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Achieving high renewable energy penetration in Western Australia using data digitisation and machine learning

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  • Tayal, Dev

Abstract

The energy industry is undergoing significant disruption. This research outlines that, whilst challenging, this disruption is also an emerging opportunity for electricity utilities.

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  • Tayal, Dev, 2017. "Achieving high renewable energy penetration in Western Australia using data digitisation and machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1537-1543.
    • Handle: RePEc:eee:rensus:v:80:y:2017:i:c:p:1537-1543
    DOI: 10.1016/j.rser.2017.07.040
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    References listed on IDEAS

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    1. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    2. Rodrigues, E.M.G. & Godina, R. & Santos, S.F. & Bizuayehu, A.W. & Contreras, J. & Catalão, J.P.S., 2014. "Energy storage systems supporting increased penetration of renewables in islanded systems," Energy, Elsevier, vol. 75(C), pages 265-280.
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    5. Denholm, Paul & Hand, Maureen, 2011. "Grid flexibility and storage required to achieve very high penetration of variable renewable electricity," Energy Policy, Elsevier, vol. 39(3), pages 1817-1830, March.
    6. Elliston, Ben & Riesz, Jenny & MacGill, Iain, 2016. "What cost for more renewables? The incremental cost of renewable generation – An Australian National Electricity Market case study," Renewable Energy, Elsevier, vol. 95(C), pages 127-139.
    7. Hedegaard, Karsten & Mathiesen, Brian Vad & Lund, Henrik & Heiselberg, Per, 2012. "Wind power integration using individual heat pumps – Analysis of different heat storage options," Energy, Elsevier, vol. 47(1), pages 284-293.
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    Citations

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    Cited by:

    1. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    2. Mathieu Pichault & Claire Vincent & Grant Skidmore & Jason Monty, 2021. "Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR," Energies, MDPI, vol. 14(9), pages 1-21, May.
    3. Wilkinson, Sam & Maticka, Martin J. & Liu, Yue & John, Michele, 2021. "The duck curve in a drying pond: The impact of rooftop PV on the Western Australian electricity market transition," Utilities Policy, Elsevier, vol. 71(C).
    4. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    5. Sam Wilkinson & Michele John & Gregory M. Morrison, 2021. "Rooftop PV and the Renewable Energy Transition; a Review of Driving Forces and Analytical Frameworks," Sustainability, MDPI, vol. 13(10), pages 1-25, May.

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