在各种网络攻击情况下确保能源消费者的能源数据交易安全

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS
Fariya Tabassum , Md. Rashidul Islam , M. Imran Azim , M.A. Rahman , Md. Omer Faruque , Sk.A. Shezan , M.J. Hossain
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引用次数: 0

摘要

由于可再生能源的使用日益增多和智能电网技术的进步,作为高效和分散式能源分配的潜在解决方案,消费者之间的双边能源交易引起了人们的极大兴趣。消费者可以利用物联网(IoT)技术和具有智能计量功能的安排建立直接的能源交换,从而消除对中间商的需求,更有效地利用可再生能源。然而,这些用户之间的直接能源交换很容易受到网络威胁,从而阻碍了安全有效的能源交易,同时也无法保护隐私。为了在用户和电网之间实现安全、无缝的能源交易,物联网设备的网络安全应作为一种可能的解决方案,具有极其重要的意义。因此,本文重点关注智能电表所促进的消费者之间能源交易的安全问题。本文旨在从消费者的角度出发,解决针对数据完整性、保密性和可用性的潜在威胁,并开发一个基于人工智能(AI)的能源交易安全综合框架。建议的结构化路线图不仅能识别受损的交易数据,还能通过替换受污染和缺失的交易数据防止消费者对此做出反应。对基于人工智能的算法进行的比较分析表明,决策树(DT)优于支持向量机(SVM)和多层感知器(MLP)。此外,拟议框架还采用了基于深度学习(DL)的模型来替换受损的交易数据。所有的数值分析以及大量的模拟结果都证明了拟议框架的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Secured energy data transaction for prosumers under diverse cyberattack scenarios
Due to the increasing use of renewable energy sources and the advancement of smart grid technology, bilateral energy transactions between prosumers have attracted significant interest as a potential solution for efficient and decentralized energy distribution. Prosumers can establish direct energy exchanges by utilizing internet of things (IoT) technologies and arrangements with smart metering capabilities, eliminating the need for middlemen and allowing for more effective use of renewable energy sources. However, these direct energy exchanges between prosumers can be susceptible to cyber-threats, which hinder secure and effective energy transactions while protecting privacy. To enable safe and seamless energy transactions among prosumers and the grid, the cyber-security of IoT devices should be of paramount significance as a possible solution. Therefore, this paper focuses on securing the energy transactions among prosumers facilitated by smart meters. It aims to address potential threats against data integrity, confidentiality, and availability from the prosumers’ point of view and develop a comprehensive framework for securing energy transactions based on artificial intelligence (AI). The proposed structured roadmap not only identifies compromised trading data but also prevents prosumers from reacting to it by replacing the contaminated as well as missing trading data. A comparative analysis on AI-based algorithms indicates that decision tree (DT) outperforms support vector machine (SVM) and multi-layer perceptron (MLP) for the proposed framework to profile the corrupted trading data identification and categorization in order to provide effective outcomes. Additionally, the proposed framework adopts a deep learning (DL)-based model for the replacement of compromised trading data. All the numerical analyses, along with extensive simulation results, justify, the efficacy of the proposed framework.
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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