Cost-oriented optimization of the location and capacity of charging stations for the electric Robotaxi fleet. (15th January 2023)

• Record Type: Journal Article
• Authors: Wang, Ning; Tian, Hangqi; Wu, Huahua; Liu, Qiaoqian; Luan, Jie; Li, Yuan
• Is Part Of: Energy. Volume 263:Part C(2023)
• Publication Date: 2023-01-15
• Subjects: Electric vehicle charging station -- Location and capacity optimization -- Monte Carlo simulation -- Particle swarm optimization -- Robotaxi; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125895
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Large-scale adoption of Robotaxi requires a comprehensive charging infrastructure as a guarantee, but the problems of insufficient capacity, low utilization, and unreasonable deployment of electric vehicle charging stations (EVCSs) are still common. In this context, this study proposed a multi-stage optimization strategy consisting of fleet sizing, charging demand simulation, model construction and solution to achieve the location and capacity optimization of EVCSs for the electric Robotaxi fleet. In stage one, a vehicle shareable network model based on graph theory was developed to determine the minimum Robotaxi fleet size required to adequately meet user travel demand, which was solved using the Hopcroft-Karp algorithm. In stage two, the specific spatio-temporal distribution of fleet charging demand was obtained by Monte Carlo simulation, considering the decision-making characteristics of Robotaxi operation and charging process. In stage three, a charging station location and capacity optimization model was established with the objective of minimizing the comprehensive costs, and an improved particle swarm optimization algorithm applying genetic operators to improve the population diversity was proposed. Finally, the effectiveness of the proposed model and algorithm was analyzed and discussed based on a case study using the real passenger order and geographic data from the city of Chengdu, China. Highlights: A multi-stage approach to achieve the EVCSs deployment optimization for the electric Robotaxi fleet. The spatio-temporal distribution of fleet charging demand was obtained by Monte Carlo simulation. The IPSO algorithm improves the convergence effect without causing a significant increase in runtime.