Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network

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...

Full description

Bibliographic Details
Main Authors: Kyung-Min Kang, Bong-Yeon Choi, Hoon Lee, Chang-Gyun An, Tae-Gyu Kim, Yoon-Seong Lee, Mina Kim, Junsin Yi, Chung-Yuen Won
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Electronics
Subjects:
hybrid AC/DC microgrid
artificial neural network
energy management system
grid-connected
stand-alone
distributed generation
Online Access:https://www.mdpi.com/2079-9292/10/16/1939
id doaj-dcf05a56b2cd4f1492c4b97b493143f9
record_format Article
spelling doaj-dcf05a56b2cd4f1492c4b97b493143f92021-08-26T13:41:34ZengMDPI AGElectronics2079-92922021-08-01101939193910.3390/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
collection DOAJ
language English
format Article
sources DOAJ
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
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
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
title 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_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_sort energy management method of hybrid ac/dc microgrid using artificial neural network
publisher MDPI AG
series Electronics
issn 2079-9292
publishDate 2021-08-01
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.
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
_version_ 1721193963680956416

Similar Items