into clusters. In the first step, each grid is considered a cluster of its own, followed by the iterative combination of the most similar clusters. ...
... This process is executed iteratively until a defined similarity threshold is reached. For example, ref. [22] evaluated several electrical parameters to cluster 271 LV grids with hierarchical classification. Ref. [23] focused on analyzing the impact of rising decentral PV generation on distribution grids, whereas [22] focused on the determination of resulting loads for the year 2030 using representative grid topologies. ...
... For example, ref. [22] evaluated several electrical parameters to cluster 271 LV grids with hierarchical classification. Ref. [23] focused on analyzing the impact of rising decentral PV generation on distribution grids, whereas [22] focused on the determination of resulting loads for the year 2030 using representative grid topologies. ...
Decarbonizing the mobility and heating sector involves increasing connected components in low-voltage grids. The simulation of distribution grids and the incorporation of an energy system are relevant instruments for evaluating the effects of these developments. However, grids are highly diversified, and with over 900,000 low-voltage grids in Germany, the simulation would require significant data management and computing capacity. A solution already applied in the literature is the simulation of representative grids. Here, we show the compatibility of clusters and representatives for grid topologies from the literature and further extend and validate them by applying accurate grid data. Our analysis indicates that clusters from the literature unify well across three key parameters but also reveals that the clusters still exclude a relevant amount of grids. Extension, reclassification, and validation using about 1200 real grids establish meta-clusters covering the spectrum of grids from rural to urban regions, focusing on residential to commercial supply tasks. We anticipate our assay to be a further relevant step toward typifying low-voltage distribution grids in Germany.
... The SLPs for electricity had been derived from measurements in Germany in the 1980s. However, behavior and electronic devices have been changing over the years, wherefore the SLPs differ from current electricity demand profiles (Bruckmeier et al. 2017). Another major issue associated with SLPs is the lack of variance, i.e. ...
Energy system modeling has been following the energy transition to investigate challenges and opportunities of future energy systems on all grid levels. Necessary input for sector-coupled energy system models are residential electricity and heat demand curves. The increasing importance of distribution grids and their modeling requires demand profiles in high spatial resolution.This paper presents a method to assign pre-generated electricity and heat demand curves to georeferenced residential buildings in Germany. We aim at overcoming fundamental shortcomings of the Standard Load Profiles and enable new possibilities for the modeling of distribution grids. Our approach provides a large variety in residential load profiles which spatially correspond to official socio-demographic data. All used input data sets as well as implemented methodology and the resulting profiles are publicly available under open source and open data licences to enable further use. Our results are validated on different aggregation levels as well as compared and discussed with the commonly used Standard Load Profiles.
... The ISAaR model developed at the Forschungsstelle für Energiewirtschaft (FfE) is an energy system model which uses linear optimization for modelling the generation, the demand and the storage systems in the electricity, heat and gas sectors [52][53][54]. The regional coupling of the electricity sector is considered by using an European transmission grid model with about 1500 grid nodes and a linearized load flow calculation. ...
The ongoing needs to develop power systems towards more environmentally friendly technologies with respect to climate change in conjunction with the continuous evolution of the respective market conditions is leading to a transition away from the traditional system operation. The upcoming challenges have motivated the development of an increasing number of models for transmission grids. Nevertheless, the high complexity of such models renders it exceedingly difficult to compare their results as well as any corresponding conclusions.
In this paper, we develop an open framework to compare a variety of pan-European transmission grid models with a strong focus on the German power system. The comparison is performed in both a qualitative and quantitative manner, depending on the investigated modeling aspect including input data, methods, system boundaries and results. The quantitative model comparison is done by performing harmonized model experiments, one for 2016 as back testing and one for 2030 for analyzing the future system.
Core elements of our comparison framework are:
We proved that our comparison framework is suitable to make similarities and differences between the different model results visible, e.g. using quadratic heat maps. To ensure transparency and to support the open modeling community, the fact sheets with the model specifications and the database with selected model results are uploaded on the open energy platform.
