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New Innovation of Smart Grid in Complex Systems: Design, Technology, and Optimization

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (7 June 2023) | Viewed by 16187

Special Issue Editors

Dr. Guillaume Guérard

E-Mail Website
Guest Editor
Léonard de Vinci Pole Universitaire, Research Center, 92 916 Paris La Défense, France
Interests: smart grids; complex systems; data mining; knowledge discovery
Dr. Marc Bui

E-Mail Website
Guest Editor
EPHE, PSL Research University, 4-14 Rue Ferrus, 75014 Paris, France
Interests: complex systems; pretopology; agent-based modeling; systemic analysis
Dr. Soufian Ben Amor

E-Mail Website
Guest Editor
LI-PARAD Laboratory EA 7432, University of Versailles, 55 Avenue de Paris, 78035 Versailles, France
Interests: smart grids; complex systems; cyber security; optimization; agent-based modeling

Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to a Special Issue of Energies entitled New Smart Grid Innovation in Complex Systems: Design, Technology, and Optimization. The energy systems form smart grids, so studying their elements, interactions, and structure as complex systems has necessitated innovative methods to control, manage, and optimize them. Many techniques and theories for smart grids have emerged in recent years. Moreover, artificial intelligence and multiagent systems are interesting topics to address the key challenges faced by smart grids, including forecasting, hybrid energy integration, vehicles to grid management, smart home management, and so on.

This Special Issue will deal with novel modeling, simulation, optimization, and management techniques for smart grids. Topics of interest for publication include, but are not limited to:

  • Smart grids;
  • Complex systems;
  • Energy systems;
  • Hybrid energy systems;
  • Artificial intelligence;
  • Optimization techniques;
  • Big Data;
  • Multiagent models;
  • Multiagent simulation

Dr. Guillaume Guérard
Dr. Marc Bui
Dr. Soufian Ben Amor
Guest Editors

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. Energies 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 2600 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

  • complex systems
  • smart grids
  • artificial intelligence
  • big data
  • autonomous systems
  • optimization techniques
  • multiagent systems
  • multiagent simulation
  • hybrid energy systems
  • demand-side management

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Published Papers (4 papers)

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Research

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27 pages, 1486 KiB  
Article
DevOps Model Appproach for Monitoring Smart Energy Systems
by Loup-Noé Lévy, Jérémie Bosom, Guillaume Guerard, Soufian Ben Amor, Marc Bui and Hai Tran
Energies 2022, 15(15), 5516; https://doi.org/10.3390/en15155516 - 29 Jul 2022
Cited by 7 | Viewed by 3268
Abstract
Energy systems are often socio-technical complex systems that are facing new challenges regarding technological and environmental changes. Because of their complex nature, they cannot be approached solely through analytical modeling, hence the inefficiency of most classical modeling approaches. In this article, a Hybrid [...] Read more.
Energy systems are often socio-technical complex systems that are facing new challenges regarding technological and environmental changes. Because of their complex nature, they cannot be approached solely through analytical modeling, hence the inefficiency of most classical modeling approaches. In this article, a Hybrid Approach based on both systemic and analytical modeling is presented and applied to a case study. From this novel approach, a model—the Multi-Institution Building Energy System—is presented. It allowed us to highlight and detail the need for greater governance of energy systems. The socio-technical solutions identified to answer the issues of governance (Accuracy, Reliability and Fairness) were DevOps methodology and the use of Distributed Microservices Architecture. Based on this framework, the design of a Decision Support System assuring and exploiting state-of-the-art scalable tools for data management and machine learning factories is described in this article. Moreover, we wish to set up the conceptual basis necessary for the design of a generic theoretical framework of optimization applicable to complex socio-technical systems in the context of the management of a shared resource. Full article
(This article belongs to the Special Issue New Innovation of Smart Grid in Complex Systems: Design, Technology, and Optimization)
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16 pages, 559 KiB  
Article
A Flexible Deep Learning Method for Energy Forecasting
by Ihab Taleb, Guillaume Guerard, Frédéric Fauberteau and Nga Nguyen
Energies 2022, 15(11), 3926; https://doi.org/10.3390/en15113926 - 26 May 2022
Cited by 14 | Viewed by 3663
Abstract
Load prediction with higher accuracy and less computing power has become an important problem in the smart grids domain in general and especially in demand-side management (DSM), as it can serve to minimize global warming and better integrate renewable energies. To this end, [...] Read more.
Load prediction with higher accuracy and less computing power has become an important problem in the smart grids domain in general and especially in demand-side management (DSM), as it can serve to minimize global warming and better integrate renewable energies. To this end, it is interesting to have a general prediction model which uses different standard machine learning models in order to be flexible enough to be used in different regions and/or countries and to give a prediction for multiple days or weeks with relatively good accuracy. Thus, we propose in this article a flexible hybrid machine learning model that can be used to make predictions of different ranges by using both standard neural networks and an automatic process of updating the weights of these models depending on their past errors. The model was tested on Mayotte Island and the mean absolute percentage error (MAPE) obtained was 1.71% for 30 min predictions, 3.5% for 24 h predictions, and 5.1% for one-week predictions. Full article
(This article belongs to the Special Issue New Innovation of Smart Grid in Complex Systems: Design, Technology, and Optimization)
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Review

