The paper presents a yearly comparison of different residential self-consumption-reducing discharge strategies for grid connected residential PV systems with the Battery Energy Storage System (BESS). Altogether, three discharge strategies are taken into consideration; base case, adaptive algorithm and an energy-market-oriented remote-controlled strategy. All of the presented strategies are strictly limited to available residential load reduction based on the current BESS regulations. Furthermore, the simulations are run on real-world measurement data. The adaptive “grid-friendly” discharge algorithm utilizes global optima and a moving-average calculation to maximize the self-energy consumption in regards to the peak grid load periods. On the other hand a remote-controlled discharge scenario is taken into consideration to maximize self-consumption and to decrease the grid load when the intraday energy prices are the highest, if there is opportunity to do so. The system size optimization equations are based on a hidden layer feedforward neural network system. The main goal of the network is to predict the corresponding equations for the optimization software. Based on the neural network results, an “easy-to-use” BESS-size economic optimization web-based application has been developed to demonstrate the feasibility of the different discharge methodologies and to make easier different manufactures’ BESS system comparisons.