AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform

A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric V...

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Main Authors: Zhishang Wang, Mark Ogbodo, Huakun Huang, Chen Qiu, Masayuki Hisada, Abderazek Ben Abdallah
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
EVs
Online Access:https://ieeexplore.ieee.org/document/9293294/
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spelling doaj-7800b26d7e6a45b59e1d250618745b832021-03-30T04:22:17ZengIEEEIEEE Access2169-35362020-01-01822640922642110.1109/ACCESS.2020.30446129293294AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid PlatformZhishang Wang0https://orcid.org/0000-0002-1716-2095Mark Ogbodo1Huakun Huang2https://orcid.org/0000-0003-2853-8892Chen Qiu3https://orcid.org/0000-0002-1128-7284Masayuki Hisada4Abderazek Ben Abdallah5https://orcid.org/0000-0003-3432-0718Adaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, JapanAdaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, JapanAizu Computer Science Laboratories, Inc., Aizuwakamatsu, JapanAdaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, JapanAizu Computer Science Laboratories, Inc., Aizuwakamatsu, JapanAdaptive Systems Laboratory, Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, JapanA Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. This paper proposes an AI-enabled, blockchain-based electric vehicle integration system, named AEBIS for power management in a smart grid platform. The system is based on an artificial neural-network and federated learning approaches for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy within a VPP platform. The evaluation results show that the proposed approach achieved high power consumption forecast with R<sup>2</sup> score of 0.938 in the conventional training scenario. When applying a federated learning approach, the accuracy decreased by only 1.7%. Therefore, with the accurate prediction of power consumption, the proposed system produces reliable and timely service to supply extra electricity from the vehicular network, decreasing the power fluctuation level. Also, the employment of AI-chip ensures a cost-efficient performance. Moreover, introducing blockchain technology in the system further achieves a secure and transparent service at the expense of an acceptable memory and latency cost.https://ieeexplore.ieee.org/document/9293294/AI-enabledblockchain-basedEVspower-managementAI-chipvirtual power plant
collection DOAJ
language English
format Article
sources DOAJ
author Zhishang Wang
Mark Ogbodo
Huakun Huang
Chen Qiu
Masayuki Hisada
Abderazek Ben Abdallah
spellingShingle Zhishang Wang
Mark Ogbodo
Huakun Huang
Chen Qiu
Masayuki Hisada
Abderazek Ben Abdallah
AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform
IEEE Access
AI-enabled
blockchain-based
EVs
power-management
AI-chip
virtual power plant
author_facet Zhishang Wang
Mark Ogbodo
Huakun Huang
Chen Qiu
Masayuki Hisada
Abderazek Ben Abdallah
author_sort Zhishang Wang
title AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform
title_short AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform
title_full AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform
title_fullStr AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform
title_full_unstemmed AEBIS: AI-Enabled Blockchain-Based Electric Vehicle Integration System for Power Management in Smart Grid Platform
title_sort aebis: ai-enabled blockchain-based electric vehicle integration system for power management in smart grid platform
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. This paper proposes an AI-enabled, blockchain-based electric vehicle integration system, named AEBIS for power management in a smart grid platform. The system is based on an artificial neural-network and federated learning approaches for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy within a VPP platform. The evaluation results show that the proposed approach achieved high power consumption forecast with R<sup>2</sup> score of 0.938 in the conventional training scenario. When applying a federated learning approach, the accuracy decreased by only 1.7%. Therefore, with the accurate prediction of power consumption, the proposed system produces reliable and timely service to supply extra electricity from the vehicular network, decreasing the power fluctuation level. Also, the employment of AI-chip ensures a cost-efficient performance. Moreover, introducing blockchain technology in the system further achieves a secure and transparent service at the expense of an acceptable memory and latency cost.
topic AI-enabled
blockchain-based
EVs
power-management
AI-chip
virtual power plant
url https://ieeexplore.ieee.org/document/9293294/
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