Abstract-A tendency to erect ever more wind turbines can be observed in order to reduce the environmental consequences of electric power generation. As a result of this, in the near future, wind turbines may start to influence the behavior of electric power systems by interacting with conventional generation and loads. Therefore, wind turbine models that can be integrated into power system simulation software are needed.In this contribution, a model that can be used to represent all types of variable speed wind turbines in power system dynamics simulations is presented. First, the modeling approach is commented upon and models of the subsystems of which a variable speed wind turbine consists are discussed. Then, some results obtained after incorporation of the model in PSS/E, a widely used power system dynamics simulation software package, are presented and compared with measurements.

Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the future power system, and to help the customers transition from a passive to an active role. In this paper, we explore for the first time in the smart grid context the benefits of using Deep Reinforcement Learning, a hybrid type of methods that combines Reinforcement Learning with Deep Learning, to perform on-line optimization of schedules for building energy management systems. The learning procedure was explored using two methods, Deep Q-learning and Deep Policy Gradient, both of them being extended to perform multiple actions simultaneously. The proposed approach was validated on the large-scale Pecan Street Inc. database. This highly-dimensional database includes information about photovoltaic power generation, electric vehicles as well as buildings appliances. Moreover, these on-line energy scheduling strategies could be used to provide realtime feedback to consumers to encourage more efficient use of electricity.

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Abstract-A tendency to erect ever more wind turbines can be observed in order to reduce the environmental consequences of electric power generation. As a result of this, in the near future, wind turbines may start to influence the behavior of electric power systems by interacting with conventional generation and loads. Therefore, wind turbine models that can be integrated into power system simulation software are needed.In this contribution, a model that can be used to represent all types of variable speed wind turbines in power system dynamics simulations is presented. First, the modeling approach is commented upon and models of the subsystems of which a variable speed wind turbine consists are discussed. Then, some results obtained after incorporation of the model in PSS/E, a widely used power system dynamics simulation software package, are presented and compared with measurements.

This paper describes the modelling and control system of a wind turbine, using a doubly fed induction generator. This configuration makes the wind turbine suitable for variable speed wind energy application. The power captured by the wind turbine is converted into electrical power by the induction generator, and it is transmitted to the grid by the stator and the rotor windings. The control system generates voltage command signals for rotor converter and grid converter, respectively, in order to control the power of the wind turbine. Reactive power exchanged with the network through the converters is set to 0 VAr. The control strategy has been developed using MATLAB/Simulink. The simulation results are presented and discussed in the conclusions.