SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications

Pedram Asef; Rahim Taheri; Mohammad Shojafar; Iosif Mporas; Rahim Tafazoli

doi:10.1109/TII.2022.3165890

SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications

Pedram Asef, Rahim Taheri, Mohammad Shojafar, Iosif Mporas, Rahim Tafazoli

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Abstract

In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g. smart grid and microgrids, in order to produce perturbation for training data.

Original language	English
Number of pages	12
Journal	IEEE Transactions on Industrial Informatics
DOIs	https://doi.org/10.1109/TII.2022.3165890
Publication status	Published - 8 Apr 2022

Keywords

Adversarial Attacks
Cyber-Physical Security
Detectors
Energy Management
Energy management systems
Hybrid Microgrid
Informatics
Internet of things (IoT)
Machine Learning
Microgrids
Prediction algorithms
Resilience
Security

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Accepted_Version
© 2022 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TII.2022.3165890
Accepted author manuscript, 4.27 MBLicence: Unspecified

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title = "SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications",

abstract = "In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g. smart grid and microgrids, in order to produce perturbation for training data.",

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AU - Taheri, Rahim

AU - Shojafar, Mohammad

AU - Mporas, Iosif

AU - Tafazoli, Rahim

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N2 - In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g. smart grid and microgrids, in order to produce perturbation for training data.

AB - In this work, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called SIEMS. The main role of the SIEMS is to maintain the state of charge at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this paper is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g. smart grid and microgrids, in order to produce perturbation for training data.

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KW - Detectors

KW - Energy Management

KW - Energy management systems

KW - Hybrid Microgrid

KW - Informatics

KW - Internet of things (IoT)

KW - Machine Learning

KW - Microgrids

KW - Prediction algorithms

KW - Resilience

KW - Security

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SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications

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