Optimal design of a hybrid PV-Battery System for on-grid applications

Photovoltaic (PV) systems with integrated battery energy storage systems (BESs) play a crucial role in promoting energy efficiency, sustainability, and the use of renewable energy sources. The main aim of this study is to develop an artificial neural network (ANN) based maximum power point tracking (MPPT) method that eliminates the drawbacks of conventional perturb & observe (P&O) and incremental conductance (INC) MPPT methods. Moreover, an optimal operation of the grid-connected hybrid PV-BES system is presented by considering the real atmospheric data taken from the Turkish State Meteorological Service. MATLAB/Simulink is used to design and test the constructed 12 kW PV system with 10 kWh capacity BES. In this study, a scenario is created for all operating modes of the hybrid system. The solar irradiation curve of a-determined day is created from the real meteorological data of Tarsus/Mersin. In addition, simulation results for constant irradiation values as 1000 W/m2 and for 500 W/m2 at 25°C are analyzed separately. It is observed from the simulation results that the presented ANN-based MPPT method gives better total harmonic distortion (THD) results than conventional MPPT methods for variable atmospheric conditions and all operating modes of the system. Especially at low irradiance values, the ANN-based MPPT illustrate better performance values for parameters such as efficiency, THD, and tracking capability. Keywords: Battery Energy Storage System (BESs), Total Harmonic Distortion (THD), ANN-Based MPPT, Grid Connected Hybrid Systems.