Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

During the last few years, attention has overwhelmingly focused on the integrated management of urban services and the demand of customers for locally-based supply. The rapid growth in developing smart measuring devices has made the underlying systems more observable and controllable. This exclusive...

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Autores principales:	Khalid Alnowibet, Andres Annuk, Udaya Dampage, Mohamed A. Mohamed
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	smart island stochastic framework energy management networked microgrid energy hub false data injection attack Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/bb2aa3dfd49b4bb0bc97b7ec2c434ded
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spelling	oai:doaj.org-article:bb2aa3dfd49b4bb0bc97b7ec2c434ded2021-11-11T19:32:11ZEffective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework10.3390/su1321118362071-1050https://doaj.org/article/bb2aa3dfd49b4bb0bc97b7ec2c434ded2021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11836https://doaj.org/toc/2071-1050During the last few years, attention has overwhelmingly focused on the integrated management of urban services and the demand of customers for locally-based supply. The rapid growth in developing smart measuring devices has made the underlying systems more observable and controllable. This exclusive feature has led the system designers to pursue the implementation of complex protocols to provide faster services based on data exchanges. On the other hand, the demands of consumers for locally-based supply could cause a disjunction and islanding behavior that demands to be dealt with by precise action. At first, keeping a centralization scheme was the main priority. However, the advent of distributed systems opened up new solutions. The operation of distributed systems requires the implementation of strong communication links to boost the existing infrastructure via smart control and supervision, which requires a foundation and effective investigations. Hence, necessary actions need to be taken to frustrate any disruptive penetrations into the system while simultaneously benefiting from the advantages of the proposed smart platform. This research addresses the detection of false data injection attacks (FDIA) in energy hub systems. Initially, a multi-hub system both in the presence of a microgrid (the interconnected smart energy hub-based microgrid system) and without it has been modeled for energy management in a way that allows them to cooperate toward providing energy with each other. Afterward, an FDIA is separately exerted to all three parts of the energy carrier including the thermal, water, and electric systems. In the absence of FDIA detection, the impact of FDIA is thoroughly illustrated on energy management, which considerably contributes to non-optimal operation. In the same vein, the intelligent priority selection based reinforcement learning (IPS-RL) method is proposed for FDIA detection. In order to model the uncertainty effects, the unscented transformation (UT) is applied in a stochastic framework. The results on the IEEE standard test system validate the system’s performance.Khalid AlnowibetAndres AnnukUdaya DampageMohamed A. MohamedMDPI AGarticlesmart islandstochastic frameworkenergy managementnetworked microgridenergy hubfalse data injection attackEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11836, p 11836 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smart island stochastic framework energy management networked microgrid energy hub false data injection attack Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	smart island stochastic framework energy management networked microgrid energy hub false data injection attack Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Khalid Alnowibet Andres Annuk Udaya Dampage Mohamed A. Mohamed Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework
description	During the last few years, attention has overwhelmingly focused on the integrated management of urban services and the demand of customers for locally-based supply. The rapid growth in developing smart measuring devices has made the underlying systems more observable and controllable. This exclusive feature has led the system designers to pursue the implementation of complex protocols to provide faster services based on data exchanges. On the other hand, the demands of consumers for locally-based supply could cause a disjunction and islanding behavior that demands to be dealt with by precise action. At first, keeping a centralization scheme was the main priority. However, the advent of distributed systems opened up new solutions. The operation of distributed systems requires the implementation of strong communication links to boost the existing infrastructure via smart control and supervision, which requires a foundation and effective investigations. Hence, necessary actions need to be taken to frustrate any disruptive penetrations into the system while simultaneously benefiting from the advantages of the proposed smart platform. This research addresses the detection of false data injection attacks (FDIA) in energy hub systems. Initially, a multi-hub system both in the presence of a microgrid (the interconnected smart energy hub-based microgrid system) and without it has been modeled for energy management in a way that allows them to cooperate toward providing energy with each other. Afterward, an FDIA is separately exerted to all three parts of the energy carrier including the thermal, water, and electric systems. In the absence of FDIA detection, the impact of FDIA is thoroughly illustrated on energy management, which considerably contributes to non-optimal operation. In the same vein, the intelligent priority selection based reinforcement learning (IPS-RL) method is proposed for FDIA detection. In order to model the uncertainty effects, the unscented transformation (UT) is applied in a stochastic framework. The results on the IEEE standard test system validate the system’s performance.
format	article
author	Khalid Alnowibet Andres Annuk Udaya Dampage Mohamed A. Mohamed
author_facet	Khalid Alnowibet Andres Annuk Udaya Dampage Mohamed A. Mohamed
author_sort	Khalid Alnowibet
title	Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework
title_short	Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework
title_full	Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework
title_fullStr	Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework
title_full_unstemmed	Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework
title_sort	effective energy management via false data detection scheme for the interconnected smart energy hub–microgrid system under stochastic framework
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bb2aa3dfd49b4bb0bc97b7ec2c434ded
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Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

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