scholarly journals Configurable DDS as Uniform Middleware for Data Communication in Smart Grids

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1839
Author(s):  
Alaa Alaerjan ◽  
Dae-Kyoo Kim ◽  
Hua Ming ◽  
Hwimin Kim
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Large Scale ◽  
Resource Constraints ◽  
Unified Modeling Language ◽  
Data Communication ◽  
Quality Data ◽  
Object Constraint Language ◽  
Wide Range ◽  
Low Performance

Data Distribution Service (DDS) has emerged as a potential solution for data communication challenges in smart grids. DDS is designed to support quality communication for large scale real-time systems through a wide range of QoS policies. However, a smart grid involves various types of communication applications running on different computing environments. Some environments have limited computing resources such as small memory and low performance, which makes it difficult to accommodate DDS. In this paper, we present a feature-based approach for tailoring DDS to configure lightweight DDS by selecting only the necessary features for the application in consideration of the resource constraints of its running environment. This allows DDS to serve as a uniform communication middleware across the smart grid, which is critical for interoperability. We analyze DDS in terms of features and design them using Unified Modeling Language (UML) and Object Constraint Language (OCL) based on inheritance and overriding. We define a formal notion of feature composition to build DDS configurations. We implemented the approach in OpenDDS and demonstrate its application to different application environments. We also experimented the approach for the efficiency of configured DDS in terms of resource utilization. The results show that configured DDS is viable for efficient and quality data communication for applications that run on an environment with limited computing capability.

Data and Communication Infrastructure in Smart Grids

2022 ◽  
pp. 380-407
Author(s):  
Abdelmadjid Recioui ◽  
Youcef Grainat
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Communication Systems ◽  
Data Communication ◽  
Future Generations ◽  
Wireless Communication Systems ◽  
Communication Link ◽  
Communication Infrastructure ◽  
Grid Applications ◽  
State Of Art

The communication infrastructure constitutes the key element in smart grids. There have been great advances to enhance the way data is communicated among the different smart grid applications. The aim of this chapter is to present the data communication part of the smart grid with some pioneering developments in this topic. A succinct review of the state of art projects to improve the communication link is presented. An illustrative simulation using LABVIEW is included with a proposed idea of introducing some newly technologies involved in the current and future generations of wireless communication systems.

scholarly journals Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2140 ◽  
Author(s):  
Sofana Reka. S ◽  
Tomislav Dragičević ◽  
Pierluigi Siano ◽  
S.R. Sahaya Prabaharan
Keyword(s):  
Smart Grid ◽  
Electricity Markets ◽  
Smart Grids ◽  
Large Scale ◽  
Business Models ◽  
Future Generation ◽  
Current Status ◽  
Complete Analysis ◽  
5G Networks ◽  
5G Network

Wireless cellular networks are emerging to take a strong stand in attempts to achieve pervasive large scale obtainment, communication, and processing with the evolution of the fifth generation (5G) network. Both the present day cellular technologies and the evolving new age 5G are considered to be advantageous for the smart grid. The 5G networks exhibit relevant services for critical and timely applications for greater aspects in the smart grid. In the present day electricity markets, 5G provides new business models to the energy providers and improves the way the utility communicates with the grid systems. In this work, a complete analysis and a review of the 5G network and its vision regarding the smart grid is exhibited. The work discusses the present day wireless technologies, and the architectural changes for the past years are shown. Furthermore, to understand the user-based analyses in a smart grid, a detailed analysis of 5G architecture with the grid perspectives is exhibited. The current status of 5G networks in a smart grid with a different analysis for energy efficiency is vividly explained in this work. Furthermore, focus is emphasized on future reliable smart grid communication with future roadmaps and challenges to be faced. The complete work gives an in-depth understanding of 5G networks as they pertain to future smart grids as a comprehensive analysis.

scholarly journals Composite Enclaves: Towards Disaggregated Trusted Execution

2021 ◽  
pp. 630-656
Author(s):  
Moritz Schneider ◽  
Aritra Dhar ◽  
Ivan Puddu ◽  
Kari Kostiainen ◽  
Srdjan Čapkun
Keyword(s):  
Case Studies ◽  
Large Scale ◽  
Trusted Computing ◽  
Design Time ◽  
Wide Range ◽  
Trusted Computing Base ◽  
Recent Developments ◽  
Configurable Hardware ◽  
Low Performance ◽  
Specialized Hardware