... The developed models inevitably contain simplifications due to missing detailed information. Several research works aim at modelling power systems as realistically as possible [2][3][4][5][6][7][8]. ...
... Also, typical low voltage (LV) grids (0.4 kV) are derived by applying cluster analysis approaches to a data set containing a collection of several real LV grids. [2] The SimBench project published a data set containing power system models of all voltage levels, ranging from LV networks to extreme high voltage (EHV) networks (380 kV) [3]. The high voltage (HV) level (110 kV) is modelled based on OpenStreetMap data [4]. ...
... The validation of these different publicly available power system models has either not been carried out yet [4], [6] or mainly focuses on topological characteristics [5], [8] and historical generation schedules and congestions [2]. However, detailed validation studies of publicly available power system models regarding operational use cases are not yet available and is thus the main contribution of this paper. ...
The confidentiality of electrical network models owned by grid operators motivated the development of publicly available models and datasets in recent years. A use case specific validation of these models is necessary to assess the significance of simulation results. In this paper, the applicability of the open_eGo model for static power flow analyzes on the 110 kV distribution grid level is evaluated. Based on a preventive security constrained optimal power flow, the annual amount of distributed generation curtailment is derived. Additionally, the aggregated reactive power demand of the 110 kV network is analyzed. Results of the open_eGo model are compared to the proprietary grid operator model of the same region. The simulation results indicate that further improvements are made by considering the non n-1 secure grid connection of generation units on the 110 kV level. Furthermore, the capability of the open_eGo model to accurately represent the aggregated reactive power demand of the 110 kV distribution network is demonstrated.
... The module for heating demand is rather simple and is described in detail in [23]. Therefore the specific heating demand of the building, which depends on age, level of refurbishment, and type is the starting point. ...
... The pump profiles were modeled with regard to heating demand, as described in [34]. See [23] for a more detailed explanation. At this step, the load profiles of the components and devices were also allocated to the three phases of the electricity system. ...
... The validation of the activity and the electric model is based on one simulation for 300 houses with 940 Households. Therefore, representative distributions for Germany based on [23] are assumed for the input parameters on household and building levels. The input parameters were already described in Section 2. A whole year is simulated. ...
The electrification of the mobility and heating sectors will significantly change the electrical behavior of households in the future. To investigate this behavior, it is important to include the heating and mobility sectors in load profile models. Existing models do not sufficiently consider these sectors. Therefore, this work aims to develop an integrated, consistent model for the electrical and thermal load of private households and their mobility behavior. The model needs to generate regionally distinct profiles depending on the building, household and resident type and should be valid for Germany. Based on a bottom-up approach, a model consisting of four components is developed. In an activity model based on a modified Markov chain process, persons are assigned to activities. The activities are then allocated to devices in the electrical and thermal models. A mobility model assigns distances to the journey activities. The results of the simulation to validate the model shows an average annual energy consumption per household of 2751 kWh and a shape of the average load profile, both in good agreement with the reference. Furthermore, the temporal distribution of the vehicles to the locations is in accordance with the reference but the annual mileage is slightly underestimated with 10,730 km.
... The heat demand curve (input data and modelling described in Köppl et al. (2017) and Müller et al. (2018)) is an essential input to calculate the baseline of heat pumps. ...
... Depending on the sector, regionalization occurs on postal code, municipal or NUTS-3 level. The methodology is described in detail in Pellinger et al. (2016), Köppl et al. (2017), and Böing et al. (2018). In general, the procedure involves the determination of a statistical indicator which correlates with the energy consumption in the respective sector (e.g., among others, employee data is used as an indicator to distribute H&HW in the industry). ...
... The result is a 59 GW el fossil fuel based power plant park. A detailed description of the relevant parameters can be found in Köppl et al. (2017). The demand and supply side scenarios provide the necessary input parameters for the energy system model ISAaR. ...
... The latter is a DC power flow approach, which is based on findings from Van Hertem, Verboomen, Purchala, Belmans, and Kling (2006) and Hagspiel et al. (2014). Details concerning the grid modeling methodology and the underlying data are described in Köppl et al. (2017) and in Böing et al. (2018). The modeled energy system consists of (CHP) power plants, storage systems, regionalized demand, RES and heating plants. ...
