An Energy Storage System for Regulating the Maximum Demand of Traction Substations
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 L...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/1/131 |
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| author | Fangyuan Zhou Zhaohui Tang Xiaolong Zhang Lebin Chou Da Tan |
| author_facet | Fangyuan Zhou Zhaohui Tang Xiaolong Zhang Lebin Chou Da Tan |
| author_sort | Fangyuan Zhou |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-011597b177c84c29b044be76602e7cf8 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-011597b177c84c29b044be76602e7cf82025-01-10T13:17:11ZengMDPI AGEnergies1996-10732024-12-0118113110.3390/en18010131An Energy Storage System for Regulating the Maximum Demand of Traction SubstationsFangyuan Zhou0Zhaohui Tang1Xiaolong Zhang2Lebin Chou3Da Tan4School of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Locomotive and Rolling Stock, Hunan Railway Professional Technology College, Zhuzhou 412001, ChinaZhuzhou CRRC Times Electric Co., Ltd., Zhuzhou 412001, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaWith 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.https://www.mdpi.com/1996-1073/18/1/131electrified railwaymaximum demandenergy storage systemshort-term forecasting of traction loadactive regulationneural network |
| spellingShingle | Fangyuan Zhou Zhaohui Tang Xiaolong Zhang Lebin Chou Da Tan An Energy Storage System for Regulating the Maximum Demand of Traction Substations Energies electrified railway maximum demand energy storage system short-term forecasting of traction load active regulation neural network |
| title | An Energy Storage System for Regulating the Maximum Demand of Traction Substations |
| title_full | An Energy Storage System for Regulating the Maximum Demand of Traction Substations |
| title_fullStr | An Energy Storage System for Regulating the Maximum Demand of Traction Substations |
| title_full_unstemmed | An Energy Storage System for Regulating the Maximum Demand of Traction Substations |
| title_short | An Energy Storage System for Regulating the Maximum Demand of Traction Substations |
| title_sort | energy storage system for regulating the maximum demand of traction substations |
| topic | electrified railway maximum demand energy storage system short-term forecasting of traction load active regulation neural network |
| url | https://www.mdpi.com/1996-1073/18/1/131 |
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