Optimization of Complex Power-to-X Plants in the Nordics

Sammanfattning

The energy sector’s transition to renewable sources is critical for mitigating climate change. Hydrogen presents a promising solution as an energy vector for decarbonizing the transport, industry, and power sectors by storing intermittent renewable energy, such as solar energy. This thesis investigates the potential of implementing Solar-toHydrogen plants in the Nordics through techno-economic optimization. The primary objective is to minimize the Levelized Cost of Hydrogen (LCOH) by leveraging revenues from side streams and ancillary grid services. The optimization framework minimizes LCOH by balancing capital expenditures, operational costs, feedstock expenses, and revenues from side streams. The model is formulated as a non-convex Mixed-Integer Quadratically Constrained Program and optimized using the Gurobi solver, which finds globally optimal solutions to complex nonlinear problems. Assuming a 50 kg/h constant hydrogen take-off and an 80 MW solar panel capacity, the optimized system achieves an LCOH of 6.70 €/kg with a 3 MW electrolyzer capacity, 208 kW fuel cell capacity, and 2000 kg hydrogen storage size. The optimal strategy involves selling solar power during high electricity prices and producing hydrogen when prices are lower, thereby maximizing revenues through participation in the Automatic Frequency Restoration Reserve market. Capital expenditure, particularly for solar panels, is the most significant factor influencing LCOH. The sustainability of Solar-to-Hydrogen plants aligns with the United Nations Sustainable Development Goals, particularly in water use, energy access, sustainable consumption, and climate action. The potential for carbon dioxide emission reduction is evaluated based on substituting fossil fuels with hydrogen. The optimization model developed in this thesis can be further refined to assess the value of flexible operation and system dimensioning in solar-to-hydrogen investment projects. Future research should explore battery integration, multiple electrolyzer types, operational capacity limits, solar power projections, and optimized solar panel capacity.