A Mixed-integer programming approach for identifying optimal levels of hydrogen fuel as a renewable energy source

Green hydrogen presents an opportunity to move towards a more sustainable and low-carbon energy future and has potential to address the current challenges faced by the world in terms of climate change and adverse health effects of using fossil fuels. While new technologies are coming up every day for more efficient solutions to address the challenges posed by climate change, this research aims to look at the current cost of liquid and gas hydrogen, considering sugarcane as the source. In this work, we model a real-world problem using a mixed-integer optimization approach and solve it using Excel's built-in optimization solver, taking into account cost functions that need to be minimized and constraints as restrictions related to the production, distribution, and storage of liquid and gas hydrogen fuel, considering sugarcane as a source. To identify the best course of action, data on demand, manufacturing costs, and storage costs were examined as potential factors as well. The research establishes a relationship between the use of hydrogen and gasoline based on their current costs while limiting overall pollution. Our results provide important insights into making decisions on using hydrogen fuel as a potential option for renewable energy.