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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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    3. Park, Jungyeon & Alvarenga, Estêvão & Jeon, Jooyoung & Li, Ran & Petropoulos, Fotios & Kim, Hokyun & Ahn, Kwangwon, 2024. "Probabilistic forecast-based portfolio optimization of electricity demand at low aggregation levels," Applied Energy, Elsevier, vol. 353(PB).
    4. Gyeongmin Kim & Jin Hur, 2021. "A Short-Term Power Output Forecasting Based on Augmented Naïve Bayes Classifiers for High Wind Power Penetrations," Sustainability, MDPI, vol. 13(22), pages 1-12, November.

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