Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network

Continuous power supply in an integrated electric system supplied by solar energy and battery storage can be optimally maintained with the use of diesel generators. This article discusses the optimum setting-point for isolated wind, photo-voltaic, diesel, and battery storage electric grid systems. O...

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Autores principales:	Ahmed Riyaz, Pradip Kumar Sadhu, Atif Iqbal, Mohd Tariq, Shabana Urooj, Fadwa Alrowais
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	hybrid grid energy system hybrid energy source battery deprivation cost genetic algorithm ANN General Works A
Acceso en línea:	https://doaj.org/article/7ebbeed4e6d14d24ac0e1dc92c7ef457
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spelling	oai:doaj.org-article:7ebbeed4e6d14d24ac0e1dc92c7ef4572021-11-11T14:13:33ZPower Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network2296-598X10.3389/fenrg.2021.774408https://doaj.org/article/7ebbeed4e6d14d24ac0e1dc92c7ef4572021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fenrg.2021.774408/fullhttps://doaj.org/toc/2296-598XContinuous power supply in an integrated electric system supplied by solar energy and battery storage can be optimally maintained with the use of diesel generators. This article discusses the optimum setting-point for isolated wind, photo-voltaic, diesel, and battery storage electric grid systems. Optimal energy supply for hybrid grid systems means that the load is sufficient for 24 h. This study aims to integrate the battery deprivation costs and the fuel price feature in the optimization model for the hybrid grid. In order to count charge–discharge cycles and measure battery deprivation, the genetic algorithm concept is utilized. To solve the target function, an ANN-based algorithm with genetic coefficients can also be used to optimize the power management system. In the objective function, a weight factor is proposed. Specific weight factor values are considered for simulation studies. On the algorithm actions, charging status, and its implications for the optimized expense of the hybrid grid, the weight factor effect is measured.Ahmed RiyazAhmed RiyazPradip Kumar SadhuAtif IqbalMohd TariqShabana UroojFadwa AlrowaisFrontiers Media S.A.articlehybrid grid energy systemhybrid energy sourcebattery deprivation costgenetic algorithmANNGeneral WorksAENFrontiers in Energy Research, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hybrid grid energy system hybrid energy source battery deprivation cost genetic algorithm ANN General Works A
spellingShingle	hybrid grid energy system hybrid energy source battery deprivation cost genetic algorithm ANN General Works A Ahmed Riyaz Ahmed Riyaz Pradip Kumar Sadhu Atif Iqbal Mohd Tariq Shabana Urooj Fadwa Alrowais Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network
description	Continuous power supply in an integrated electric system supplied by solar energy and battery storage can be optimally maintained with the use of diesel generators. This article discusses the optimum setting-point for isolated wind, photo-voltaic, diesel, and battery storage electric grid systems. Optimal energy supply for hybrid grid systems means that the load is sufficient for 24 h. This study aims to integrate the battery deprivation costs and the fuel price feature in the optimization model for the hybrid grid. In order to count charge–discharge cycles and measure battery deprivation, the genetic algorithm concept is utilized. To solve the target function, an ANN-based algorithm with genetic coefficients can also be used to optimize the power management system. In the objective function, a weight factor is proposed. Specific weight factor values are considered for simulation studies. On the algorithm actions, charging status, and its implications for the optimized expense of the hybrid grid, the weight factor effect is measured.
format	article
author	Ahmed Riyaz Ahmed Riyaz Pradip Kumar Sadhu Atif Iqbal Mohd Tariq Shabana Urooj Fadwa Alrowais
author_facet	Ahmed Riyaz Ahmed Riyaz Pradip Kumar Sadhu Atif Iqbal Mohd Tariq Shabana Urooj Fadwa Alrowais
author_sort	Ahmed Riyaz
title	Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network
title_short	Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network
title_full	Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network
title_fullStr	Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network
title_full_unstemmed	Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network
title_sort	power management of hybrid grid system with battery deprivation cost using artificial neural network
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/7ebbeed4e6d14d24ac0e1dc92c7ef457
work_keys_str_mv	AT ahmedriyaz powermanagementofhybridgridsystemwithbatterydeprivationcostusingartificialneuralnetwork AT ahmedriyaz powermanagementofhybridgridsystemwithbatterydeprivationcostusingartificialneuralnetwork AT pradipkumarsadhu powermanagementofhybridgridsystemwithbatterydeprivationcostusingartificialneuralnetwork AT atifiqbal powermanagementofhybridgridsystemwithbatterydeprivationcostusingartificialneuralnetwork AT mohdtariq powermanagementofhybridgridsystemwithbatterydeprivationcostusingartificialneuralnetwork AT shabanaurooj powermanagementofhybridgridsystemwithbatterydeprivationcostusingartificialneuralnetwork AT fadwaalrowais powermanagementofhybridgridsystemwithbatterydeprivationcostusingartificialneuralnetwork
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Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network

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