Abstract
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.
Highlights
The integration of renewable energy sources (RESs) brings an increasing number of stochastic disturbances into power systems [1,2,3,4] and considerably reduces the system inertia [5,6,7], which incurs higher rate of change of frequency (RoCoF) than ever before [8,9], and sometimes even serious incidents [10]
The regional RoCoF is an important indicator for the safe operation of the power system, which needs to be carefully considered in operational planning
This paper proposes a fast-algorithmic assessment for the probabilistic distribution of regional RoCoF, which is more advantageous as it needs less time compared with scenario-based simulation (SBS) and provides a more straightforward calculating procedure than cumulant-based method (CBM)
Summary
The integration of renewable energy sources (RESs) brings an increasing number of stochastic disturbances into power systems [1,2,3,4] and considerably reduces the system inertia [5,6,7], which incurs higher rate of change of frequency (RoCoF) than ever before [8,9], and sometimes even serious incidents [10]. Taking all the points above into consideration, the paper proposes a novel fast-algorithmic evaluation to efficiently determine the probabilistic distribution of regional RoCoF, which demonstrates a clear superiority over the time-consuming SBS and the complicated CBM. 3. The proposed AS-LSM could determine the probabilistic distribution of regional RoCoF influenced by the correlation of wind speed distribution more accurately than AS-CBM (i.e., CBM based on AS). Based on the above analysis, power disturbance to a region equals the sum of the active power disturbance distributed to the individual generator bus in this. ∆Pi (0+ ) w.r.t the stochastic output of RES (PDist ) can be computed by (4)
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