Optimal design of a hydrogen supply chain taking into account economic costs, energy consumption, and carbon emissions-A case study of Shanghai, China

The problem of global warming is becoming more and more serious, and effective reduction of carbon dioxide emissions has become a focus of attention for countries around the world. In the field of transportation, hydrogen-powered heavy-duty trucks are expected to replace traditional trucks with their low carbon emissions and environmental advantages. Building a hydrogen supply chain optimization model that takes complex urban road conditions and hydrogen data into account can increase economic benefits while reducing carbon emissions. However, such models face challenges of objective optimization and lack of case data. Here, we develop a mixedinteger linear model using a bi-objective optimization approach and an extended epsilon-constraint approach to optimize the cost, energy consumption, and carbon emissions at different stages of the hydrogen supply chain. The model we developed further reduces the computational complexity while satisfying the 3-objective optimization. In addition, for the first time, we bring the actual data of Shanghai, China into the model to obtain the optimized hydrogen supply chain including the optimal hydrogen station construction and the optimal hydrogen transportation path. Our model provides guidance and inspiration for future hydrogen supply chain optimization in cities with complex transportation environments.