Summary

Pedro Giron from the Fraunhofer Institute for Energy Economics presents an innovative approach to optimizing heat pump flexibilities for diverse building types in large cities. The focus is on integrating heat pumps as a flexible alternative in urban energy systems, considering seasonal behaviors, peak load impacts, and regulatory frameworks. Giron discusses the complexities and challenges in modeling and optimizing these systems to achieve efficient and sustainable energy solutions.

To gain deeper insights and access exclusive content, we encourage you to fill out the form and unlock more valuable information.

Challenges

One of the primary challenges addressed is the seasonal variation in heat pump performance. The nominal capacity and electrical consumption of heat pumps fluctuate significantly with changes in outdoor temperatures. During winter, peak load hours and their impact on the network become critical issues. Additionally, the optimization problem is compounded by the heterogeneous nature of buildings, including different construction years, heating systems, and refurbishment statuses. Managing these variables and integrating them into a cohesive model presents a significant challenge.

Solution

To tackle these challenges, Giron and his team employ advanced clustering methods to group building profiles and reduce problem complexity. They use representative weeks and clustering techniques to simulate and optimize energy consumption patterns for different building types. The model incorporates various physical and economic inputs, such as demand profiles and thermal output capacities. Simplifications, such as multivariate regression for temperature and output calculations, help streamline the optimization process. Furthermore, the integration of thermal storage systems, with temperature-dependent loss calculations, adds another layer of precision to the model.

Results

The optimization model yields valuable insights into the performance and flexibility of heat pumps during peak winter periods. For instance, the flexibility provided by thermal storage systems during winter does not significantly reduce peak loads compared to non-flexible scenarios. The optimal strategy often results in new peaks, particularly during shutdown periods. This highlights the importance of considering the structural mass of buildings as additional heat storage and the potential rebound effects post-shutdown. The inclusion of dynamic tariffs and the integration of photovoltaic (PV) and battery storage systems further complicate the optimization but provide more comprehensive solutions.