In view of the characteristics of high - proportion renewable energy and power electrification in the new distribution system, in order to achieve comprehensive state perception and flexible regulation and control, a two - layer planning model for the collaborative configuration of intelligent terminals and soft open points (SOP) is proposed. The upper - layer model, based on the “remote measurement” function of “two - remote” intelligent terminals and the “remote control” function of “three - remote” intelligent terminals, optimizes the location of intelligent terminals with the goal of minimizing investment costs and system operation costs while ensuring comprehensive state perception of the distribution network. The lower - layer model, on the basis of the intelligent terminal location optimization model, configures the capacity of SOP with the goal of minimizing network losses and voltage deviations. An improved particle swarm optimization algorithm is used to solve the proposed two - layer planning model. Finally, the feasibility of the proposed planning method is verified through an analysis of a four - feeder example based on the IEEE 33 - bus system.

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Optimized Allocation Method of Intelligent Terminals and Soft Open Point Considering Economic Reconfiguration

  • Xianyi He,
  • Wei Xiong,
  • Xufeng Yuan,
  • Zhiyang Lu,
  • Huajun Zheng,
  • Yutao Xu

摘要

In view of the characteristics of high - proportion renewable energy and power electrification in the new distribution system, in order to achieve comprehensive state perception and flexible regulation and control, a two - layer planning model for the collaborative configuration of intelligent terminals and soft open points (SOP) is proposed. The upper - layer model, based on the “remote measurement” function of “two - remote” intelligent terminals and the “remote control” function of “three - remote” intelligent terminals, optimizes the location of intelligent terminals with the goal of minimizing investment costs and system operation costs while ensuring comprehensive state perception of the distribution network. The lower - layer model, on the basis of the intelligent terminal location optimization model, configures the capacity of SOP with the goal of minimizing network losses and voltage deviations. An improved particle swarm optimization algorithm is used to solve the proposed two - layer planning model. Finally, the feasibility of the proposed planning method is verified through an analysis of a four - feeder example based on the IEEE 33 - bus system.