P1 Poster abstract: a three-phase electricity grid model of a single family houseSimon Grafenhorst\(^{*}\), Kevin Förderer and Veit HagenmeyerInstitute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany Correspondence: Simon Grafenhorst (grafenhorst@kit.edu)Energy Informatics 2023, 6(Suppl 2):P1 Summary Various models of the distribution grid are developed to assess the impact of imbalanced loads on the three phases or to conduct a state estimation with different kinds of measurement infrastructure installed in the grid. Sometimes very detailed models of small areas of the grid are also used to enhance load disaggregation techniques. In this paper, we present a detailed three-phase residential house model that can be integrated into low voltage distribution grid models. It enables both a detailed analysis of the impact of individual asymmetrical three-phase load and generation systems and the state estimation of the whole grid based on all kinds of measurement infrastructure. The model is based on a real house and validated with different load scenarios. We show that the model enables the identification of the impact of individual devices when integrating it into a distribution grid model. Other use cases include the evaluation of load disaggregation and state estimation algorithms for low voltage grids. The Python source code to duplicate and use our model is published as open-source. Introduction The load and the generation in the distribution grid either are assumed to be equal on the three phases, or sum up to an equal total in many simulations. However, small decentralized photovoltaic (PV) generation systems, electric vehicle (EV) chargers and widely popular household appliances such as electric kettles and hair dryers often introduce high currents on only one phase and therefore lead to unbalanced load of the three phases.Because the historic grid networks were not designed with electric vehicle charging and distributed generation in mind, unregulated installation of such infrastructures can lead to problems. In sparsely instrumented distribution grids, these problems can not be identified, let alone localized. With a detailed distribution grid model and a corresponding power flow simulation it can be calculated [1] how much distributed generation can be installed in a grid area. However, as can be seen in the case study presented in this paper, a simulation of all three phases can be necessary.In this present paper we sum up related works concerned with the modeling of load and generation in three-phase distribution grids and use cases for a three-phase house model. Then we outline the process of creating a three-phase electricity grid model of a single family house with using the electrical plans of a house as a starting point. Afterwards, the validation of the generated model is conducted by comparing the simulated voltage drop coming from resistive loads with real world experiments. The model can help with detailed power flow analysis in the distribution grid by allowing for a more granular view on the three-phase electricity distribution grid. The model as well as an exemplary use case are distributed as open-source Python code [2]. Contribution The detailed model of the low voltage grid in the house allows for granular, three-phase power flow calculations. Because our model is based on a real house, it is validated and provided as an open source model, it can be used for further research. To the best of our knowledge, this is the first open model of a three-phase residential house for Pandapower and one of only few examples of three-phase power flow simulation with Pandapower. Furthermore, the outlined validation process of our model in combination with the source code of the model itself can serve as a template for the creation of a customized model for other research efforts.With the case study presented in this paper, we show that modeling the loads and generation systems in three-phase distribution grids accurately can be of interest in certain scenarios. Future work on realistic scenarios can provide valuable insights into the distribution grid state. Related work The distribution grid model used in [3] consists of over 5000 end users, two substations and multiple cycles. This is done for only one phase, as it is assumed that the PV inverters and the loads are perfectly balanced between the phases. Similarly, the power flow is assumed to be balanced in [4] as they simulate the impact of PV in low-voltage distribution grids in Sweden.However, other papers are concerned with an imbalance of the three phases in the distribution grid caused for example by charging electric vehicles [5] or decentralized energy generation [6]. In [7] the optimal size of a battery to support a residential PV system was evaluated. They identified the generalization of a large number of households as a problem of multiple other papers. With a more detailed model of a residential house, a less general but more optimal solution for the house in question can be obtained. The model To create the model we look at the electrical cabling and floor plans of the house. We extract the type of the cabling used and estimate the lengths of the lines. Furthermore, we are able to determine the exact topology of the cabling from these plans. The house model is based on the real-world houses at the KIT Energy Lab 2.0. The house consists of two floors with a base of 9.57 m by 7.6 m and a total living area of 107.5 m\(^{2}\). Two rooms, a kitchen, a restroom, and a storage room form the bottom floor and three more rooms are located upstairs. A staircase in the middle of the house connects the two floors.All outlets, lights and heating systems are part of the model. The lengths of the lines are estimated based on the floor plans and the line inductances are derived from the data sets provided by the wiring manufacturer. With this data, new standard cabling types are created as Pandapower standard types. The cables between the sockets and the distribution panels are of type NYM-J 3×2.5 mm\(^{2}\) with a line resistance of \(R=7.41\Omega /km\). From here on to the house connection, all three