Electric Power ›› 2022, Vol. 55 ›› Issue (10): 124-131.DOI: 10.11930/j.issn.1004-9649.202010035

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Evaluation of Renewable Energy Accommodation Capacity of High Voltage Distribution Networks Considering Regulation Potential of Flexible Resources

WANG Lili1, WANG Hao2, REN Zhouyang2, SUN Yihao1   

  1. 1. State Grid Henan Electric Power Company Economic Research Institute, Zhengzhou 450007, China;
    2. State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), Chongqing 400044, China
  • Received:2020-10-13 Revised:2020-12-02 Online:2022-10-28 Published:2022-10-20
  • Supported by:
    This work is supported by Natural Science Foundation of Chongqing, China (No.cstc2019jcyj-msxmX0092), National Natural Science Foundation of China (No.51677012) and State Grid Henan Electric Power Company Economic Research Institute (No.SGHAYJ00GHJS2000040).

Abstract: The rapid growth of grid-connected installed capacity of wind power and photovoltaic power has brought great challenges to the safe operation of the power system. Exploiting the flexible resources of the power grid is an effective way to ensure the healthy development of renewable energy. Therefore, this paper proposes an evaluation method for accommodation capacity of high voltage distribution networks considering the regulation potential of flexible resources. Firstly, a set of typical scenarios of wind power, photovoltaic and load are produced based on the fuzzy C-means clustering algorithm. Then, taking into account the flexibility of hydropower and power grid, a renewable energy planning model is established with the maximum renewable energy accommodation capacity of distribution networks as objective and with a comprehensive consideration of the normal operation states and the N–1 contingency states of distribution networks. Finally, the effectiveness of the proposed model and method are verified through case study of an 110 kV high voltage distribution network.

Key words: generation flexibility, grid flexibility, renewable energy planning, typical scenarios, N–1 contingency states

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