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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 8888

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Special Issue Editor

Dr. Moez Esseghir

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Guest Editor

Laboratory of Computer Science and Digital Society (LIST3N), University of Technology of Troyes (UTT), 10010 Troyes, France
Interests: computer networks; IoT; energy efficiency; cloud–edge; AI; smart grid

Special Issue Information

Dear Colleagues,

The Internet, and more generally networks, are today almost mandatory in each application context. They are one of the most important key enablers for applications to be considered as smart, like those used in smart agriculture or smart industry. Since the number of users and connected devices in networks keep growing, more computational and consequently more energy resources are required. This has a big impact on the environment, specifically in pollution driven by electronical components and by the carbon footprint. Over many decades, researchers have started to tackle that problem and to propose sustainable solutions like recycling and exploiting renewable energies. Still, challenges are growing as technologies, infrastructures, architectures, and applications are always evolving, requiring more and more energy. It is worth noticing that the issue is not only a matter of reducing energy consumption but also of more efficiently managing available resources and specifically energy. The smart grid, with new services like storage, can be a key enabler to efficiently manage the energy demand for networks.

This Special Issue calls for original proposals from academia and industry on improving energy efficiency in networks targeting, but not limited to, algorithms, protocols, and architectures.

Topics of interest include but are not limited to the following:

Energy-efficient networking;
IA energy issues in networks;
Smart grid management/storage service for networks;
Energy-efficient secure networks;
6G energy issues;
Centric-user approaches;
Energy consumption models for physical and virtual network components;
Autonomous vehicles and UAV energy-efficient systems;
Energy-efficient IoT and industrial IoT networks.

Dr. Moez Esseghir
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. 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

network technologies
next generation communication networks
network protocols
network architectures
virtualized functions and infrastructures
energy consumption
energy efficiency
energy management
smart grid
artificial intelligence
user-centric

Published Papers (5 papers)

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Research

21 pages, 5993 KiB

Open AccessArticle

Intelligent Control of the Energy Storage System for Reliable Operation of Gas-Fired Reciprocating Engine Plants in Systems of Power Supply to Industrial Facilities

by Pavel Ilyushin, Sergey Filippov, Aleksandr Kulikov, Konstantin Suslov and Dmitriy Karamov

Energies 2022, 15(17), 6333; https://doi.org/10.3390/en15176333 - 30 Aug 2022

Cited by 11 | Viewed by 1146

Abstract

Gas-fired reciprocating engine plants (GREPs) are widely used in power supply systems of industrial facilities, which allows for ensuring the operation of electrical loads in case of accidents in the power system. Operating experience attests to the fact that during islanded operations, GREPs are shut down by process protections or protective relays in the event of severe disturbances. This leads to complete load shedding, which is accompanied by losses and damage to industrial facilities. Severe disturbances include the following ones: large load surges on GREPs due to one of them being switched off, the group starting of electric motors, and load shedding (more than 50%) during short circuits or disconnection of process lines. Energy storage systems (ESS) have the ability to compensate for instantaneous power imbalances to prevent GREPs from switching off. The authors of this study have developed methods for intelligent control of the ESS that allow one to solve two problems: prevention of GREPs shutdowns under short-term frequency and voltage deviations as well as preservation of the calendar and cycling lifetime of battery storage (BS) of the GREP. The first method does not require performing the calculation of adjustments of control actions for active and reactive power on the ESS online but rather determines them by the value of frequency deviations and the voltage sag configuration, which greatly simplifies the system of automatic control of the ESS. The second method, which consists in dividing the steady-state power/frequency characteristic into sections with different droops that are chosen depending on the current load of the ESS and the battery state of charge, and offsetting it according to a specified pattern, allows for preventing the premature loss of power capacity of the ESS BS. Full article

(This article belongs to the Special Issue Energy in Networks)

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17 pages, 867 KiB

Open AccessArticle

Providing Convenient Indoor Thermal Comfort in Real-Time Based on Energy-Efficiency IoT Network

by Bouziane Brik, Moez Esseghir, Leila Merghem-Boulahia and Ahmed Hentati

Energies 2022, 15(3), 808; https://doi.org/10.3390/en15030808 - 23 Jan 2022

Cited by 9 | Viewed by 2356

Abstract

Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. It enables not only remote real-time detection of situations, but also a timely reaction to reduce the damage made by harmful situations in targeted buildings. In this paper, we first design a new Internet of Things (IoT) architecture in order to provide remote availability of both indoor and outdoor conditions, with respect to the limited energy of IoT devices. We then build a multi-output prediction model of indoor parameters using a random forest learning algorithm, and based on a longitudinal real dataset of one year. Our prediction model considers outdoor conditions to predict the indoor ones. Hence, it helps to detect discomfort situations in real-time when comparing predicted variables to real ones. Furthermore, when detecting an indoor thermal discomfort, we provide a new genetic-based algorithm to find the most suitable values of indoor parameters, enabling the improvement of the indoor occupants’ thermal comfort. Numerical results show the efficiency of our prediction scheme, reaching an accuracy of 96%, as well as our genetic-based scheme in optimizing the indoor thermal parameters by 85%. Full article

