Optimal integration of hybrid renewable energy systems for decarbonized urban electrification and hydrogen mobility

This study addresses cost-optimal sizing and energy management of a grid-integrated solar photovoltaic, wind turbine hybrid renewable energy system integrated with electrolyzer and hydrogen storage tank to simultaneously meet electricity and hydrogen demands considering the case study of Dijon, France. Mixed Integer Linear Programming optimization problem is formulated to evaluate two objective case scenarios: single objective and multi-objective, minimizing total annual costs and grid carbon emission footprint. The study incorporates various technical, economic, and environmental indicators focusing on the impact of sensitivity lying on various grid electricity purchase rates within the French electricity market prices. The results highlight that rising grid prices drive increased integration of renewable sources, while lower prices favor ultimate grid dependency. Constant hydrogen demand necessitates the installation of two electrolyzers. Notably, grid electricity prices above 60 €/MWh, result increase in the size of the hydrogen tank and electrolyzer operation to prevent renewable energy losses. Grid prices above 140 €/MWh depict 70% of electrical and 80% of electrolyzer demand provided by the renewable generation resulting in a carbon emission below 0.0416 Mt of CO 2 and 0.643 kgCO 2 /kgH 2 . Conversely, grid prices below 20 €/MWh lead ultimately to 100% grid dependency with a higher carbon emission of approximately 0.14 Mt of CO 2 and 4.13 kgCO 2 /kgH 2 reducing the total annual cost to 41.63 Million €. Increase in grid prices from 20€/MWh to 180 €/MWh resulted in increase of hydrogen specific costs from 1.23 to 3.58 €/kgH 2 . Finally, the Pareto front diagram is employed to illustrate the trade-off between total annual cost and carbon emission due to grid imports, aiding in informed decision-making. • Sizing and energy management for the production of decarbonized H 2 for urban public transportation. • Maximization of renewable share for H 2 production and electric demand. • Sensitivity analysis of the grid electricity price on sizing and total annual cost of ownership. • MILP optimization formulation and low computing effort resolution. • Multi-objective resolution with total costs and CO 2 emissions.