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Smart grid control demands delegation of liabilities to distributed, rather small energy resources in contrast to todays large control power units. Distributed energy scheduling constitutes a complex task for optimization algorithms regarding the underlying high-dimensional, multimodal and nonlinear problem structure. Additionally, the necessity for abstraction from individual capabilities is given while integrating energy units into a general optimization model. For predictive scheduling with high penetration of renewable energy resources, agent-based approaches using classifier-based decoders for modeling individual flexibilities have shown good performance. On the other hand, such decoder-based methods are currently designed for single entities and not able to cope with ensembles of energy resources. Combining training sets randomly sampled from individually modeled energy units, results in folded distributions with unfavorable properties for training a decoder. Nevertheless, this happens to be a quite frequent use case, e. g. when a hotel, a small business, a school or similar with an ensemble of co-generation, heat pump, solar power, and controllable consumers wants to take part in decentralized predictive scheduling. We use a Simulated Annealing approach to correct the unsuitable distribution of instances in the aggregated ensemble training set prior to deriving a flexibility model. Feasibility is ensured by integrating individual flexibility models of the respective energy units as boundary penalty while the mutation drives instances from the training set through the feasible region of the energy ensemble. Applicability is demonstrated by several simulations using established models for energy unit simulation.

Unfolding Ensemble Training Sets for Improved Support Vector Decoders in Energy Management