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The uncertainties involved in measuring national solar photovoltaic electricity generation

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  • Huxley, O.T.
  • Taylor, J.
  • Everard, A.
  • Briggs, J.
  • Tilley, K.
  • Harwood, J.
  • Buckley, A.

Abstract

The measurement of near real-time national solar PV electricity output is an increasingly important component for managing electricity systems. PV systems embedded in the distribution network are problematic for system operators since their output is not directly visible through existing transmission system metering. Typically, national solar PV output is measured indirectly by upscaling the output from a sample of reference PV systems using an estimate of the national capacity. National PV output is then used, along with similar measurements for other embedded technologies such as wind, to train and validate electricity forecasts which ensure efficient electricity market operation. For the first time and using Great Britain as a case study, we investigate the accuracy of this general approach by characterising different sources of uncertainty in national PV output measurements. We find that the capacity error, at ±5%, dominates the yield calculation error, at < ±1% and leads to an overall error in GB solar PV output estimates of ±5.1%. We conclude that solar PV measurements, and consequently national electricity demand forecasts, are currently limited by the state of national PV capacity registers. To make PV output measurements more accurate nations must develop a coherent and comprehensive approach to PV system registration, and output measurement methods must move away from using static solar PV capacity registers towards estimating an operational grid-connected solar PV capacity. This could involve moving away from capacity registers altogether and instead estimating capacity from other sources, for example, from network power flows, satellite imagery, or more likely, a combination of complementary datasets.

Suggested Citation

  • Huxley, O.T. & Taylor, J. & Everard, A. & Briggs, J. & Tilley, K. & Harwood, J. & Buckley, A., 2022. "The uncertainties involved in measuring national solar photovoltaic electricity generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    • Handle: RePEc:eee:rensus:v:156:y:2022:i:c:s1364032121012636
    DOI: 10.1016/j.rser.2021.112000
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    1. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Pizarro-Alonso, Amalia & Ravn, Hans & Münster, Marie, 2019. "Uncertainties towards a fossil-free system with high integration of wind energy in long-term planning," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    4. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    5. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.
    6. Schepel, Veikko & Tozzi, Arianna & Klement, Marianne & Ziar, Hesan & Isabella, Olindo & Zeman, Miro, 2020. "The Dutch PV portal 2.0: An online photovoltaic performance modeling environment for the Netherlands," Renewable Energy, Elsevier, vol. 154(C), pages 175-186.
    7. Notton, Gilles & Paoli, Christophe & Ivanova, Liliana & Vasileva, Siyana & Nivet, Marie Laure, 2013. "Neural network approach to estimate 10-min solar global irradiation values on tilted planes," Renewable Energy, Elsevier, vol. 50(C), pages 576-584.
    8. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    9. 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.
    10. Richard Green & Iain Staffell, 2016. "Electricity in Europe: exiting fossil fuels?," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 32(2), pages 282-303.
    11. Drew, Daniel R. & Coker, Phil J. & Bloomfield, Hannah C. & Brayshaw, David J. & Barlow, Janet F. & Richards, Andrew, 2019. "Sunny windy sundays," Renewable Energy, Elsevier, vol. 138(C), pages 870-875.
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