Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks

Controllers; Cost reduction; Electric load dispatching; Energy management; Microgrids; Neural networks; Control approach; Intelligent controllers; Microgrid; Multi micro-grids; Network-based; Performance; Power plant system; Scheduling control; System conditions; Virtual power plants; Scheduling

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Main Authors: Abdolrasol M.G.M., Hannan M.A., Suhail Hussain S.M., Ustun T.S., Sarker M.R., Ker P.J.
Other Authors: 35796848700
Format: Article
Published: MDPI 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-259592023-05-29T17:05:47Z Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks Abdolrasol M.G.M. Hannan M.A. Suhail Hussain S.M. Ustun T.S. Sarker M.R. Ker P.J. 35796848700 7103014445 22035146400 43761679200 57537703000 37461740800 Controllers; Cost reduction; Electric load dispatching; Energy management; Microgrids; Neural networks; Control approach; Intelligent controllers; Microgrid; Multi micro-grids; Network-based; Performance; Power plant system; Scheduling control; System conditions; Virtual power plants; Scheduling This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algo-rithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers� robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN�s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency. � 2021 by the authors. Licensee MDPI, Basel, Switzerland. Final 2023-05-29T09:05:47Z 2023-05-29T09:05:47Z 2021 Article 10.3390/en14206507 2-s2.0-85117415327 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117415327&doi=10.3390%2fen14206507&partnerID=40&md5=6bb64f94db43fc6ea3a3275134058f92 https://irepository.uniten.edu.my/handle/123456789/25959 14 20 6507 All Open Access, Gold MDPI Scopus
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/
description Controllers; Cost reduction; Electric load dispatching; Energy management; Microgrids; Neural networks; Control approach; Intelligent controllers; Microgrid; Multi micro-grids; Network-based; Performance; Power plant system; Scheduling control; System conditions; Virtual power plants; Scheduling
author2 35796848700
author_facet 35796848700
Abdolrasol M.G.M.
Hannan M.A.
Suhail Hussain S.M.
Ustun T.S.
Sarker M.R.
Ker P.J.
format Article
author Abdolrasol M.G.M.
Hannan M.A.
Suhail Hussain S.M.
Ustun T.S.
Sarker M.R.
Ker P.J.
spellingShingle Abdolrasol M.G.M.
Hannan M.A.
Suhail Hussain S.M.
Ustun T.S.
Sarker M.R.
Ker P.J.
Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks
author_sort Abdolrasol M.G.M.
title Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks
title_short Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks
title_full Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks
title_fullStr Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks
title_full_unstemmed Energy management scheduling for microgrids in the virtual power plant system using artificial neural networks
title_sort energy management scheduling for microgrids in the virtual power plant system using artificial neural networks
publisher MDPI
publishDate 2023
_version_ 1806425897003122688