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Enhancing the voltage stability of distribution network during PV ramping conditions with variable speed drive loads

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  • Maharjan, Salish
  • Sampath Kumar, Dhivya
  • Khambadkone, Ashwin M.

Abstract

The Photovoltaic (PV) sources have become the most popular renewable energy resources as it is modular and are deployed quickly. However, the distributed PVs are highly volatile and can have high ramping characteristics where the ramping can go up to 90% of its capacity in about 20 s due to events such as the passing of fast-moving clouds. This kind of PV ramps with high magnitudes can lead to voltage instability in distribution networks that are usually dominated by Induction motor (IM) loads. This paper explains the mechanism of such an instability analytically using Q-V analysis with distribution feeder and IM load characteristics. Highlighting the gradual replacement of IM loads by Variable Speed Drive (VSD) loads due to energy efficiency policies, the paper demonstrates the enhanced voltage stability under VSD loads by both time simulation and analytical approach. Furthermore, the analysis is extended to a realistic distribution network namely, United Kingdom General Distribution System (UKGDS) and extensive case studies are conducted to analyze the voltage stability during PV ramping events at various load compositions of IMs and VSDs. Moreover, the minimum VSD penetration level required to avert the voltage instability at various PV penetration levels have also been determined.

Suggested Citation

  • Maharjan, Salish & Sampath Kumar, Dhivya & Khambadkone, Ashwin M., 2020. "Enhancing the voltage stability of distribution network during PV ramping conditions with variable speed drive loads," Applied Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:appene:v:264:y:2020:i:c:s0306261920302452
    DOI: 10.1016/j.apenergy.2020.114733
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    References listed on IDEAS

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    1. Wang, Licheng & Yan, Ruifeng & Saha, Tapan Kumar, 2019. "Voltage regulation challenges with unbalanced PV integration in low voltage distribution systems and the corresponding solution," Applied Energy, Elsevier, vol. 256(C).
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    Cited by:

    1. Wu, Wei & Li, Peng & Fu, Xiaopeng & Yan, Jinyue & Wang, Chengshan, 2022. "Flexible Shifted-Frequency analysis for Multi-Timescale simulations of active distribution networks," Applied Energy, Elsevier, vol. 321(C).
    2. Jinhua Zhang & Liding Zhu & Shengchao Zhao & Jie Yan & Lingling Lv, 2023. "Optimal Configuration of Energy Storage Systems in High PV Penetrating Distribution Network," Energies, MDPI, vol. 16(5), pages 1-21, February.
    3. Tianhao Song & Xiaoqing Han & Baifu Zhang, 2021. "Multi-Time-Scale Optimal Scheduling in Active Distribution Network with Voltage Stability Constraints," Energies, MDPI, vol. 14(21), pages 1-20, November.
    4. Tsao, Yu-Chung & Beyene, Tsehaye Dedimas & Thanh, Vo-Van & Gebeyehu, Sisay Geremew & Kuo, Tsai-Chi, 2022. "Power distribution network design considering the distributed generations and differential and dynamic pricing," Energy, Elsevier, vol. 241(C).
    5. Abdullahi Oboh Muhammed & Muhyaddin Rawa, 2020. "A Systematic PVQV-Curves Approach for Investigating the Impact of Solar Photovoltaic-Generator in Power System Using PowerWorld Simulator," Energies, MDPI, vol. 13(10), pages 1-21, May.
    6. Polat, Onder & Gul, Omer, 2022. "Development of a probabilistic short-term voltage quality assessment method with the weak point detection capability through the dynamic analyses," Applied Energy, Elsevier, vol. 326(C).
    7. Wen, Haoran & Du, Yang & Chen, Xiaoyang & Lim, Eng Gee & Wen, Huiqing & Yan, Ke, 2023. "A regional solar forecasting approach using generative adversarial networks with solar irradiance maps," Renewable Energy, Elsevier, vol. 216(C).
    8. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    9. Chen, Xiaoyang & Du, Yang & Lim, Enggee & Fang, Lurui & Yan, Ke, 2022. "Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control," Renewable Energy, Elsevier, vol. 195(C), pages 147-166.

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