Abstract
With the rapid development of energy storage technology, onboard energy storage systems(OESS) have been applied in modern railway systems to help reduce energy consumption. In addition, regenerative braking energy utilization is becoming increasingly important to avoid energy waste in the railway systems, undermining the sustainability of urban railway transportation. However, the intelligent energy management of the trains equipped with OESSs considering regenerative braking energy utilization is still rare in the field. This paper considers the stochastic characteristics of the regenerative braking power distributed in railway power networks. It concurrently optimizes the train trajectory with OESS and regenerative braking energy utilization. The expected regenerative braking power distribution can be obtained based on the Monte-Carlo simulation of the train timetable. Then, the integrated optimization using mixed integer linear programming (MILP) can be conducted and combined with the expected available regenerative braking energy. A generic four-station railway system powered by one traction substation is modeled and simulated for the study. The results show that by applying the proposed method, 68.8% of the expected regenerative braking energy in the environment will be further utilized. The expected amount of energy from the traction substation is reduced by 22.0% using the proposed train control method to recover more regenerative braking energy from improved energy interactions between trains and OESSs.
| Original language | English |
|---|---|
| Number of pages | 19 |
| Journal | IEEE Transactions on Transportation Electrification |
| Publication status | Accepted/In press - Apr 2024 |
Bibliographical note
This research project is supported and sponsored in part by the National Natural Science Foundation of China under Grant 52302380 and Grant 61603306, in part by the Funding by Science and Technology Projects in Guangzhou No. 2023A04J0312, in part by the National Science Foundation of Guangdong Province, China (Grant No.2023A1515012949), and in part by the Research Development Fund (RDF-18-01-04) of Xi’an Jiaotong-Liverpool University.
Access to Document
- WuC2024Energy-Efficient_AAM
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