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Currently, the huge potential of data centre waste heat for the decarbonization of the heating sector is often ignored. Here, a feasibility study is presented for covering the heat demand of two districts of Frankfurt (Germany) mainly by data centre waste heat. Contrary to many other studies, the total heat demand (144 GWh/a) is to be covered almost exclusively with waste heat integration, the district heating network is not existing yet and the buildings to be connected are not particularly energy efficient. In this study, the potential and demand are estimated, a heating supply concept is presented and evaluated regarding costs, heat pump capacity, and storage size. As a result, the utilisation of data centre waste heat is not only possible, but it is the most promising way for decarbonizing heating in the area under consideration. With high-capacity heat pumps (37 MWth), gas boilers (20 MWth) and a thermal energy storage for daily peaks, 97.5% of the heat demand can be covered with waste heat usage. It is economically favourable compared to individual heat pumps for all types of buildings and with the proposed concept, CO2 emissions in the district heating network area are reduced by 78% on average. [ABSTRACT FROM AUTHOR]

Dynamic thermo-hydraulic simulations of district heating networks (DHN) are essential to investigate novel concepts for their sustainable design and operation. To develop solutions for a particular case study, numerous long-term simulations are required. Therefore, computational effort for simulation is critical. Heat consumers (HC) are numerous and determine the dynamics of mass flows and return temperatures in the DHN. Thus, the way in which HCs are modeled has significant impact on the computational effort and the results of the simulation. This article presents a novel Modelica-based model for HCs that builds on an existing simplified modeling approach (open-loop design). The calculation of mass flow and return temperature is improved in terms of robustness, plausible behavior and low computational effort. In particular, the model reacts to limited differential pressure and supply temperatures to ensure plausible behavior across all operating conditions, including undersupply situations. The model is successfully tested using an exemplary DHN. The analysis proves that the HC model itself requires little time to simulate. Nevertheless, it significantly influences the simulation time for the entire DHN, which varies by a factor of five for the investigated system depending on the HC model. Fast dynamics, including a bypass in the model and correction of deviations between set point and actual heat load increase the simulation time, so users should sensibly choose how to use these options. HC models triggering many state events result in high computational effort. Compared to other simple HC models, the proposed model produces more plausible results while maintaining at least equal simulation performance (for models without bypass) or even improving it (for models with bypass, CPU time is reduced by at least 35%). [ABSTRACT FROM AUTHOR]

The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications, such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer mainly originates from (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study's database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both shallow and deep learning algorithms from the Python libraries Scikit-Learn and Keras are evaluated. A Long Short-Term Memory (LSTM) model achieves the best results (the median of R2 is 0.94). The ex post model architecture makes the model suitable for anomaly detection in energy monitoring systems. [ABSTRACT FROM AUTHOR]

Large scale heat pumps (HP) are an important technology that will link district heating (DH) systems to the electricity sector in future smart energy systems. This paper examines the feasibility of the integration of a river water HP at a combined heat and power plant in Germany. It is part of a more extensive study about the transformation of a DH system in an urban district towards a 4th generation DH system. The focus is on operational characteristics and economic efficiency of electricity price-driven heat production. A novel method for estimating the coefficient of performance (COP) of two-stage ammonia HPs based on the difference between sink and source temperature is presented. The HP achieves a seasonal COP in the range of 3.4 to 3.7. The 15-year simulation with the software energyPRO shows that electricity-price driven operation is especially relevant for lower heat loads during the non-heating season. The correlation between volatility of electricity market price change and flexible operation is analysed. Finally, the levelized cost of heat for four designs with heat outputs of the HP from 4.7 MWth to 6.1 MWth and increasing storage sizes are compared. The results indicate that electricity costs are reduced in more flexible systems, but cost parity to the minimum dimensioning is not yet reached with the underlying economic framework conditions. However, the parameters that benefit the economic efficiency of more flexible systems are discussed. [ABSTRACT FROM AUTHOR]

Focuses on a study which investigated the influence of domestic hot water (DHW) load profiles with a constant total yearly heat demand for a solar combisystem. Realistic load profiles for DHW; Definition of the fraction energy savings; Relations of the thermosyphon loop; Distribution of the solar energy gain.

Abstract: To increase the fractional energy savings achieved with solar thermal combisystems the store volume may be increased. Installation of large stores in single-family houses is, however, often limited by space constraints. In this article the influence of the store dimensions, as well as internal and external auxiliary volume configurations, are investigated for large solar water stores by annual dynamic TRNSYS simulations. The results show that store sizes up to 4m3 may be used in solar heating systems with 30m2 collector area. It is further shown that well-insulated stores are rather insensitive to the geometry. Stores deviating from the conventional dimensions still yield high fractional energy savings. Furthermore, the simulations show that the performance of an internal auxiliary volume configuration in most cases exceeds that of a solution with an external auxiliary unit. The practical limitations of very thin auxiliary volumes must, however, be further investigated. [Copyright &y& Elsevier]