Economic optimization of combined electricity feed in and hydrogen production

Abstract

This master's thesis introduces a Python-based optimization tool for maximizing annual profits in energy systems. Core components include photovoltaic (PV) systems and inverters, supplemented by modules addressing grid limitations, wind turbines, batteries, hydrogen generation, and daily hydrogen demand. Applying Mixed-Integer-Linear-Programming, the tool ensures practical design sizes, guided by techno-economic parameters drawn from reputable sources. A case study on the Baltic Sea island of Rügen at a 50-hectare area, reveals that integrating electricity feed in with hydrogen production can be conomically advantageous. The optimal system design, achieving an ROI exceeding 5%, features PV, inverters, wind turbines, electrolyzers, compressors, and H2 storage tanks. In every simulated scenario wind turbines are included in the optimized solutions, which indicates an economic advantage of wind turbines over PV in such systems. Exclusive PV-based hydrogen production is economically viable without daily demand constraints. While limited grid capacity modestly impacts economic indicators, it significantly influences system design. Fluctuations in electricity and H2 prices, along with market data changes, reveal the importance of extended simulations. Battery systems remain economically unviable, and PV systems with alternative orientations fail to enhance economic outcomes. The conclusion emphasizes the economic feasibility of combining electricity feed-in and hydrogen production, contingent on favorable electricity prices or higher returns from selling renewable H2. A sensitivity analysis highlights the impact of decreasing H2 sales prices, emphasizing the need for comprehensive simulations over extended plant lifetimes for accurate results.