2024 AIChE Annual Meeting

(373af) Navigating the Energy Transition Towards Hydrogen As an Energy Carrier: A Mathematical Programming Approach for Scheduling Hydrogen Supply Chain Operations

Authors

Deshpande, D. - Presenter, Indian Institute of Technology Madras
Srinivasan, R., Indian Institute of Technology Madras
Karimi, I., National University of Singapore
Energy transition refers to a global shift from a fossil fuel-based energy mix (coal, crude oil, and natural gas (NG)) to one based on renewable energy sources such as solar, wind, hydroelectric, and geothermal power1,2. The major goals of the energy transition are enhancing energy security by diversifying energy sources, mitigating climate change impacts, reducing greenhouse gas emissions, and fostering sustainable economic development. In this context, hydrogen and its derivates, such as liquid hydrogen, ammonia, and methanol, are emerging as clean energy carriers capable of substituting conventional fossil fuels, leading to the energy transition. To ensure the flow of hydrogen and its derivates from the exporter to the importer and from the importer to the end user, scheduling and planning of the hydrogen supply chain (HSC) operations on a global scale is crucial. The global HSC consists of hydrogen production, conversion to hydrogen carriers (HCs), storage of the HCs at the export terminal, maritime transportation of the HCs from the export terminal to the import terminal, storage and re-conversion of HCs to hydrogen, and distribution of hydrogen to end-users. In this paper, we present a scheduling problem for an HSC on a global scale with the objective of minimizing the total cost of the supply chain.

HSC scheduling and planning problems in the existing literature have been studied for a portion of the supply chain,i.e., from the exporter to the importer or from the importer to end-users. Kim et al.3 developed an optimization framework for international HSCs to minimize the levelized cost of hydrogen. A mixed-integer nonlinear programming (MINLP) model was proposed considering uncertainties in hydrogen production and demand and time lags due to shipping. However, their model did not consider the distribution of hydrogen from the import terminal to end-users. He et al.4 developed an HSC planning model considering various production technologies (electrolysis, steam methane reforming with and without carbon capture and storage), storage (pressure vessel and geological storage), conditioning (compression or liquefaction), and transportation (via trucks and pipelines). A power grid and gas network supplied electricity, and NG required for hydrogen production, respectively. The objective of their model was to minimize the capital and operating costs of the HSC. However, their model only considered the hydrogen demand for fuel cell electric vehicles (both light and heavy duty) and did not consider other end-use sectors such as power, iron and steel, cement, and petrochemicals. Also, hydrogen transportation to end-users through railcars was not considered. Oh et al.5 developed a mixed integer linear programming (MILP) model for planning a green HSC for Peninsular Malaysia. The developed MILP model included hydrogen conversion to various HCs (liquid hydrogen (LH2), liquid organic hydrogen carriers (LOHCs), and ammonia), on-site storage facilities, centralized storage terminals, and distribution via different modes of transportation (pipeline, tube trailer, tanker trucks, and rail cars). However, their MILP model does not consider methanol as an HC. To summarize, some problems in the existing literature do not consider the distribution of hydrogen from the import terminal to the end-users. In contrast, some neglect the major end-users and only consider certain HCs and modes of transportation. We seek to address these research gaps through our model.

In this paper, we present an HSC scheduling problem considering the transport of hydrogen from the export terminal to the import terminal, the distribution from the import terminal to the end-users, and the export of HCs. The problem considers features such as maritime transport of liquid hydrogen from the export terminal to the import terminal, regasification of liquid hydrogen, domestic hydrogen production from renewable energy and fossil fuels, conversion of the regasified hydrogen into HCs (ammonia, methanol, and LOHCs), export of HCs, and distribution to end-users. A MILP model is developed to minimize the cost of the HSC. The model helps in decision-making regarding the scheduling of different transportation modes, the choice of HCs for transportation, and the quantities of HCs transported and exported. The proposed MILP model is demonstrated on a case study having a planning horizon of three months. The modes of transportation considered for the case study are LH2 tanker trucks, tube trailers and pipelines (for compressed hydrogen), railcars, and cryogenic tanker trucks (for LOHCs). The industrial end-use sectors considered are power, iron and steel, fertilizer, cement, and petrochemicals. The case study is implemented in IBM ILOG CPLEX Optimization Studio v12.10.0. The computational results of the case study are also presented. We believe that the results from this paper would help develop critical insights regarding the scheduling of HSC operations on a global scale.

References

(1) What is Energy Transition? https://www.spglobal.com/en/research-insights/articles/what-is-energy-t… (accessed 2024-04-07).

(2) What do we mean by energy transition? https://www.enelgreenpower.com/learning-hub/energy-transition (accessed 2024-04-07).

(3) Kim, S.; Park, J.; Chung, W.; Adams, D.; Lee, J. H. Techno-Economic Analysis for Design and Management of International Green Hydrogen Supply Chain under Uncertainty: An Integrated Temporal Planning Approach. Energy Convers. Manag. 2024, 301, 118010. https://doi.org/10.1016/j.enconman.2023.118010.

(4) He, G.; Mallapragada, D. S.; Bose, A.; Heuberger, C. F.; Gencer, E. Hydrogen Supply Chain Planning With Flexible Transmission and Storage Scheduling. IEEE Trans. Sustain. Energy 2021, 12 (3), 1730–1740. https://doi.org/10.1109/TSTE.2021.3064015.

(5) Oh, H. X.; Ng, D. K. S.; Andiappan, V. Decision Support Model for Planning Optimal Hydrogen Supply Chains. Ind. Eng. Chem. Res. 2023, 62 (38), 15535–15552. https://doi.org/10.1021/acs.iecr.3c01088.