An effective distributed approach based machine learning for energy negotiation in networked microgrids

In recent years, the definition of the distributed energy management frameworks has become the core of research due to its distinguished advantages including the less time-consuming, more accurate and secure than the centralized frameworks. In this sense, this work proposes an effective energy manag...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jian Chen, Khalid Alnowibet, Andres Annuk, Mohamed A. Mohamed
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Energy negotiation Distributed optimization approach Networked microgrid Uncertainty Reinforcement learning Energy management Energy industries. Energy policy. Fuel trade HD9502-9502.5
Acceso en línea:	https://doaj.org/article/8d2697d7a9254aeca77ddcfbff1eb691
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8d2697d7a9254aeca77ddcfbff1eb691
record_format	dspace
spelling	oai:doaj.org-article:8d2697d7a9254aeca77ddcfbff1eb6912021-11-18T04:48:20ZAn effective distributed approach based machine learning for energy negotiation in networked microgrids2211-467X10.1016/j.esr.2021.100760https://doaj.org/article/8d2697d7a9254aeca77ddcfbff1eb6912021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2211467X21001449https://doaj.org/toc/2211-467XIn recent years, the definition of the distributed energy management frameworks has become the core of research due to its distinguished advantages including the less time-consuming, more accurate and secure than the centralized frameworks. In this sense, this work proposes an effective energy management framework for a distributed networked microgrid (DNM) based on the primal-dual method of multipliers (PDMM). The networked microgrid is comprised of a wind turbine (WT), battery unit and photovoltaic (PV) system which supply the demands of the networked microgrid. The power negotiation process among the networked microgrid' segments will continue until obtaining an adequate solution to achieve the best performance of the proposed microgrid. A Reinforcement Learning (RL) approach is proposed that can increase the accuracy of modeling uncertainty of the parameters of the DNM compared to other manners. To make the secured communication platform, a secured energy transaction structure based on the directed acyclic graph (DAG) approach, is proposed as one of the other aims of this paper. The results indicate the effectiveness of the proposed model and its applicability for the networked microgrid.Jian ChenKhalid AlnowibetAndres AnnukMohamed A. MohamedElsevierarticleEnergy negotiationDistributed optimization approachNetworked microgridUncertaintyReinforcement learningEnergy managementEnergy industries. Energy policy. Fuel tradeHD9502-9502.5ENEnergy Strategy Reviews, Vol 38, Iss , Pp 100760- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Energy negotiation Distributed optimization approach Networked microgrid Uncertainty Reinforcement learning Energy management Energy industries. Energy policy. Fuel trade HD9502-9502.5
spellingShingle	Energy negotiation Distributed optimization approach Networked microgrid Uncertainty Reinforcement learning Energy management Energy industries. Energy policy. Fuel trade HD9502-9502.5 Jian Chen Khalid Alnowibet Andres Annuk Mohamed A. Mohamed An effective distributed approach based machine learning for energy negotiation in networked microgrids
description	In recent years, the definition of the distributed energy management frameworks has become the core of research due to its distinguished advantages including the less time-consuming, more accurate and secure than the centralized frameworks. In this sense, this work proposes an effective energy management framework for a distributed networked microgrid (DNM) based on the primal-dual method of multipliers (PDMM). The networked microgrid is comprised of a wind turbine (WT), battery unit and photovoltaic (PV) system which supply the demands of the networked microgrid. The power negotiation process among the networked microgrid' segments will continue until obtaining an adequate solution to achieve the best performance of the proposed microgrid. A Reinforcement Learning (RL) approach is proposed that can increase the accuracy of modeling uncertainty of the parameters of the DNM compared to other manners. To make the secured communication platform, a secured energy transaction structure based on the directed acyclic graph (DAG) approach, is proposed as one of the other aims of this paper. The results indicate the effectiveness of the proposed model and its applicability for the networked microgrid.
format	article
author	Jian Chen Khalid Alnowibet Andres Annuk Mohamed A. Mohamed
author_facet	Jian Chen Khalid Alnowibet Andres Annuk Mohamed A. Mohamed
author_sort	Jian Chen
title	An effective distributed approach based machine learning for energy negotiation in networked microgrids
title_short	An effective distributed approach based machine learning for energy negotiation in networked microgrids
title_full	An effective distributed approach based machine learning for energy negotiation in networked microgrids
title_fullStr	An effective distributed approach based machine learning for energy negotiation in networked microgrids
title_full_unstemmed	An effective distributed approach based machine learning for energy negotiation in networked microgrids
title_sort	effective distributed approach based machine learning for energy negotiation in networked microgrids
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/8d2697d7a9254aeca77ddcfbff1eb691
work_keys_str_mv	AT jianchen aneffectivedistributedapproachbasedmachinelearningforenergynegotiationinnetworkedmicrogrids AT khalidalnowibet aneffectivedistributedapproachbasedmachinelearningforenergynegotiationinnetworkedmicrogrids AT andresannuk aneffectivedistributedapproachbasedmachinelearningforenergynegotiationinnetworkedmicrogrids AT mohamedamohamed aneffectivedistributedapproachbasedmachinelearningforenergynegotiationinnetworkedmicrogrids AT jianchen effectivedistributedapproachbasedmachinelearningforenergynegotiationinnetworkedmicrogrids AT khalidalnowibet effectivedistributedapproachbasedmachinelearningforenergynegotiationinnetworkedmicrogrids AT andresannuk effectivedistributedapproachbasedmachinelearningforenergynegotiationinnetworkedmicrogrids AT mohamedamohamed effectivedistributedapproachbasedmachinelearningforenergynegotiationinnetworkedmicrogrids
_version_	1718425051265499136

An effective distributed approach based machine learning for energy negotiation in networked microgrids

Ejemplares similares