This project aims to advance the capabilities of the existing Python-based application by enhancing its techno-economic optimization and life cycle assessment (LCA) functionalities for electrolytic hydrogen production. Future development will focus on: i) incorporating additional e-fuesl production, ii) improving the accuracy and scalability of the Mixed-Integer Linear Programming (MILP) models, iii) adding new types of electrolyser technolgies and iv) expanding the environmental impact assessment to include more comprehensive sustainability metrics. Enhanced user interfaces and scenario analysis tools will be developed to facilitate broader adoption and customizable policy and market condition simulations. The goal is to create a robust, flexible decision-support tool that can adapt to emerging technologies and evolving hydrogen economies worldwide.

1. Enhanced version of the e-Hydrogen Cost Optimizer application with improved MILP models, expanded renewable energy technology options, and advanced environmental impact metrics integrated.
2. Comprehensive scenario-based techno-economic optimization reports analyzing system performance and sustainability under varying policy, market, technology conditions, and locations.
3. A peer-reviewed publication presenting the methodology, new app capabilities, and case study results demonstrating the optimized cost and environmental benefits of advanced electrolytic hydrogen production systems developed with the tool.