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Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems

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  • Chen, Jun
  • Rabiti, Cristian

Abstract

Hybrid energy systems consisting of multiple energy inputs and multiple energy outputs have been proposed to be an effective element to enable ever increasing penetration of clean energy. In order to better understand the dynamic and probabilistic behavior of hybrid energy systems, this paper proposes a model combining Fourier series and autoregressive moving average (ARMA) to characterize historical weather measurements and to generate synthetic weather (e.g., wind speed) data. In particular, Fourier series is used to characterize the seasonal trend in historical data, while ARMA is applied to capture the autocorrelation in residue time series (e.g., measurements with seasonal trends subtracted). The generated synthetic wind speed data is then utilized to perform probabilistic analysis of a particular hybrid energy system configuration, which consists of nuclear power plant, wind farm, battery storage, natural gas boiler, and chemical plant. Requirements on component ramping rate, economic and environmental impacts of hybrid energy systems, and the effects of deploying different sizes of batteries in smoothing renewable variability, are all investigated.

Suggested Citation

  • Chen, Jun & Rabiti, Cristian, 2017. "Synthetic wind speed scenarios generation for probabilistic analysis of hybrid energy systems," Energy, Elsevier, vol. 120(C), pages 507-517.
    • Handle: RePEc:eee:energy:v:120:y:2017:i:c:p:507-517
    DOI: 10.1016/j.energy.2016.11.103
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    6. Hui Hwang Goh & Gumeng Peng & Dongdong Zhang & Wei Dai & Tonni Agustiono Kurniawan & Kai Chen Goh & Chin Leei Cham, 2022. "A New Wind Speed Scenario Generation Method Based on Principal Component and R-Vine Copula Theories," Energies, MDPI, vol. 15(7), pages 1-21, April.
    7. Diego Francisco Larios & Enrique Personal & Antonio Parejo & Sebastián García & Antonio García & Carlos Leon, 2020. "Operational Simulation Environment for SCADA Integration of Renewable Resources," Energies, MDPI, vol. 13(6), pages 1-37, March.
    8. Wang, Zheng-Xin & Li, Qin & Pei, Ling-Ling, 2018. "A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors," Energy, Elsevier, vol. 154(C), pages 522-534.
    9. Jia Zhou & Hany Abdel-Khalik & Paul Talbot & Cristian Rabiti, 2021. "A Hybrid Energy System Workflow for Energy Portfolio Optimization," Energies, MDPI, vol. 14(15), pages 1-28, July.
    10. Marisol Garrouste & Michael T. Craig & Daniel Wendt & Maria Herrera Diaz & William Jenson & Qian Zhang & Brendan Kochunas, 2023. "Techno-Economic Analysis of Synthetic Fuel Production from Existing Nuclear Power Plants across the United States," Papers 2309.12085, arXiv.org.
    11. Karakoyun, Ece Cigdem & Avci, Harun & Kocaman, Ayse Selin & Nadar, Emre, 2023. "Deviations from commitments: Markov decision process formulations for the role of energy storage," International Journal of Production Economics, Elsevier, vol. 255(C).
    12. Baker, T.E. & Epiney, A.S. & Rabiti, C. & Shittu, E., 2018. "Optimal sizing of flexible nuclear hybrid energy system components considering wind volatility," Applied Energy, Elsevier, vol. 212(C), pages 498-508.
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