Summary

Sebastian Berg and Lena Rosin present an in-depth look at evaluating flexibility for prosumers, focusing on optimizing energy systems in hospitals. They explain how their team uses advanced mathematical optimization models to improve energy efficiency and shift energy demand to times of high renewable energy availability. The presentation includes a case study of a hospital, demonstrating the practical application of their models and the significant impact on energy management.

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

Challenges

The primary challenge in this research is managing the energy systems of hospitals, which have high and consistent energy demands. Hospitals typically rely on combined heat and power (CHP) systems, which produce both electricity and heat. However, these systems often run at full load, leading to excess heat production that must be dissipated, especially during times of low heat demand like in the summer. Another challenge is integrating dynamic electricity tariffs, which vary throughout the day, into the optimization models to encourage energy use when renewable energy is abundant.

Solution

To address these challenges, the research team employs a modular optimization model using the open-source Python package oemof. This model includes various components such as energy sources, demands, converters, and storage, allowing for detailed simulation and optimization of energy systems. The team developed specific constraints and objective functions tailored to the hospital's needs, including load-shifting capabilities and part-load operation for the CHP system. They also incorporated real-time data and dynamic tariffs to optimize energy use and reduce costs.

Results

The optimization models provided significant insights into how hospitals can shift their energy demands to align with renewable energy availability. For example, during winter, the optimized CHP system could run at full load, meeting both the electricity and heating demands efficiently. In summer, the system could operate in part-load mode, avoiding excess heat production. The models also demonstrated the potential for thermal storage to shift heating loads to periods of high renewable energy availability, reducing reliance on the grid during peak times and lowering operational costs.