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QUANTIFYING THE UNCERTAINTY OF SOLAR PHOTOVOLTAIC SOFT COSTS IN THE “COST OF RENEWABLE ENERGY SPREADSHEET TOOL” (CREST) MODEL

Published online by Cambridge University Press:  11 June 2020

S. M. Syal*
Affiliation:
Stanford University, United States of America
E. F. MacDonald
Affiliation:
Stanford University, United States of America

Abstract

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While solar photovoltaics are projected to grow, major financial barriers exist that impede installation. Soft costs (human-driven costs) can account for over half of total project costs and are often simplified in typical models. We use the National Renewable Energy Laboratory's “Cost of Renewable Energy Spreadsheet Tool” to quantify uncertainty of three soft cost inputs and their influence on the output cost of energy using variance-based sensitivity indices. We then suggest how the development process and model can be redesigned to represent the complexities of this socio-technical system.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2020. Published by Cambridge University Press

References

Bird, L. et al. (2018), Review of Interconnection Practices and Costs in the Western States, [online] National Renewable Energy Laboratory. Available at: https://www.nrel.gov/docs/fy18osti/71232.pdf (accessed 1.8.2019). https://doi.org/10.2172/1435904CrossRefGoogle Scholar
Blair, N. et al. (2017), System Advisor Model (SAM) General Description (Version 2017.9.5). [online] National Renewable Energy Laboratory. Available at: https://www.nrel.gov/docs/fy18osti/70414.pdf (accessed 30.10.2019). https://doi.org/10.2172/1440404)CrossRefGoogle Scholar
BP. (2019), BP Energy Outlook, 2019 Edition, [online] BP. Available at: https://www.bp.com/energyoutlook. (accessed 1.8.2019)Google Scholar
Carroll, M. (2019), “Considerations for Transferring Agricultural Land to Solar Panel Energy Production”, [online] North Carolina Cooperative Extension. Available at: https://craven.ces.ncsu.edu/considerations-for-transferring-agricultural-land-to-solar-panel-energy-production/ (accessed 1.8.2019).Google Scholar
Department of Energy. (2019), Soft Costs, [online] Available at: https://www.energy.gov/eere/solar/soft-costs (accessed 1.4.2019).Google Scholar
Friedman, B. et al. (2013), Benchmarking Non-Hardware Balance-of-System (Soft) Costs for U.S. Photovoltaic Systems, Using a Bottom-up Approach and Installer Survey - Second Edition. [online] National Renewable Energy Laboratory. Available at: https://www.nrel.gov/docs/fy14osti/60412.pdf (accessed 30.10.2019). https://doi.org/10.2172/1107461CrossRefGoogle Scholar
Fu, R., Feldman, D. and Margolis, R. (2018), U.S. Solar Photovoltaic System Cost Benchmark: Q1 2018, [online] National Renewable Energy Laboratory. Available at: https://www.nrel.gov/docs/fy19osti/72399.pdf (accessed 30.10.2019). https://doi.org/10.2172/1483475CrossRefGoogle Scholar
Hubbell, R. et al. (2009), Renewable Energy Finance Tracking Initiative (REFTI) Solar Trend Analysis, [online] National Renewable Energy Laboratory. Available at: https://www.nrel.gov/docs/fy12osti/53531.pdf (accessed 30.10.2019). https://dx.doi.org/10.2172/1052498Google Scholar
National Renewable Energy Laboratory. (2011), Renewable Energy Project Finance: CREST Cost of Energy Models, [online] National Renewable Energy Laboratory. Available at: https://www.nrel.gov/analysis/crest.html (accessed 1.9.2019).Google Scholar
North Carolina Clean Energy Technology Center. (2017), Balancing Agricultural Productivity with Ground-Based Solar Photovoltaic (PV) Development, [online] North Carolina State University. Available at: http://ncsolarcen-prod.s3.amazonaws.com/wp-content/uploads/2017/10/Balancing-Ag-and-Solar-final-version-update.pdf (accessed 15.9.2019).Google Scholar
Ong, S. et al. (2013), Land-Use Requirements for Solar Power Plants in the United States, [online] National Renewable Energy Laboratory. Available at: https://www.nrel.gov/docs/fy13osti/56290.pdf (accessed 30.10.2019). https://doi.org/10.2172/1086349CrossRefGoogle Scholar
Owen, A.B. (2013), “Variance components and generalized Sobol’ indices”, SIAM/ASA Journal of Uncertainty Quantification, Vol. 1, pp. 1941. https://doi.org/10.1137/120876782CrossRefGoogle Scholar
Pianosi, F. et al. (2016), “Sensitivity analysis of environmental models: A systematic review with practical workflow”, Environmental Modelling and Software, Vol. 79, pp. 214232. https://doi.org/10.1016/j.envsoft.2016.02.008Google Scholar
PVSyst. (2019), PVSyst Photovoltaic Software, [online] PVSyst. Available at: https://www.pvsyst.com/ (accessed 1.11.2019).Google Scholar
Romich, E. (2017), Considerations for Utility Scale Solar Farm Land Lease Agreements, [online] The Ohio State University. Available at: https://u.osu.edu/extensioncd/2017/02/23/considerations-for-utility-scale-solar-farm-land-lease-agreements/comment-page-1/ (accessed 15.10.2019).Google Scholar
Saltelli, A. et al. (2004), Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models, John Wiley & Sons Ltd, West Sussex. https://doi.org/10.1002/0470870958Google Scholar
Seshadri, P. and Parks, G. (2017), “Effective-Quadratures (EQ): Polynomials for Computational Engineering Studies”, Journal of Open Source Software, Vol. 2 No. 11, p. 166. https://doi.org/10.21105/joss.00166CrossRefGoogle Scholar
Sobol’, I.M. (1993), “Sensitivity Estimates for Nonlinear Mathematical Models”, Mathematical Modelling and Computational Experiments, Vol. 1 No. 4, pp. 407414.Google Scholar
Solar Energy Industries Association. (2016), Guide to Land Leases for Solar, [online] Solar Energy Industries Association. Available at: https://www.seia.org/research-resources/seia-guide-land-leases-solar (accessed 1.10.2019).Google Scholar
Strategic Solar Group. (2019), What Is the Average Solar Farm Lease Rate, [online] Strategic Solar Group. Available at: https://strategicsolargroup.com/what-is-the-average-solar-farm-lease-rate/ (accessed 1.9.2019).Google Scholar
US Department of Energy. (2016a), Solar Energy Evolution and Diffusion Studies 2 - State Energy Strategies (SEEDSS2-SES), [online] US Solar Energy Technologies Office. Available at: https://www.energy.gov/eere/solar/funding-opportunity-announcement-solar-energy-evolution-and-diffusion-studies-2-state (accessed 1.10.2019).Google Scholar
US Department of Energy. (2016b), Soft Cost Fact Sheet, [online] Sunshot US Department of Energy. Available at: https://www.energy.gov/sites/prod/files/2016/05/f32/SC%20Fact%20Sheet-508.pdf (accessed 1.9.2019).Google Scholar
Valentin Software. (2019), PV*SOL Premium, [online] Valentin Software. Available at: https://www.valentin-software.com/en/products/photovoltaics/57/pvsol-premium (accessed 1.11.2019).Google Scholar