  The rapid expansion of wind and solar energy leads to an increasing
volatility in the electricity generation. Previous studies have shown that
storage devices provide an opportunity to balance fluctuations in the power
grid. An economical operation of these devices is linked to solutions of
probabilistic optimization problems, due to the fact that future generation is
not deterministic in general. For this reason, reliable forecast methods as
well as appropriate robust optimization algorithms take an increasingly
important role in future power operation systems. Taking an uncertain
availability of electricity into account, we present a method to calculate
cost-optimal charging strategies for energy storage units. The proposed method
solves subproblems which result from a sliding window approach applied on a
linear program by utilizing statistical measures. The prerequisite of this
method is a recently developed fast algorithm for storage-related optimization
problems. To present the potential of the proposed method, a Power-To-Heat
storage system is investigated as an example using today's available forecast
data and a robust statistical measure. Second, the novel approach proposed here
is compared with a common robust optimization method for stochastic scenario
problems. In comparison, the proposed method provides lower acquisition costs,
especially for today's available forecasts, and is more robust under
perturbations in terms of deteriorating predictions, both based on empirical
analyses. Furthermore, the introduced approach is applicable to general
cost-optimal operation problems and also real-time optimization concerning
uncertain acquisition costs.
