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Investigation of spatial correlation on optimal power flow with high penetration of wind power: A comparative study

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  • Quan, Hao
  • Lv, Junjie
  • Guo, Jian
  • Zhang, Wenjie

Abstract

Spatial correlation is a critical characteristic of adjacent wind farms, but its importance is always ignored in wind power modeling, further resulting in modeling bias. In order to investigate the necessity of involving spatial correlation and validate whether it has effects on power system, this paper proposes a method to construct the spatial correlation model and applies it into an IEEE test system. Specifically, a 7-dimensional t-copula method is used to capture the spatial dependence by forming a multi-variable distribution function, from which different scenarios are generated. Subsequently, a k-means clustering method is used for scenario reduction. As for the case study, a modified IEEE 39-bus test system with 7 wind farms deriving from the AEMO dataset is utilized to conduct optimal power flow under the circumstance with and without considering spatial dependence. Based on this, the cost of generation, including traditional fuel cost and levelized cost of wind power is obtained. Through comparing the cost results and analyzing the gap, it can be concluded that the spatial correlation has significant effects that when levelized cost of wind power exceeds a particular value, the generation cost can be greatly underestimated if ignoring spatial correlation with a high penetration of wind power.

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  • Quan, Hao & Lv, Junjie & Guo, Jian & Zhang, Wenjie, 2022. "Investigation of spatial correlation on optimal power flow with high penetration of wind power: A comparative study," Applied Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:appene:v:316:y:2022:i:c:s0306261922004366
    DOI: 10.1016/j.apenergy.2022.119034
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    References listed on IDEAS

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    Cited by:

    1. Jiao, P.H. & Chen, J.J. & Cai, X. & Zhao, Y.L., 2024. "Fuzzy semi-entropy based downside risk to low-carbon oriented multi-energy dispatch considering multiple dependent uncertainties," Energy, Elsevier, vol. 287(C).

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