Crown scheduling is a static scheduling approach for sets of parallelizable tasks with a common deadline, aiming to minimize energy consumption on parallel processors with frequency scaling. We demonstrate that crown schedules are robust, i.e. that the runtime prolongation of one task by a moderate percentage does not cause a deadline transgression by the same fraction. In addition, by speeding up some tasks scheduled after the prolonged task, the deadline can still be met at a moderate additional energy consumption. We present a heuristic to perform this re-scaling online and explore the tradeoff between additional energy consumption in normal execution and limitation of deadline transgression in delay cases. We evaluate our approach with scheduling experiments on synthetic and application task sets. Finally, we consider influence of heterogeneous platforms such as ARM’s big.LITTLE on robustness.