Hybrid energy storage system using bidirectional single-inductor multiple-port converter with model predictive control in DC microgrids
This paper presents a bidirectional single-inductor multiple-port (BSIMP) converter for integrating hybrid energy storage system (HESS) into DC microgrids, where the HESS is the combination of different types of energy storages (ESs). A control method based on model predictive control (MPC) is propo...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/143254 |
| _version_ | 1824454777981894656 |
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| author | Wang, Benfei Xian, Liang Manandhar, Ujjal Ye, Jian Zhang, Xinan Gooi, Hoay Beng Ukil, Abhisek |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Wang, Benfei Xian, Liang Manandhar, Ujjal Ye, Jian Zhang, Xinan Gooi, Hoay Beng Ukil, Abhisek |
| author_sort | Wang, Benfei |
| collection | NTU |
| description | This paper presents a bidirectional single-inductor multiple-port (BSIMP) converter for integrating hybrid energy storage system (HESS) into DC microgrids, where the HESS is the combination of different types of energy storages (ESs). A control method based on model predictive control (MPC) is proposed to regulate the BSIMP converter for the HESS. With the MPC-based method, the ESs are controlled following their characteristics. Simultaneously, the DC microgrid bus voltage can be maintained against the fluctuations of the load consumption and renewable generation. Compared to conventional bidirectional single-input single-output converters, the proposed BSIMP converter significantly reduces the component count, system size and cost to integrate the HESS into DC microgrid. To verify the performance of the proposed BSIMP converter with MPC-based method, a series of hardware-in-loop (HIL) experimental cases are studied. Moreover, the comparison with previous works is conducted. The HIL experimental and comparison results demonstrate the successful regulation of HESS using the BSIMP converter in DC microgrid. |
| first_indexed | 2025-02-19T03:27:43Z |
| format | Journal Article |
| id | ntu-10356/143254 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:27:43Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1432542021-01-28T07:48:12Z Hybrid energy storage system using bidirectional single-inductor multiple-port converter with model predictive control in DC microgrids Wang, Benfei Xian, Liang Manandhar, Ujjal Ye, Jian Zhang, Xinan Gooi, Hoay Beng Ukil, Abhisek School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Single-inductor Multiple-port Converter Model Predictive Control This paper presents a bidirectional single-inductor multiple-port (BSIMP) converter for integrating hybrid energy storage system (HESS) into DC microgrids, where the HESS is the combination of different types of energy storages (ESs). A control method based on model predictive control (MPC) is proposed to regulate the BSIMP converter for the HESS. With the MPC-based method, the ESs are controlled following their characteristics. Simultaneously, the DC microgrid bus voltage can be maintained against the fluctuations of the load consumption and renewable generation. Compared to conventional bidirectional single-input single-output converters, the proposed BSIMP converter significantly reduces the component count, system size and cost to integrate the HESS into DC microgrid. To verify the performance of the proposed BSIMP converter with MPC-based method, a series of hardware-in-loop (HIL) experimental cases are studied. Moreover, the comparison with previous works is conducted. The HIL experimental and comparison results demonstrate the successful regulation of HESS using the BSIMP converter in DC microgrid. Accepted version 2020-08-17T04:32:55Z 2020-08-17T04:32:55Z 2019 Journal Article Wang, B., Xian, L., Manandhar, U., Ye, J., Zhang, X., Gooi, H. B., & Ukil, A. (2019). Hybrid energy storage system using bidirectional single-inductor multiple-port converter with model predictive control in DC microgrids. Electric Power Systems Research, 173, 38-47. doi:10.1016/j.epsr.2019.03.015 0378-7796 https://hdl.handle.net/10356/143254 10.1016/j.epsr.2019.03.015 2-s2.0-85064178880 173 38 47 en Electric Power Systems Research © 2019 Duke University Press. All rights reserved. This paper was published by Elsevier B.V. in Electric Power Systems Research and is made available with permission of Duke University Press. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Single-inductor Multiple-port Converter Model Predictive Control Wang, Benfei Xian, Liang Manandhar, Ujjal Ye, Jian Zhang, Xinan Gooi, Hoay Beng Ukil, Abhisek Hybrid energy storage system using bidirectional single-inductor multiple-port converter with model predictive control in DC microgrids |
| title | Hybrid energy storage system using bidirectional single-inductor multiple-port converter with model predictive control in DC microgrids |
| title_full | Hybrid energy storage system using bidirectional single-inductor multiple-port converter with model predictive control in DC microgrids |
| title_fullStr | Hybrid energy storage system using bidirectional single-inductor multiple-port converter with model predictive control in DC microgrids |
| title_full_unstemmed | Hybrid energy storage system using bidirectional single-inductor multiple-port converter with model predictive control in DC microgrids |
| title_short | Hybrid energy storage system using bidirectional single-inductor multiple-port converter with model predictive control in DC microgrids |
| title_sort | hybrid energy storage system using bidirectional single inductor multiple port converter with model predictive control in dc microgrids |
| topic | Engineering::Electrical and electronic engineering Single-inductor Multiple-port Converter Model Predictive Control |
| url | https://hdl.handle.net/10356/143254 |
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