Bidirectional Charging (Vehicle-to-Home) in Home Energy Management Systems: Exploring Potentials with a Simulation Tool

The home will become the most important link between heat, electricity and mobility. For instance, the concept of Vehicle-to-Home (V2H) allows to use the average long parking times of electric vehicles for energy management applications in the household. In this study, we focus on developing a simulation model in the Home Energy Management System (HEMS) to explore the impact of bidirectional charging on household energy supply. Bidirectional charging allows electric vehicles not only to take energy but also to feed energy back to supply other consumers. The study addresses the following research question: What is the potential for increasing self-sufficiency through bidirectional charging in a modern single-family house with HEMS assets? First of all, bidirectional charging was researched in initial pilot projects, and the findings were used to validate the results. Furthermore, load profiles for household loads, heat loads (heat pumps) and production profiles (photovoltaics) were collected. Based on the findings from the literature, a simulation model was developed that considers the dynamic interactions between the electric vehicle and the system components in the household. Different scenarios of bidirectional charging could be simulated and compared e.g. with a unidirectional system. In addition, different parameters could be adapted and analyzed through a sensitivity analysis. Parameters of photovoltaic power, orientation of the photovoltaic system, capacity of vehicle battery and home storage, as well as daily consumption by kilometers driven were varied. The results of the simulations show that bidirectional charging has the potential to increase the self-sufficiency of renewable energy (in this case photovoltaics) in the household, to reduce electricity costs and indirectly to reduce the load from the energy grid. It is important to say that the potential strongly depends on individual user behavior, photovoltaic power and the presence of the vehicle on site. This study contributes to the scientific literature by presenting a simulation model for bidirectional charging in HEMS. The results provide important insights for households and the simulation model can be a decision support tool for the choice and sizing of system components in the HEMS. In addition, the study provides input for further research and development in the field of electromobility and home energy management systems

Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms

Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) 1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector

Fördern unsere Medien die Salafisten? Dynamiken, Verantwortung und Grenzen der Berichterstattung über salafistische Gruppen

Dies ist der 19. Artikel unseres Blogfokus „Salafismus in Deutschland“. Medien sind Erfüllungsgehilfen der Salafisten. Jedes Mal wenn über eine Aktion von Salafisten berichtet wird, wird die Gruppe bekannter. Berichterstattungen machen neugierig und animieren dazu, ins Internet zu gehen. Zugleich diskreditieren Medien die Muslime. Berichte über Salafismus werfen immer auch ein schlechtes Licht auf die Religion des Islam. Nur was ist die Schlussfolgerung daraus? Sollten Medien das Phänomen besser verschweigen? Wie sollte eine verantwortliche Abwägung von Medienschaffenden aussehen? Der Beitrag geht diesen Fragen nach..

Gewalt gegen Frauen mit türkischem Migrationshintergrund in Deutschland. Diskurse zwischen Skandalisierung und Bagatellisierung

Schröttle M. Gewalt gegen Frauen mit türkischem Migrationshintergrund in Deutschland. Diskurse zwischen Skandalisierung und Bagatellisierung. In: Schneiders TG, ed. Islamfeindlichkeit. Wenn die Grenzen der Kritik verschwimmen. Wiesbaden: VS Verlag für Sozialwissenschaften; 2009: 269-287

Energiesystemanalyse mit mobiler Messtechnik: Systematische Auswertung von Messkampagnen in KMU und öffentlichen Gebäuden

Bisher fehlt es an einer systematischen Mess- und Analysemethode zum Einsatz mobiler Messtechnik für Strommessungen in KMU und öffentlichen Gebäuden. In diesem Beitrag werden standardisierte Datenanalysen vorgestellt und auf die Messdatensätze durchgeführter Messkampagnen (Kurzzeitmessungen) angewendet. Die Ergebnisse zeigen, dass eine Anwendung der standardisierten Datenanalysen trotz Diversität der Messobjekte zu vergleichbaren Ergebnissen führt. So können relevante Strom- und Grundlast-Verbraucher identifiziert werden. Statistische Parameter erlauben die automatisierte Einordnung von erfassten Messpunkten auf der Grundlage des Lastverhaltens. Technische und bauliche Limitationen können die Aussagekraft der Analyseergebnisse einschränken. Die Ergebnisse bilden die Grundlage zur Weiterentwicklung des systematischen Einsatzes mobiler Messtechnik im Zuge von Energiesystemanalysen in KMU und öffentlichen Gebäuden

Mobile measurement case - Saving energy with innovative digital technologies

Innovative digitale Technologien bieten kleinen und mittleren Unternehmen (KMU) neue Möglichkeiten, ihre Energieeffizienz und ihr Energiemanagement zu verbessern. Allerdings ist unklar, inwieweit KMU in der Lage sind, von den Vorteilen dieser neuen Technologien zu profitieren und diese optimal für sich zu nutzen. Das Forschungsprojekt „Smarte Technologien für Unternehmen“ hat deshalb praxisorientierte Schritte und Lösungsansätze für KMU entwickelt

Energy System Analysis with Mobile Measurement Technology: Developing a Standardized Data Analysis Concept for Short-term Measurements in SMEs

Submetering with mobile measurement technology offers substantial benefits for energy system analysis in small and medium-sized enterprises (SMEs). However, the effective and systematic analysis of submetering datasets is challenging due to the lack of a standardized concept and the limited measurement period. Based on empirically collected submetering data from selected case studies, this study investigates which data analyses are relevant for a systematic data analysis approach. In addition, key indicators and limitations of these analyses are examined and discussed. The results indicate that the use of standardized data analyses can contribute to a more efficient and targeted energy system analysis. Limitations of standardized data analyses point to the need for individual detailed analyses. In conclusion, an approach for a standardized data analysis concept is developed

Testing Applications for Home Energy Management in the Field

Poster Presentation at the DPES Research Day, (25-11-2022) Testing Applications for Home Energy Management in the Fiel

Socio-Technical Analysis of Energy Demand Behaviour and Energy Consumption of Households Equipped with Smart Home Systems

German energy and climate policy pursues the goal of reducing greenhouse gas emissions in the long term. So that politics, industry and research institutes can find suitable strategies in this respect, information is needed on changes in the energy demand behaviour of final consumers. This paper analyses the energy consumption of 120 households equipped with smart home. It is examined whether statements about the participants and their environment can be made on the basis of the energy consumption (heat and electricity). In addition, the change in energy demand behaviour due to the use of smart home systems is analysed. For this purpose, the raw data of the field study will first be validated. Subsequently, procedures as well as methodologies, taking into account data sciences and analytics, are presented, which are used to visualize and analyse the energy consumption data. An outlook shows potentials for further studies in this field