(This article belongs to the Special Issue Energy in Networks)

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20 pages, 486 KiB

Open AccessEditor’s ChoiceArticle

Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning

by Mohammad Sadeghi, Shahram Mollahasani and Melike Erol-Kantarci

Energies 2021, 14(22), 7481; https://doi.org/10.3390/en14227481 - 09 Nov 2021

Cited by 2 | Viewed by 1466

Abstract

Microgrids are empowered by the advances in renewable energy generation, which enable the microgrids to generate the required energy for supplying their loads and trade the surplus energy to other microgrids or the macrogrid. Microgrids need to optimize the scheduling of their demands and energy levels while trading their surplus with others to minimize the overall cost. This can be affected by various factors such as variations in demand, energy generation, and competition among microgrids due to their dynamic nature. Thus, reaching optimal scheduling is challenging due to the uncertainty caused by the generation/consumption of renewable energy and the complexity of interconnected microgrids and their interplay. Previous works mainly rely on modeling-based approaches and the availability of precise information on microgrid dynamics. This paper addresses the energy trading problem among microgrids by minimizing the cost while uncertainty exists in microgrid generation and demand. To this end, a Bayesian coalitional reinforcement learning-based model is introduced to minimize the energy trading cost among microgrids by forming stable coalitions. The results show that the proposed model can minimize the cost up to 23% with respect to the coalitional game theory model. Full article

(This article belongs to the Special Issue Energy in Networks)

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20 pages, 3690 KiB

Open AccessArticle

Evaluation of Primary User Power Impact for Joint Optimization of Energy Efficiency in Cognitive Radio Networks

by Tian Yang, Moez Esseghir, Lyes Khoukhi and Su Pan

Energies 2021, 14(21), 7012; https://doi.org/10.3390/en14217012 - 26 Oct 2021

Viewed by 1203

Abstract

Energy efficiency (EE) is of great concern in cognitive radio networks since the throughput and energy consumption of secondary users (SUs) vary with the sensing time. However, the conditions of the detection probability and false alarm probability should be respected to better protect primary users (PUs) and to improve the sensing performance of SUs. Additionally, the PUs’ minimum averaged power provision should also be regarded as a key problem of interactive linking to SUs. Therefore, an integrated design between the PU and SUs is desired for the coordination of the whole cognitive radio system, especially regarding the satisfaction of EE and performance metrics. This study formulates sensing constraints in a unified way and calculates the minimum SNR of SUs, based on which the essential PU power provision is computed. Furthermore, EE is proved as a decreasing function with the PU’s active ratio, where the maximum EE is obtained corresponding to the minimum QoS requirements of the sensing process. Hence, a bisection-based method is proposed to maximize EE, which is considered as a concave function of SUs’ sensing time and has only one unique optimum. EE’s optimization was analyzed under different fusion rules for diverse SNR conditions. The optimum was also studied with sensing performance targets for various cases of PU power provision. Full article

(This article belongs to the Special Issue Energy in Networks)

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18 pages, 629 KiB

Open AccessArticle

Centralized Energy Prediction in Wireless Sensor Networks Leveraged by Software-Defined Networking

by Gustavo A. Nunez Segura and Cintia Borges Margi

Energies 2021, 14(17), 5379; https://doi.org/10.3390/en14175379 - 30 Aug 2021

Cited by 6 | Viewed by 1754

Abstract

Resource Constraints in Wireless Sensor Networks are a key factor in protocols and application design. Furthermore, energy consumption plays an important role in protocols decisions, such as routing metrics. In Software-Defined Networking (SDN)-based networks, the controller is in charge of all control and routing decisions. Using energy as a metric requires such information from the nodes, which would increase packets traffic, impacting the network performance. Previous works have used energy prediction techniques to reduce the number of packets exchanged in traditional distributed routing protocols. We applied this technique in Software-Defined Wireless Sensor Networks (SDWSN). For this, we implemented an energy prediction algorithm for SDWSN using Markov chain. We evaluated its performance executing the prediction on every node and on the SDN controller. Then, we compared their results with the case without prediction. Our results showed that by running the Markov chain on the controller we obtain better prediction and network performance than when running the predictions on every node. Furthermore, we reduced the energy consumption for topologies up to 49 nodes for the case without prediction. Full article

(This article belongs to the Special Issue Energy in Networks)

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