A standalone photovoltaic/battery energy-powered water quality monitoring system based on narrowband internet of things for aquaculture: Design and implementation

This study presents a standalone photovoltaic (PV) /battery energy storage (BES) -powered water quality monitoring system based on the narrowband internet of things (NB-IoT) for aquaculture. (1) A PV/BES system was used as the main energy system of the monitoring system. The PV and BES capacities were optimized to provide uninterrupted electrical energy to the monitoring system, taking into account two techno-economic criteria: a maximum reliability index (RI) and a minimum levelized cost of energy (LCOE). Additionally, sensitivity analyses were conducted to investigate the effects of changes in PV generation and system consumption on the RI to improve the resilience of the PV/BES system. (2) The NB-IoT-based remote monitoring system was developed to aggregate water quality parameters such as dissolved oxygen, potential of hydrogen, temperature, turbidity, and salinity in order to provide early warning of severe water quality. Subsequently, the water quality data were used to calculate the water quality suitability index (WQSI). In addition, electrical measuring devices were installed to measure relevant electrical parameters such as PV power, system consumption, BES power, and state of charge. Grafana was then used to process and visualize these water quality and electrical parameters in real-time for the end-users. The proposed system was tested at an aquaculture pond in Rayong Province, Thailand. From the energy system viewpoint, the optimal techno-economic size of the PV/BES system was determined to be a PV capacity of 50 Wp and a BES capacity of 480 Wh, with an RI of 100% and a minimum LCOE of 0. 61 /kWh. The experimental results revealed that the system could operate continuously and stably without losing power supply. Furthermore, the results demonstrated that the proposed system achieved adequate communication reliability, with a packet loss rate of 0. 89%, thereby allowing for reliable near real-time monitoring of the WQSI.