Pricing-aware Real-time Charging Scheduling and Charging Station Expansion for Large-scale Electric Buses

Abstract

We are witnessing a rapid growth of electrified vehicles due to the ever-increasing concerns on urban air quality and energy security. Compared to other types of electric vehicles, electric buses have not yet been prevailingly adopted worldwide due to their high owning and operating costs, long charging time, and the uneven spatial distribution of charging facilities. Moreover, the highly dynamic environment factors such as unpredictable traffic congestion, different passenger demands, and even the changing weather can significantly affect electric bus charging efficiency and potentially hinder the further promotion of large-scale electric bus fleets. To address these issues, in this article, we first analyze a real-world dataset including massive data from 16,359 electric buses, 1,400 bus lines, and 5,562 bus stops. Then, we investigate the electric bus network to understand its operating and charging patterns, and further verify the necessity and feasibility of a real-time charging scheduling. With such understanding, we design busCharging , a pricing-aware real-time charging scheduling system based on Markov Decision Process to reduce the overall charging and operating costs for city-scale electric bus fleets, taking the time-variant electricity pricing into account. To show the effectiveness of busCharging , we implement it with the real-world data from Shenzhen, which includes GPS data of electric buses, the metadata of all bus lines and bus stops, combined with data of 376 charging stations for electric buses. The evaluation results show that busCharging dramatically reduces the charging cost by 23.7% and 12.8% of electricity usage simultaneously. Finally, we design a scheduling-based charging station expansion strategy to verify our busCharging is also effective during the charging station expansion process.

Smart Integration of Electric Buses in Cities: A Technological Review

“…Similar results were found in [77][78][79][80]. Wang et al [81] proposed a pricing-aware real-time charging scheduling system and managed to reduce the charging cost by 24% and the electricity usage by 13%. Arif et al [82] integrated an energy storage system (ESS) and a PV system in a bus depot to reduce charging cost and the peak load on the grid, while Raab et al [83] developed a charging strategy for a BEB depot integrated in the energy management of a virtual power plant operator.…”

“…As a result, offline strategies will not be able to give a reliable charging scheme. Until now, only the authors of [81] paid attention to real-time charging scheduling, so further research is required.…”

Research Progress and Prospects of Public Transportation Charging Station Layout Methods

“…Their results revealed that the model could ensure the optimal deployment of facilities for electric bus systems. Based on factors such as the traffic conditions, passenger demand, and weather, which can significantly affect the charging efficiency of electric buses, Wang [24] designed a real-time bus charging scheduling system based on the Markov decision process to analyze charging and operating costs, and it could significantly reduce the charging costs and electricity consumption of buses. In addition to addressing stochastic issues such as ridership and weather, Esmaeilnejad [25] optimized the passenger waiting times and operational costs of bus routes.…”

Limitations and suggestions of electric transit charge scheduling

“…Regarding the simulation method, Markov decision process is introduced to describe the charging events (Wang et al 2020). The average reward reinforcement learning method is implemented for problem-solving (Chen and Liang 2020).…”