phases are in the same cable, a NYM-J 5×6 mm\(^{2}\) with a line resistance of \(R=3.08\Omega /km\). Smart home controllers, for example the floor heating controllers, are conencted using NYM-J 3×1.5 mm\(^{2}\) lines with a resistance of \(R=12.1\Omega /km\).The resulting model is shown in Fig. 1. Between the root node (connection to the public grid) and the distribution panels, one line represents all three phases. The distribution panels are represented by larger round black nodes. Each of these contains a Janitza UMG 604-Pro Power Analyzer. The three strains coming from each of these distribution panels represent the three phases. The red, green and blue nodes represent power outlets or appliances connected to only one phase.Fig. 1 (abstract P1) Distribution grid with four houses. Blue nodes represent phase S with the loads connected to the enlarged triangles. Green nodes represent phase R with the PV systems connected to the enlarged squares Model validation Validation of the house model is done by comparing the power flow simulations of the model with real world measurements. The parameters we are not able to extract from the wiring and floor plans are the lengths of the lines, which we therefore estimate during the development of the model. To validate the length of the lines, we place different resistive loads at different outlets within the house and measure the voltage drop across the individual lines. The voltage drop of a line depends on the resistance, which depends on the line length and the type of cable. Parameters of the cable are taken from the manufacturer’s data sheet. Therefore, we are able to calculate the line length in the house from the voltage drop observed with different loads. The power flow simulation is validated by comparing the voltage drop observed in the real world with the voltage drop obtained from executing the simulation.While this validation process results in line lengths that are within 10 % of our estimates, we are unable to specify contact resistances and errors resulting from these. Case study In the following simulation, four identical houses contain PV systems that are connected to phase R. These PV systems generate 3 kW of real power each. Household appliances and an EV charger are connected to phase S. All loads combined sum up to a total of 7 kW per house. In total, the small distribution grid area modeled in this scenario generates a total of 12 kW on phase R and draws a total of 28 kW on phase S. On the right side of the distribution grid area in Fig. 1, an external grid component is connected in the simulation that represents a power source for phase S and power sink for phase R. In the real world, this could be a transformer connecting the low voltage area to a medium voltage grid.At the measurement point marked as “M” in Fig. 1, the simulation outputs a voltage drop of 10.6 V on Phase S and a voltage rise of 3.8 V on Phase R. However, modeling both loads on the same phase results in a voltage drop of just 6.5 V at the measurement point. These results show that modeling asymmetric loads is important for grid state analysis and installation of new grid infrastructure. Conclusion The case study we conduct in this paper makes it clear that a three-phase electricity distribution grid model is necessary for accurately simulating the impact of various appliances, decentralized generation and EV charging infrastructure. With the knowledge gained from such simulations, placement of grid-supporting infrastructure can be optimized to provide the greatest benefit. To conclude, the three-phase house model has numerous use cases. These include the realistic assessment of the impact of imbalanced load and generation, the development of new algorithms and research regarding the observability of the distribution grid with decentralized measurement devices, and the evaluation of load monitoring techniques without the preparatory work of creating a custom house model. Funding This research has been funded by the German Federal Ministry for Economic Affairs and Climate Action (TrafoKommune project, funding reference: 03EN3008F) Availability of data and materials [2]: https://github.com/KIT-IAI/ThreePhaseHouseGrid Author’s contributions Conceptualization and methodology: SG and KF; Model development: SG; validation: SG; writing-original draft: SG; writing-review and editing: SG, KF and VH; supervision: KF and VH; funding acquisition: KF and VH. All authors read and approved the final manuscript. Conflict of interest The authors declare that they have no competing interests. References 1. Mulenga, E., Bollen, M.H.J., Etherden, N.: A review of hosting capacity quantification methods for photovoltaics in low-voltage distribution grids. International Journal of Electrical Power & Energy Systems 115, 105445 (2020). Accessed 2022-09-12 2. Simon Grafenhorst, Kevin Förderer, Veit Hagenmeyer: ThreePhaseHouseGrid, Karlsruhe (2023). https://github.com/KIT-IAI/ThreePhaseHouseGrid 3. Luthander, R., Lingfors, D., Widén, J.: Large-scale integration of photovoltaic power in a distribution grid using power curtailment and energy storage. Solar Energy 155, 1319–1325 (2017). Accessed 2023-01-10 4. Widén, J., Wäckelgård, E., Paatero, J., Lund, P.: Impacts of distributed photovoltaics on network voltages: Stochastic simulations of three Swedish low-voltage distribution grids. Electric Power Systems Research 80(12), 1562–1571 (2010). Accessed 2023-03-30 5. Islam, M.R., Lu, H., Hossain, M.J., Li, L.: Mitigating unbalance using distributed network reconfiguration techniques in distributed power generation grids with services for electric vehicles: A review. Journal of Cleaner Production 239, 117932 (2019). Accessed 2023-03-31 6. Pinthurat, W., Hredzak, B., Konstantinou, G., Fletcher, J.: Techniques for compensation of unbalanced conditions in LV distribution networks with integrated renewable generation: An overview. Electric Power Systems Research 214, 108932 (2023). Accessed 2023-03-31 7. Li, J.: Optimal sizing of grid-connected photovoltaic battery systems for residential houses in Australia. Renewable Energy 136, 1245–1254 (2019). Accessed 2023-01-11

