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Managing solar-PV variability with geographical dispersion: An Ontario (Canada) case-study

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  • Rowlands, Ian H.
  • Kemery, Briana Paige
  • Beausoleil-Morrison, Ian

Abstract

The purpose of this article is to determine whether the geographic dispersion of solar-photovoltaic panels reduces variability in energy production. Following this, three questions are posed: 1) If geographic dispersion reduces variability, how dispersed should the panels be?; 2) What happens during peak price periods?; and 3) How are these insights affected by consideration of system-wide demand? Using measured and modelled weather data on an hourly basis from 16 locations across Ontario (Canada), hourly energy production figures for 1000 kW of solar-photovoltaic panels are generated for 2003, 2004, and 2005. Geographical dispersion of panels across multiple locations (as compared to the deployment of all panels in one location, namely, Toronto, Ontario) leads to, in particular instances, energy production profiles that have lower variability, greater total energy production, and a higher correlation value with the Ontario-wide system. Further research is needed both to isolate particularly-advantageous combinations and to broaden the investigation to consider alternative performance metrics, additional analytical techniques and land-use implications.

Suggested Citation

  • Rowlands, Ian H. & Kemery, Briana Paige & Beausoleil-Morrison, Ian, 2014. "Managing solar-PV variability with geographical dispersion: An Ontario (Canada) case-study," Renewable Energy, Elsevier, vol. 68(C), pages 171-180.
    • Handle: RePEc:eee:renene:v:68:y:2014:i:c:p:171-180
    DOI: 10.1016/j.renene.2014.01.034
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    References listed on IDEAS

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    Cited by:

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    2. Rosenbloom, Daniel & Meadowcroft, James, 2014. "Harnessing the Sun: Reviewing the potential of solar photovoltaics in Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 488-496.
    3. Richardson, David B. & Harvey, L.D.D., 2015. "Strategies for correlating solar PV array production with electricity demand," Renewable Energy, Elsevier, vol. 76(C), pages 432-440.
    4. Caldas, M. & Alonso-Suárez, R., 2019. "Very short-term solar irradiance forecast using all-sky imaging and real-time irradiance measurements," Renewable Energy, Elsevier, vol. 143(C), pages 1643-1658.
    5. Shivashankar, S. & Mekhilef, Saad & Mokhlis, Hazlie & Karimi, M., 2016. "Mitigating methods of power fluctuation of photovoltaic (PV) sources – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1170-1184.
    6. Jiang, Hou & Lu, Ning & Yao, Ling & Qin, Jun & Liu, Tang, 2023. "Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis," Renewable Energy, Elsevier, vol. 208(C), pages 726-736.
    7. Tang, Yuchen & Cheng, John W.M. & Duan, Qinwei & Lee, Cheuk Wing & Zhong, Jin, 2019. "Evaluating the variability of photovoltaics: A new stochastic method to generate site-specific synthetic solar data and applications to system studies," Renewable Energy, Elsevier, vol. 133(C), pages 1099-1107.
    8. Keeratimahat, Kanyawee & Bruce, Anna & MacGill, Iain, 2021. "Analysis of short-term operational forecast deviations and controllability of utility-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 167(C), pages 343-358.

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