The ever-rising computation demand is forcing the move from the CPU to heterogeneous specialized hardware, which is readily available across modern datacenters through disaggregated infrastructure. On the other hand, trusted execution environments (TEEs), one of the most promising recent developments in hardware security, can only protect code confined in the CPU, limiting TEEs’ potential and applicability to a handful of applications. We observe that the TEEs’ hardware trusted computing base (TCB) is fixed at design time, which in practice leads to using untrusted software to employ peripherals in TEEs. Based on this observation, we propose composite enclaves with a configurable hardware and software TCB, allowing enclaves access to multiple computing and IO resources. Finally, we present two case studies of composite enclaves: i) an FPGA platform based on RISC-V Keystone connected to emulated peripherals and sensors, and ii) a large-scale accelerator. These case studies showcase a flexible but small TCB (2.5 KLoC for IO peripherals and drivers), with a low-performance overhead (only around 220 additional cycles for a context switch), thus demonstrating the feasibility of our approach and showing that it can work with a wide range of specialized hardware.

scholarly journals Exploring the Privacy Issues of Smart-Grid Infrastructure – A Network Security Perspective

2019 ◽  
pp. 165-169
Keyword(s):  
Network Security ◽  
Smart Grid ◽  
Real Time ◽  
Smart Grids ◽  
Data Communication ◽  
Grid Infrastructure ◽  
Smart Meters ◽  
Holistic View ◽  
The Public ◽  
Public Networks

The proposed smart grid infrastructure aims to make use of the existing public networks such as internet for data communication between consumer premises to the public power utility network. The smart-grid adopts smart-meters which basically collect vast amount of data to provide a holistic view of the connected load behavior and preferences pattern related to power and water consumption. The smart-grids provide benefits to the utilities and consumers alike. For utilities the benefits are real time data collection, ease of power management, and reduced personnel requirement. The benefits for the users on the other hand include availability of real time usage data, providing information on ways to minimize power consumption, monetary savings and so on. Since, the smart-grid uses existing public networks the utilities do not have the burden of installing any new infrastructure (except for installing the smart-meters), thus an added advantage. But, the downside of using the public network is susceptibility to a variety of network attacks, if not guarded well against. This paper talks about the various network security vulnerabilities that exist and the measures to patch the same before employing in the smart grid networks.

scholarly journals Evaluation of big data frameworks for analysis of smart grids

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohammad Hasan Ansari ◽  
Vahid Tabatab Vakili ◽  
Behnam Bahrak
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Cyber Security ◽  
Smart Grids ◽  
Large Scale ◽  
Rapid Development ◽  
Massive Data ◽  
Grid Data ◽  
Data Generator ◽  
And Performance

AbstractWith the rapid development of smart grids and increasing data collected in these networks, analyzing this massive data for applications such as marketing, cyber-security, and performance analysis, has gained popularity. This paper focuses on analysis and performance evaluation of big data frameworks that are proposed for handling smart grid data. Since obtaining large amounts of smart grid data is difficult due to privacy concerns, we propose and implement a large scale smart grid data generator to produce massive data under conditions similar to those in real smart grids. We use four open source big data frameworks namely Hadoop-Hbase, Cassandra, Elasticsearch, and MongoDB, in our implementation. Finally, we evaluate the performance of different frameworks on smart grid big data and present a performance benchmark that includes common data analysis techniques on smart grid data.

Data and Communication Infrastructure in Smart Grids

2021 ◽  
pp. 1-28
Author(s):  
Abdelmadjid Recioui ◽  
Youcef Grainat
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Communication Systems ◽  
Data Communication ◽  
Future Generations ◽  
Wireless Communication Systems ◽  
Communication Link ◽  
Communication Infrastructure ◽  
Grid Applications ◽  
State Of Art

The communication infrastructure constitutes the key element in smart grids. There have been great advances to enhance the way data is communicated among the different smart grid applications. The aim of this chapter is to present the data communication part of the smart grid with some pioneering developments in this topic. A succinct review of the state of art projects to improve the communication link is presented. An illustrative simulation using LABVIEW is included with a proposed idea of introducing some newly technologies involved in the current and future generations of wireless communication systems.

scholarly journals Evaluation of Sequence Learning Models for Large Commercial Building Load Forecasting

2019 ◽  
Author(s):  
Cristina Nichiforov ◽  
Grigore Stamatescu ◽  
Iulia Stamatescu ◽  
Ioana Fagarasan
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Energy Management ◽  
Smart Grids ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Critical Role ◽  
Load Forecasting ◽  
Quality Data

Buildings have started to play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large scale energy management strategies from the supply side to the consumer side. When the buildings integrate local renewable energy generation in the form of renewable energy resources they become prosumers and this reflects into additional complexity into the operation of the interconnected complex energy systems. A class of methods of modelling the energy consumption patterns of the building have recently emerged as black-box input-output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produces by non-deterministic processes underlying the energy consumption. We present an application of a class of neural networks, namely deep learning techniques for time series sequence modelling with the goal of accurate and reliable building energy load forecasting. The Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects and are considered suitable for further used in future in situ energy management at the building and neighbourhood levels.

