Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN
In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/4/1787 |
| _version_ | 1797621223190953984 |
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| author | Tae-Gyu Kim Hoon Lee Chang-Gyun An Junsin Yi Chung-Yuen Won |
| author_facet | Tae-Gyu Kim Hoon Lee Chang-Gyun An Junsin Yi Chung-Yuen Won |
| author_sort | Tae-Gyu Kim |
| collection | DOAJ |
| description | In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration. |
| first_indexed | 2024-03-11T08:52:45Z |
| format | Article |
| id | doaj.art-59f44f1539994f6aa490bc6d6db7713b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T08:52:45Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-59f44f1539994f6aa490bc6d6db7713b2023-11-16T20:17:48ZengMDPI AGEnergies1996-10732023-02-01164178710.3390/en16041787Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANNTae-Gyu Kim0Hoon Lee1Chang-Gyun An2Junsin Yi3Chung-Yuen Won4Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of KoreaIn grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration.https://www.mdpi.com/1996-1073/16/4/1787energy management strategydistributed generationinterlinking converterartificial neural networkhybrid AC/DC microgrid |
| spellingShingle | Tae-Gyu Kim Hoon Lee Chang-Gyun An Junsin Yi Chung-Yuen Won Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN Energies energy management strategy distributed generation interlinking converter artificial neural network hybrid AC/DC microgrid |
| title | Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN |
| title_full | Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN |
| title_fullStr | Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN |
| title_full_unstemmed | Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN |
| title_short | Hybrid AC/DC Microgrid Energy Management Strategy Based on Two-Step ANN |
| title_sort | hybrid ac dc microgrid energy management strategy based on two step ann |
| topic | energy management strategy distributed generation interlinking converter artificial neural network hybrid AC/DC microgrid |
| url | https://www.mdpi.com/1996-1073/16/4/1787 |
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