Space heating applications account for a high share of global greenhouse gas emissions. To increase the renewable share of heat generation, seasonal thermal energy storage (STES) can be used to make thermal energy from fluctuating renewable sources available in times of high demand. A popular STES technology is pit thermal energy storage (PTES), where heat is stored underground, using water as a storage medium. To evaluate the use of PTES in an energy system, easily adaptable, publicly accessible and tool independent models are needed. In this paper, we improve an existing PTES model developed in the Modelica modeling language. The model is cross-compared with a more detailed and previously validated COMSOL model, considering different amounts of insulation, showing a deviation of 2–13% in the observed annual charged and discharged amount of heat. The results indicate that the presented model is well suited for early design stage and an exemplary case study is performed to demonstrate its applicability in a system context. Dimensions of system components are optimized for the levelized cost of heat (LCOH), both with and without subsidies, highlighting the importance of subsidies for the transition towards climate friendly heating solutions, as the gas boiler use is reduced from 47.6% to 2.7%.

In this study, a method is proposed to expand the utilization of an existing calculation model for a floor heat exchanger (HX) from room scale to small district scale. The model, namely Trnsys Type 653, is typically employed for the simulation of single or simultaneously controlled parallel heating circuits. It uses a simplified approach to calculate the heat exchange between fluid and screed, taking the HX effectiveness as an input. In order to calculate the effectiveness based on the HX design, fluid properties and mass flow rate, a Python model is developed to be coupled with Type 653. The results are compared to a reference finite element model set up in COMSOL^® and depend on the HX design. The highest deviations range from over 1 K for 35 min to over 2 K for 175 min, while the lowest deviations range from below 0.5 K to below 1 K. Furthermore, the simplification of the floor HX model is analyzed by summarizing heating circuits from single rooms to a whole flat and from single flats to a whole floor. This approach results in deviations of approximately 2 and 4%, respectively, in the overall transferred heat over longer periods of time, while the switch-on frequency of the controller in an exemplary day is halved. While further analysis is required, the described simplifications seem promising for detailed district simulations with relatively low computational effort.

As renewables become more established in the electricity grid, the focus, and therefore adaptability, will need to shift from the generation side to the demand side. Since the building sector accounts for a large share of the energy demand, it will be strongly affected by this development. One possibility for adaptation is so-called demand side management (DSM). To assess the contribution of the building sector to energy flexibility, some key performance indicators (KPIs) have already been developed in previous work. In this study, we investigate and statistically compare two control strategies for temporarily raising the room temperature—one rule-based and one schedule-based—with regard to their influence on the characterization of the building mass as a type of thermal energy storage. In each case, we determine the thermal energy demand of a residential district based on a dynamic simulation that occurred for a period of one year. The rule-based control assigns in the median approximately 60% (mean: 41%) less capacity to the building mass than the schedule-based control for the same boundary conditions. The calculation of the time-independent heating load results in a median difference of 34% (mean: 36%). In addition, the establishment of energy-flexible control in the evening hours just before a night-time reduction in the room temperature has a negative impact on the efficiency of the thermal storage.