A Shape-Based Clustering Framework for Time Aggregation in the Presence of Variable Generation and Energy Storage
Nima Sarajpoor, Logan Rakai, Juan Arteaga, and Hamidreza Zareipour
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A common solution to mitigate the complexity of power system studies is time aggregation. This is to replace the actual data set for all time intervals with representative time periods. Previous research confirms that when energy storage systems are involved in the study, preserving the overall shape of the original data is crucial. This paper proposes a new time aggregation framework to incorporate a shape-based distance to jointly extract representative periods of wind and demand data. The duration curve of the net demand is used as a data-based validation index to compare the performance of the proposed method against other techniques. Also, a 3-bus case study that includes a wind resource, an energy storage system, and two conventional generators is designed. Four model-based validation indices are defined and applied for performance measurement, including the annual operation cost of the system, the annual wind curtailment in the system, the energy throughput of the storage facility, and the daily average of the state of the charge of the energy storage for each 365 days of the year.