The paper proposes an optimal management strategy for a system composed by a battery and a photovoltaic power plant. This integrated system is called to deliver the photovoltaic power and to simultaneously provide droop-based primary frequency regulation to the main grid. The battery state-of-energy is controlled by power offset signals, which are determined using photovoltaic energy generation forecasts and predictions of the energy required to operate frequency regulation. A two level control architecture is developed. A day-ahead planning algorithm schedules the energy profile which is traded at the day-ahead market and defines the primary control reserve that the integrated system is able to provide in the considered day. During the day operations, a second level algorithm corrects the dispatched plan using updated information, in order to guarantee a continuous and reliable service. Both control algorithms take into account the uncertainties of the photovoltaic generation and of the frequency dynamics using stochastic optimization.

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 turbines 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.

This paper proposes a solution for the day-ahead planning and the real-time control of an integrated system composed of a PhotoVoltaic plant (PV) and a Battery Energy Storage System (BESS). The day-ahead algorithm provides the optimal daily energy delivery profile based on a prediction of the PV production. The objective is to maximize the power delivery, according to the request of specific profile shape. The real- time operation algorithm dynamically regulates the BESS power exchange, in order to realize the scheduled power profile. Both the proposed algorithms exploit chance-constrained stochastic optimization, in order to take into account the uncertainties of the PV power predictions. Simulation results show the effectiveness of the proposed solutions, while their validation is achieved by experimental tests, executed on a LV microgrid controlled by a Distribution Management System (DMS).