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18 pages, 2172 KiB  
Review
The Role of Internet of Things on Electric Vehicle Charging Infrastructure and Consumer Experience
by Nnaemeka V. Emodi, Udochukwu B. Akuru, Michael O. Dioha, Patrick Adoba, Remeredzai J. Kuhudzai and Olusola Bamisile
Energies 2023, 16(10), 4248; https://doi.org/10.3390/en16104248 - 22 May 2023
Cited by 9 | Viewed by 4975
Abstract
The drive for net-zero emission and global decarbonization spurred the need for a worldwide transition towards cleaner energy options. The fossil-fuel-dominated global transportation system is a target for these initiatives, accounting for 37% of recent carbon emissions. This has accelerated the adoption of [...] Read more.
The drive for net-zero emission and global decarbonization spurred the need for a worldwide transition towards cleaner energy options. The fossil-fuel-dominated global transportation system is a target for these initiatives, accounting for 37% of recent carbon emissions. This has accelerated the adoption of electric vehicles (EVs) into the global market to cut down carbon emissions and improve efficiency in the transportation sector. In the face of this growth, limitations in EV charging infrastructure still loom large amongst EV consumers. Resolving this bottleneck requires systematic approaches to ensure seamless operation and integration into the existing transport systems. This study examines the critical role of IoT in addressing the challenges of EV public charging through reviewing the literature to understand the inter-relation and highlighting its attendant impact on consumer experience. Findings show that while IoT serves as a strong tool to foster public interest through favorable public policy, its novel and innovative nature faces developmental challenges based on existing government policies that could hinder the interest of potential investors. Therefore, governments should consider evaluating existing policies and practices to ascertain their suitability for IoT adoption in EVs, ensuring that they do not constitute unintentional barriers. Full article
(This article belongs to the Special Issue New Innovation of Smart Grid in Complex Systems: Design, Technology, and Optimization)
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28 pages, 5119 KiB  
Review
On the Development of Overcurrent Relay Optimization Problem for Active Distribution Networks
by Rene Prenc, Michele Rojnić, Dubravko Franković and Saša Vlahinić
Energies 2022, 15(18), 6528; https://doi.org/10.3390/en15186528 - 7 Sep 2022
Cited by 8 | Viewed by 3244
Abstract
The goal of this review paper will be to address complex optimization functions in the area of overcurrent relay optimization, to inspect and valorize their objectives and to critically examine their application in real distribution networks. Special emphasis will be put on observing [...] Read more.
The goal of this review paper will be to address complex optimization functions in the area of overcurrent relay optimization, to inspect and valorize their objectives and to critically examine their application in real distribution networks. Special emphasis will be put on observing the optimization of discrimination time between primary and backup relay pairs, and methods which prevent the latter becoming penalty functions will be shown. Furthermore, the impact of distributed generation units on the overcurrent relay optimization problem will be elaborated in detail. Protection settings of all the existing and new relays must be thoroughly inspected before the connection of new production units, because contributing fault currents may have a significant effect on the solution of the overcurrent optimization problem. This is because the relays being used are of an inverse-type. Next, meshed network operation will be critically assessed in comparison with the actual practice of radial operation, and some topologies will be highlighted for future research, while others will be discarded. The distribution network in mesh operation implicitly suggests using numerical relays on both sides of protected elements (usually lines), so their total number will be much higher than in radial operation. Finally, a relatively new concept of adaptive distribution network protection will be reviewed, and its potential application to the overcurrent relay optimization problem will be examined. The purpose of this review paper will be to lay a groundwork for future research papers by inspecting the overall development of the overcurrent relay optimization problem for contemporary and future active distribution networks. Full article
(This article belongs to the Special Issue New Innovation of Smart Grid in Complex Systems: Design, Technology, and Optimization)
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