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Der steigende Energiebedarf der Rechenzentren stellt eine Herausforderung bei der Erreichung der Klimaschutzziele sowie der Realisierung der Energiewende dar. Auch die sogenannte graue Energie – also die Energie, die für Herstellung, Transport, Lagerung und Entsorgung der Komponenten benötigt wird – kann ganz erhebliche Anteile am Gesamtenergiebedarf ausmachen. Dieser Artikel stellt eine selbstentwickelte Modellstruktur vor, welche die graue Energie in Rechenzentren erstmalig berechnet.

With the increasing amount of renewable energy in the energy mix, the growing demand for materials required to manufacture renewable technologies becomes a potential limiting factor for their further implementation. When assessing the consequences of using these materials, it is necessary to consider their physical availability and their environmental impacts. Currently, methodologies such as material flow analysis and life cycle assessment make it possible to analyze a product's complete material requirements throughout its life cycle and the related environmental impacts. However, these methods currently focus on aspects such as material depletion, or global warming potential, without consideration of criticality, potential for replacement, or material efficiency. The indicators fail to take into consideration the temporal variations of material stocks. This chapter provides an overview on the existing methods and their indicators for assessing raw materials consumption and highlights their limitations when it comes to evaluating the total impacts of new energy technologies.

Including criticality into Life Cycle Assessment (LCA) has always been challenging to achieve but desirable to accomplish. In this article, we present a new approach for the evaluation of resource consumption of products by building comparison values based on Life Cycle Impact Assessment (LCIA) combined with weighted criticality values to show the direct impacts of criticality on LCA results. For this purpose, we develop an impact indicator based on the Abiotic Depletion Potential (ADP) of natural resources and use the two main parameters defined by the EU to determine the criticality of a material - the economic importance and the supply risk – in our case studies to build the Criticality Weighted Abiotic Depletion Potentials (CWADPs), one for each parameter. These indicators allow identifying and measuring the impacts of criticality when comparing the results of resource depletion using the ADP methodology and the results that incorporate criticality. The comparison of the CWADPs to the corresponding EU criticality values and its thresholds it reflects the equivalent criticality of the assessed product. This information reflects the impacts of criticality on LCA and assesses the total resource consumption of critical materials in a system.Keywords: Life Cycle Assessment, criticality, resources, materials, sustainability indicator

Because of their low emissions and possible contribution to sustainable development, both mobile and stationary fuel cells show promising tendencies to play an important role in the future. However, the polymer exchange membrane fuel cell (PEMFC) contains significant amounts of platinum, a material considered critical within the European Union. Using material flow analysis, this paper seeks to examine how the implementation of mobile and stationary fuel cells will affect demand for critical raw materials and to what degree recycling presents a viable option for reducing the pressure on primary production. Based on a number of developed scenarios, it is demonstrated that the platinum requirements arising from a more widespread adoption of neither fuel cell vehicles nor household heating systems is likely to cause a depletion of platinum deposits in the near future. However, both technologies may increase the pressure on the already constricted platinum market.

This paper develops and evaluates a set of metrics to evaluate the holistic energy efficiency of data centers. The Cumulative Energy Efficiency (CEE) and Cumulated Performance Efficiency (CPE) are metrics developed considering criteria for indicators for the evaluation of resource efficiency, while also taking into account aspects of sustainability and primary resource depletion. The metrics are calculated using Cumulative Energy Demand as resource indicator, which is analyzed through a Life Cycle Assessment of products. The useful energy and performance is then compared to this resource depletion indicator. The metrics are then tested with a case study of a server used in a data center. The results indicate a CEE=0,260 and a CPE=789 ops/J. When comparing these values to other well established metrics, the developed metrics account for embodied energy and energy transformation losses for the whole energy supply chain.

Sustainable recovery of critical metals (CM) from Waste Electrical and Electronic Equipment (WEEE) in the European Union (EU) requires information for detailed analysis, monitoring and decision-making. Related knowledge is currently insufficient or disseminated through the network of stakeholders. This paper assesses the requirements of an adequate Database Management System (DBMS) with participation of different actors involved in the recovery of critical metals and analyses the difficulties and the possibilities found for its implementation. The authors define a conceptual scheme of a DBMS to assess the information requirements and to establish the interactions between different actors of the WEEE supply chain, with the aim of supplying standardized information for management and research. Barriers are studied through a survey to identify obstacles for its elaboration. Limitations for its development are addressed and practical solutions for its elaboration are presented.

Due to the pressing necessity of switching to alternative sources of energy, fuel cells (FC) for household applications have emerged as an alternative for decentralized energy supply. Despite the advancement on technological developments, concerns on the availability of critical raw materials required for the fabrication of these technologies may limit their implementation. This document analyses the demand of platinum for mass implementation of stationary fuel cells for household energy supply in Germany, through an analysis of the potential market, lifecycle of the product, material demands, and material flows. Different scenarios show that improving recycling rates and extending operation lifetime can help to mitigate the material needs even in the most demanding scenarios and reach material reuse targets. Methods and results can also be applied for other regions where FC have this potential applicability. Quality of data and its effects on the results need also to be further analyzed.