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Reducing forecasting error by optimally pooling wind energy generation sources through portfolio optimization

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  • Han, Chanok
  • Vinel, Alexander

Abstract

Generation variability is generally accepted as one of the key challenges in enabling wider penetration of renewable energy sources in general, and wind energy in particular. It is widely documented that it is often possible to reduce the severity of generation intermittency by pooling together generation from geographically (or technologically) diverse sources. This paper aims at evaluating the potential for a similar approach targeted at addressing the related issue of limited predictability of wind energy generation. Specifically, a portfolio optimization model for intelligently constructing a wind energy portfolio for a given harvesting region with the goal of reducing the prediction error is proposed. The mathematical model, based on Conditional Value-at-Risk (CVaR) optimization methodology, is used to evaluate potential improvement in (day ahead) generation predictability for a collection of locations in the USA. The study concludes that pooling indeed can significantly reduce wind energy generation forecasting error, with the effect largely dependent on the size of the harvesting region. Further, if advanced optimization techniques are used, it is possible to balance this reduction with average generation output. Consequently, the results imply that the positive effect of pooling diverse wind resources can be an important factor in planning for generation expansion projects.

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  • Han, Chanok & Vinel, Alexander, 2022. "Reducing forecasting error by optimally pooling wind energy generation sources through portfolio optimization," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023471
    DOI: 10.1016/j.energy.2021.122099
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    1. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren & Liu, Jizhen, 2017. "Measurement and statistical analysis of wind speed intermittency," Energy, Elsevier, vol. 118(C), pages 632-643.
    2. Handschy, Mark A. & Rose, Stephen & Apt, Jay, 2017. "Is it always windy somewhere? Occurrence of low-wind-power events over large areas," Renewable Energy, Elsevier, vol. 101(C), pages 1124-1130.
    3. Gersema, Gerke & Wozabal, David, 2018. "Risk-optimized pooling of intermittent renewable energy sources," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 217-230.
    4. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    5. Katzenstein, Warren & Fertig, Emily & Apt, Jay, 2010. "The variability of interconnected wind plants," Energy Policy, Elsevier, vol. 38(8), pages 4400-4410, August.
    6. Bianchi, Robert J. & Bornholt, Graham & Drew, Michael E. & Howard, Michael F., 2014. "Long-term U.S. infrastructure returns and portfolio selection," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 314-325.
    7. Shahriari, Mehdi & Blumsack, Seth, 2018. "The capacity value of optimal wind and solar portfolios," Energy, Elsevier, vol. 148(C), pages 992-1005.
    8. Mauleón, Ignacio & Hamoudi, Hamid, 2017. "Photovoltaic and wind cost decrease estimation: Implications for investment analysis," Energy, Elsevier, vol. 137(C), pages 1054-1065.
    9. Shahriari, Mehdi & Blumsack, Seth, 2017. "Scaling of wind energy variability over space and time," Applied Energy, Elsevier, vol. 195(C), pages 572-585.
    10. Tan, Zhongfu & Wang, Guan & Ju, Liwei & Tan, Qingkun & Yang, Wenhai, 2017. "Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand r," Energy, Elsevier, vol. 124(C), pages 198-213.
    11. Bett, Philip E. & Thornton, Hazel E., 2016. "The climatological relationships between wind and solar energy supply in Britain," Renewable Energy, Elsevier, vol. 87(P1), pages 96-110.
    12. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
    13. Conor Sweeney & Ricardo J. Bessa & Jethro Browell & Pierre Pinson, 2020. "The future of forecasting for renewable energy," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(2), March.
    14. Rombauts, Yannick & Delarue, Erik & D’haeseleer, William, 2011. "Optimal portfolio-theory-based allocation of wind power: Taking into account cross-border transmission-capacity constraints," Renewable Energy, Elsevier, vol. 36(9), pages 2374-2387.
    15. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    16. Huang, Junling & Lu, Xi & McElroy, Michael B., 2014. "Meteorologically defined limits to reduction in the variability of outputs from a coupled wind farm system in the Central US," Renewable Energy, Elsevier, vol. 62(C), pages 331-340.
    17. Boomsma, Trine Krogh & Meade, Nigel & Fleten, Stein-Erik, 2012. "Renewable energy investments under different support schemes: A real options approach," European Journal of Operational Research, Elsevier, vol. 220(1), pages 225-237.
    18. Moreno, Blanca & Díaz, Guzmán, 2019. "The impact of virtual power plant technology composition on wholesale electricity prices: A comparative study of some European Union electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 100-108.
    19. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    20. Vinel, Alexander & Mortaz, Ebrahim, 2019. "Optimal pooling of renewable energy sources with a risk-averse approach: Implications for US energy portfolio," Energy Policy, Elsevier, vol. 132(C), pages 928-939.
    21. Sahin, Cem & Shahidehpour, Mohammad & Erkmen, Ismet, 2012. "Generation risk assessment in volatile conditions with wind, hydro, and natural gas units," Applied Energy, Elsevier, vol. 96(C), pages 4-11.
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