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Flexible AC transmission system (FACTS) controller play an essential role in increasing the penetration level of renewable energy resources owing to their ability in continuously controlling the active and reactive power flow in the network. This paper presents a probabilistic multi-objective optimization approach to obtain the optimal sizes and locations of static var compensators (SVCs) and thyristor-controlled series capacitors (TCSCs) in a power transmission network with high penetration level of wind generation. The objective of the problem is to maximize the system loadability while minimizing the network power losses and the installation cost of the FACTS controllers. In this study, the uncertainties associated with wind power generation and the correlated load demand are considered. The uncertainties are handled in this work using the points estimation method. Moreover, the dynamic line ratings (DLRs) of the transmission lines are considered in this work. In this case, the maximum transmission capacity of transmission lines is estimated dynamically according to the weather conditions. Considering the DLRs or transmission lines is expected to avoid unrealistic congestion in the network, and hence, improve its loadability. The optimization problem is solved using the multi-objective teaching-learning based optimization (MO-TLBO) algorithm to find the best locations and ratings for the FACTS controllers. Additionally, a technique based on the fuzzy decisionmaking approach is employed to extract one of the Pareto optimal solutions as the best compromise. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results demonstrate the effectiveness of the proposed approach and show that the maximum loadability limit of the study system increases when considering the DLR. This limit can be enhanced to 123.0% without FACTS controller and 137.0 % ,130 % and 132.0% by SVC, TCSC and (SVC-TCSC) respectively.

Analyzing the impacts of large on-grid photovoltaic (PV) systems on the performance of the electric network is an essential task prior to the installation of these systems. To quantify these impacts, a method based on chronological simulations can be used. The main advantage of this method is its ability to provide information about the impacts of the fluctuation of the power generated from the PV systems. However, this method requires performing extensive analysis and simulations, making it impractical for utility studies, especially if long historical data with subhourly time resolution is used. In this paper, a new method that utilizes the data efficiently while preserving the temporal information of the generated PV power is proposed. The method takes advantage of the clustering techniques to group together segments of the output PV power having similar features. Hence, a representative segment for each group can be chosen and used in the analysis and simulations. This representative segment can provide information about the expected performance of other segments of the group. The validity and usefulness of the proposed method are demonstrated by identifying the suitable size and site of a large PV system.