Abstract: An apparatus for performing the following: the apparatus maintains, in a database, one or more trained machine learning algorithms for predicting an optimal charging strategy for a time interval based on one or more values of a set of prediction parameters relating to a point of common coupling and one or more electrical load devices and on a state of charge level of the battery energy storage system. The apparatus obtains one or more recent values of the set of prediction parameters relating to one or more previous time intervals and predicts, using the one or more trained machine learning algorithm, an optimal charging strategy for a next time interval based on the one or more recent values and a current state of charge level of the battery energy storage system. Finally, the apparatus operates the battery energy storage system using the predicted optimal charging strategy.

Abstract: A method of controlling battery energy storage comprises a renewable active power source (PR), a battery energy storage system (BESS), at least one static load (PLoad) and at least one high dynamic load PH.LOAD. Active power demand of the system in the point of common coupling is at quasi-constant level. The method further includes gathering constant active power limit from the external grid (PGRID) and historical data of active power profiles of: predicting the following active power profiles for day+1, calculating required active power of the battery energy storage system, setting daily peak of state of charge and minimum state of charge of the battery energy storage system, and verifying whether daily peak of state of charge and minimum state of charge ensure that instantaneous values of state of charge through the entire day of the battery energy storage system is within range of 20% to 80%.

Abstract: An apparatus for performing the following: the apparatus maintains, in a database, one or more trained machine learning algorithms for predicting an optimal charging strategy for a time interval based on one or more values of a set of prediction parameters relating to a point of common coupling and one or more electrical load devices and on a state of charge level of the battery energy storage system. The apparatus obtains one or more recent values of the set of prediction parameters relating to one or more previous time intervals and predicts, using the one or more trained machine learning algorithm, an optimal charging strategy for a next time interval based on the one or more recent values and a current state of charge level of the battery energy storage system. Finally, the apparatus operates the battery energy storage system using the predicted optimal charging strategy.