Investigating green hydrogen production operated by redundant energy on a solar PV mini-grid through matlab simulation and artificial neural network

Abstract

Green hydrogen is an important part of the transition to a net zero economy, due to its potential to deliver high amounts of energy without pollution. One important opportunity to produce green hydrogen is the use of redundant energy on solar PV mini-grids. Redundant energy is the potential generation of a mini-grid that is never utilized due to low demand and the mismatch of peak solar PV generation and peak demand. In this study, a typical rural community solar PV mini-grid is simulated using Matlab Simulink and incorporated with a water electrolyser for hydrogen production. Having run the simulation for 365 days of the year, it was revealed that the mini-grid can produce a daily average of 1.18–2.16 kg, a monthly average of 36.59–64.84 kg and a yearly total of 609.26 kg of hydrogen. Subsequently, an artificial neuron network was trained with data acquired from the simulation. With varying configurations of the number of neurons and hidden layers, it was found that the model successfully predicts hydrogen production on the mini-grid with the highest performance (RMSE = 39.85 g and R2 = 0.9979) seen when the number of hidden layers is 40 and the number of neurons per hidden layer is 3.