Future Directions to the Application of Distributed Fog Computing in Smart Grid Systems

2018 ◽  
pp. 162-195 ◽  
Author(s):  
Arash Anzalchi ◽  
Aditya Sundararajan ◽  
Longfei Wei ◽  
Amir Moghadasi ◽  
Arif Sarwat
Keyword(s):  
Smart Grid ◽  
Large Scale ◽  
New Technologies ◽  
Fog Computing ◽  
Data Communication ◽  
Grid Systems ◽  
Challenges And Opportunities ◽  
Future Challenges ◽  
Large Scale Integration ◽  
Integration Of Renewables

The rapid growth of new technologies in power systems requires real-time monitoring and control of bidirectional data communication and electric power flow. Cloud computing has centralized architecture and is not scalable towards the emerging internet of things (IoT) landscape of the grid. Further, under large-scale integration of renewables, this framework could be bogged down by congestion, latency, and subsequently poor quality of service (QoS). This calls for a distributed architecture called fog computing, which imbibes both clouds as well as the end-devices to collect, process, and act upon the data locally at the edge for low latency applications prior to forwarding them to the cloud for more complex operations. Fog computing offers high performance and interoperability, better scalability and visibility, and greater availability in comparison to a grid relying only on the cloud. In this chapter, a prospective research roadmap, future challenges, and opportunities to apply fog computing on smart grid systems is presented.

Future Directions to the Application of Distributed Fog Computing in Smart Grid Systems

2019 ◽  
pp. 2186-2212 ◽  
Author(s):  
Arash Anzalchi ◽  
Aditya Sundararajan ◽  
Longfei Wei ◽  
Amir Moghadasi ◽  
Arif Sarwat
Keyword(s):  
Smart Grid ◽  
Large Scale ◽  
New Technologies ◽  
Fog Computing ◽  
Data Communication ◽  
Grid Systems ◽  
Challenges And Opportunities ◽  
Future Challenges ◽  
Large Scale Integration ◽  
Integration Of Renewables

The rapid growth of new technologies in power systems requires real-time monitoring and control of bidirectional data communication and electric power flow. Cloud computing has centralized architecture and is not scalable towards the emerging internet of things (IoT) landscape of the grid. Further, under large-scale integration of renewables, this framework could be bogged down by congestion, latency, and subsequently poor quality of service (QoS). This calls for a distributed architecture called fog computing, which imbibes both clouds as well as the end-devices to collect, process, and act upon the data locally at the edge for low latency applications prior to forwarding them to the cloud for more complex operations. Fog computing offers high performance and interoperability, better scalability and visibility, and greater availability in comparison to a grid relying only on the cloud. In this chapter, a prospective research roadmap, future challenges, and opportunities to apply fog computing on smart grid systems is presented.

scholarly journals Muti-technique observations and modelling of the gas and dust phases of protoplanetary disks

2009 ◽  
Vol 5 (H15) ◽  
pp. 767-767
Author(s):  
C. Pinte ◽  
F. Ménard ◽  
G. Duchěne ◽  
J. C. Augereau
Keyword(s):  
Gas Phase ◽  
Observational Data ◽  
Large Scale ◽  
Scattered Light ◽  
Protoplanetary Disks ◽  
Quality Data ◽  
Data Sets ◽  
Wide Range ◽  
Dust Component ◽  
Chemical Conditions

A wide range of high-quality data is becoming available for protoplanetary disks. From these data sets many issues have already been addressed, such as constraining the large scale geometry of disks, finding evidence of dust grain evolution, as well as constraining the kinematics and physico-chemical conditions of the gas phase. Most of these results are based on models that emphasise fitting observations of either the dust component (SEDs or scattered light images or, more recently, interferometric visibilities), or the gas phase (resolved maps in molecular lines). In this contribution, we present a more global approach which aims at interpreting consistently the increasing amount of observational data in the framework of a single model, in order to to better characterize both the dust population and the gas disk properties, as well as their interactions. We present results of such modeling applied to a few disks (e.g. IM Lup, see Figure) with large observational data-sets available (scattered light images, polarisation maps, IR spectroscopy, X-ray spectrum, CO maps). These kinds of multi-wavelengths studies will become very powerful in the context of forthcoming instruments such as Herschel and ALMA.

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