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We present a method to turn the results of model-based optimisations into resilient and comprehensible control strategies. Our approach is to define priority lists for all available technologies in a district energy system. Using linear discriminant analysis and the results of the optimisations, these are then assigned to discrete time steps using a set of possible steering parameters. In contrast to the model-based optimisations, the deduced control strategies do not need predictions or even perfect foresight but solely rely on data about the present. The case study using priority lists presents results in terms of emissions and prices that are only about 5% off the linear optimum. Considering that the priority lists only need information about the present, the results of the control strategies obtained using the proposed method can be considered competitive.

Load-forecasting problems have already been widely addressed with different approaches, granularities and objectives. Recent studies focused not only on deep learning methods but also on forecasting loads on single building level. This study aims to research problems and possibilities arising by using different load-forecasting techniques to manage loads. For that behavior of two neural networks, Long Short-Term Memory and Feed-Forward Neural Network as well as two statistical methods, standardized load profiles and personalized standardized load profiles are analyzed and assessed by using a sliding-window forecast approach. The results show that personalized standardized load profiles (MAE: 3.99) can perform similar to deep learning methods (for example, LSTM MAE: 4.47). However, because of the simplistic approach, load profiles are not able to adapt to new patterns. As a case study for evaluating the support of load-forecasting for applications in energy management systems, the integration of charging stations into an existing building is simulated by using load-forecasts to schedule the charging procedures. It is shown that forecast- based controlled charging can have a significant impact by lowering overload peaks exceeding the house connection point power limit (controlled charging 20.24 kW, uncontrolled charging: 65.15 kW) while slightly increasing average charging duration. It is concluded that integration of high flexible loads can be supported by using forecast-based energy management systems with regards to their limitations.

There are 1454 district heating systems in Germany. Most of them are fossil based and with high temperature levels, which is neither efficient nor sustainable and needs to be changed for reaching the 2050 climate goals. In this paper, we present a case study for transforming a high to low temperature district heating system which is more suitable for renewable energy supply. With the Carnot Toolbox, a dynamic model of a potential district heating system is simulated and then transformed to a low temperature supply. A sensitivity analysis is carried out to see the system performance in case space constrains restrict the transformation. Finally, an economic comparison is performed. Results show that it is technically possible to perform the transformation until a very low temperature system. The use of decentralized renewable sources, decentralized heat storage tanks and the placement of a heat pump on each building are the key points to achieve the transformation. Regarding the sensitivity analysis, the transformation is worth doing until the seasonal storage and solar collector field sizes are reduced to 60% and 80% of their values in the reference case, respectively. The economic analysis shows, however, that it is hard for highly efficient low temperature renewable based heat networks to compete with district heating systems based on a centralized fossile CHP solution. Thus, though the presented transformation is technically possible, there is a strong need to change existing economic schemes and policies for fostering a stronger promotion of renewable energy policies in the heat sector.

This edited open access book gives a comprehensive overview of small and lightweight electric three- and four-wheel vehicles with an international scope. The present status of small electric vehicle (SEV) technologies, the market situation and main hindering factors for market success as well as options to attain a higher market share including new mobility concepts are highlighted. An increased usage of SEVs can have different impacts which are highlighted in the book in regard to sustainable transport, congestion, electric grid and transport-related potentials. To underline the effects these vehicles can have in urban areas or rural areas, several case studies are presented covering outcomes of pilot projects and studies in Europe. A study of the operation and usage in the Global South extends the scope to a global scale. Furthermore, several concept studies and vehicle concepts on the market give a more detailed overview and show the deployment in different applications.

Decarbonization requires new energy systems components to mitigate fossil fuel dependency, for instance electric vehicles and heat pumps, forming a sector integrated energy system. Energy management is a promising approach to integrate these devices more efficiently by orchestrating the respective consumption and generation. This study investigates the advantage of an advanced energy management algorithm that is applied to varying energy system scenarios. The energy management algorithm is based on economic principles and the system topology is represented by a rooted tree. Grid elements form parents, which act as auctioneers and devices act according to type specific demand and supply functions. This algorithm is compared to an approach where devices are not coordinated, at a system scale of six households. In order to account for different characteristics of the energy system, the different scenarios are defined according to a morphological analysis and are analysed by means of Monte-Carlo simulation. These scenarios vary the PV generation, heating technology, and building insulation. It is shown that the algorithm reduces peak loads across all scenarios by around 15 kW. Other key performance indicators, such as own consumption and self-sufficiency show a dependency on the scenarios, although the algorithm outperforms the reference in each one, achieving an increase in own consumption of at least 13 p.p. and 22 p.p. in terms of self-sufficiency.