Speakers
Description
The e-Hydrogen Cost Optimizer is a Python-based user-defined application that combines techno-economic optimization and environmental modeling to evaluate the feasibility and sustainability of green hydrogen production systems. Built with an accessible GUI and modular codebase, the e-Hydrogen Cost Optimizer integrates key components such as photovoltaic (PV) and wind turbine generation, electrolyzer, energy and hydrogen storage. The application allows users to define system configurations and boundary conditions, and it includes a location-based retrieval of solar and wind resource data to estimate renewable electricity availability. Once an optimized system layout is determined (minimizing the levelized cost of hydrogen (LCOH)) its technical specifications (e.g., installed capacity, system size, electricity profiles) are used as direct inputs to a life cycle assessment. The tool is designed to interoperate with Brightway, enabling transparent, scenario-based environmental evaluations. By linking system design with environmental assessment, the e-Hydrogen Cost Optimizer is currently being used to support sustainable energy planning at multiple scales, including its application in strategic urban projects such as The Line by NEOM in Saudi Arabia, where green hydrogen integration plays a key role in shaping future zero-carbon cities.
Join us to see how this application is built!
GitHub link: here
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| Do you need special material (e.g. online whiteboard)? | Basic equipment for a PowerPoint presentation. |
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| How much time do you ideally wish for your contribution? | 30 minutes |
