Real-Time Energy Data Extraction and Analysis of a Nanogrid System in BVRIT Campus for Critical Loads Using IoT Modules

This paper presents a real-time method for extracting and analyzing energy data from a nanogrid system at the BVRIT campus using Internet of Things devices. The nanogrid system aims to store solar energy in batteries during the day and power critical AC loads at night. It includes six LED lamps, a street light, and a ceiling fan. The Nanogrid system comprises two 12 V, 150 Ah batteries, a 24 V, 1500 VA, 1200 W inverter, and a timer switch activating loads at 6 PM and deactivating them at 5 AM. Two IoT modules, each with Pzem004T & ESP8266 NodeMCU, monitor energy flow from the solar PV to the battery and from the battery to the AC loads. The real-time energy data analysis was done for three loading conditions with one-minute data extraction intervals. The battery efficiency was also calculated for all the three different loading conditions. An efficiency of 51.72% and 51.90% was obtained for loading conditions 1 and 3; for loading condition 2, the efficiency was found to be 43.22%, which implies that variation in loading condition impacts the battery efficiency.