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A statistical cognitive model to assess impact of spatially correlated wind production on market behaviors

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  • Rahimiyan, Morteza

Abstract

Over the last decade, the share of wind power production has rapidly increased. Thus, the market clearing outcomes have been exposed by inherent power production uncertainty of wind farms. This uncertainty may inevitably affect the behaviors of strategic producers in exercising their market power. In this paper, a statistical cognitive model is proposed to simulate the strategic behaviors in presence of the wind power uncertainty. To this end, an approach based on Copula theory is used to characterize wind power uncertainty considering spatial correlation among diverse wind farms. Moreover, the proposed cognitive model allows strategic producers to learn how to exercise their market power.

Suggested Citation

  • Rahimiyan, Morteza, 2014. "A statistical cognitive model to assess impact of spatially correlated wind production on market behaviors," Applied Energy, Elsevier, vol. 122(C), pages 62-72.
    • Handle: RePEc:eee:appene:v:122:y:2014:i:c:p:62-72
    DOI: 10.1016/j.apenergy.2014.02.004
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Arjmand, Reza & Rahimiyan, Morteza, 2016. "Statistical analysis of a competitive day-ahead market coupled with correlated wind production and electric load," Applied Energy, Elsevier, vol. 161(C), pages 153-167.
    2. Fang, Xin & Hodge, Bri-Mathias & Du, Ershun & Zhang, Ning & Li, Fangxing, 2018. "Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach," Applied Energy, Elsevier, vol. 230(C), pages 531-539.
    3. Arjmand, Reza & Rahimiyan, Morteza, 2016. "Impact of spatio-temporal correlation of wind production on clearing outcomes of a competitive pool market," Renewable Energy, Elsevier, vol. 86(C), pages 216-227.
    4. González-Aparicio, I. & Zucker, A., 2015. "Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain," Applied Energy, Elsevier, vol. 159(C), pages 334-349.
    5. Exizidis, Lazaros & Kazempour, S. Jalal & Pinson, Pierre & de Greve, Zacharie & Vallée, François, 2016. "Sharing wind power forecasts in electricity markets: A numerical analysis," Applied Energy, Elsevier, vol. 176(C), pages 65-73.
    6. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Liu, Zhou & Liu, Wen & Chen, Zhe & Blaabjerg, Frede, 2020. "Scheduling of wind-battery hybrid system in the electricity market using distributionally robust optimization," Renewable Energy, Elsevier, vol. 156(C), pages 47-56.
    7. Talari, Saber & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "Stochastic modelling of renewable energy sources from operators' point-of-view: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1953-1965.
    8. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    9. Chang-Gi Min & Jong Keun Park & Don Hur & Mun-Kyeom Kim, 2015. "The Economic Viability of Renewable Portfolio Standard Support for Offshore Wind Farm Projects in Korea," Energies, MDPI, vol. 8(9), pages 1-20, September.
    10. Fang, Xin & Hodge, Bri-Mathias & Jiang, Huaiguang & Zhang, Yingchen, 2019. "Decentralized wind uncertainty management: Alternating direction method of multipliers based distributionally-robust chance constrained optimal power flow," Applied Energy, Elsevier, vol. 239(C), pages 938-947.

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