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Enhancing risk control ability of distribution network for improved renewable energy integration through flexible DC interconnection

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  • Xiao, Hao
  • Pei, Wei
  • Deng, Wei
  • Ma, Tengfei
  • Zhang, Shizhong
  • Kong, Li

Abstract

The increased penetration of distributed renewable energy into modern distribution networks has resulted in a significant rise in the system operational risk. However, the risk control ability of traditional open-loop AC distribution networks is greatly limited due to the inherent restriction of the network structure, which hinders the integration of larger-scale renewable energy into the power grid. To address this issue, this paper developed an effective risk assessment model and risk averse method for fully utilizing flexible DC interconnections to enhance the risk control ability and to improve the penetration of renewable energy in distribution networks. Moreover, aiming at the complex coupling risk characteristics between the AC and DC system that are imposed by the DC interconnection and considering the inefficiency of traditional Monte Carlo sampling risk assessment methods, an effective point-estimation-based risk assessment method is proposed that conducts a limited number of sequential AC/DC power flow calculations with estimation points constructed from random input variables, thereby the operational risk of the system is effectively realized. On this basis, a risk-averse multi-objective optimization model is further established for minimizing both the operational risk and the expected network power loss, and an improved kriging-model-assisted multi-object gray wolf algorithm is proposed for quickly supporting the model solution. Finally, a fuzzy clustering method is presented for effectively identifying the optimal Pareto solution. A case study is conducted on an AC/DC hybrid system with three AC networks and one DC network. The results demonstrate that the proposed method can substantially improve the risk assessment efficiency compared with the traditional Monte Carlo method. In addition, the risk-averse optimization method can effectively reduce the system risk and improve the operational reliability as well as the penetration of renewable energy.

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  • Xiao, Hao & Pei, Wei & Deng, Wei & Ma, Tengfei & Zhang, Shizhong & Kong, Li, 2021. "Enhancing risk control ability of distribution network for improved renewable energy integration through flexible DC interconnection," Applied Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:appene:v:284:y:2021:i:c:s030626192031761x
    DOI: 10.1016/j.apenergy.2020.116387
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    6. Guo, Yi & Ming, Bo & Huang, Qiang & Wang, Yimin & Zheng, Xudong & Zhang, Wei, 2022. "Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery," Applied Energy, Elsevier, vol. 309(C).
    7. Xiao, Hao & Pei, Wei & Wu, Lei & Ma, Li & Ma, Tengfei & Hua, Weiqi, 2023. "A novel deep learning based probabilistic power flow method for Multi-Microgrids distribution system with incomplete network information," Applied Energy, Elsevier, vol. 335(C).
    8. Wei Dai & Yang Gao & Hui Hwang Goh & Jiangyi Jian & Zhihong Zeng & Yuelin Liu, 2024. "A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power," Energies, MDPI, vol. 17(6), pages 1-20, March.

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