  The distributed schedule optimization of energy storage constitutes a
challenge. Such algorithms often expect an input set containing all feasible
schedules or respectively require to efficiently search the schedule space. It
is hardly possible to accomplish this with energy storage due to its high
flexibility. In this paper, the problem is introduced in detail and addressed
by a metaheuristic algorithm, which generates a preselection of schedules.
Three contributions are presented to achieve this goal: First, an extension for
a distributed schedule optimization allowing a simultaneous optimization is
developed. Second, an evolutionary algorithm is designed to generate optimized
schedules. Third, the algorithm is extended to include an arbitrary local
criterion. It is shown that the presented approach is suitable to schedule
electric energy storage in real households and industries with different
generator and storage types.
