Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems. (1st July 2023)

• Record Type: Journal Article
• Authors: Aghajan-Eshkevari, Saleh; Ameli, Mohammad Taghi; Azad, Sasan
• Is Part Of: Applied energy. Volume 341(2023)
• Publication Date: 2023-07-01
• Subjects: Electric vehicles -- Active and reactive power management -- Distribution network -- Transportation system -- Trip chain -- Optimal routing; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2023.121126
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A comprehensive two-stage framework for optimal routing and active and reactive power management of EVs. Employing the trip chain method for modelling EVs' daily trips in the transportation network. Considering the characteristics and constraints of both transportation and distribution systems in the optimization model. Integrating various factors affecting the EVs' energy consumption into the proposed model. Considering the benefits of EV owners and distribution system operator in the objective function of the optimization problem. Utilizing the maximum capability of EVs to discharge power during peak price periods. Abstract: With the increasing penetration level of electric vehicles (EVs) in the transportation network, it is necessary to control the charging and discharging process of EVs to avoid operational issues in the power grid. Therefore, in this paper, a two-stage framework is presented for EVs optimal routing and their active and reactive power control in the distribution grid. In the first stage, the EVs mobility in the transportation system is taken into consideration. On this basis, EV daily trips in the transportation network are modelled using the trip chain method. Also, the Dijkstra algorithm is utilized for optimal routing with the shortest travel time between the origin and the destination node. Furthermore, the effects of ambient temperature, traffic congestion and road type on the EVs energy consumption are assessed. In the second stage, optimal active and reactive power exchange of EVs with the distribution grid is carried out. Thus, a mixed-integer linear programming (MILP) model is proposed that simultaneously considers EV owners' and the distribution network operator's benefits as the optimization goals. The EV battery degradation cost due to the discharging of active power is also integrated into the objective function of the optimization problem. The proposed framework is implemented on a standard IEEE 33-bus system coupled with a 30-node transportation network. The simulation results show that the presented method can reduce the loss cost of the distribution grid and benefit EV owners compared to different charging strategies. In addition, with the battery technology development, the proposed framework can significantly improve the distribution system operation, decrease environmental issues, and reduce EV owner's costs.