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Sampling the Search Space of Energy Resources for Self-organized, Agent-based Planning of Active Power Provision
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Datum
2013
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Shaker Verlag
Zusammenfassung
The future smart energy grid demands for new control paradigms that are able to incorporate a huge number of rather
small, distributed and individually configured energy resources. In order to allow for a transition of the current central
market and network structure to a decentralized smart grid, with small units pooling together to jointly trade their
electricity production on specialized markets, self-organization concepts will become indispensable as an efficient
management approach. In order to enable ahead of time planning of electricity that incorporates global objectives and
individually constrained distributed search spaces in such highly dynamic environment, meta-models of constrained
spaces of operable schedules are indispensable for efficient communication and uniform access. An essential prerequisite
for building-up machine learning based domain models of individually constrained search spaces is a training
set of operable example schedules. Drawing such a sample from an electricity unit s simulation model is a challenging
task due to the high dimensionality of the problem. We present two computationally feasible sampling methods
and analyze their complexity and appropriateness. Moreover, the embedding of these methods and the interplay of
sampling and simulation in a multi agent simulation is presented.