Hybrid System Optimization Model for Hydrogen Production, Case Study: South Africa

This paper proposes a hybrid (wind, solar PV, grid) system optimization model for hydrogen production. Wind and solar PV supply a proton exchange membrane electrolyzer to produce hydrogen. However, due to the intermittent nature of wind and solar resources, the grid supplies insufficient energy to meet the proton exchange membrane load demand. The optimization model is achieved using a multi-objective non-dominated sorting genetic algorithm and a Python framework, Pymoo. The objective functions considered are the cost of electricity and efficiency to be minimized and maximized respectively at high reliability. The cost is defined using the cost of deficient energy purchased from the grid based on the time-of-use tariff structure and efficiency is defined for the hybrid system. Furthermore, reliability is based on the Loss of Power Supply Probability. An investigation using South Africa's chosen six Renewable Energy Development Zones as case studies are conducted to evaluate and validate the proposed hybrid system optimization model.