Encoding distributed search spaces for virtual power plants

The optimization task in many virtual power plant (VPP) scenarios comprises the search for appropriate schedules in search spaces from distributed energy resources. In scenarios with a decoupling of plant modeling and plant control, these search spaces are distributed as well. If merely the controller unit of a plant knows about the subset of operable schedules that are allowed to be considered by the central scheduling unit, then these sets have to be effectively communicated. We discuss an approach of learning the envelope that separates operable from non-operable schedules inside the space of all schedules by means of support vector data description. Then, only the comparatively small set of support vectors has to be transmitted as a classifier for distinguishing schedules during optimization. We applied this approach to simulated VPP.