Decision Support Model for Planning Optimal Hydrogen Supply Chains

Climate change has prompted policymakers to implement new solutions to decarbonize the energy sector. In this context, hydrogen has been identified as an alternative energy carrier to decarbonize the energy sector. Thus, this study proposes a mathematical decision-making model to identify the minimum cost of establishing the hydrogen supply chain. The proposed model is developed by considering various echelons in the hydrogen supply chain such as conditioning, storage, transportation, and distribution. The model developed in this work is demonstrated using two scenarios. The two scenarios analyzed the techno-economic feasibility of the supply chain for three different end users of hydrogen. These end users are oil refinery industries, power plants, and hydrogen refueling stations. The first scenario assumes that the pipeline's installation cost is paid by the operator. In the second scenario, the pipeline installation cost was fractionated to explore the impact of cost sharing among the stakeholders. Liquefaction, cryogenic liquid tanks, tankers, and railway tankers were selected in the optimal supply chain for oil refinery industries and power plants. For refueling stations, the optimal supply chain consisted of compression, a high-pressure vessel, a tube trailer, and a railway tube car. Moreover, it was found that the distance and hydrogen demand were the two most significant deciding factors in both scenarios. This led to liquefied hydrogen being chosen as a form of delivery for oil refineries and power plants and gaseous hydrogen for refueling stations.

Automated summary

This study proposes a mathematical decision-making model to identify the minimum cost of establishing the hydrogen supply chain. The research finds that liquefied hydrogen is suitable for delivery to oil refineries and power plants, while gaseous hydrogen is suitable for delivery to refueling stations. Distance and hydrogen demand are identified as the two most significant deciding factors.