Dataset on effects of learning curve models on onshore wind and solar PV cost developments in the USA

Published: 3 November 2021| Version 1 | DOI: 10.17632/82nfwvcgdz.1
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Description

This dataset includes input data to estimate learning-by-deploying (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States of America (USA). Using different learning curve approaches the simulated technological-based cost developments are also presented. Coefficient of determination (R squared) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technological-based costs.

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Steps to reproduce

1. Select country and energy technologies to be investigated. Onshore wind and solar PV in the USA were selected for this study. 2. Collect data on cumulative installed capacity (domestic and global), annual public RD&D expenses (domestic and global), and technology capital cost for selected energy technologies. 3. Set relevant parameters to estimate learning curves for selected energy technologies. Parameters considered in this study include capital cost structure, domestic content and international trade, annual depreciation rate of knowledge and time lag for RD&D investment effects, and international knowledge spillover effects. 4. Organise and curate data based on analysis objectives. The aim of this study is to analyse the effects of different learning curve approaches to describe energy technology cost changes over time. Data collected for this study are presented in two Microsoft Excel Comma Separated Values (.csv) files as described in <LearningCurvesDataset-description.pdf> (see ‘Description of data’ Section). 5. Copy the code included in <Learning_Curves.py>. 6. Paste the code to an integrated development environment (IDE) (e.g. PyCharm, Spyder, Jupyter Notebook). The .csv files must be in the same folder as the code as the path is considered relative. 7. Run the code. 8. Input energy technology, period of analysis (i.e. initial and final years), and learning curve approach to be investigated. For this study three periods were selected for onshore wind, and two periods for solar PV. Also, five alternative model structure definitions and specifications were selected. Description of each model formulation and periods may be found within the study. 9. Repeat steps 7 and 8 for each energy technology, period of analysis, and model formulation to be investigated. 10. Create a summary file with results conducted. <Wind_and_SolarPV_summary.xlsx> file contains a summary of observed and simulated technological-based costs, learning rates, coefficient of determination, and RMSE for each one of the energy technologies selected, periods of analysis, and model formulations proposed within the study.

Institutions

The University of Melbourne

Categories

Clean Energy Investment, Renewable Energy, Technological Learning, Knowledge Stock

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