Accurate renewable energy resource assessments are necessary for energy system planning to meet climate goals, yet inconsistencies in methods and data can produce significant differences in results. This paper introduces ETHOS.REFLOW, a Python-based workflow manager that ensures transparency and reproducibility in energy potential assessments. The tool enables reproducible analyses with minimal effort by automating the entire workflow, from data acquisition to reporting. We demonstrate its functionality by estimating the technical offshore wind potential of the North Sea, for fixed-foundation and mixed-technology (including floating turbines) scenarios. Two methods for turbine siting (explicit placement vs. uniform power density) and wind datasets are compared. Results show a maximum installable capacity of 768-861 GW and an annual yield of 2,961-3,047 TWh, with capacity factors between 41% and 46% and significant temporal variability. ETHOS.REFLOW offers a robust framework for reproducible energy potential studies, enabling energy system modelers to build on existing work and fostering trust in findings.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873006 | PMC |
| http://dx.doi.org/10.1016/j.patter.2025.101172 | DOI Listing |
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