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An Open-Access Web-Based Tool to Access Global, Hourly Wind and Solar PV Generation Time-Series Derived from the MERRA Reanalysis Dataset

Author

Listed:
  • Madeleine McPherson

    (Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, ON M5S 1A4, Canada)

  • Theofilos Sotiropoulos-Michalakakos

    (Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, ON M5S 1A4, Canada)

  • LD Danny Harvey

    (Department of Geography, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada)

  • Bryan Karney

    (Department of Civil Engineering, University of Toronto, 35 St. George Street, Toronto, ON M5S 1A4, Canada)

Abstract

Wind and solar energy resources are an increasingly large fraction of generation in global electricity systems. However, the variability of these resources necessitates new datasets and tools for understanding their economics and integration in electricity systems. To enable such analyses and more, we have developed a free web-based tool (Global Renewable Energy Atlas & Time-series, or GRETA) that produces hourly wind and solar photovoltaic (PV) generation time series for any location on the globe. To do so, this tool applies the Boland–Ridley–Laurent and Perez models to NASA’s (National Aeronautics and Space Administration) Modern-Era Retrospective Analysis for Research and Applications (MERRA) solar irradiance reanalysis dataset, and the Archer and Jacobson model to the MERRA wind reanalysis dataset to produce resource and power data, for a given technology’s power curve. This paper reviews solar and wind resource datasets and models, describes the employed algorithms, and introduces the web-based tool.

