Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Active Distribution System Planning Considering Battery Swapping Station for Low-carbon Objective using Immune Binary Firefly Algorithm

  • Shi, Ji-Ying (Dept. of Electrical and Electronic Engineering, Tianjin University) ;
  • Li, Ya-Jing (Dept. of Electrical and Electronic Engineering, Tianjin University) ;
  • Xue, Fei (Electric Power Research Institute, State Grid Ningxia Electric Power Company) ;
  • Ling, Le-Tao (Dept. of Electrical and Electronic Engineering, Tianjin University) ;
  • Liu, Wen-An (ZiBo Power Supply Company, State Grid Shandong Electric Power Company) ;
  • Yuan, Da-Ling (Dept. of Electrical and Electronic Engineering, Tianjin University) ;
  • Yang, Ting (Dept. of Electrical and Electronic Engineering, Tianjin University)
  • Received : 2017.03.30
  • Accepted : 2017.10.10
  • Published : 2018.03.01
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Abstract

Active distribution system (ADS) considering distributed generation (DG) and electric vehicle (EV) is an effective way to cut carbon emission and improve system benefits. ADS is an evolving, complex and uncertain system, thus comprehensive model and effective optimization algorithms are needed. Battery swapping station (BSS) for EV service is an essential type of flexible load (FL). This paper establishes ADS planning model considering BSS firstly for the minimization of total cost including feeder investment, operation and maintenance, net loss and carbon tax. Meanwhile, immune binary firefly algorithm (IBFA) is proposed to optimize ADS planning. Firefly algorithm (FA) is a novel intelligent algorithm with simple structure and good convergence. By involving biological immune system into FA, IBFA adjusts antibody population scale to increase diversity and global search capability. To validate proposed algorithm, IBFA is compared with particle swarm optimization (PSO) algorithm on IEEE 39-bus system. The results prove that IBFA performs better than PSO in global search and convergence in ADS planning.

Keywords

References

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