Abstract-Distributed energy scheduling constitutes a tough task for optimization algorithms, as the underlying problem structure is highdimensional, multimodal and non-linear. For this reason, metaheuristics and especially distributed algorithms have been in the focus of research for several years with promising results. The modeling of the distributed energy units' flexibility is a specific research task, with different concepts like comfort-level based approaches, enumeration of possible schedules, and continuous schedule representation using machine learning and decoder techniques. Although a continuous representation of flexibility has shown better results regarding the global optimization goal, there have been hints that the susceptibility to local minima traps enlarges compared to the enumeration of distinct schedules. In this contribution, we present an exemplary system for predictive scheduling of distributed energy units consisting of a continuous flexibility modelling approach and a fully distributed planning heuristic. A prestudy is presented, where we analyze the problem structure regarding local minima and describe planned work to reduce the heuristic's susceptibility to be kept in these.

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