Smart grid encounters edge computing: opportunities and applications

Feng, Cheng; Wang, Yi; Chen, Qixin; Ding, Yi; Štrbac, Goran; Chen, Kang

doi:10.1016/j.adapen.2020.100006

Cited by 100 publications

(68 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, the smart power grid has proved to be a key component for sustainable next-generation energy systems. Smart Grid Energy Management System (SGEMS), in collaboration with the internet, has paved the way to tackle with the energy generation, transmission, storage, consumption, and market using the Internet-based smart operation cloud platforms, to perform remote control optimization and improve operation efficiency by delivering the proper services [1,19,20]. SGEMS has the ability to operate across its domain, whether a city, a local community, or a university campus.…”

Section: Smart Power Distribution Systemsmentioning

confidence: 99%

“…Smart grid is a collective platform of multiple technologies connected to a common network, either in a centralized or decentralized way. The smart grid network consists of the end devices, grid operators, producers and consumers [1,2]. Load forecasting greatly influences the smart grid operations, including energy purchasing, energy storage, peak load shaving, load dispatching, and electric vehicle scheduling for charging/discharging in the smart power distribution system.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional power grids have been evolved to smart grids in which electricity consumers are replaced with prosumers that can produce and consume electricity. Distributed energy resources (DERs) such as energy storage systems (ESSs), renewable energy sources (RESs), and electric vehicles (EVs) are playing a vital role in the transition towards the smart grid [1,3].…”

Section: Introductionmentioning

confidence: 99%

“…The transition from the conventional and unidirectional power grid to smart power grids has made the opportunities available to deal with the demand and supply curve intelligently. With IoT assistance, the energy management in smart power grids is able to reach every corner of a city or a local area to collect the data and the behavior of the grid entities [1,2], which paved the way to utilize that data to improve the system at a higher level (city/microgrid).…”

mentioning

confidence: 99%

See 3 more Smart Citations

Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System

2021

View full text Add to dashboard Cite

An efficient energy management system is integrated with the power grid to collect information about the energy consumption and provide the appropriate control to optimize the supply–demand pattern. Therefore, there is a need for intelligent decisions for the generation and distribution of energy, which is only possible by making the correct future predictions. In the energy market, future knowledge of the energy consumption pattern helps the end-user to decide when to buy or sell the energy to reduce the energy cost and decrease the peak consumption. The Internet of things (IoT) and energy data analytic techniques have provided the convenience to collect the data from the end devices on a large scale and to manipulate all the recorded data. Forecasting an electric load is fairly challenging due to the high uncertainty and dynamic nature involved due to spatiotemporal pattern consumption. Existing conventional forecasting models lack the ability to deal with the spatio-temporally varying data. To overcome the above-mentioned challenges, this work proposes an encoder–decoder model based on convolutional long short-term memory networks (ConvLSTM) for energy load forecasting. The proposed architecture uses encode consisting of multiple ConvLSTM layers to extract the salient features in the data and to learn the sequential dependency and then passes the output to the decoder, having LSTM layers to make forecasting. The forecasting results produced by the proposed approach are favorably comparable to the existing state-of-the-art and better than the conventional methods with the least error rate. Quantitative analyses show that a mean absolute percentage error (MAPE) of 6.966% for household energy consumption and 16.81% for city-wide energy consumption is obtained for the proposed forecasting model in comparison with existing encoder–decoder-based deep learning models for two real-world datasets.

show abstract

Section: Smart Power Distribution Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System

2021

View full text Add to dashboard Cite

show abstract

“…A more recent concept than cloud computing, edge computing (EC), which consists of data processing performed close to the IoT sensor or device instead in the cloud, has begun to be promoted as a suitable option for SG management. EC provides several benefits for the SG [94] that are also useful for energy management in buildings: reduction of processing latency for time-sensitive applications (load control, DR) and support for the application of cognitive solutions (data fusion, reinforcement learning), while at the same time fostering interoperability among the different elements and systems of the SG and the interactions between the SG and these systems (users, buildings, energy sources).…”

mentioning

confidence: 99%

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

et al. 2021

View full text Add to dashboard Cite

During the last decade, the smart grid (SG) concept has started to become a reality, mainly thanks to the technical progress achieved in telecommunications, informatics and power electronics, among other domains, leading to an evolution of the traditional electrical grid into an intelligent one. Nowadays, the SG can be seen as a system of smart systems that include cyber and physical parts from different technologies that interact with each other. In this context, intelligent buildings (IBs) constitute a paradigm in which such smart systems are able to guarantee the comfort of residents while ensuring an appropriate tradeoff of energy production and consumption by means of an energy management system (EMS). These interconnected EMSs remain the objective of potential cyber-attacks, which is a major concern. Therefore, this paper conducts a survey, from a multidisciplinary point of view, of some of the main security and privacy issues related to IBs as part of the SG, including an overview of EMS, smart meters, and the main communication networks employed to connect IBs to the overall SG. Future research directions towards a security enhancement from both technical and human perspectives are also provided.

show abstract

Sustainable edge computing: Challenges and future directions

Arroba,

Buyya,

Cárdenas

et al. 2024

Softw Pract Exp

View full text Add to dashboard Cite

The advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

show abstract

Smart grid encounters edge computing: opportunities and applications

Cited by 100 publications

References 77 publications

Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System

Efficient Energy Management Based on Convolutional Long Short-Term Memory Network for Smart Power Distribution System

Intelligent Buildings in Smart Grids: A Survey on Security and Privacy Issues Related to Energy Management

Sustainable edge computing: Challenges and future directions

Contact Info

Product

Resources

About