The interconnections of power systems are extended to improve operating conditions and increase their adequacy and security. Furthermore, with the increasing penetration of renewable energy sources such as wind turbines (WTs), probabilistic assessment of these systems' performance is very important, especially in risk management, bidding strategies, and operational decisions. Interline power flow controller (IPFC) is one of the flexible AC transmission system (FACTS) devices, which can increase power transfer capability and maximize the use of the existing transmission network. In the structure of the IPFC, there are two converters whose settings should be determined optimally to get the maximum benefit from it. This paper introduces a probabilistic multi-objective optimization method for the allocation of the IPFC to reduce the active power losses and improve the power flow index (PFI) of the lines with considering the IPFC cost using the multiobjective particle swarm optimization (MOPSO) algorithm. The uncertainties are taken to account in loads and wind speed of WTs. Also, the k-means-based data clustering method (DCM) is used for the probabilistic assessment of this problem for the first time, and its performance is compared with the Monte Carlo simulation (MCS) method. The efficiency of the proposed approach is investigated on the IEEE 30-bus test system.

The optimal location and setting of the unified power flow controller (UPFC) along with power plant generators' output parameters to enhance power systems reliability in the presence of uncertain variables are determined in this paper. For this purpose, the expected power not served (EPNS) is used as a reliability index based on the total required load shedding at all buses in case of single contingencies such as the outage of lines or generators. The EPNS and components of system's normal operating conditions such as active power losses, voltage deviation index (VDI), as well as economic components, namely, the cost of power generation and UPFC allocation, are taken into consideration in the objective function. The particle swarm optimization (PSO) algorithm optimizes this objective function. Afterward, the firefly algorithm (FA) determines the minimum quantity of required load shedding based on all possible solutions obtained by PSO. Because of the probabilistic nature of loads and renewable generations like wind power generation (WPG), an accurate probabilistic assessment is essential. Therefore, the two‐point estimate method (2PEM) is used, and its performance is compared with the Latin hypercube sampling (LHS) method. The proposed solution method is evaluated on the IEEE 14‐bus and the IEEE 57‐bus test systems and results are discussed.