A Grid-Connected ANFIS-MPPT Based Solar PV System and Hybrid Energy Storage

The rising popularity of renewable energy sources (RES) has escalated due to limited fossil fuel and environmental concerns. However, due to weather dependency, renewable energy generation is uncertain. In order to ensure maximum renewable generation and store the energy for later use, a methodology was shown in this paper. Maximum power point tracking (MPPT) controllers can significantly improve Solar photo-voltaic (PV) performance. Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller was implemented for a grid-connected PV system considering different levels of load demand, and weather conditions such as solar insolation and temperature. In a time of no renewable generation and peak energy demand, Energy storage can be a sustainable solution. A hybrid energy storage (HES) system consisting of both battery (BAT) and super-capacitor (SC) performs the best in time of peak load demand, and this was implemented in the system. In the time of no renewable generation, HES is able to provide the deficit energy to the grid. A smart energy management system (EMS) is proposed in this study which can effortlessly manage the energy between generation, energy storage, load demand, and existing grid. The proposed model was tested considering real hourly data. The output response of each component was analyzed to measure the performance and redundancy of the system.