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A novel chicken swarm and teaching learning based algorithm for electric vehicle charging station placement problem

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  • Deb, Sanchari
  • Gao, Xiao-Zhi
  • Tammi, Kari
  • Kalita, Karuna
  • Mahanta, Pinakeswar

Abstract

The current concern about the ever-escalating demand for energy, exhaustive nature of fossil fuels, global warming accompanied by climate change has necessitated the development of an alternate pollution-free mode of commute. Electric Vehicles (EV) are an environmentally friendly alternative to reduce the reliance on fossil fuel and pollution. For public acceptance of EVs, functionality and accessibility of charging stations is of paramount importance. Improper planning of EV charging stations, however, is a threat to the power grid stability. EV charging stations must be placed in the transport network in such a way that the safe limit of distribution network parameters is not violated. Thus, charging station placement problem is an intricate problem involving convolution of transport and distribution networks. A novel and simple approach of formulating the charging station placement problem is presented in this work. This approach takes into account integrated cost of charging station placement as well as penalties for violating grid constraints. For obtaining an optimal solution of this placement problem, two efficient evolutionary algorithms, such as Chicken Swarm Optimization (CSO) and Teaching Learning Based Optimization algorithm (TLBO) are amalgamated together thereby extracting the best features of the both algorithms. The efficacy of the proposed algorithm is tested by solving selected standard benchmark problems as well as charging station placement problem. The result of this hybrid algorithm is further compared with other algorithms used for this purpose.

Suggested Citation

  • Deb, Sanchari & Gao, Xiao-Zhi & Tammi, Kari & Kalita, Karuna & Mahanta, Pinakeswar, 2021. "A novel chicken swarm and teaching learning based algorithm for electric vehicle charging station placement problem," Energy, Elsevier, vol. 220(C).
    • Handle: RePEc:eee:energy:v:220:y:2021:i:c:s0360544220327523
    DOI: 10.1016/j.energy.2020.119645
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    References listed on IDEAS

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    Cited by:

    1. Zhao, Zhonghao & Lee, Carman K.M. & Huo, Jiage, 2023. "EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning," Energy, Elsevier, vol. 267(C).
    2. Duan, Ditao & Poursoleiman, Roza, 2021. "Modified teaching-learning-based optimization by orthogonal learning for optimal design of an electric vehicle charging station," Utilities Policy, Elsevier, vol. 72(C).
    3. Sami M. Alshareef & Ahmed Fathy, 2023. "Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks," Mathematics, MDPI, vol. 11(15), pages 1-30, July.
    4. Zixuan Wang & Qingyuan Yang & Chuwen Wang & Lanxi Wang, 2023. "Spatial Layout Analysis and Evaluation of Electric Vehicle Charging Infrastructure in Chongqing," Land, MDPI, vol. 12(4), pages 1-18, April.
    5. Amaro García-Suárez & José-Luis Guisado-Lizar & Fernando Diaz-del-Rio & Francisco Jiménez-Morales, 2021. "A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 13(10), pages 1-14, May.
    6. Gururaghav Raman & Gurupraanesh Raman & Jimmy Chih-Hsien Peng, 2022. "Resilience of urban public electric vehicle charging infrastructure to flooding," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    7. Essam H. Houssein & Sanchari Deb & Diego Oliva & Hegazy Rezk & Hesham Alhumade & Mokhtar Said, 2021. "Performance of Gradient-Based Optimizer on Charging Station Placement Problem," Mathematics, MDPI, vol. 9(21), pages 1-16, November.
    8. Zeinab Teimoori & Abdulsalam Yassine, 2022. "A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation," Sustainability, MDPI, vol. 14(21), pages 1-23, October.

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