A linear programming model for power system planning with hydrogen integration

The imperative to decarbonize the economy has positioned hydrogen as a clean fuel for hard-to-abate sectors and as a buffer for renewable energy integration. For the United Arab Emirates (UAE), with abundant solar resources, the large-scale role of hydrogen in the power system remains underexplored. This study develops an hourly-resolution linear programming optimization model to evaluate the economic and environmental benefits of hydrogen integration as both an energy storage medium and an industrial commodity. The model optimizes solar PV, wind, nuclear, and natural gas generation, along with batteries and a hydrogen subsystem including underground hydrogen storage, to meet projected 2030 electricity (203 TWh) and hydrogen (1.4 Mton) demand targets. Implemented in Python and solved with Gurobi, the model identifies a cost-optimal system that yields a levelized cost of electricity of $0.065/kWh and hydrogen of $2.56/kg. The results highlight vulnerability to carbon taxation and gas price volatility, while confirming hydrogen's cost-effectiveness for storage and demand supply.