Reliability assessment method of composite power system with wind farms and its application in capacity credit evaluation of wind farms

This paper presents a non-sequential Monte Carlo Simulation (MCS)-based method for the reliability assessment of composite power system with wind farms (WFs). A multistate probability table and its corresponding Spearman's rank correlation coefficient (SRCC) are combined to represent the power outputs of WFs, which makes the multistate model of WFs compatible with the non-sequential MCS while considering the dependence among power outputs of WFs. By constructing a system state array with encoding conversion, a state merging technique is proposed, which significantly reduces the number of system states to be evaluated. In addition, the parallel computing technique is employed to accelerate the contingency analysis for the merged system states. Furthermore, the capacity credit (CC) of WFs considering both wind power correlation and transmission network constraints is evaluated based on the proposed reliability assessment method. Finally, the effectiveness of the proposed reliability assessment method and its application in the CC evaluation are demonstrated using extensive numerical studies on several modified test systems.