This paper presents a method for the forecasting of the voltage and the frequency at the point of connection between a battery energy storage system installed at The University of Manchester and the local low voltage distribution grid. The techniques are to be used in a real-time controller for optimal management of the storage system. The forecasters developed in this study use an Artificial Neural Network (ANN)-based technique and can predict the grid quantities with two different time widows: one second and one minute ahead. The developed ANNs have been implemented in a dSPACE based real-time controller and all forecasters show very good performance, with correlations coefficients greater than 0.85, and Mean Absolute Percentage Errors of less than 0.2 %.
