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Stochastic Optimization-Based hosting capacity estimation with volatile net load deviation in distribution grids

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  • Cho, Yongjun
  • Lee, Eunjung
  • Baek, Keon
  • Kim, Jinho

Abstract

With the increasing penetration rates of variable renewable energies (VREs), estimating the maximum network capacity without adversely impacting the reliability or voltage quality for power system operation, that is, the hosting capacity (HC), is a significant issue. For system operators, it is challenging to secure flexible resources that can respond to the volatility of the net load resulting from the intermittent generation characteristics of VREs and the appearance of various consumers. Thus, this study proposed a hosting capacity estimation framework that considers the net load deviation. It thereby overcomes the abrupt net load deviation for the economic accommodation of VREs. To evaluate proposed framework, the qualification of the proposed net load deviation limit as a new performance index and investigation for improving HC was verified via performance violation analysis. The proposed net load filter exhibited excellent performance in capturing the net load deviation without distorting the conventional performance index. To consider the net load deviation magnitude and intensity effect on system operation, a multi-time stochastic optimization model was formulated. The proposed framework was tested on an IEEE 33-radial bus system to investigate the effects of the net load deviation limit on the HC, and its potential as a performance index was analyzed. Finally, as an application of the proposed model to help the system operator’s precise decision making, the VRE accommodation costs was quantitatively suggested.

Suggested Citation

  • Cho, Yongjun & Lee, Eunjung & Baek, Keon & Kim, Jinho, 2023. "Stochastic Optimization-Based hosting capacity estimation with volatile net load deviation in distribution grids," Applied Energy, Elsevier, vol. 341(C).
    • Handle: RePEc:eee:appene:v:341:y:2023:i:c:s0306261923004397
    DOI: 10.1016/j.apenergy.2023.121075
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    References listed on IDEAS

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    1. Islam, Md. Zahidul & Lin, Yuzhang & Vokkarane, Vinod M. & Yu, Nanpeng, 2023. "Robust learning-based real-time load estimation using sparsely deployed smart meters with high reporting rates," Applied Energy, Elsevier, vol. 352(C).

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