A Computationally Efficient Sizing Method of Flexible Resources for Grid-connected Microgrids

In this paper a computationally efficient sizing method is proposed for flexible resources of grid-connected microgrids to efficiently obtain the optimal ratings of flexible resources with a large number of uncertainty scenarios considered. Initially, based on the historical data method, the scenarios are generated to represent GCMG uncertainty, which are then partitioned into the over-power scenario and under-power scenario set. The worst case of the over-power scenario set is used to determine the feasible range of grid-tied transformers and the minimum capacities of battery storage. The capacities of battery storage are optimized based on the battery storage dynamics with the under-power scenario set. Finally, simulation comparisons with a general MILP-based planning method shows the effectiveness and extraordinary computational performance of the proposed method.