Energy Storage Control for Dispatching Photovoltaic Power

The strong growth of the solar power generation industry requires an increasing need to predict the profile of solar power production over a day and develop highly efficient and optimized stand-alone and grid-connected photovoltaic systems. Moreover, the opportunities offered by Battery Energy Storage Systems (BESSs) coupled with PV systems require an ability to forecast the load power to optimize the size of the entire system composed of PV panels and storage devices. This paper presents a sizing and control strategy of BESSs for dispatching a photovoltaic generation farm in the 1-hour ahead and day-ahead markets. The forecasting of the solar irradiation and load power consumption is performed by developing a predictive model based on a feedforward neural network trained with the Levenberg-Marquardt back-propagation learning algorithm.