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Decision problems encountered in practice often possess a highly dynamic anduncertain nature. In particular fast changing forecasts for parameters (e.g.,photovoltaic generation forecasts in the context of energy management) poselarge challenges for the classical rolling horizon framework. Within this work,we propose an online scheduling algorithm for a rolling horizon framework,which directly uses short-term forecasts and observations of the uncertainty.The online scheduling algorithm is based on insights and results fromcombinatorial online optimization problems and makes use of key properties ofrobust optimization. Applied within a robust energy management approach, weshow that the online scheduling algorithm is able to reduce the totalelectricity costs within a local microgrid by more than 85% compared to aclassical rolling horizon framework and by more than 50% compared to atailor-made dynamic, yet still offline rolling horizon framework. A detailedanalysis provides insights into the working of the online scheduling algorithmunder different underlying forecast error distributions.

Threshold-Based Algorithms for an Online Rolling Horizon Framework Under Uncertainty -- With an Application to Energy Management