The substitution of fossil fueled final energy consumption through electrical appliances and processes (electrification), in combination with an increased share of emission free electricity production, poses a promising deep decarbonization strategy. To reveal the effect of high demand‐side electrification rates on the transmission grid and electricity supply‐side a case‐study analysis for the German market is performed. A reference scenario with low demand‐side electrification and low grid congestion is compared to high demand‐side electrification scenarios with two different shares of renewable electricity production of total electrical load: “Elec61” and “Elec75.” The analysis shows that an increase of the electrical load from ~500 TWh to ~760 TWh leads to heightened stress for the transmission grid and therefore more curtailment in both electrification scenarios. In Elec61, which exhibits the same share of renewable electricity production as the reference scenario, the integration of 19 TWh of flexible power‐to‐heat in district heating networks reduces the market driven curtailment of renewable feed‐in, highlighting the value of flexible electrical loads for the integration of variable renewable energy sources. Although a drastic increase of installed renewable electricity production capacity occurs in Elec61 (+109 GW) and Elec75 (+178 GW) compared to the reference scenario, fossil fueled power plants are still being dispatched frequently in times of high electrical load and low renewable energy feed. In the examined scenarios, deep decarbonization through electrification was not possible because the decrease of the CO2‐coefficient of power generation resulting from an increase in the installed capacity of variable renewable energy sources was insufficient.
This article is categorized under:
• Wind Power > Systems and Infrastructure
• Energy and Climate > Systems and Infrastructure
• Energy Systems Analysis > Systems and Infrastructure
... The presented scenarios are supplemented by energy system scenario data for the European neighboring countries following "Vision 2" of the TYNDP2016 [9] and power plant data from [10]. Using regionalization algorithms, described in [11] and [12], the installed capacities of RES, load data and power plants are distributed regionally. After intersection with weather data of the year 2012 renewable power generation profiles and load profiles are generated. ...
... The simulation model used is named "ISAaR: Integrated Simulation Model for Planning the Operation and Expansion of Power Plants with Regionalization", which is described in detail in [7], [11] and [12]. ISAaR is a linear optimization model with an objective function aimed at minimizing the overall system costs. ...
... Possible remaining grid congestion situations are solved by curtailing vRES in combination with positive redispatch. A description of the mathematical formulation of the congestion management cascade is given in [11]. The dispatch of PtH units is fixed within the congestion management simulation. ...
The electrification of fossil fueled processes and applications is frequently quoted as an essential component for the deep decarbonization of the German energy system. A prerequisite for decarbonization through electrification is low-emission electricity. In the German case, this leads to the so-called "electrification dilemma", as both a reduction of the emission-intensive conventional power plant park and the significant increase in electricity consumption are driving the demand for RES and guaranteed capacities drastically. In a simulation-based scenario analysis, two measures to reduce emissions in power generation in a high electrification regime are compared: CO2-allowance pricing and a lignite phase-out. Using the DC power flow formulation, the effects on the German transmission grid are determined. The results show that both the coal phase-out and an increase in the CO2-prices lead to a significant reduction in the average CO2-coefficient of power generation. Simultaneously the capacity gap increases significantly, but only for a few hours per year.
Due to climate targets of the German government, the share of renewable energy in the power grid will be increased and the number of grid participants connected to the low voltage level of the power grid will rise. This leads to new requirements in voltage control, especially in low voltage distribution grids. In order to achieve a stable power grid in future, further development of control strategies is necessary. In this paper, a load recognition concept, which was tested on simulative data in previous work, is further developed to reduce simulation effort. Additionally, the concept is adapted for real hardware influences and active grid participants complicating the recognition task. Thus, the main contribution of this study is the successful application of the methodology within a hardware-based test grid containing a charging electric vehicle. Using a convolutional neural network in a time series classification setting, the recognition rates in this use-case exceeded 99 % while benefiting from an asymmetric charging behavior. Due to these promising results, future voltage control strategies could be supported based on gained information through integration of the presented concept.
