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Sequential Predictive Scheduling in Partitioned Data Domains

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Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 717))

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Following the long-term goal of substituting conventional, fossil power generation completely with cleaner, renewable energy will consequently lead to an integration of a large share of small energy generation units imposing large problem sizes for coordination. Hardly predictable, stochastic feed-in makes the problem even harder. Predictive scheduling is a frequent task in energy grid control and has been widely studied for some decades. But, the expected huge number of entities leads to a need for new techniques reducing the computational effort for coordination. For a group of energy resources, a schedule has to be found for each single entity in the group that fulfills several objectives at the same time and resembles jointly a wanted target schedule. Considering day-ahead scenarios with 96-dimensional schedules imposes additional challenges to this already hard combinatorial problem. We explore the effects of reducing complexity by partitioning the data domain of the optimization problem for a sequential approach that integrates energy models for constraint handling directly into the optimization process. We explore the effects of different partitioning schemes and evaluate the trade-off between accuracy and effort with several simulation studies.

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Bremer, J., Hinrichs, C., Martens, S., Sonnenschein, M. (2018). Sequential Predictive Scheduling in Partitioned Data Domains. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 717. Springer, Cham.

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