Support vector based encoding of distributed energy resources' feasible load spaces

The sets of feasible load schedules that distributed energy resources are able to operate, jointly define the search space within many virtual power plant optimization tasks. If a centralized approach is considered, a central, single scheduling unit needs to know for each energy resource what schedules comply with all given constraints, because only these are operable and might be taken into account for optimization. As many constraints depend on state or time, sets of currently operable alternatives have repeatedly to be communicated to the scheduler in order to avoid central modeling of each single resource. We here present a support vector based approach for learning a highly efficient geometric representation of the space of feasible alternatives for operable schedules. This description is communicated to the scheduler and the encoded information implicitly contains all constraints and therefore makes their modeling dispensable at scheduler side.