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Reliability assessment of a wind power delivery system

Author

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  • R Karki
  • J Patel

Abstract

Growing environmental concerns have led to renewable energy policies, such as the renewable portfolio standard, that have set ambitious targets for wind power penetration in electric power systems throughout the world. In many cases, the geographical locations with good wind resources are not close to the main load centres. Wind is a highly variable energy source and, therefore, transmission system planning for wind delivery is very different from conventional transmission planning. It becomes extremely important to assess the availability of wind power against the cost of transmission facility connecting to the power grid. This paper presents an analytical method to determine appropriate transmission line sizing based on its contribution to the overall system risk. The studies can also be used to evaluate the reliability contribution of the combined wind generation and transmission systems. The effects of generation, transmission, and load parameters on risk-based transmission line sizing are presented using a test system. The methodology and results presented in this paper should be useful in wind site selection and transmission system planning for delivering wind power.

Suggested Citation

  • R Karki & J Patel, 2009. "Reliability assessment of a wind power delivery system," Journal of Risk and Reliability, , vol. 223(1), pages 51-58, March.
    • Handle: RePEc:sae:risrel:v:223:y:2009:i:1:p:51-58
    DOI: 10.1243/1748006XJRR218
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    References listed on IDEAS

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    1. Berry, Trent & Jaccard, Mark, 2001. "The renewable portfolio standard:: design considerations and an implementation survey," Energy Policy, Elsevier, vol. 29(4), pages 263-277, March.
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    Cited by:

    1. M’hammed Sahnoun & David Baudry & Navonil Mustafee & Anne Louis & Philip Andi Smart & Phil Godsiff & Belahcene Mazari, 2019. "Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2981-2997, December.

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