IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v89y2016icp606-615.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115305176
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2015.12.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Tom Baker, "undated". "Insurance and the Law," University of Connecticut School of Law Working Papers uconn_ucwps-1004, University of Connecticut School of Law.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu-Chung Tsao & Thuy-Linh Vu, 2023. "Electricity pricing, capacity, and predictive maintenance considering reliability," Annals of Operations Research, Springer, vol. 322(2), pages 991-1011, March.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leigh Johnson, 2013. "Index Insurance and the Articulation of Risk-Bearing Subjects," Environment and Planning A, , vol. 45(11), pages 2663-2681, November.
    2. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland," Energy Economics, Elsevier, vol. 58(C), pages 186-198.
    3. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    4. Marc A. Ragin & Benjamin L. Collier & Johannes G. Jaspersen, 2021. "The effect of information disclosure on demand for high‐load insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 161-193, March.
    5. Mohammad Rahmati & David A. Hyman & Bernard Black & Charles Silver, 2016. "Insurance Crisis or Liability Crisis? Medical Malpractice Claiming in Illinois, 1980–2010," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 13(2), pages 183-204, June.
    6. Di Cosmo, Valeria & Malaguzzi Valeri, Laura, 2018. "Wind, storage, interconnection and the cost of electricity generation," Energy Economics, Elsevier, vol. 69(C), pages 1-18.
    7. 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.
    8. Koehler, Jonathan J. & Gershoff, Andrew D., 2003. "Betrayal aversion: When agents of protection become agents of harm," Organizational Behavior and Human Decision Processes, Elsevier, vol. 90(2), pages 244-261, March.
    9. Aaron Doyle, 2011. "Introduction: Insurance and Business Ethics," Journal of Business Ethics, Springer, vol. 103(1), pages 1-5, April.
    10. Derrick W. H. Fung & Jason J. H. Yeh, 2018. "Inherent Virtue or Inevitable Evil: The Effects of Directors' and Officers' Insurance on Firm Value," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 21(2), pages 243-288, September.
    11. Christian Lahnstein, 2011. "Tort Law and the Ethical Responsibilities of Liability Insurers: Comments from a Reinsurer’s Perspective," Journal of Business Ethics, Springer, vol. 103(1), pages 87-94, April.
    12. Ronnie J. Phillips & David Nickerson, 2011. "Underwriting in Property-Casualty Insurance Markets: Regulation, Risk and Volatility," NFI Working Papers 2011-WP-19, Indiana State University, Scott College of Business, Networks Financial Institute.
    13. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    14. Yang, L. & Entchev, E., 2014. "Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique," Applied Energy, Elsevier, vol. 134(C), pages 197-203.
    15. Gillan, Stuart L. & Panasian, Christine A., 2014. "On Litigation Risk and Disclosure Complexity: Evidence from Canadian Firms Cross-Listed in the US," The International Journal of Accounting, Elsevier, vol. 49(4), pages 426-454.
    16. Boubakri, Narjess & Bouslimi, Lobna, 2016. "Directors’ and officers’ liability insurance and analyst forecast properties," Finance Research Letters, Elsevier, vol. 19(C), pages 22-32.
    17. 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.
    18. Georges Dionne & Casey Rothschild, 2014. "Economic Effects of Risk Classification Bans," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 39(2), pages 184-221, September.
    19. Slednev, Viktor & Bertsch, Valentin & Ruppert, Manuel & Fichtner, Wolf, 2017. "Highly resolved optimal renewable allocation planning in power systems under consideration of dynamic grid topology," MPRA Paper 79706, University Library of Munich, Germany.
    20. Lin, Chen & Officer, Micah S. & Zou, Hong, 2011. "Directors' and officers' liability insurance and acquisition outcomes," Journal of Financial Economics, Elsevier, vol. 102(3), pages 507-525.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:89:y:2016:i:c:p:606-615. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.