Energy management system for PV-battery microgrid based on model predictive control

There had been increase of the usage of renewable energy sources to supply electricity in remote areas. However, microgrid consists of renewable sources such as solar Photovoltaic (PV) poses challenges for a reliable energy supply due to its intermittent nature. This paper presents a microgrid model...

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Main Authors: Basir Khan, M.R., Pasupuleti, J., Al-Fattah, J., Tahmasebi, M.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-129812020-02-24T04:02:03Z Energy management system for PV-battery microgrid based on model predictive control Basir Khan, M.R. Pasupuleti, J. Al-Fattah, J. Tahmasebi, M. There had been increase of the usage of renewable energy sources to supply electricity in remote areas. However, microgrid consists of renewable sources such as solar Photovoltaic (PV) poses challenges for a reliable energy supply due to its intermittent nature. This paper presents a microgrid model with Energy Management System based on Model Predictive Control (MPC). The microgrid comprises of PV, and battery storage system. The goal of the EMS is to deliver a reliable and optimal generation from multiple sources in the microgrid. Moreover, the MPC will also provide controls for the battery charging for a smooth PV output. The model simulated based on actual load profile and renewable resource such as solar radiation. Several disturbances such as variation of load, generation and PV shading has been simulated to measure the performance of the EMS. The results illustrate the system ability to exhibits robust performance in variety of conditions. © 2019 Institute of Advanced Engineering and Science. All rights reserved. 2020-02-03T03:28:15Z 2020-02-03T03:28:15Z 2019 Article 10.11591/ijeecs.v15.i1.pp20-25 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description There had been increase of the usage of renewable energy sources to supply electricity in remote areas. However, microgrid consists of renewable sources such as solar Photovoltaic (PV) poses challenges for a reliable energy supply due to its intermittent nature. This paper presents a microgrid model with Energy Management System based on Model Predictive Control (MPC). The microgrid comprises of PV, and battery storage system. The goal of the EMS is to deliver a reliable and optimal generation from multiple sources in the microgrid. Moreover, the MPC will also provide controls for the battery charging for a smooth PV output. The model simulated based on actual load profile and renewable resource such as solar radiation. Several disturbances such as variation of load, generation and PV shading has been simulated to measure the performance of the EMS. The results illustrate the system ability to exhibits robust performance in variety of conditions. © 2019 Institute of Advanced Engineering and Science. All rights reserved.
format Article
author Basir Khan, M.R.
Pasupuleti, J.
Al-Fattah, J.
Tahmasebi, M.
spellingShingle Basir Khan, M.R.
Pasupuleti, J.
Al-Fattah, J.
Tahmasebi, M.
Energy management system for PV-battery microgrid based on model predictive control
author_facet Basir Khan, M.R.
Pasupuleti, J.
Al-Fattah, J.
Tahmasebi, M.
author_sort Basir Khan, M.R.
title Energy management system for PV-battery microgrid based on model predictive control
title_short Energy management system for PV-battery microgrid based on model predictive control
title_full Energy management system for PV-battery microgrid based on model predictive control
title_fullStr Energy management system for PV-battery microgrid based on model predictive control
title_full_unstemmed Energy management system for PV-battery microgrid based on model predictive control
title_sort energy management system for pv-battery microgrid based on model predictive control
publishDate 2020
_version_ 1662758797581484032
score 13.160551