Tokenized market learning-based transaction scheduling for hydrogen–carbon chemistry consortium-based green energy communities with stakeholder welfare and sustainable transportation

The transition toward sustainable cities requires integrated energy planning frameworks that coordinate multiple technologies, policy instruments, and social considerations. This study proposes a robust optimization framework for rich-renewables eco-sustainable urban communities, where multi-energy hubs including electricity, thermal, cooling, and hydrogen systems are jointly managed under uncertainty. A scenario-independent static robust model is developed to ensure reliable operation under renewable intermittency, supported by sensitivity analyses. The framework introduces hydrogen chemistry consortium processes, integrating electrolyzers, methanation, fuel cells, and carbon capture, utilization, and storage to enhance renewable utilization and reduce emissions. Both stationary storage systems and electric public transportation fleets are incorporated to provide distributed and mobile energy flexibility. Demand-side management and policy mechanisms, including carbon taxation and cap-and-trade, are embedded to align operations with environmental targets. A digital-social welfare layer evaluates affordability and equitable access. Simulation results across multiple scenarios demonstrate that the proposed framework reduces operational costs by over 45%, improves grid independence by more than 35%, and achieves emission reductions exceeding 90%. Welfare indicators also show significant improvement, confirming the effectiveness of the integrated approach.