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The high rate of change of frequency (RoCoF) issue incurred by the integration of renewable energy sources (RESs) into a modern power system significantly threatens the grid security, and thus needs to be carefully examined in the operational planning. However, severe fluctuation of regional frequency responses concerned by system operators could be concealed by the conventional assessment based on aggregated system frequency response. Moreover, the occurrence probability of a high RoCoF issue is actually a very vital factor during the system planner’s decision-making. Therefore, a fast-algorithmic evaluation method is proposed to determine the probabilistic distribution of regional RoCoF for the operational planning of a RES penetrated power system. First, an analytical sensitivity (AS) that quantifies the relationship between the regional RoCoF and the stochastic output of the RES is derived based on the generator and network information. Then a linear sensitivity-based analytical method (LSM) is established to calculate the regional RoCoF and the corresponding probabilistic distribution, which takes much less computational time when comparing with the scenario-based simulation (SBS) and involves much less complicated calculation procedure when comparing with the cumulant-based method (CBM). The effectiveness and efficiency of the proposed method are verified in a modified 16-machine 5-area IEEE benchmark system by numerical SBS and analytical CBM.

The application of large scale energy storage makes wind farms more dispatchable, which lowers operating risks to the grid from interconnected large scale wind farms. In order to make full use of the flexibility and controllability of energy storage to improve the schedulability of wind farms, this paper presents a rolling and dispatching control strategy with a battery energy storage system (BESS) based on model predictive control (MPC). The proposed control scheme firstly plans expected output, i.e., dispatching order, of a wind/battery energy storage hybrid system based on the predicted output of the wind farm, then calculates the order in the predictive horizon with the receding horizon optimization and the limitations of energy storage such as state of charge and depth of charge/discharge to maintain the combination of active output of the wind farm and the BESS to track dispatching order at the extreme. The paper shows and analyses the effectiveness of the proposed strategy with different sizes of capacity of the BESS based on the actual output of a certain actual wind farm in the northeast of China. The results show that the proposed strategy that controls the BESS could improve the schedulability of the wind farm and maintain smooth output of wind/battery energy storage hybrid system while tracking the dispatching orders. When the capacity of the BESS is 20% or the rated capacity of the wind farm, the mean dispatching error is only 0.153% of the rated capacity of the wind farm.