| Autor: |
Fazeli, Seyed Sajjad, Venkatachalam, Saravanan, Chinnam, Ratna Babu, Murat, Alper |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
IEEE Transactions on Intelligent Transportation Systems 2020 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/TITS.2020.2979363 |
| Popis: |
Electric vehicles (EVs) provide a cleaner alternative that not only reduces greenhouse gas emissions but also improves air quality and reduces noise pollution. The consumer market for electrical vehicles is growing very rapidly. Designing a network with adequate capacity and types of public charging stations is a challenge that needs to be addressed to support the current trend in the EV market. In this research, we propose a choice modeling approach embedded in a two-stage stochastic programming model to determine the optimal layout and types of EV supply equipment for a community while considering randomness in demand and drivers' behaviors. Some of the key random data parameters considered in this study are: EV's dwell time at parking {\sv location}, battery's state of charge, distance from home, willingness to walk, drivers' arrival patterns, and traffic on weekdays and weekends. The two-stage model uses the sample average approximation method, which asymptotically converges to an optimal solution. To address the computational challenges for large-scale instances, we propose an outer approximation decomposition algorithm. We conduct extensive computational experiments to quantify the efficacy of the proposed approach. In addition, we present the results and a sensitivity analysis for a case study based on publicly available data sources. |
| Databáze: |
arXiv |
| Externí odkaz: |
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