Modeling phase change materials (PCMs) has been a topic of research interest in the past, carried out experimentally and by means of computational fluid dynamics (CFD). The implemented solidification and melting (SM) model in Ansys Fluent-based on the enthalpy-porosity formulation is widely used in the literature. To the authors’ knowledge, few publications apply the apparent heat capacity (AHC) method in Ansys Fluent and even fewer have discussed both. The SM approach applies a linear relationship of the liquid fraction between solidus and liquidus temperature although it is known that the phase transition follows a non-linear behavior, which can be captured using the AHC method as a curve shape and location of the specific heat capacity containing information about the nature of phase transition behavior. Important factors in modeling are the temperature dependent thermophysical material properties density, viscosity, and thermal conductivity. They are often considered constant in the respective phase (solid or liquid) with a (linear) transition over the melting range. Temperature-dependent density is taken into account by using the Boussinesq approximation to model convective heat transfer. SM and AHC are compared to the analytical solution of the two-phase Stefan problem. As this does not include gravity and thus natural convection behavior, an additional comparison to two different PCMs, one from literature and a second data set gained in a new experiment is provided. The present work helps to evaluate the differences between the SM and AHC approach and to decide which is better suited for intended studies.

Decarbonising the energy system requires high shares of variable renewable generation and sector coupling like power to heat. In addition to heat supply, heat pumps can be used in future energy systems to provide flexibility to the electricity system by using the thermal storage potential of the building stock and buffer tanks to shift electricity demand to hours of high renewable electricity production. Bridging the gap between two methodological approaches, we coupled a detailed building technology operation model and the open-source energy system model Balmorel to evaluate the flexibility potential that decentral heat pumps can provide to the electricity system. Austria in the year 2030 serves as an example of a 100% renewable-based electricity system (at an annual national balance). Results show that system benefits from heat pump flexibility are relatively limited in extent and concentrated on short-term flexibility. Flexible heat pumps reduce system cost, CO₂ emissions, and photovoltaics and wind curtailment in all scenarios. The amount of electricity shifted in the assessed standard flexibility scenario is 194 GWh_el and accounts for about 20% of the available flexible heat pump electricity demand. A comparison of different modelling approaches and a deterministic sensitivity analysis of key input parameters complement the modelling. The most important input parameters impacting heat pump flexibility are the flexible capacity (determined by installed capacity and share of control), shifting time limitations, and cost assumptions for the flexibility provided. Heat pump flexibility contributes more to increasing low residual loads (up to 22% in the assessed scenarios) than decreasing residual load peaks. Wind power integration benefits more from heat pump flexibility than photovoltaics because of the temporal correlation between heat demand and wind generation.

Background: Residential buildings with heat pumps show promising possibilities for demand-side management. The operation optimization of such heating systems can lead to cost reduction and, at the same time, change electricity consumption patterns, which is especially prevalent in the case of a variable price signal. In this work, we deal with the following question: How does the volatility of a variable retail electricity price change the energy consumption of buildings with a smart energy management system? Methods: In this context, we take Austria as an example and aggregate the findings of individual households to the national stock of single-family houses. This is done by simulating and optimzing heating operation in single representative buildings. The aggregation is done based on national building information statistics. Results: This part of the Austrian building stock could shift 19.7 GWh of electricity per year through thermal inertia using a real-time electricity price from 2021. We show the future potential under the assumption of three electricity price trends for 2030, representing different decarbonisation ambition levels. The trends show that higher decarbonisation levels which lead to higher electricity prices increase the incentive to shift electric loads. Conclusions: Real time pricing turns out to be an effective incentive for buildings to shift electric loads by pre-heating the building mass. However, cost savings for individuals are relatively low which is why additional monetary incentives are needed to tap into that potential. Increased daily peak-to-peak demand from these buildings has to put into perspective to the overall grid load.