Optimizing District Energy System Design: The Role of Temporal Resolution in Simulation Accuracy

Presentation Description: Data-driven decisions are critical as systems simultaneously aim for optimized performance and long-term sustainability. This poster highlights key findings on the relationship between temporal resolution of time series input data (e.g., hourly vs. 15-minute demand profiles) and the resulting accuracy of simulations created with oemof. The practical challenges of simulation -accurate data collection, model maintenance, and workflow management- will also be discussed. The project explores how simulation tools can support campuses in designing more economic, efficient, and sustainable district energy systems.

Case Study: As an intern for Max Brahms at HTW (University of Applied Sciences) Berlin, through the DAAD RISE scholarship program, I developed and applied simulation models to explore optimal design strategies for district energy systems. Using the oemof modeling library and Gurobi, a Mixed Integer Linear Programming (MILP) solver, I represented system layout, costs, emissions, and demand data to identify optimal technology configurations and capacities. To evaluate the impact of temporal resolution, I wrote code to up- and down-sample datasets, enabling direct comparisons across different levels of detail. The methodology explored could inform utility master planning and decisions regarding the necessary resolution of data collected for internal analysis.