In many virtual power plant (VPP) scenarios, numerous individually configured units within a VPP have to be scheduled regarding both global constraints (i.e. external market demands) and local constraints (i.e. technical, economical or ecological aspects for each unit). Approaches for global and local constraint handling have been discussed in the relevant literature independently. A hybrid approach is proposed that combines a decentralized combinatorial optimization heuristic with the encoding of individually constrained search spaces into unconstrained representations by means of support vector data description. The approach is applied to simulated VPP.
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