Rethinking the economics and flexibility of U.S. nuclear power through hydrogen integration and policy support

NPPH2 v1.0.1 This release provides the modeling and analysis workflow associated with the forthcoming Nature Communications article: Rethinking the economics and flexibility of U.S. nuclear power through hydrogen integration and policy support NPPH2 is a Python-based workflow for analyzing flexible nuclear-hydrogen systems. It integrates hourly operational optimization, techno-economic analysis, life cycle assessment, sensitivity analysis, and figure generation for nuclear power plants that can sell electricity to the grid, produce hydrogen, and participate in ancillary service markets. What Is Included This release includes the core codebase used to support the study, including: Hourly dispatch optimization for nuclear-hydrogen hybrid systems Multi-ISO electricity market analysis for CAISO, ERCOT, ISONE, MISO, NYISO, PJM, and SPP Modeling of nuclear generation, electrolyzers, hydrogen storage, and battery storage Ancillary service market participation and revenue evaluation Techno-economic analysis for retrofit and greenfield nuclear-hydrogen cases Reactor-level batch studies using plant metadata Life cycle assessment workflows downstream of optimization and TEA results Electricity price sensitivity analysis and result aggregation Plotting workflows for publication-style figures Main Workflows The repository supports several major workflows: executables/opt/opt_main.py for single-ISO optimization executables/opt/opt_cs1.py for reactor-level batch optimization executables/tea/tea_main.py for ISO-level techno-economic analysis executables/tea/tea_cs1.py for reactor-level techno-economic analysis executables/lca/run_lca.py for life cycle assessment executables/sensitivity/sa_price_sensitivity.py for electricity price sensitivity analysis plotting/plotting.ipynb for figure generation Installation Create a Python environment and install the required dependencies: python -m venv .venv source .venv/bin/activate pip install -r requirements.txt Some optimization workflows require a working Pyomo-compatible solver. The study workflows were developed with Gurobi support. Citation If you use this repository, please cite the associated article: Honglin Li, Jianqiao Huang, Bikash Poudel, Mucun Sun, Tyler Westover, Daniel Wendt, and Jie Zhang.Rethinking the economics and flexibility of U.S. nuclear power through hydrogen integration and policy support.Nature Communications, forthcoming. A CITATION.cff file is included for citation metadata. DOI and final publication details should be added after publication. Notes Input data are organized under input/hourly_data/. Generated optimization, TEA, LCA, sensitivity, and figure outputs are organized under output/. The repository contains analysis scripts, intermediate workflows, and supporting utilities used during the development of the study. Users should review configuration files under src/opt/config.py and src/tea/config.py before running full-scale studies.