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Shunt capacitor allocation by considering electric vehicle charging stations and distributed generators based on optimization algorithm

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  • Lin, Lihua
  • Shen, Shujian
  • Liao, Yunlin
  • Wang, Chuanliang
  • Shahabi, Laleh

Abstract

The two-stage optimization bear smell search algorithm (BSSA) based on fuzzy theory is proposed for determining the optimal location and size of dispersed generation sources, shunt capacitors, and electrical charging stations in distribution systems. In BSSA, in the first stage, the fuzzy method is used for measuring optimal Distributed Generations (DGs) and Shunt Capacitors (SCs) to improve the power factor, re-generation, power losses, and voltage specifications of the distribution system. In the second stage, the distribution system is considered as integrated with DGs and SCs, and the fuzzy BSSA is used to identify the optimal locations for the Electric Vehicle (EV) charging stations and number of vehicles in these stations. Moreover, in the proposed model, a lithium-ion EV battery is used for charging and the characteristic curves are used for analyzing the generated load current. The bear smell search algorithm, which is inspired by bears’ hunting in nature, is a novel meta-heuristic algorithm for solving optimization problems. If the complexity of optimization problems increases, this algorithm suffers from low convergence speed, which increases computation time. To resolve this problem, we propose a new development for this algorithm. Moreover, we examine the effects of EV load increase and uncertainty in DGs and the load distribution system on the performance of the distribution system. The simulation results show that the advantages of BSSA are more than those of the other optimization methods. The simulation results in a 51-bus distribution network indicate that the proposed method has better performance based on numerical analyses.

Suggested Citation

  • Lin, Lihua & Shen, Shujian & Liao, Yunlin & Wang, Chuanliang & Shahabi, Laleh, 2022. "Shunt capacitor allocation by considering electric vehicle charging stations and distributed generators based on optimization algorithm," Energy, Elsevier, vol. 239(PE).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221025317
    DOI: 10.1016/j.energy.2021.122283
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    References listed on IDEAS

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    1. Ahmadian, Ali & Sedghi, Mahdi & Aliakbar-Golkar, Masoud & Elkamel, Ali & Fowler, Michael, 2016. "Optimal probabilistic based storage planning in tap-changer equipped distribution network including PEVs, capacitor banks and WDGs: A case study for Iran," Energy, Elsevier, vol. 112(C), pages 984-997.
    2. Awasthi, Abhishek & Venkitusamy, Karthikeyan & Padmanaban, Sanjeevikumar & Selvamuthukumaran, Rajasekar & Blaabjerg, Frede & Singh, Asheesh K., 2017. "Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm," Energy, Elsevier, vol. 133(C), pages 70-78.
    3. Tolabi, H.B. & Ara, A. Lashkar & Hosseini, R., 2020. "A new thief and police algorithm and its application in simultaneous reconfiguration with optimal allocation of capacitor and distributed generation units," Energy, Elsevier, vol. 203(C).
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    Cited by:

    1. Zhang, Chao & Yin, Wanjun & Wen, Tao, 2024. "An advanced multi-objective collaborative scheduling strategy for large scale EV charging and discharging connected to the predictable wind power grid," Energy, Elsevier, vol. 287(C).

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