Electric Vehicle Route Optimization Considering Time-of-Use Electricity Price by Learnable Partheno-Genetic Algorithm

• Published in: IEEE transactions on smart grid Vol. 6; no. 2; pp. 657 - 666
• Main Authors: Yang, Hongming, Yang, Songping, Xu, Yan, Cao, Erbao, Lai, Mingyong, Dong, Zhaoyang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Biological cells; Charging stations; Distribution costs; Electric vehicle (EV); Electricity; learnable partheno-genetic algorithm (LPGA); Loading; Optimization; time-of-use (TOU) electricity price; Traveling salesman problem; vehicle route optimization; Vehicles; Electric vehicle (EV); learnable partheno-genetic algorithm (LPGA); time-of-use (TOU) electricity price; vehicle route optimization
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2014.2382684

In the context of energy saving and carbon emission reduction, the electric vehicle (EV) has been identified as a promising alternative to traditional fossil fuel-driven vehicles. Due to a different refueling manner and driving characteristic, the introduction of EVs to the current logistics system can make a significant impact on the vehicle routing and the associated operation costs. Based on the traveling salesman problem, this paper proposes a new optimal EV route model considering the fast-charging and regular-charging under the time-of-use price in the electricity market. The proposed model aims to minimize the total distribution costs of the EV route while satisfying the constraints of battery capacity, charging time and delivery/pickup demands, and the impact of vehicle loading on the unit electricity consumption per mile. To solve the proposed model, this paper then develops a learnable partheno-genetic algorithm with integration of expert knowledge about EV charging station and customer selection. A comprehensive numerical test is conducted on the 36-node and 112-node systems, and the results verify the feasibility and effectiveness of the proposed model and solution algorithm.