The state estimator is a fundamental part of the overall monitoring and control systems for power grids. Up to date, in most cases state estimators data are supplied only from traditional supervisory control and data acquisition systems, which collects the real-time measurements from remote terminal units installed in substations. Nowadays, phasor measurement units technology provides more accurate and higher precision real-time measurements than those from remote terminal units. Additionally, phasor measurement units are able to provide both magnitude and phase angle of the measured voltages or currents. This paper reports on the implementation of a state estimation algorithm able to combine measurements obtained from remote terminal units with those from phasor measurement units. Effectiveness of the algorithm has been evaluated on two wellknown benchmark power systems.

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The security assessment of power systems represents one of the principal studies that must be carried out in energy control centers. In this context, small-signal stability analysis is very important to determine the corresponding control strategies to improve security under stressed operating conditions of power systems. This chapter details a practical approach for assessing the stability of power system's equilibrium points in real time based on the concept of trajectory sensitivity theory. This approach provides complementary information to that given by selective modal analysis: it determines how the state variables linked with the critical eigenvalues are affected by the system's parameters and also determines the way of judging how the system's parameters affect the oscillatory behavior of a power system. The WSCC 9bus and a 190-buses equivalent system of the Mexican power system are used to demonstrate the generality of the approach as well as how its application in energy management systems is suitable for power system operation and control.

Nowadays, the large-scale integration of wind energy conversion systems in transmission networks is one of the most effective and practical options for generating electricity from renewable sources. However, the increase in wind energy penetration causes the operation of electrical power systems to become more dependent and vulnerable to variations in wind speed. Within this context, it is necessary to perform probabilistic power flow (PPF) studies to maintain system integrity in the face of stochastic wind power generation. This paper describes and implements a power flow method that considers the uncertainties that arise from wind generation behavior. This PPF approach, based on a point estimation technique, is validated against thousands of Monte Carlo simulations.