A Novel Edge-Supported Cost-Efficient Resource Management Approach for Smart Grid System

Mishra, Jyotirmaya; Sheetlani, Jitendra; Reddy, K. Hemant Kumar; Roy, Diptendu Sinha

doi:10.1007/978-981-10-7871-2_36

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…One of the most frequently used definitions of SG is the ability to integrate information, two-way, cyber-secure communication technologies, and computational intelligence across the vast bulk of the electricity supply chain (generation, transmission, distribution, and consumption) in order to achieve a system that is clean, safe, secured, reliable, resilient, efficient, and sustainable [1,2,96]. It is considered the next generation of power grids [12,97], and also one of the main subsets of smart cities powered by IoT [98].…”

Section: Smart Grid: a General Overviewmentioning

confidence: 99%

“…Smart grids (SG) are widely recognized as the "next-generation power grids" comprising sophisticated cyber-secured information and communication technologies (ICT), bidirectional information and electricity infrastructure, and network-integrated computational intelligence [1][2][3]. The concept of SGs evolved around the year 2000 as a feasible solution to the substantial challenges connected with the old (legacy) power grid system [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Smart devices (e.g., smart meters, sensors, and smart electrical appliances) are utilized in SGs to generate data via different applications and services. Currently, many SG designs rely significantly on cloud computing to store, analyse, and process data in preparation for future control commands and decision-making purposes [1,11,12]. However, existing traditional cloud computing platforms, on the other hand, have a number of shortcomings, including large communication latency, network and deployment complexities, all of which restrict their use in real-time applications [13].…”

Section: Introductionmentioning

confidence: 99%

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Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Molokomme

Onumanyi

Abu‐Mahfouz

2022

JSAN

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The rapid development of new information and communication technologies (ICTs) and the deployment of advanced Internet of Things (IoT)-based devices has led to the study and implementation of edge computing technologies in smart grid (SG) systems. In addition, substantial work has been expended in the literature to incorporate artificial intelligence (AI) techniques into edge computing, resulting in the promising concept of edge intelligence (EI). Consequently, in this article, we provide an overview of the current state-of-the-art in terms of EI-based SG adoption from a range of angles, including architectures, computation offloading, and cybersecurity concerns. The basic objectives of this article are fourfold. To begin, we discuss EI and SGs separately. Then we highlight contemporary concepts closely related to edge computing, fundamental characteristics, and essential enabling technologies from an EI perspective. Additionally, we discuss how the use of AI has aided in optimizing the performance of edge computing. We have emphasized the important enabling technologies and applications of SGs from the perspective of EI-based SGs. Second, we explore both general edge computing and architectures based on EI from the perspective of SGs. Thirdly, two basic questions about computation offloading are discussed: what is computation offloading and why do we need it? Additionally, we divided the primary articles into two categories based on the number of users included in the model, either a single user or a multiple user instance. Finally, we review the cybersecurity threats with edge computing and the methods used to mitigate them in SGs. Therefore, this survey comes to the conclusion that most of the viable architectures for EI in smart grids often consist of three layers: device, edge, and cloud. In addition, it is crucial that computation offloading techniques must be framed as optimization problems and addressed effectively in order to increase system performance. This article typically intends to serve as a primer for emerging and interested scholars concerned with the study of EI in SGs.

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Section: Smart Grid: a General Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Molokomme

Onumanyi

Abu‐Mahfouz

2022

JSAN

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“…Various researchers [3,14,16,21,70] have proposed features of edge computing that fit to the requirements of smart grid applications implementation. The following are the features: Support for real-time, that is, reducing latency and responsive time [38,[47][48][49][50][51], Support for Scalability [68], Decentralization [6, 16-18, 51, 52, 68], Consistency [19], Heterogeneity [20][21][22] , Intelligent coordination [23][24][25][26][27] , Optimal design / Resource utilization [28][29][30][31][32][33][34][35][36], Privacy and Security [16,17,37,[39][40][41][42], Data Mining and Storage [43][44][45], Virtualization [46], Energy Efficiency [51,71].…”

Section: Research Question Onementioning

confidence: 99%

A Critical Review of Edge and Fog Computing for Smart Grid Applications

Gilbert

Naiman

Kimaro

et al. 2019

IFIP Advances in Information and Communication Technology

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Smart grid and cloud computing architectures have been perfectly suiting each other naturally. As a result, over the years cloud computing architectures have dominated the implementations of smart grid applications to address computing needs. However, due to continuing additions of heterogeneous (sensing and actuating) devices, emergence of Internet of Things (IoT), and massive amount of data collected across the grids for analytics, have contributed to the complexity of smart grids, making cloud computing architectures no longer suitable to provide smart grid services effectively. Edge and Fog computing approaches have relieved the cloud computing architectures of problems related to network congestion, latency and locality by shift of control, intelligence and trust to the edge of the network. In this paper, a systematic literature review is used to explore the research trend of the actual implementations of edge and fog computing for smart grid applications. A total of 70 papers were reviewed from the popular digital repositories. The study has revealed that, there is significant increase in the number of smart grid applications that have exploited the use edge and fog computing approaches. The study also shows that, considerable number of the smart grid applications are related to energy optimizations and intelligent coordination of smart grid resources. There are also challenges and issues that hinder smooth adoption of edge and fog computing for smart grid applications, which include security, interoperability and programming models.

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Secure Data Access Control With Fair Accountability in Smart Grid Data Sharing: An Edge Blockchain Approach

Yang

Guan

et al. 2021

IEEE Internet Things J.

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A Novel Edge-Supported Cost-Efficient Resource Management Approach for Smart Grid System

Cited by 3 publications

References 15 publications

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

Edge Intelligence in Smart Grids: A Survey on Architectures, Offloading Models, Cyber Security Measures, and Challenges

A Critical Review of Edge and Fog Computing for Smart Grid Applications

Secure Data Access Control With Fair Accountability in Smart Grid Data Sharing: An Edge Blockchain Approach

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