Physically Consistent Design of Hybrid Renewable Energy Systems for Green Methanol: A Chained Load Modeling and Techno-Economic Approach

Physically Consistent Design of Hybrid Renewable Energy Systems for Green Methanol: A Chained Load Modeling and Techno-Economic Approach Sunay TÜRKDOĞAN1*, Kübra AL2 1 Department of Electrical and Electronic Engineering, Faculty of Engineering Yalova University, Yalova, Türkiye 77200 2 Department of Energy Systems Engineering, Faculty of Engineering Yalova University, Yalova, Türkiye 77200 *Corresponding author. ORCID ID 0000-0002-9690-325X E-mail address: [e-mail] Description of Supplementary Data and HOMER Pro Files The supplementary materials accompanying this study contain the complete digital twin models and high-resolution numerical datasets required to replicate the reported findings. The files are organized into four primary directories reflecting the optimization and robustness stages of the research: Main Simulation: Contains the primary architecture model (MainSimulation.homer) and detailed CSV files for all nine evaluated configurations (Cases 1–9), documenting hourly dispatch behavior and techno-economic outcomes. H2 Tank Size Simulations: Includes discrete HOMER models and corresponding results for the 100 kg, 150 kg, 200 kg, and 250 kg parametric sweep, used to validate the techno-economic optimum for chemical storage. Main Simulation Sensitivity Analysis: Provides the modeling files and result matrices evaluating the system's economic resilience under extreme capital cost fluctuations (e.g., +300% electrolyzer and +50% PV multipliers). PEM Efficiency Simulation: Contains simulation data for the 65% and 75% electrolyzer efficiency scenarios, illustrating the system's sensitivity to part-load and non-ideal operating conditions. The repository is organized into distinct sub-folders corresponding to the primary simulation stages, hydrogen storage parametric sweep, and sensitivity analyses. Furthermore, the repository includes two comprehensive Python implementations: (i) the MCDA_Analysis.py script, which performs the CRITIC-weighted stochastic TOPSIS analysis, Monte Carlo iterations (Equal Weight and Environmental Priority), and Dirichlet-based weight perturbations; and (ii) the Optimization_Validation.py script, which implements the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) used for capacity cross-validation. These resources are provided for academic and non-commercial use to ensure the full reproducibility of the proposed Chained Load Modeling framework.