Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network
This paper proposes an artificial neural network (ANN)-based energy management system (EMS) for controlling power in AC–DC hybrid distribution networks. The proposed ANN-based EMS selects an optimal operating mode by collecting data such as the power provided by distributed generation (DG), the load...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/16/1939 |
| _version_ | 1797524135798112256 |
|---|---|
| author | Kyung-Min Kang Bong-Yeon Choi Hoon Lee Chang-Gyun An Tae-Gyu Kim Yoon-Seong Lee Mina Kim Junsin Yi Chung-Yuen Won |
| author_facet | Kyung-Min Kang Bong-Yeon Choi Hoon Lee Chang-Gyun An Tae-Gyu Kim Yoon-Seong Lee Mina Kim Junsin Yi Chung-Yuen Won |
| author_sort | Kyung-Min Kang |
| collection | DOAJ |
| description | This paper proposes an artificial neural network (ANN)-based energy management system (EMS) for controlling power in AC–DC hybrid distribution networks. The proposed ANN-based EMS selects an optimal operating mode by collecting data such as the power provided by distributed generation (DG), the load demand, and state of charge (SOC). For training the ANN, profile data on the charging and discharging amount of ESS for various distribution network power situations were prepared, and the ANN was trained with an error rate within 10%. The proposed EMS controls each power converter in the optimal operation mode through the already trained ANN in the grid-connected mode. For the experimental verification of the proposed EMS, a small-scale hybrid AD/DC microgrid was fabricated, and simulations and experiments were performed for each operation mode. |
| first_indexed | 2024-03-10T08:53:03Z |
| format | Article |
| id | doaj.art-dcf05a56b2cd4f1492c4b97b493143f9 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T08:53:03Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-dcf05a56b2cd4f1492c4b97b493143f92023-11-22T07:24:49ZengMDPI AGElectronics2079-92922021-08-011016193910.3390/electronics10161939Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural NetworkKyung-Min Kang0Bong-Yeon Choi1Hoon Lee2Chang-Gyun An3Tae-Gyu Kim4Yoon-Seong Lee5Mina Kim6Junsin Yi7Chung-Yuen Won8Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, KoreaMando Corporation, Seongnam 13486, KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, KoreaThis paper proposes an artificial neural network (ANN)-based energy management system (EMS) for controlling power in AC–DC hybrid distribution networks. The proposed ANN-based EMS selects an optimal operating mode by collecting data such as the power provided by distributed generation (DG), the load demand, and state of charge (SOC). For training the ANN, profile data on the charging and discharging amount of ESS for various distribution network power situations were prepared, and the ANN was trained with an error rate within 10%. The proposed EMS controls each power converter in the optimal operation mode through the already trained ANN in the grid-connected mode. For the experimental verification of the proposed EMS, a small-scale hybrid AD/DC microgrid was fabricated, and simulations and experiments were performed for each operation mode.https://www.mdpi.com/2079-9292/10/16/1939hybrid AC/DC microgridartificial neural networkenergy management systemgrid-connectedstand-alonedistributed generation |
| spellingShingle | Kyung-Min Kang Bong-Yeon Choi Hoon Lee Chang-Gyun An Tae-Gyu Kim Yoon-Seong Lee Mina Kim Junsin Yi Chung-Yuen Won Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network Electronics hybrid AC/DC microgrid artificial neural network energy management system grid-connected stand-alone distributed generation |
| title | Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network |
| title_full | Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network |
| title_fullStr | Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network |
| title_full_unstemmed | Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network |
| title_short | Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network |
| title_sort | energy management method of hybrid ac dc microgrid using artificial neural network |
| topic | hybrid AC/DC microgrid artificial neural network energy management system grid-connected stand-alone distributed generation |
| url | https://www.mdpi.com/2079-9292/10/16/1939 |
| work_keys_str_mv | AT kyungminkang energymanagementmethodofhybridacdcmicrogridusingartificialneuralnetwork AT bongyeonchoi energymanagementmethodofhybridacdcmicrogridusingartificialneuralnetwork AT hoonlee energymanagementmethodofhybridacdcmicrogridusingartificialneuralnetwork AT changgyunan energymanagementmethodofhybridacdcmicrogridusingartificialneuralnetwork AT taegyukim energymanagementmethodofhybridacdcmicrogridusingartificialneuralnetwork AT yoonseonglee energymanagementmethodofhybridacdcmicrogridusingartificialneuralnetwork AT minakim energymanagementmethodofhybridacdcmicrogridusingartificialneuralnetwork AT junsinyi energymanagementmethodofhybridacdcmicrogridusingartificialneuralnetwork AT chungyuenwon energymanagementmethodofhybridacdcmicrogridusingartificialneuralnetwork |