Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources

A two-stage planning form of multi-energy supply optimization such as power, cooling, and heating is presented in this paper as a micro energy grid (MEG) To cover the effect of uncertainty in renewable energy sources (RES), the scheduling cycle is considered in this paper. Next, the results of the d...

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Autores principales:	Muhammad Hammad Saeed, Wang Fangzong, Sultan Salem, Yousaf Ali Khan, Basheer Ahmad Kalwar, Ashk Fars
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Optimization Microgrid planning Renewable resources Artificial bee colony algorithm Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/ef770379ae1244309098a615cc65c841
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spelling	oai:doaj.org-article:ef770379ae1244309098a615cc65c8412021-12-04T04:34:45ZTwo-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources2352-484710.1016/j.egyr.2021.10.123https://doaj.org/article/ef770379ae1244309098a615cc65c8412021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721011422https://doaj.org/toc/2352-4847A two-stage planning form of multi-energy supply optimization such as power, cooling, and heating is presented in this paper as a micro energy grid (MEG) To cover the effect of uncertainty in renewable energy sources (RES), the scheduling cycle is considered in this paper. Next, the results of the day-ahead prediction are considered as random variables for the upper-layer model. To realize the random variables at the lower layer, the revised model of energy storage and the demand response (DR) planning model are considered. Finally, the modified version of the artificial bee colony (ABC) algorithm is utilized to find the optimal solution. The improved ABC algorithm is a shape-memory method based on the collective intelligence and behavior of bees in a colony for finding the best nutrition source. In the improved ABC algorithm, with information exchange between the bees, based on Newton’s law of universal gravitation, the full potential of this algorithm is used to find the optimal solution given the constraints applied to the system. The proposed method is applied to a real system and the results show that the two-stage optimization algorithm and the proposed intelligent algorithm obtained the simultaneous optimization of different energy forms. The obtained numerical analysis results in test cases prove the following points: (1) The optimal synergistic supply of multiple energy forms has been provided based on the two-stage optimization algorithm and solution approach. (2) The surplus energy can be converted to natural gas by the power-to-gas converter (P2G) based on power cascade conversion in a multi-directional mode. (3) To get some revenue, the MEG is flexible enough to cooperate with the upper-grade energy network. (4) The DR-based price can smooth the load shape and increase the MEG operation revenue using some supplementary features. Also, P2G can sequentially develop the flexible multidirectional energy conversion in energy - gas - energy - cooling as a cascade. When the evaluated P2G energy rises by 450 kW, the total GST output raises by 1244 kWh. For more economic benefits, MEG can be connected to the upstream energy grid. Load management also increases the net revenue of the system.Muhammad Hammad SaeedWang FangzongSultan SalemYousaf Ali KhanBasheer Ahmad KalwarAshk FarsElsevierarticleOptimizationMicrogrid planningRenewable resourcesArtificial bee colony algorithmElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 8912-8928 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Optimization Microgrid planning Renewable resources Artificial bee colony algorithm Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Optimization Microgrid planning Renewable resources Artificial bee colony algorithm Electrical engineering. Electronics. Nuclear engineering TK1-9971 Muhammad Hammad Saeed Wang Fangzong Sultan Salem Yousaf Ali Khan Basheer Ahmad Kalwar Ashk Fars Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources
description	A two-stage planning form of multi-energy supply optimization such as power, cooling, and heating is presented in this paper as a micro energy grid (MEG) To cover the effect of uncertainty in renewable energy sources (RES), the scheduling cycle is considered in this paper. Next, the results of the day-ahead prediction are considered as random variables for the upper-layer model. To realize the random variables at the lower layer, the revised model of energy storage and the demand response (DR) planning model are considered. Finally, the modified version of the artificial bee colony (ABC) algorithm is utilized to find the optimal solution. The improved ABC algorithm is a shape-memory method based on the collective intelligence and behavior of bees in a colony for finding the best nutrition source. In the improved ABC algorithm, with information exchange between the bees, based on Newton’s law of universal gravitation, the full potential of this algorithm is used to find the optimal solution given the constraints applied to the system. The proposed method is applied to a real system and the results show that the two-stage optimization algorithm and the proposed intelligent algorithm obtained the simultaneous optimization of different energy forms. The obtained numerical analysis results in test cases prove the following points: (1) The optimal synergistic supply of multiple energy forms has been provided based on the two-stage optimization algorithm and solution approach. (2) The surplus energy can be converted to natural gas by the power-to-gas converter (P2G) based on power cascade conversion in a multi-directional mode. (3) To get some revenue, the MEG is flexible enough to cooperate with the upper-grade energy network. (4) The DR-based price can smooth the load shape and increase the MEG operation revenue using some supplementary features. Also, P2G can sequentially develop the flexible multidirectional energy conversion in energy - gas - energy - cooling as a cascade. When the evaluated P2G energy rises by 450 kW, the total GST output raises by 1244 kWh. For more economic benefits, MEG can be connected to the upstream energy grid. Load management also increases the net revenue of the system.
format	article
author	Muhammad Hammad Saeed Wang Fangzong Sultan Salem Yousaf Ali Khan Basheer Ahmad Kalwar Ashk Fars
author_facet	Muhammad Hammad Saeed Wang Fangzong Sultan Salem Yousaf Ali Khan Basheer Ahmad Kalwar Ashk Fars
author_sort	Muhammad Hammad Saeed
title	Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources
title_short	Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources
title_full	Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources
title_fullStr	Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources
title_full_unstemmed	Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources
title_sort	two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/ef770379ae1244309098a615cc65c841
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Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources

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