Reducing detrimental communication failure impacts in microgrids by using deep learning techniques
Reducing detrimental communication failure impacts in microgrids by using deep learning techniques
A Microgrid (MG), like any other smart and interoperable power system, requires device-to-device (D2D) communication structures in order to function effectively. This communication system, however, is not immune to intentional or unintentional failures. This paper discusses the effects of communication link failures on MG control and management and proposes solutions based on enhancing message content to mitigate their detritus impact. In order to achieve this goal, generation and consumption forecasting using deep learning (DL) methods at the next time steps is used. The architecture of an energy management system (EMS) and an energy storage system (ESS) that are able to operate in coordination is introduced and evaluated by simulation tests, which show promising results and illustrate the efficacy of the proposed methods. It is important to mention that, in this paper, three dissimilar topics namely MG control/management, DL-based forecasting, and D2D communication architectures are employed and this combination is proven to be capable of achieving the aforesaid objective.
Artificial Neural Networks, Time series forecasting, deep learning, machine-to-machine communication, microgrid, artificial neural networks, time series forecasting
Arbab Zavar, Babak
d6134ca5-c0a0-48d9-8d88-8a719683f5b3
Sharkh, Suleiman
c8445516-dafe-41c2-b7e8-c21e295e56b9
Palacious-Garcia, Emilio J
7a71e21e-f044-43c9-bf99-01a345ba3989
Vasquez, Juan C
fd4ba40a-1faa-441f-9253-16298480c3a5
Guerrero, J.M.
29397923-9151-44e1-b74b-98160dda4099
11 August 2022
Arbab Zavar, Babak
d6134ca5-c0a0-48d9-8d88-8a719683f5b3
Sharkh, Suleiman
c8445516-dafe-41c2-b7e8-c21e295e56b9
Palacious-Garcia, Emilio J
7a71e21e-f044-43c9-bf99-01a345ba3989
Vasquez, Juan C
fd4ba40a-1faa-441f-9253-16298480c3a5
Guerrero, J.M.
29397923-9151-44e1-b74b-98160dda4099
Arbab Zavar, Babak, Sharkh, Suleiman, Palacious-Garcia, Emilio J, Vasquez, Juan C and Guerrero, J.M.
(2022)
Reducing detrimental communication failure impacts in microgrids by using deep learning techniques.
Sensors (Basel, Switzerland), 22 (16), [6006].
(doi:10.3390/s22166006).
Abstract
A Microgrid (MG), like any other smart and interoperable power system, requires device-to-device (D2D) communication structures in order to function effectively. This communication system, however, is not immune to intentional or unintentional failures. This paper discusses the effects of communication link failures on MG control and management and proposes solutions based on enhancing message content to mitigate their detritus impact. In order to achieve this goal, generation and consumption forecasting using deep learning (DL) methods at the next time steps is used. The architecture of an energy management system (EMS) and an energy storage system (ESS) that are able to operate in coordination is introduced and evaluated by simulation tests, which show promising results and illustrate the efficacy of the proposed methods. It is important to mention that, in this paper, three dissimilar topics namely MG control/management, DL-based forecasting, and D2D communication architectures are employed and this combination is proven to be capable of achieving the aforesaid objective.
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sensors-22-06006
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Published date: 11 August 2022
Additional Information:
Funding Information:
This work was supported by Aalborg University Talent Programme 2016 with the Research Project: The Energy Internet—Integrating Internet of Things into the Smart Grid. This work was supported by VILLUM FONDEN under the VILLUM Investigator Grant (no. 25920): Center for Research on Microgrids (CROM); www.crom.et.aau.dk (accessed on 19 July 2022).
Publisher Copyright:
© 2022 by the authors.
Keywords:
Artificial Neural Networks, Time series forecasting, deep learning, machine-to-machine communication, microgrid, artificial neural networks, time series forecasting
Identifiers
Local EPrints ID: 469461
URI: http://eprints.soton.ac.uk/id/eprint/469461
ISSN: 1424-8220
PURE UUID: 7c57469c-9d2b-4071-b347-67f1b1007a4f
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Date deposited: 15 Sep 2022 16:33
Last modified: 17 Mar 2024 02:41
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Author:
Babak Arbab Zavar
Author:
Emilio J Palacious-Garcia
Author:
Juan C Vasquez
Author:
J.M. Guerrero
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