Modelling and Energy Management Strategy in a Grid Connected Solar PV-battery Energy Storage System

Abstract

Currently,the world is experiencing fast growth in electricity demand for both its domestic and industrial use. The use of green energy as an alternative to the generation of electricity by fossil fuel is currently on the rise. The energy produced from renewable sources is intermittent and this poses a challenge combining it with the electrical power grid. Because of theunreliability of renewable energy sources, a complementary source like a battery storage system is provided to solve the intermittency problem.This study aims to model and perform Energy Management on a grid-connected solar PV-battery energy storage system. The management system is expected to use the energy obtained from renewable sources and the grid efficiently and reduce the grid electricity need. The solar PVsuppliespower to the load when sunlight is sufficient and charges the battery when its power exceeds the load demand. The battery storage system supplies the load when solar PV is unable to produce enough power to serve the load. The grid intervenes and supplies the load when the battery power drops below the load demand. MATLAB Simulink software is used for modellingand simulation because of its effectiveness. The Arduino-based microcontroller is chosen to perform real-time energy management because of its effectiveness in switching actions and flexibility of expansion. One Sun Earth Solar Power 90 W solar panel, 150 AH lead-acid battery and 60 W incandescent lamp are the main Micro-grid components in this study. Raspberry Pi is introduced (new to related literature about PV) to forecast the next day solar PV power production. Simulation resultsshow that the load was served at all times by either the micro sources or the grid. The system model is based on weather conditions and load demand of the study area. The system is economical because it reduces the grid electricity need, prolongs the battery life, next day PV power production is predictable and consequently the system caters for possible grid failure.