Developed a rolling horizon optimization example notebook for the PyPSA/PyPSA repository, focusing on energy system modeling and optimization under forecast uncertainty. The work involved initializing a network in Python, configuring wind and battery components, and implementing both perfect-forecast and rolling-horizon optimization approaches to enable side-by-side comparison. Data visualization techniques were used to illustrate the impact of different planning horizons, providing a reproducible reference for users. The notebook was integrated with updated documentation and included PNG visualizations to reinforce results. Pre-commit CI auto fixes were applied to maintain code quality, supporting onboarding and adoption of advanced optimization workflows in PyPSA.