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Dispatchable region for distributed renewable energy generation in reconfigurable AC–DC distribution networks with soft open points

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  • Li, Shuhan
  • Li, Zhigang
  • Shahidehpour, Mohammad
  • Huang, Wenjing
  • Zheng, J.H.

Abstract

The dispatchable region (DR) describes the ability of a power system to adapt to the variability of distributed renewable energy (DRE) generation. Switchable devices such as soft open points (SOPs) and tie switches enable the AC–DC distribution network to flexibly cope with DRE power fluctuations. Currently, research on DRs seldom considers the effects of SOPs and reconfiguration with tie switches in AC–DC distribution networks, which prevents accurate evaluation of DR for this type of network. To bridge this gap, this paper formulates DRs for DRE generation in an AC–DC distribution network considering SOPs and reconfiguration with tie switches. The original model for deriving DRs is typically nonconvex, and the DR boundaries are intractable because of nonlinear power flow equations and binary variables of tie switches. Accordingly, this paper employs the outer polyhedral approximation and convex hull relaxation to approximate the original DR. An efficient adaptive constraint generation algorithm is developed to search for boundaries of the approximated DR. The numerical results show that the proposed model can effectively adapt to the DR of reconfigurable AC–DC distribution networks with SOPs and verify the accuracy and computational efficiency of the proposed method.

Suggested Citation

  • Li, Shuhan & Li, Zhigang & Shahidehpour, Mohammad & Huang, Wenjing & Zheng, J.H., 2024. "Dispatchable region for distributed renewable energy generation in reconfigurable AC–DC distribution networks with soft open points," Applied Energy, Elsevier, vol. 371(C).
  • Handle: RePEc:eee:appene:v:371:y:2024:i:c:s0306261924010870
    DOI: 10.1016/j.apenergy.2024.123704
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    Cited by:

    1. Yue Chen & Qiuyu Lu & Kaiyue Zeng & Yinguo Yang & Pingping Xie, 2024. "Flexibility-Oriented AC/DC Hybrid Grid Optimization Using Distributionally Robust Chance-Constrained Method," Energies, MDPI, vol. 17(19), pages 1-18, September.

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