ETHOS.REFLOW: An open-source workflow for reproducible renewable energy potential assessments

Area of Science:

• Renewable energy resource assessment
• Energy system modeling
• Climate change mitigation

Background:

• Inconsistent methods and data in renewable energy assessments lead to significant result variations.
• Accurate assessments are vital for energy system planning to meet climate goals.

Purpose of the Study:

• Introduce ETHOS.REFLOW, a Python-based workflow manager for transparent and reproducible energy potential assessments.
• Automate the entire workflow from data acquisition to reporting to minimize effort for reproducible analyses.

Main Methods:

• Demonstrate ETHOS.REFLOW by estimating North Sea offshore wind potential (fixed-foundation and mixed-technology).
• Compare two turbine siting methods (explicit placement vs. uniform power density) and different wind datasets.
• Utilize a Python-based workflow manager for automated data handling and analysis.

Main Results:

• Estimated maximum installable offshore wind capacity: 768-861 GW.
• Projected annual energy yield: 2,961-3,047 TWh.
• Calculated capacity factors: 41%-46%, with notable temporal variability.

Conclusions:

• ETHOS.REFLOW provides a robust framework for reproducible energy potential studies.
• The tool enhances trust in findings and enables energy system modelers to build upon existing work.
• Standardized workflows improve the reliability of renewable energy resource assessments.