Locating electric vehicle charging stations with service capacity using the improved whale optimization algorithm |
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Affiliation: | 1. Department of Electrical and Electronics Engineering, Gudlavalleru Engineering College, Gudlavalleru, Krishna District, Andhra Pradesh 521356, India;2. School of Computing and Engineering, University of Missouri Kansas City, Kansas City, MO 64110, USA;3. Department of Electrical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India;4. Department of Electrical Engineering, University of Sharjah, Sharjah, P.O. Box 27272 United Arab Emirates |
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Abstract: | This study proposes an Improved Whale Optimization Algorithm (IWOA) that, on the basis of Whale Optimization Algorithm (WOA) designed by Mirjalili and Lewis (2016), introduces Gaussian mutation operator, differential evolution operator, and crowding degree factor to the algorithm framework. Test results with nine classic examples show that IWOA significantly improves WOA’s precision and computing speed. We also model the locating problem of Electric Vehicle (EV) charging stations with service risk constraints and apply IWOA to solve it. This paper introduces service risk factors, which include the risk of service capacity and user anxiety, establishing the EV charging station site selection model considering service risk. Computational results based on a large-scale problem instance suggest that both the model and the algorithm are effective to apply in practical locating planning projects and help reduce social costs. |
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Keywords: | Electric vehicle charging station location Whale optimization algorithm Gaussian variation Differential evolution Crowding factor Service risk |
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