Current and Future Role of Natural Gas Supply Chains in the Transition to a Low-Carbon Hydrogen Economy: A Comprehensive Review on Integrated Natural Gas Supply Chain Optimisation Models

Abstract

Natural gas is the most growing fossil fuel due to its environmental advantages. For the economical transportation of natural gas to distant markets, physical (i.e., liquefaction and compression) or chemical (i.e., direct and indirect) monetisation options must be considered to reduce volume and meet the demand of different markets. Planning natural gas supply chains is a complex problem in today’s turbulent markets, especially considering the uncertainties associated with final market demand and competition with emerging renewable and hydrogen energies. This review study evaluates the latest research on mathematical programming (i.e., MILP and MINLP) as a decision-making tool for designing and planning natural gas supply chains under different planning horizons. The first part of this study assesses the status of existing natural gas infrastructures by addressing readily available natural monetisation options, quantitative tools for selecting monetisation options, and single-state and multistate natural gas supply chain optimisation models. The second part investigates hydrogen as a potential energy carrier for integration with natural gas supply chains, carbon capture utilisation, and storage technologies. This integration is foreseen to decarbonise systems, diversify the product portfolio, and fill the gap between current supply chains and the future market need of cleaner energy commodities. Since natural gas markets are turbulent and hydrogen energy has the potential to replace fossil fuels in the future, addressing stochastic conditions and demand uncertainty is vital to hedge against risks through designing a responsive supply chain in the project’s early design stages. Hence, hydrogen supply chain optimisation studies and the latest works on hydrogen–natural gas supply chain optimisation were reviewed under deterministic and stochastic conditions. Only quantitative mathematical models for supply chain optimisation, including linear and nonlinear programming models, were considered in this study to evaluate the effectiveness of each proposed approach.