{"title":"Secured energy data transaction for prosumers under diverse cyberattack scenarios","authors":"","doi":"10.1016/j.segan.2024.101555","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Energy Grids & Networks","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352467724002844","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.