Solar panel adoption increases in residential areas, so electricity grids face growing pressure from peak feed-in, especially during hours with high radiation during summer months. In this thesis, the potential of a Smart Heat Transfer Station (SHTS) to alleviate these challenges by converting surplus solar-generated electricity into usable heat for district heating networks (DHN) is investigated. The SHTS aims to improve local energy system performance by enhancing flexibility, reducing grid feed-in congestion, and increasing renewable self-consumption.

A Python-based simulation model is developed to evaluate the technical and economic performance of the SHTS under realistic conditions. The model integrates solar panels, a heat pump, an electric boiler, and a tank thermal energy storage system (TTES).

The strategy proved robust enough to perform effectively under varying solar radiation and thermal demand conditions. Simulation results show that the SHTS can significantly reduce electricity feed-in, by using up to 86\% of generated solar electricity internally, and lower $CO_2$ emissions by replacing district heating demand with local heat production. Optimal configurations depend on a trade-off between system efficiency, emissions, and cost. High solar availability, a well-sized TTES, and a balanced combination of heat pump and electric boiler capacities are found to be critical for the performance of the SHTS.


Despite spatial constraints and regulatory uncertainties, the SHTS presents a promising, scalable solution for energy systems with high solar panel integration. It offers grid operators relief from feed-in congestion without reinforcements in the electricity grid and behavioral change from end users. Additionally, this allows heat providers to increase the share of renewable heat in their supply, while ensuring the service remains both affordable and reliable for end users.


This study presents a practical, simulation-based framework with rule-based control for evaluating and optimizing innovative systems, such as the SHTS, providing insights into how SHTSs can enhance the utilization of locally generated energy. By identifying the key technical and economic conditions for successful implementation, it lays the groundwork for real-world deployment. Future research should focus on integrating predictive control algorithms, which can improve system responsiveness by anticipating solar availability and demand patterns. Additionally, future work should examine regulatory and spatial constraints and analyze the aggregated behavior of multiple SHTS units.