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Modified teaching-learning-based optimization by orthogonal learning for optimal design of an electric vehicle charging station

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  • Duan, Ditao
  • Poursoleiman, Roza

Abstract

The provision of a safe environment has led to the growth of electric vehicles (EVs), whose propagation in the market depends on features such as price, battery technology, economy, and improvement of charging stations. This paper proposes a charging station for plug-in electric vehicles (PEVs) connected to the distribution system, along with the energy storage system's batteries, diesel generator, and photovoltaic panels. The charging facilities are also designed and optimized at three levels of fast, medium, and slow speeds. Since this model integrates many decision variables and cannot be accurately solved by traditional mathematical methods, a new modified optimization algorithm is presented. The modified teaching-learning-based optimization (TLBO) based on orthogonal learning (OL), or OLTLBO, is proposed to solve the optimization problem. The results confirm that the model successfully uses all the available options to design the EVCS.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:juipol:v:72:y:2021:i:c:s0957178721000874
    DOI: 10.1016/j.jup.2021.101253
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    References listed on IDEAS

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    Cited by:

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    3. Singh, Priyanka & Kottath, Rahul, 2022. "Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices," Utilities Policy, Elsevier, vol. 79(C).

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