Abstract-The increasing pervasion of information and communication technology (ICT) in energy systems allows for the development of new control concepts on all voltage levels. In the distribution grid, this development is accompanied by a still increasing penetration with distributed energy resources like photovoltaic (PV) plants, wind turbines or small scale combined heat and power (CHP) plants. Combined with shiftable loads and electrical storage, these energy units set up a new flexibility potential in the distribution grid that can be tapped with ICT-based control following the long-term goal of substituting conventional power generation. In this contribution, we propose an architectural model and algorithms for the self-organization of these distributed energy units within dynamic virtual power plants (DVPP) along with first results from a feasibility study of the integrated process chain from market-driven DVPP formation to product delivery.

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.

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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.

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The current upheaval in the electricity sector demands distributed generation schemes that take into account individually configured energy units and new grid structures. At the same time, this change is heading for a paradigm shift in controlling these energy resources within the grid. Pro-active scheduling of active power within a (from a controlling perspective) loosely coupled group of distributed energy resources demands for distributed planning and optimization methods that take into account the individual feasible region in local search spaces modeled by surrogate models. We propose a method that uses support vector based black-box models for re-constructing feasible regions for automated, local solution repair during scheduling and combine it with a distributed greedy approach for finding an appropriate partition of a desired target schedule into operable schedules for each participating energy unit.

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