Data and Code for: "The Hidden Cost of Resilience: How Financial Risk Aversion Paradoxically Undermines Green Hydrogen in Isolated Energy Hubs"
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2026
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| author | TOZAR, Ali |
| author_facet | TOZAR, Ali |
| contents | <p>This repository contains the dataset, optimization models, and simulation scripts required to reproduce the results and figures presented in the article "The Hidden Cost of Resilience: How Financial Risk Aversion Paradoxically Undermines Green Hydrogen in Isolated Energy Hubs".</p> <p>The study investigates how investor risk aversion impacts the design and operation of a multi-vector energy hub on the Orkney Islands, integrating wind power, electrolysis, fuel cells, heat pumps, and thermal storage. </p> <p>Repository Structure:<br>* 01_data_preparation.py: Prepares the annual hourly dataset, performs k-medoids clustering to derive representative days, and generates Monte Carlo scenarios for renewable energy source (RES) forecast uncertainty. <br>* 02_optimization.py: The core two-stage stochastic mixed-integer linear programming (MILP) model. It performs the risk-aversion lambda sweep, counterfactual scenarios, and sensitivity analyses. Computations are distributed across parallel cores to efficiently handle the large processing load.<br>* 03_plot_figures.py: Generates the grid-free, publication-quality figures used in the main text and supplementary material. The script automatically exports and backs up the underlying data for every plotted graph into CSV format for full transparency.<br>* 04_uncertainty_analysis.py: Runs the Monte Carlo uncertainty propagation by solving dispatch-only linear programs across various forecast error levels.<br>* plot_fig24_only.py: A standalone script to generate the final uncertainty propagation figure.<br>* Data Files: Includes the raw input data (full_year_data.csv) and the exported simulation results (e.g., Main_Results_Lambda_Sweep.csv, MC_Uncertainty_Results.csv).</p> <p>Instructions for Use:<br>1. Ensure all dependencies (Pyomo, Pandas, NumPy, Scikit-learn, Matplotlib) and an appropriate MILP solver are installed.<br>2. Run 01_data_preparation.py to generate the clustered data and uncertainty scenarios.<br>3. Execute 02_optimization.py to solve the optimization models and generate the core numerical results.<br>4. Run 04_uncertainty_analysis.py to compute the Monte Carlo uncertainty propagations.<br>5. Run 03_plot_figures.py and plot_fig24_only.py to reproduce all manuscript figures and save their corresponding plot data.</p> <p>Keywords:<br>Green Hydrogen; Multi-Vector Energy Hub; Financial Risk Aversion; Two-Stage Stochastic Optimization; Monte Carlo Simulation; Pyomo; Energy Systems Modeling</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18771474 |
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| publishDate | 2026 |
| publisher | Zenodo |
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| spellingShingle | Data and Code for: "The Hidden Cost of Resilience: How Financial Risk Aversion Paradoxically Undermines Green Hydrogen in Isolated Energy Hubs" TOZAR, Ali Green Hydrogen Multi-Vector Energy Hub Financial Risk Aversion Two-Stage Stochastic Optimization Monte Carlo Simulation Energy Systems Modeling <p>This repository contains the dataset, optimization models, and simulation scripts required to reproduce the results and figures presented in the article "The Hidden Cost of Resilience: How Financial Risk Aversion Paradoxically Undermines Green Hydrogen in Isolated Energy Hubs".</p> <p>The study investigates how investor risk aversion impacts the design and operation of a multi-vector energy hub on the Orkney Islands, integrating wind power, electrolysis, fuel cells, heat pumps, and thermal storage. </p> <p>Repository Structure:<br>* 01_data_preparation.py: Prepares the annual hourly dataset, performs k-medoids clustering to derive representative days, and generates Monte Carlo scenarios for renewable energy source (RES) forecast uncertainty. <br>* 02_optimization.py: The core two-stage stochastic mixed-integer linear programming (MILP) model. It performs the risk-aversion lambda sweep, counterfactual scenarios, and sensitivity analyses. Computations are distributed across parallel cores to efficiently handle the large processing load.<br>* 03_plot_figures.py: Generates the grid-free, publication-quality figures used in the main text and supplementary material. The script automatically exports and backs up the underlying data for every plotted graph into CSV format for full transparency.<br>* 04_uncertainty_analysis.py: Runs the Monte Carlo uncertainty propagation by solving dispatch-only linear programs across various forecast error levels.<br>* plot_fig24_only.py: A standalone script to generate the final uncertainty propagation figure.<br>* Data Files: Includes the raw input data (full_year_data.csv) and the exported simulation results (e.g., Main_Results_Lambda_Sweep.csv, MC_Uncertainty_Results.csv).</p> <p>Instructions for Use:<br>1. Ensure all dependencies (Pyomo, Pandas, NumPy, Scikit-learn, Matplotlib) and an appropriate MILP solver are installed.<br>2. Run 01_data_preparation.py to generate the clustered data and uncertainty scenarios.<br>3. Execute 02_optimization.py to solve the optimization models and generate the core numerical results.<br>4. Run 04_uncertainty_analysis.py to compute the Monte Carlo uncertainty propagations.<br>5. Run 03_plot_figures.py and plot_fig24_only.py to reproduce all manuscript figures and save their corresponding plot data.</p> <p>Keywords:<br>Green Hydrogen; Multi-Vector Energy Hub; Financial Risk Aversion; Two-Stage Stochastic Optimization; Monte Carlo Simulation; Pyomo; Energy Systems Modeling</p> |
| title | Data and Code for: "The Hidden Cost of Resilience: How Financial Risk Aversion Paradoxically Undermines Green Hydrogen in Isolated Energy Hubs" |
| topic | Green Hydrogen Multi-Vector Energy Hub Financial Risk Aversion Two-Stage Stochastic Optimization Monte Carlo Simulation Energy Systems Modeling |
| url | https://doi.org/10.5281/zenodo.18771474 |