Modelling and optimization of solar integrated green hydrogen supply chain management: a genetic approach

Abstract

The continued growth in energy demand and a pressing need to lower greenhouse gas emissions have shifted the focus toward clean and sustainable energy alternatives. The purpose of this research study is to produce green hydrogen powered by renewable energy sources that reduces the dependency on fossil fuels and supports the transition to a low-carbon energy system. The main goal of this study is to design and optimize a sustainable and cost-effective green hydrogen supply chain network using solar energy and treated wastewater. A mixed-integer linear programming model is designed to minimize the system’s total cost, which consists of installation, production, storage and transportation costs. Financial indicators like net present value, internal rate of return and payback period are evaluated for strategic and investment decision management. A genetic algorithm is used to optimize the total cost of the network by locating the optimum solution. Numerical example reveals that the solar plant installation cost and the green hydrogen plant installation cost are the main contributors to the cost, which account for 64.71% and 32.91%, respectively. Sensitivity analysis highlights that the capital investment and the transport mode significantly influence the total cost. It is found that pipelines are 8.5% economical than compressed hydrogen trucks for long distances. A positive net present value and 18.21 % internal rate of return confirm the financial stability of the proposed supply chain. The proposed framework provides valuable insights for policymakers, planners and industry stakeholders, helping to accelerate hydrogen and support the global framework.