Time Aggregation in Presence of Multiple Variable Energy Resources

This paper proposes a new time aggregation method that can properly reflect the spatial aspect of the data sources alongside their temporal behaviour when multiple variable energy resources are involved. Each representative period, constructed by the proposed method, can include more than one representative day, which, in itself, represents a group of data sources that are highly associated with each other. It also preserves the spatial relationship among data sources as a whole by constructing a hybrid centroid. Two case studies, each with different data sources and networks, with and without energy storage, are considered for this study. In the second case, we add an storage to reflect the significance of preserving shape of patterns in the representative periods. The duration curves of wind farms' and solar farms' generations, electricity demand, and net demand are used as data-based evaluation indices to compare the performance of the proposed method against other approaches. In addition, an IEEE 24-bus and a 6-bus test system are considered for the case studies. Two model-based evaluation indices are used to measure the performance of the clustering methods, including the power flow of all the lines in the system and the annual operation cost of the system. In addition, the performance of the proposed method in the presence of energy storage is evaluated and compared with existing methods.