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Insurance strategy for mitigating power system operational risk introduced by wind power forecasting uncertainty

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  • Yang, Hongming
  • Qiu, Jing
  • Meng, Ke
  • Zhao, Jun Hua
  • Dong, Zhao Yang
  • Lai, Mingyong

Abstract

The increasing penetration of wind power significantly affects the reliability of power systems due to its intrinsic intermittency. Wind generators participating in electricity markets will encounter operational risk (i.e. imbalance cost) under current trading mechanism. The imbalance cost arises from the service for mitigating supply-demand imbalance caused by inaccurate wind forecasts. In this paper, an insurance strategy is proposed to cover the possible imbalance cost that wind power producers may incur. First of all, a novel method based on Monte Carlo simulations is proposed to estimate insurance premiums. The impacts of insurance excesses on premiums are analyzed as well. Energy storage system (ESS) is then discussed as an alternative approach to balancing small wind power forecasting errors, whose loss claims would be blocked by insurance excesses. Finally, the ESS and insurance policy are combined together to mitigate the imbalance risks of trading wind power in real-time markets. With the proposed approach, the most economic power capacity of ESS can be determined under different excess scenarios. Case studies prove that the proposed ESS plus insurance strategy is a promising risk aversion approach for trading wind power in real-time electricity markets.

Suggested Citation

  • Yang, Hongming & Qiu, Jing & Meng, Ke & Zhao, Jun Hua & Dong, Zhao Yang & Lai, Mingyong, 2016. "Insurance strategy for mitigating power system operational risk introduced by wind power forecasting uncertainty," Renewable Energy, Elsevier, vol. 89(C), pages 606-615.
    • Handle: RePEc:eee:renene:v:89:y:2016:i:c:p:606-615
    DOI: 10.1016/j.renene.2015.12.007
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    References listed on IDEAS

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    1. Swinand, Gregory P. & O'Mahoney, Amy, 2015. "Estimating the impact of wind generation and wind forecast errors on energy prices and costs in Ireland," Renewable Energy, Elsevier, vol. 75(C), pages 468-473.
    2. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Error analysis of short term wind power prediction models," Applied Energy, Elsevier, vol. 88(4), pages 1298-1311, April.
    3. Tom Baker, "undated". "Insurance and the Law," University of Connecticut School of Law Working Papers uconn_ucwps-1004, University of Connecticut School of Law.
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    Cited by:

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    2. Yan, Xingyu & Abbes, Dhaker & Francois, Bruno, 2017. "Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators," Renewable Energy, Elsevier, vol. 106(C), pages 288-297.
    3. Lahouar, A. & Ben Hadj Slama, J., 2017. "Hour-ahead wind power forecast based on random forests," Renewable Energy, Elsevier, vol. 109(C), pages 529-541.
    4. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & Xiong, Yi & Huang, Heming, 2017. "Model of selecting prediction window in ramps forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 98-107.

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