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Stochastic modeling to represent wind power generation and demand in electric power system based on real data

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  • Verdejo, Humberto
  • Awerkin, Almendra
  • Saavedra, Eugenio
  • Kliemann, Wolfgang
  • Vargas, Luis

Abstract

A methodology to model two types of random perturbation that affect the operation of electric power systems (EPS) are presented. The first uncertainty is wind power generation and is represented by a one-dimensional and by a multidimensional continuous stochastic process. The second one is power demand, and is modeled by using an hybrid structure based on harmonic regression and the Ornstein–Uhlenbeck (O–U) process. The stochastic models are applied to a real Chilean case, using real data for parametric estimation and validation models.

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  • Verdejo, Humberto & Awerkin, Almendra & Saavedra, Eugenio & Kliemann, Wolfgang & Vargas, Luis, 2016. "Stochastic modeling to represent wind power generation and demand in electric power system based on real data," Applied Energy, Elsevier, vol. 173(C), pages 283-295.
    • Handle: RePEc:eee:appene:v:173:y:2016:i:c:p:283-295
    DOI: 10.1016/j.apenergy.2016.04.004
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    6. Loukatou, Angeliki & Howell, Sydney & Johnson, Paul & Duck, Peter, 2018. "Stochastic wind speed modelling for estimation of expected wind power output," Applied Energy, Elsevier, vol. 228(C), pages 1328-1340.
    7. Jie Zhu & Buxiang Zhou & Yiwei Qiu & Tianlei Zang & Yi Zhou & Shi Chen & Ningyi Dai & Huan Luo, 2023. "Survey on Modeling of Temporally and Spatially Interdependent Uncertainties in Renewable Power Systems," Energies, MDPI, vol. 16(16), pages 1-19, August.
    8. Chen, F. & Huang, G.H. & Fan, Y.R. & Chen, J.P., 2017. "A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning," Applied Energy, Elsevier, vol. 187(C), pages 291-309.
    9. Tao, Laifa & Ma, Jian & Cheng, Yujie & Noktehdan, Azadeh & Chong, Jin & Lu, Chen, 2017. "A review of stochastic battery models and health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 716-732.
    10. Loukatou, Angeliki & Johnson, Paul & Howell, Sydney & Duck, Peter, 2021. "Optimal valuation of wind energy projects co-located with battery storage," Applied Energy, Elsevier, vol. 283(C).
    11. Busiswe Skosana & Mukwanga W. Siti & Nsilulu T. Mbungu & Sonu Kumar & Willy Mulumba, 2023. "An Evaluation of Potential Strategies in Renewable Energy Systems and Their Importance for South Africa—A Review," Energies, MDPI, vol. 16(22), pages 1-27, November.
    12. Verdejo, Humberto & Awerkin, Almendra & Becker, Cristhian & Olguin, Gabriel, 2017. "Statistic linear parametric techniques for residential electric energy demand forecasting. A review and an implementation to Chile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 512-521.
    13. Usman Zafar & Neil Kellard & Dmitri Vinogradov, 2022. "Multistage optimization filter for trend‐based short‐term forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 345-360, March.
    14. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    15. Jin, S.W. & Li, Y.P. & Nie, S. & Sun, J., 2017. "The potential role of carbon capture and storage technology in sustainable electric-power systems under multiple uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 467-480.
    16. Jónsdóttir, Guðrún Margrét & Milano, Federico, 2019. "Data-based continuous wind speed models with arbitrary probability distribution and autocorrelation," Renewable Energy, Elsevier, vol. 143(C), pages 368-376.

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