Frontiers in Energy Research (Nov 2023)

A fast-partitioning decision method for demand side resources based on grid resilience assessment

  • Yueping Kong,
  • Shihai Yang,
  • Meimei Duan,
  • Yuqi Zhou,
  • Zecheng Ding,
  • Tingquan Zhang,
  • Ju Sheng

DOI
https://doi.org/10.3389/fenrg.2023.1301175
Journal volume & issue
Vol. 11

Abstract

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With the large-scale renewable energy integrated into the distribution grid, the grid’s regulating ability and disturbance tolerance are weakening. When partitioning demand-side resources, it is necessary to enhance resilience to ensure the reliability of electric power. This paper proposes a fast-partitioning method that considers resilience, structure, and functionality to adapt to the evolving requirements of the distribution system. Specifically, the comprehensive partition index system is constructed with the resilience assessment index reflecting the ability of partitions to withstand and mitigate the effects of faults, the modularity index based on electrical distance, and regional power balance indexes. Meanwhile, a modified genetic algorithm is proposed to calculate the comprehensive partition index. The modified algorithm first uses a sensitivity matrix to perform initial partitions and construct initial populations. Then, it utilizes a triangular network adjacency matrix for chromosome encoding, significantly reducing the algorithm’s search space and enhancing partitioning efficiency. Finally, the applicability and effectiveness of the proposed method are verified through simulation analysis of the IEEE 28-node system.

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