An Energy Storage System for Regulating the Maximum Demand of Traction Substations

Fangyuan Zhou (), Zhaohui Tang, Xiaolong Zhang, Lebin Chou and Da Tan
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Fangyuan Zhou: School of Automation, Central South University, Changsha 410083, China
Zhaohui Tang: School of Automation, Central South University, Changsha 410083, China
Xiaolong Zhang: School of Locomotive and Rolling Stock, Hunan Railway Professional Technology College, Zhuzhou 412001, China
Lebin Chou: Zhuzhou CRRC Times Electric Co., Ltd., Zhuzhou 412001, China
Da Tan: School of Automation, Central South University, Changsha 410083, China

Energies, 2024, vol. 18, issue 1, 1-19

Abstract: With the development of electrified railways towards high speed and heavy load, the peak power of traction loads is increasing, and the maximum demand and negative sequence current of traction substations are also increasing. Therefore, this article proposes an energy storage system (ESS) based on Li-ion batteries for regulating the maximum demand of traction substations. An ESS is connected to the DC bus of a railway power conditioner (RPC), which is connected to the two power supply arms of the traction substation. In response to the large fluctuation of traction load, this paper proposes a maximum demand active regulation method based on short-term prediction of traction load. The short-term prediction of traction load adopts a time series short-term load prediction method based on BP neural network error correction. Then, based on the load prediction value of the traction substation and the state of charge of the ESS, a collaborative control strategy for ESS and RPC is formulated to enable RPC to achieve a negative sequence suppression function simultaneously. Finally, simulation experiments were conducted using MATLAB, and the results showed that compared with the traditional maximum demand regulation method based on peak power reference values, the method proposed in this paper significantly reduces the number of ESS charging and discharging cycles, improves the regulation effect of maximum demand, and has a higher net income during the lifecycle. At the same time, it also takes into account the negative sequence current suppression function, thereby improving the comprehensive economic benefits of railways and the quality of power grids.

Keywords: electrified railway; maximum demand; energy storage system; short-term forecasting of traction load; active regulation; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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