Partitioning the data domain of combinatorial problems for sequential optimization

Following the long-term goal of substituting conventional power generation with cleaner energy will lead to an integration of a large share of small energy generation units imposing large problem sizes for coordination. The expected huge number of entities leads to a need for new techniques reducing the computational effort for coordination. Predictive scheduling is a frequent task in energy grid control. For a number of energy resources, schedules have to be found that fulfill several objectives at the same time. 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.