Optimal storage sizing using two-stage stochastic optimization for intra-hourly dispatch

With the increasing penetration of renewable energy sources into the electric power grid, a heightened amount of attention is being given to the topic of energy storage, a popular solution to account for the variability of these sources. Energy storage systems (ESS) can also be beneficial for load-levelling and peak-shaving, as well as reducing the ramping of generators. However, the optimal energy and power ratings for these devices is not immediately obvious. In this paper, the energy capacity and power rating of the ESS is optimized using two-stage stochastic optimization. In order to capture the wind and load variations in the different days throughout the year, it is advantageous to use a large number of scenarios. Optimizing generator outputs and storage decisions at the intra-hour level with a high number of scenarios will result in a very large optimization problem, and thus scenario reduction is employed. A relationship between the variance of the system price for each scenario and the optimal storage size determined for that scenario is shown. The correlation between these parameters allows for a natural clustering of similar scenarios. Scenario reduction is performed by exploiting this relationship in conjunction with centroid-linkage clustering, and stochastic optimization with the reduced number of scenarios is used to determine the optimal ESS size.