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This paper proposes an efficient approach for probabilistic transmission expansion planning (TEP) that considers load and wind power generation uncertainties. The Benders decomposition algorithm in conjunction with Monte Carlo simulation is used to tackle the proposed probabilistic TEP. An upper bound on total load shedding is introduced in order to obtain network solutions that have an acceptable probability of load curtailment. The proposed approach is applied on Garver six-bus test system and on IEEE 24-bus reliability test system. The effect of contingency analysis, load and mainly wind production uncertainties on network expansion configurations and costs is investigated. It is shown that the method presented can be used effectively to study the effect of increasing wind power integration on TEP of systems with high wind generation uncertainties.Index Terms-Benders decomposition, Monte Carlo simulation, probabilistic contingency analysis, transmission expansion planning, wind power generation.

Utilization of phasor measurement units (PMUs) in the monitoring, protection and control of power systems has become increasingly important in recent years. The aim of the optimal PMU placement (OPP) problem is to provide the minimal PMU installations to ensure full observability of the power system. Several methods, based on mathematical and heuristic algorithms, have been suggested for the OPP problem. This paper presents a thorough description of the state of the art of the optimization methods applied to the OPP problem, analyzing and classifying current and future research trends in this field.