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Applied Sciences
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Communications for Smart Grids, Energy Internet, and Digital Grids
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Communications for Smart Grids, Energy Internet, and Digital Grids

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 7949

Special Issue Editors

Prof. Dr. Roberto Rojas-Cessa

E-Mail Website
Guest Editor
Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: networking; network measurement; switching; routing; peer-to-peer
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed Mohamed

E-Mail Website
Co-Guest Editor
Department of Electrical Engineering, Grove School of Engineering City University of New York, City College, New York, NY 10031, USA
Interests: smart grids; critical infrastructure interdependency; transportation electrification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Recent advances in computing have provided a stronger capability in communicating and analyzing the sensed information on the status of the power grid, loads, and factors that influence load forecasting and management of the grid operation. Furthermore, the rise of data-controlled power grids is leading to the emergence of paradigms that aim at enhancing the management of the grid and increasing its efficiency. Examples of such paradigms are Energy Internet and digital (micro) grids. This Special Issue aims to collect original works on the applications of communications in the smart grid, the associated challenges, and a forecast of the upcoming problems, solutions, and technologies that make the grid smarter, efficient, and highly resilient and reliable. The scope of this Special Issue also encompasses works that consider the problems associated with future microgrids that comprise a dense network of alternative energy sources.

This Special Issue seeks original works on topics including, but not exclusive to:

- Issues and challenges of future smart grids;

- Data-controlled microgrids;

- Sensor networks in power grids;

- Machine and deep learning case studies;

- Distributed decision-making algorithms;

- Cyber-physical systems and modeling;

- Smart grids and cloud computing;

- Visualization of power-data techniques;

- Data management and analytics;

- Management of renewable energy;

- Grids of the future;

- Energy Internet;

- Digital power grids;

- 5G and mmWave on the power grid;

- Impact of energy storage and electrical vehicles on the power grid.

Prof. Dr. Roberto Rojas-Cessa
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • issues and challenges of the future smart grids 
  • data-controlled microgrids 
  • sensor networks in power grids 
  • machine and deep learning case studies 
  • distributed decision-making algorithms 
  • cyber-physical systems and modeling 
  • smart grids and cloud computing 
  • visualization of power-data techniques 
  • data management and analytics 
  • management of renewable energy 
  • grids of the future 
  • Energy Internet 
  • digital power grids 
  • 5G and mmWave on the power grid 
  • impact of energy storage and electrical vehicles on the power grid

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

18 pages, 2348 KiB  
Article
MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
by Wenxin Lei, Hong Wen, Jinsong Wu and Wenjing Hou
Appl. Sci. 2021, 11(7), 3101; https://doi.org/10.3390/app11073101 - 31 Mar 2021
Cited by 46 | Viewed by 4905
Abstract
Advanced communication and information technologies enable smart grids to be more intelligent and automated, although many security issues are emerging. Security situational awareness (SSA) has been envisioned as a potential approach to provide safe services for power systems’ operation. However, in the power [...] Read more.
Advanced communication and information technologies enable smart grids to be more intelligent and automated, although many security issues are emerging. Security situational awareness (SSA) has been envisioned as a potential approach to provide safe services for power systems’ operation. However, in the power cloud master station mode, massive heterogeneous power terminals make SSA complicated, and failure information cannot be promptly delivered. Moreover, the dynamic and continuous situational space also increases the challenges of SSA. By taking advantages of edge intelligence, this paper introduces edge computing between terminals and the cloud to address the drawbacks of the traditional power cloud paradigm. Moreover, a deep reinforcement learning algorithm based on the edge computing paradigm of multiagent deep deterministic policy gradient (MADDPG) is proposed. The minimum processing cost under the premise of minimum detection error rate is taken to analyze the smart grids’ SSA. Performance evaluations show that the algorithm under this paradigm can achieve faster convergence and the optimal goal, namely the provision of real-time protection for smart grids. Full article
(This article belongs to the Special Issue Communications for Smart Grids, Energy Internet, and Digital Grids)
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18 pages, 854 KiB  
Article
Minimizing Energy and Computation in Long-Running Software
by Erol Gelenbe and Miltiadis Siavvas
Appl. Sci. 2021, 11(3), 1169; https://doi.org/10.3390/app11031169 - 27 Jan 2021
Cited by 6 | Viewed by 2436
Abstract
Long-running software may operate on hardware platforms with limited energy resources such as batteries or photovoltaic, or on high-performance platforms that consume a large amount of energy. Since such systems may be subject to hardware failures, checkpointing is often used to assure the [...] Read more.
Long-running software may operate on hardware platforms with limited energy resources such as batteries or photovoltaic, or on high-performance platforms that consume a large amount of energy. Since such systems may be subject to hardware failures, checkpointing is often used to assure the reliability of the application. Since checkpointing introduces additional computation time and energy consumption, we study how checkpoint intervals need to be selected so as to minimize a cost function that includes the execution time and the energy. Expressions for both the program’s energy consumption and execution time are derived as a function of the failure probability per instruction. A first principle based analysis yields the checkpoint interval that minimizes a linear combination of the average energy consumption and execution time of the program, in terms of the classical “Lambert function”. The sensitivity of the checkpoint to the importance attributed to energy consumption is also derived. The results are illustrated with numerical examples regarding programs of various lengths and showing the relation between the checkpoint interval that minimizes energy consumption and execution time, and the one that minimizes a weighted sum of the two. In addition, our results are applied to a popular software benchmark, and posted on a publicly accessible web site, together with the optimization software that we have developed. Full article
(This article belongs to the Special Issue Communications for Smart Grids, Energy Internet, and Digital Grids)
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