Due to the increase of volatile renewable energy resources, additional flexibility will be necessary in the electricity system in the future to ensure a technically and economically efficient network operation. Although home energy management systems hold potential for a supply of flexibility to the grid, private end users often neglect or even ignore recommendations regarding beneficial behavior. In this work, the social acceptance and requirements of a participatively developed home energy management system with focus on (i) system support optimization, (ii) self-consumption and self-sufficiency optimization, and (iii) additional comfort functions are determined. Subsequently, the socially-accepted flexibility potential of the home energy management system is estimated. Using methods of online household survey, cluster analysis, and energy-economic optimization, the socially-accepted techno-economic potential of households in a three-community cluster sample area is computed. Results show about a third of the participants accept the developed system. This yields a shiftable load of nearly 1.8 MW within the small sample area. Furthermore, the system yields the considerably larger monetary surplus on the supplier-side due to its focus on system support optimization. New electricity market opportunities are necessary to adequately reward a systemically useful load behavior of households.

Sustainable energy consumption behavior in households is a decisive factor in contributing to climate protection. To provide residents comprehensible insights into their household’s energy consumption data, nonintrusive load monitoring on appliance level can assist. To give consumers a simple and comprehensible overview of their consumption data, which they also find aesthetically pleasing, in this work a two-stage experimental design is used to compare the perceived aesthetic appearance of different types of electricity consumption graphs on appliance level (aggregated or time-dependent) and their triggered emotion. First, a representative survey covers the general aesthetics assessment in a 2x3 mixed factorial design. Second, an in-depth eye-tracking and facial expression investigation is used to track and quantify emotions on different electricity consumption graphs on application level and their perceived advantages and disadvantages. The main results provide comprehensive insights into the aesthetics of electricity consumption visualizations to trigger positive emotions, in particular users’ preferences for simple and conventional graphs like bar charts.

In this study, a reaction mechanism is presented that is optimized for the simulation of the dual fuel combustion process using n-heptane and a mixture of methane/propane as surrogate fuels for diesel and natural gas, respectively. By comparing the measured and calculated ignition delay times (IDTs) of different homogeneous methane–propane–n-heptane mixtures, six different n-heptane mechanisms were investigated and evaluated. The selected mechanism was used for computational fluid dynamics (CFD) simulations to calculate the ignition of a diesel spray injected into air and a natural gas–air mixture. The observed deviations between the simulation results and the measurements performed with a rapid compression expansion machine (RCEM) and a combustion vessel motivated the adaptation of the mechanism by adjusting the Arrhenius parameters of individual reactions. For the identification of the reactions suitable for the mechanism adaption, sensitivity and flow analyzes were performed. The adjusted mechanism is able to describe ignition phenomena in the context of natural gas–diesel, i.e., dual fuel combustion.

The global electric vehicle market is rapidly growing, which is an important step, as electro-mobility is an essential driver for achieving climate targets. However, acceptance of electric vehicles is still lacking among potential users. To overcome their skepticism, it is necessary to understand the decisive factors for the consumers’ decision-making process. Therefore, this work investigates essential drivers and barriers to electric vehicle adoption derived from international literature. Electric vehicle users from the electro-mobility-club Austria were interviewed by means of an online survey. Importance-performance analysis was conducted to analyze the data. Interpretations are supported by means of receiver operating characteristic analysis. The results show that the environmental performance of electric vehicles is a key motivator for participants and that this is already perceived as satisfying. Major barriers governments and manufactures should address are acquisition cost, range and time consumption associated with the usage of electric vehicles.

Energy consumption at the household level has a significant environmental impact. However, it is often difficult for end users to understand this complex topic. Hence, end users need to sight and understand their own energy data by means of appropriate visualizations to achieve a sustainable change towards more efficient energy consumption behavior. By means of an experimental online survey, the comprehension of energy data visualizations with regard to the daily electricity consumption of a multi-person household was surveyed. A total of n = 538 respondents were asked about their comprehension at three levels of difficulty, considering the response duration. The results show that comprehension differs depending on the energy data visualization type and its information density. This work contributes to determine appropriate visualization types to unleash potential for more energy efficient end user behavior.

This work investigates a novel distance-based electricity tariff applicable to energy communities. Based on a mixed integer linear optimization model computational studies are conducted to investigate the effects of this tariff on a small-scale community consisting of 22 consumers and 5 prosumers. Additionally, the potential of electric storage systems supporting the use of locally produced energy is investigated. The results of the computational model prove that an energy community generates incentives photovoltaic plants and the installation of energy storage systems. Complementary to the simulations, the flexibility options are supported by a user survey focusing on technology attributes for local energy market models, proving that there is a significant awareness for the use of energy storage systems.

Black box modeling is a fast and efficient way of creating models for generating the heat demand of a district heating networks. A sufficient amount of high quality data has to be collected to form the basis for a valid model that can serve as training and test stand for the models. The model parameters and their influence on the heat demand are investigated and a model structure is derived. With this structure, five data mining algorithms, namely Multiple Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbor (k-NN) and Artificial Neural Networks (ANN) are utilized for creating the load models for a small district heating network located in southeast of Austria. Except for LR, all algorithms showed a good performance. They are well suited for that kind of task. K-NN has the best regression score metric with an average MAPE of 13.49 %.

The work presents the development of a Hardware-in-the-Loop test method which enables the solving of integrated energy design problems at a system and concept level for the heat and cold supply of NZEBs based on electrically and thermally driven heat pumps and chillers. The main result is the realization of a test rig consisting of hardware (e.g. thermally driven chiller, or heat pump etc.), of software (e.g. LabView, TRNSYS) and the interface for the interaction of hardware and software.

In recent years, power systems have undergone changes in technology and definition of the associated stakeholders. With the increase in distributed renewable generation and small- to medium-sized consumers starting to actively participate on the supply side, a suitable incorporation of decentralized agents into the power system is required. A promising scheme to support this shift is given by local electricity markets. These provide an opportunity to extend the liberal wholesale markets for electrical power found in Europe and the United States to the communal level. Compared to these more established markets, local electricity markets, however, neither have few practical implementations nor standardized frameworks. In order to fill this research gap and classify the types of local electricity markets, the presented paper therefore starts with the challenges that these markets attempt to solve. This is then extended to an analysis of the theoretical and practical background with a focus on these derived challenges. The theoretical background is provided in the form of an introduction to state-of-the-art models and the associated literature, whereas the practical background is provided in form of a summary of ongoing and recent projects on local electricity markets. As a result, this paper presents a foundation for future research and projects attempting to approach the here presented challenges in distribution of generation, integration of demand response, decentralization of markets and legal and social issues via local electricity markets.