The Unified Planning Framework (UPF) provides convenient access to automated planning technology. It allows for problem formulation independent of a planning engine and the utilization of planners available on the system. However, choosing a suitable parameter configuration of the planning engine for a given problem constitutes a significant challenge. Manually finding a high-quality configuration requires domain knowledge and a considerable time investment, contradicting the intended ease-of-use of the UPF. This issue is addressed by Algorithm Configuration (AC) techniques, which aim to automatically find high-quality configurations. Algorithm runtime as well as quality of solutions found by the parameterized algorithm have been shown to be improved by AC methods in wide-ranging problem settings, which includes planning. We integrate three state-of-the-art AC methods into the UPF and perform AC runs with planning engines which are integrated in the UPF. To this end, we perform AC in runtime, solution quality and anytime planning scenarios on problem instance sets from several International Planning Competitions (IPC). We demonstrate that AC methods provide performance improvements for the IPC.