This paper presents a model-based control technique to provide the contribution of wind power generators to primary frequency regulation in electric power systems. Models of individual wind power generators and wind farm (WF) as a whole are presented and the proposed control strategy is detailed. It consists of a central controller, a central Kalman filter (KF), and some local KFs, one for each wind turbine. The central controller is disabled in normal operation conditions and its task is to set the power reference for each wind turbine, overwriting the local reference, when a disturbance occurs. Central KF is in charge of estimating the external load variation, while each local KF estimates wind speed and the wind turbine’s dynamical state. The key feature of this approach is that each wind turbine can react to grid disturbances in a different way, which depends on wind speed as seen by the wind turbine itself and by its dynamical conditions. Real wind data and a large WF connected to the grid in a dedicated simulation environment have been used to test the effectiveness of the proposed control strategy

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The paper aims at describing two different control strategies for a combined system composed by a Vanadium Redox Flow Battery and a wind farm. A brief overview of the dynamic models used at describing the storage system and the wind turbines is presented. The focus is then devoted to the description of the two controllers, which task is to grant the desired power output at the point of connection of the system to the main network. The two control strategies called respectively Power Control and Energy Control are analyzed and their effectiveness is tested. The wind turbines are, in fact, fed with turbulent winds and the storage is controlled to perform a series of charges and discharges in order to have the desired global output. Their implementation and the dynamic simulations are performed in the Matlab-Simulink environment

Frequency stability in power systems is a key driver for the maintenance of supply quality. Thermal loads, if properly managed, can play an important role in providing support to the frequency regulation. In this framework, the paper presents a control strategy to enable a load aggregator to manage a set of building cooling systems to contribute both to primary and secondary regulation. The proposed strategy uses the Model Predictive Control approach. Frequency support is provided without compromising the natural mission of the controlled loads, i.e., the end-user thermal comfort. The introduced method is tested by means of software-in-the-loop simulation studies. The implemented testing framework emulates real-time operation of a building aggregate within a benchmark network with high penetration of wind generation. Results show the ability of the control algorithm to optimally coordinate the contribution of thermal loads both to primary and secondary frequency regulation. Keywords-Model predictive control, demand control, primary and secondary frequency control, ancillary services.

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