Charging and relocating optimization for electric vehicle car-sharing: An event-based strategy improvement approach. (15th September 2020)

• Record Type: Journal Article
• Authors: Lu, Xiaonong; Zhang, Qiang; Peng, Zhanglin; Shao, Zhen; Song, Hao; Wang, Wanying
• Is Part Of: Energy. Volume 207(2020)
• Publication Date: 2020-09-15
• Subjects: Electrical vehicle car sharing -- Event-based optimization -- Charging and relocating decision -- Gradient-based algorithm; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118285
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: In this paper, we study the charging and relocating problem for an electrical vehicle car-sharing (EVCS) system, aiming to dynamically match the user request, electrical load and vehicle supply at the lowest total cost of charging and lost sales. The scheduling problem is first formulated as a stochastic sequential decision program. To solve the strategy for an EVCS system with multiple city regions, we deploy the distributional event-based dynamic optimization approach that can coordinate the serving, charging and relocating decisions of shared electrical vehicles (SEV). To maximize the daily income of system, a gradient-based strategy iterative algorithm is applied to solve the scheduling problem. Finally, a computational experiment is performed, and the results show that the proposed optimization framework is applicable to the EVCS system scheduling problem by its efficiency as well as the capability to handle the fluctuating user requests and electrical load. Highlights: The problem of optimizing charging and relocating strategy for EVCS system is addressed. The uncertainty of user request and electrical load in different city area is considered. The state and action are defined to describe the operation process of system. A novel event-based dynamic optimization model with distributed decision is presented. The gradient-based strategy iterative algorithm is applied to solve the event-based strategy.