Towards sustainable city transport fleet mobility: robust energy scheduling for urban energy ecosystem coupled with autonomous electric taxis

In the paper a sustainable Urban Integrated Energy Systems (U-IES) with Autonomous Electric Taxis (AETs) as active agents is described, and a robust energy scheduling model for U-IES with AETs is accordingly formulated. The model operationalizes a set of urban problems including real-time electricity price fluctuations, and grid inefficiencies with coordinated demand response (DR) programs enabled by AETs bidirectional energy trading capability. The framework, employing mixed-integer linear programing and machine learning powered renewable energy forecasting, optimizes the energy dispatch in an uncertain environment. Simulation results show 10.01% decrease in total operational cost and 98.72% utilization of locally available renewable resources, respectively. Synchronized DR of AETs and urban loads eliminate the dependance on diesel generators totally and save the pollution cost by 9.03%, compared with 6.52% in signal end operation under various grid interaction levels. The results indicate that utilizing a combined operation of AETs and urban loads can lead to remarkable improvement in the flexibility, reliability, and economy of urban energy systems. The approach lays the foundation for the construction of low-carbon cities in practice as it can help the DR implementation, the smart grid application and maximize local RE use.

Text 123348.pdf - Published Version Restricted to Repository staff only Download (6MB) Official URL or Download Paper: https://www.sciencedirect.com/science/article/pii/...

Actions (login required)

View Item