Suggested Citation

  • Madeleine McPherson & Theofilos Sotiropoulos-Michalakakos & LD Danny Harvey & Bryan Karney, 2017. "An Open-Access Web-Based Tool to Access Global, Hourly Wind and Solar PV Generation Time-Series Derived from the MERRA Reanalysis Dataset," Energies, MDPI, vol. 10(7), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1007-:d:104865
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    as
    1. Ingeborg Graabak & Magnus Korpås, 2016. "Variability Characteristics of European Wind and Solar Power Resources—A Review," Energies, MDPI, vol. 9(6), pages 1-31, June.
    2. Ang Sha & Marco Aiello, 2016. "A Novel Strategy for Optimising Decentralised Energy Exchange for Prosumers," Energies, MDPI, vol. 9(7), pages 1-22, July.
    3. Henrik Lund & Finn Arler & Poul Alberg Østergaard & Frede Hvelplund & David Connolly & Brian Vad Mathiesen & Peter Karnøe, 2017. "Simulation versus Optimisation: Theoretical Positions in Energy System Modelling," Energies, MDPI, vol. 10(7), pages 1-17, June.
    4. Laslett, Dean & Creagh, Chris & Jennings, Philip, 2016. "A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data," Renewable Energy, Elsevier, vol. 96(PA), pages 1003-1014.
    5. Petr Mastny & Jan Moravek & Martin Vojtek & Jiri Drapela, 2017. "Hybrid Photovoltaic Systems with Accumulation—Support for Electric Vehicle Charging," Energies, MDPI, vol. 10(7), pages 1-24, June.
    6. Cradden, Lucy C. & McDermott, Frank & Zubiate, Laura & Sweeney, Conor & O'Malley, Mark, 2017. "A 34-year simulation of wind generation potential for Ireland and the impact of large-scale atmospheric pressure patterns," Renewable Energy, Elsevier, vol. 106(C), pages 165-176.
    7. Kubik, M.L. & Brayshaw, D.J. & Coker, P.J. & Barlow, J.F., 2013. "Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland," Renewable Energy, Elsevier, vol. 57(C), pages 558-561.
    8. Huber, Matthias & Dimkova, Desislava & Hamacher, Thomas, 2014. "Integration of wind and solar power in Europe: Assessment of flexibility requirements," Energy, Elsevier, vol. 69(C), pages 236-246.
    9. de Boer, Harmen Sytze & Grond, Lukas & Moll, Henk & Benders, René, 2014. "The application of power-to-gas, pumped hydro storage and compressed air energy storage in an electricity system at different wind power penetration levels," Energy, Elsevier, vol. 72(C), pages 360-370.
    10. Sinden, Graham, 2007. "Characteristics of the UK wind resource: Long-term patterns and relationship to electricity demand," Energy Policy, Elsevier, vol. 35(1), pages 112-127, January.
    11. Bosch, Jonathan & Staffell, Iain & Hawkes, Adam D., 2017. "Temporally-explicit and spatially-resolved global onshore wind energy potentials," Energy, Elsevier, vol. 131(C), pages 207-217.
    12. Hannes Kunz & Nathan John Hagens & Stephen B. Balogh, 2014. "The Influence of Output Variability from Renewable Electricity Generation on Net Energy Calculations," Energies, MDPI, vol. 7(1), pages 1-23, January.
    13. Li, Zhigang & Qiu, Feng & Wang, Jianhui, 2016. "Data-driven real-time power dispatch for maximizing variable renewable generation," Applied Energy, Elsevier, vol. 170(C), pages 304-313.
    14. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    15. Prasad, Abhnil A. & Taylor, Robert A. & Kay, Merlinde, 2017. "Assessment of solar and wind resource synergy in Australia," Applied Energy, Elsevier, vol. 190(C), pages 354-367.
    16. Zhou, Wei & Lou, Chengzhi & Li, Zhongshi & Lu, Lin & Yang, Hongxing, 2010. "Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems," Applied Energy, Elsevier, vol. 87(2), pages 380-389, February.
    17. Baptiste François & Sara Martino & Lena S. Tøfte & Benoit Hingray & Birger Mo & Jean-Dominique Creutin, 2017. "Effects of Increased Wind Power Generation on Mid-Norway’s Energy Balance under Climate Change: A Market Based Approach," Energies, MDPI, vol. 10(2), pages 1-18, February.
    18. Boccard, Nicolas, 2009. "Capacity factor of wind power realized values vs. estimates," Energy Policy, Elsevier, vol. 37(7), pages 2679-2688, July.
    19. Federica Cucchiella & Idiano D’Adamo & Massimo Gastaldi, 2017. "Economic Analysis of a Photovoltaic System: A Resource for Residential Households," Energies, MDPI, vol. 10(6), pages 1-15, June.
    20. Ionica Oncioiu & Anca Gabriela Petrescu & Eugenia Grecu & Marius Petrescu, 2017. "Optimizing the Renewable Energy Potential: Myth or Future Trend in Romania," Energies, MDPI, vol. 10(6), pages 1-14, May.
    21. Oliveira, Amauri P. & Escobedo, João F. & Machado, Antonio J. & Soares, Jacyra, 2002. "Correlation models of diffuse solar-radiation applied to the city of São Paulo, Brazil," Applied Energy, Elsevier, vol. 71(1), pages 59-73, January.
    22. Holger C. Hesse & Rodrigo Martins & Petr Musilek & Maik Naumann & Cong Nam Truong & Andreas Jossen, 2017. "Economic Optimization of Component Sizing for Residential Battery Storage Systems," Energies, MDPI, vol. 10(7), pages 1-19, June.
    23. Boilley, Alexandre & Wald, Lucien, 2015. "Comparison between meteorological re-analyses from ERA-Interim and MERRA and measurements of daily solar irradiation at surface," Renewable Energy, Elsevier, vol. 75(C), pages 135-143.
    24. Gueymard, Christian A., 2005. "Importance of atmospheric turbidity and associated uncertainties in solar radiation and luminous efficacy modelling," Energy, Elsevier, vol. 30(9), pages 1603-1621.
    25. Torres, J.L. & De Blas, M. & García, A. & de Francisco, A., 2010. "Comparative study of various models in estimating hourly diffuse solar irradiance," Renewable Energy, Elsevier, vol. 35(6), pages 1325-1332.
    26. Noorian, Ali Mohammad & Moradi, Isaac & Kamali, Gholam Ali, 2008. "Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces," Renewable Energy, Elsevier, vol. 33(6), pages 1406-1412.
    27. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    28. Jacovides, C.P. & Tymvios, F.S. & Assimakopoulos, V.D. & Kaltsounides, N.A., 2006. "Comparative study of various correlations in estimating hourly diffuse fraction of global solar radiation," Renewable Energy, Elsevier, vol. 31(15), pages 2492-2504.
    29. Kevin Ummel, 2013. "Planning for Large-Scale Wind and Solar Power in South Africa: Identifying Cost-Effective Deployment Strategies Using Spatiotemporal Modeling-Working Paper 340," Working Papers 340, Center for Global Development.
    30. Olauson, Jon & Bergkvist, Mikael, 2015. "Modelling the Swedish wind power production using MERRA reanalysis data," Renewable Energy, Elsevier, vol. 76(C), pages 717-725.
    31. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
    32. Boland, John & Ridley, Barbara & Brown, Bruce, 2008. "Models of diffuse solar radiation," Renewable Energy, Elsevier, vol. 33(4), pages 575-584.
    33. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
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    2. Arjmand, Reza & Monroe, Jacob & McPherson, Madeleine, 2023. "The role of emerging technologies in Canada's electricity system transition," Energy, Elsevier, vol. 278(PA).
    3. Kena Likassa Nefabas & Lennart Söder & Mengesha Mamo & Jon Olauson, 2021. "Modeling of Ethiopian Wind Power Production Using ERA5 Reanalysis Data," Energies, MDPI, vol. 14(9), pages 1-17, April.
    4. McPherson, Madeleine & Ismail, Malik & Hoornweg, Daniel & Metcalfe, Murray, 2018. "Planning for variable renewable energy and electric vehicle integration under varying degrees of decentralization: A case study in Lusaka, Zambia," Energy, Elsevier, vol. 151(C), pages 332-346.
    5. McPherson, Madeleine & Tahseen, Samiha, 2018. "Deploying storage assets to facilitate variable renewable energy integration: The impacts of grid flexibility, renewable penetration, and market structure," Energy, Elsevier, vol. 145(C), pages 856-870.
    6. Lim, Kai Zhuo & Lim, Kang Hui & Wee, Xian Bin & Li, Yinan & Wang, Xiaonan, 2020. "Optimal allocation of energy storage and solar photovoltaic systems with residential demand scheduling," Applied Energy, Elsevier, vol. 269(C).
    7. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.

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