Coordinated optimization of logistics scheduling and electricity dispatch for electric logistics vehicles considering uncertain electricity prices and renewable generation
Abstract
Electric logistics vehicles (ELVs) have the potential to significantly reduce pollution and carbon emissions, which are considered highly promising for achieving green logistics. However, the challenges posed by time-consuming charging processes, indirect carbon emissions, and fluctuating electricity prices hinder the economic viability of ELVs. To enhance the competitiveness of ELVs compared to internal combustion engine vehicles, this study introduces a coordinated optimization framework that aims to minimize both logistics and electricity costs within an urban logistics system. In terms of logistics, a simultaneous loading and partial charging strategy is proposed to improve the logistics efficiency of ELVs by effectively utilizing the charging time. In terms of electricity, we integrate suburban renewable energy sources with ELVs to reduce indirect carbon emissions resulting from thermal power generation. Additionally, ELVs are aggregated to participate in bidding in the day-ahead and real-time electricity markets through charging scheduling. To address uncertainties related to electricity prices and renewable generation, optimal logistics scheduling and electricity dispatch strategies for ELVs are formulated as a stochastic programming problem. This problem is then solved using a specially designed matheuristic approach. The effectiveness of the proposed coordinated optimization framework is confirmed through case studies on a large-scale urban logistics system.
Yuanyi Chen
PhD Student
Simon Hu
Assistant Professor
