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Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators

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  • Xydas, Erotokritos
  • Qadrdan, Meysam
  • Marmaras, Charalampos
  • Cipcigan, Liana
  • Jenkins, Nick
  • Ameli, Hossein

Abstract

Accurate information regarding the uncertainty of short-term forecast for aggregate wind power is a key to efficient and cost effective integration of wind farms into power systems. This paper presents a methodology for producing wind power forecast scenarios. Using historical wind power time series data and the Kernel Density Estimator (KDE), probabilistic wind power forecast scenarios were generated according to a rolling process. The improvement achieved in the accuracy of forecasts through frequent updating of the forecasts taking into account the latest realized wind power was quantified. The forecast scenarios produced by the proposed method were used as inputs to a unit commitment and optimal dispatch model in order to investigate how the uncertainty in wind forecast affect the operation of power system and in particular gas-fired generators.

Suggested Citation

  • Xydas, Erotokritos & Qadrdan, Meysam & Marmaras, Charalampos & Cipcigan, Liana & Jenkins, Nick & Ameli, Hossein, 2017. "Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators," Applied Energy, Elsevier, vol. 192(C), pages 382-394.
    • Handle: RePEc:eee:appene:v:192:y:2017:i:c:p:382-394
    DOI: 10.1016/j.apenergy.2016.10.019
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    1. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    2. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.
    3. Song, Zhe & Jiang, Yu & Zhang, Zijun, 2014. "Short-term wind speed forecasting with Markov-switching model," Applied Energy, Elsevier, vol. 130(C), pages 103-112.
    4. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    5. Frieder Borggrefe & Karsten Neuhoff, 2011. "Balancing and Intraday Market Design: Options for Wind Integration," Discussion Papers of DIW Berlin 1162, DIW Berlin, German Institute for Economic Research.
    6. Jooyoung Jeon & James W. Taylor, 2012. "Using Conditional Kernel Density Estimation for Wind Power Density Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 66-79, March.
    7. Flores, P. & Tapia, A. & Tapia, G., 2005. "Application of a control algorithm for wind speed prediction and active power generation," Renewable Energy, Elsevier, vol. 30(4), pages 523-536.
    8. Wang, J. & Botterud, A. & Bessa, R. & Keko, H. & Carvalho, L. & Issicaba, D. & Sumaili, J. & Miranda, V., 2011. "Wind power forecasting uncertainty and unit commitment," Applied Energy, Elsevier, vol. 88(11), pages 4014-4023.
    9. Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
    10. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
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    3. Chinmoy, Lakshmi & Iniyan, S. & Goic, Ranko, 2019. "Modeling wind power investments, policies and social benefits for deregulated electricity market – A review," Applied Energy, Elsevier, vol. 242(C), pages 364-377.
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    7. Hugo Tavares Vieira Gouveia & Ronaldo Ribeiro Barbosa De Aquino & Aida Araújo Ferreira, 2018. "Enhancing Short-Term Wind Power Forecasting through Multiresolution Analysis and Echo State Networks," Energies, MDPI, vol. 11(4), pages 1-19, April.
    8. Jiang, Ping & Yang, Hufang & Heng, Jiani, 2019. "A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting," Applied Energy, Elsevier, vol. 235(C), pages 786-801.
    9. Xu, Jian & Chen, Yuanfeng & Liao, Siyang & Sun, Yuanzhang & Yao, Liangzhong & Fu, Haobo & Jiang, Xueyi & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao, 2019. "Demand side industrial load control for local utilization of wind power in isolated grids," Applied Energy, Elsevier, vol. 243(C), pages 47-56.
    10. 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.
    11. Yu, Ruiguo & Liu, Zhiqiang & Li, Xuewei & Lu, Wenhuan & Ma, Degang & Yu, Mei & Wang, Jianrong & Li, Bin, 2019. "Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space," Applied Energy, Elsevier, vol. 238(C), pages 249-257.
    12. Kim, Deockho & Hur, Jin, 2018. "Short-term probabilistic forecasting of wind energy resources using the enhanced ensemble method," Energy, Elsevier, vol. 157(C), pages 211-226.
    13. 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).
    14. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
    15. Ruhang Xu & Zhilin Liu & Zhuangzhuang Yu, 2019. "Exploring the Profitability and Efficiency of Variable Renewable Energy in Spot Electricity Market: Uncovering the Locational Price Disadvantages," Energies, MDPI, vol. 12(14), pages 1-30, July.
    16. Ameli, Hossein & Qadrdan, Meysam & Strbac, Goran, 2017. "Value of gas network infrastructure flexibility in supporting cost effective operation of power systems," Applied Energy, Elsevier, vol. 202(C), pages 571-580.
    17. Eryilmaz, Serkan & Devrim, Yilser, 2019. "Theoretical derivation of wind plant power distribution with the consideration of wind turbine reliability," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 192-197.
    18. Sani Hassan, Abubakar & Cipcigan, Liana & Jenkins, Nick, 2017. "Optimal battery storage operation for PV systems with tariff incentives," Applied Energy, Elsevier, vol. 203(C), pages 422-441.
    19. Wang, Yun & Hu, Qinghua & Meng, Deyu & Zhu, Pengfei, 2017. "Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model," Applied Energy, Elsevier, vol. 208(C), pages 1097-1112.
    20. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Yang, Zhaoming & Zhang, Jinjun, 2021. "Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model," Renewable Energy, Elsevier, vol. 174(C), pages 952-970.
    21. Peng, Xu & Tao, Xiaoma, 2018. "Cooperative game of electricity retailers in China's spot electricity market," Energy, Elsevier, vol. 145(C), pages 152-170.

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