Our case explores how to design and validate a system that performs robust energy optimization under uncertainty and incomplete information. Participants will analyze how trading and dispatch decisions for assets like solar and batteries can lead to unexpected financial outcomes when forecasts, market prices, or system behavior deviate from assumptions. The focus is not on modeling, but on ensuring reliability through testing, validation, and reproducibility.

To successfully solve the case, students need strong analytical thinking, basic knowledge of mathematics or optimization, structured problem-solving skills, and an interest in software engineering, system design, and testing methodologies.