Probabilistic multi-objective optimization method for interline power flow controller (IPFC) allocation in power systems

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 multi-objective 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.

Automated summary

This paper introduces a probabilistic multi-objective optimization method for the allocation of the IPFC to reduce active power losses and improve the power flow index of the lines with considering the IPFC cost. It also discusses how to consider uncertainties and uses a data clustering method for probabilistic assessment.