Johannes Geis developed a rolling horizon optimization example notebook for the PyPSA/PyPSA repository, focusing on energy system modeling and optimization under forecast uncertainty. He implemented an end-to-end workflow in Python, initializing a network with wind and battery components and comparing perfect-forecast and rolling-horizon optimization strategies. The notebook includes data visualization to illustrate results and serves as a reproducible reference for users evaluating planning horizons. Johannes updated documentation and integrated pre-commit CI auto fixes to maintain code quality. His work provides a ready-to-run demonstration that supports onboarding and adoption of advanced optimization techniques in energy system analysis.