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Factors influencing calculation of capacity value of wind power: A case study of the Australian National Electricity Market (NEM)

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  • Nguyen, Christy
  • Ma, Chunbo
  • Hailu, Atakelty
  • Chalak, Morteza

Abstract

Calculation of wind power capacity values for risk assessment of power system adequacy has attracted great attention in the literature. And the most popular approach has been the Effective Load Carrying Capability (ELCC) method which allows for the consideration of key factors such as wind capacity factor, forced outage rates (F.O.R) of conventional power stations, system reliability targets, and the correlation between wind availability and system load. However, comparatively little attention has been paid to analysing the effects of other factors such as the number of wind farms and wind installed capacity, the length of historic time series data on demand and wind resources. This paper provides an in-depth analysis of how these factors influence the calculation of capacity value for the Australian National Electricity Market (NEM) power system using metered half-hourly wind and load data for 2006 to 2013. The analysis incorporates the periods with extreme risk events. Our results show that capacity values depend greatly on the design of the simulation model used, and highlight the importance of capturing wind and load data points relating to extremely high demand periods. We compare our NEM-wide estimates to recent estimates for the State of South Australia.

Suggested Citation

  • Nguyen, Christy & Ma, Chunbo & Hailu, Atakelty & Chalak, Morteza, 2016. "Factors influencing calculation of capacity value of wind power: A case study of the Australian National Electricity Market (NEM)," Renewable Energy, Elsevier, vol. 90(C), pages 319-328.
    • Handle: RePEc:eee:renene:v:90:y:2016:i:c:p:319-328
    DOI: 10.1016/j.renene.2016.01.007
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    References listed on IDEAS

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    1. Voorspools, Kris R. & D'haeseleer, William D., 2006. "An analytical formula for the capacity credit of wind power," Renewable Energy, Elsevier, vol. 31(1), pages 45-54.
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    Cited by:

    1. Zhou, Ella & Cole, Wesley & Frew, Bethany, 2018. "Valuing variable renewable energy for peak demand requirements," Energy, Elsevier, vol. 165(PA), pages 499-511.
    2. Paik, Chunhyun & Chung, Yongjoo & Kim, Young Jin, 2021. "ELCC-based capacity credit estimation accounting for uncertainties in capacity factors and its application to solar power in Korea," Renewable Energy, Elsevier, vol. 164(C), pages 833-841.
    3. Xu, Xiaomin & Niu, Dongxiao & Xiao, Bowen & Guo, Xiaodan & Zhang, Lihui & Wang, Keke, 2020. "Policy analysis for grid parity of wind power generation in China," Energy Policy, Elsevier, vol. 138(C).
    4. Bamooeifard, Alireza, 2020. "Future studies in Iran development plans for wind power, a system dynamics modeling approach," Renewable Energy, Elsevier, vol. 162(C), pages 1054-1064.
    5. Bromley-Dulfano, Isaac & Florez, Julian & Craig, Michael T., 2021. "Reliability benefits of wide-area renewable energy planning across the Western United States," Renewable Energy, Elsevier, vol. 179(C), pages 1487-1499.
    6. Samal, Rajat Kanti & Tripathy, M., 2019. "A novel distance metric for evaluating impact of wind integration on power systems," Renewable Energy, Elsevier, vol. 140(C), pages 722-736.
    7. Mastropietro, Paolo & Rodilla, Pablo & Batlle, Carlos, 2019. "De-rating of wind and solar resources in capacity mechanisms: A review of international experiences," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 253-262.

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