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Energy Management Optimization and Resource Allocation for Electric Vehicles and Quantum Computing in Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (17 August 2023) | Viewed by 5293

Special Issue Editor

Dr. Dhaou Said

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: electric vehicles; smart cities; game theory; power engineering computing; power grids; power markets; pricing; artificial intelligence; power system stability; renewable energy sources

Special Issue Information

Dear Colleagues,

The objective of this Special Issue is to address and disseminate the latest results on various aspects of electrified mobility and its challenges and impacts on the Smart Grid.

In the context of the Modern Power Grid, the success of the Connected Electric Vehicle (CEV) concept depends on its range capability and its battery energy management system in terms of its capacity, the charging and discharging rate, and the availability of the EV Supply Equipment (EVSE). Thus, batteries are expected to be the key to decarbonizing global transport and energy sectors. The CEV is co-locating the consumption and control of electricity based on its specific architecture and the capabilities of Information and Communication Technologies (ICTs). With all these technological improvements that are available today, coupled with the need to make the mobility and the energy management system more efficient, led us to rethink and redesign the way to manage the energy and transportation sector and the way their participants are involved.

The CEVs and the Renewable Energy Sources (RES)s are expected to combat current environmental problems such as air pollution, GHG emissions, climate change, etc. Today, RESs, which can be based on wind, solar, geothermal, and biomass sources, are clean energy sources and can be integrated with recent technological innovations. Thus, energy consumption/production management will be more efficient and optimized for communities. This would include the integration of technologies such as the blockchain, artificial intelligence (AI), mobile technology, and the Internet of Things (IoT) to build innovations, developing new opportunities for the deregulated energy market. As a result, energy management techniques will be crucially linked to the data which can be captured by sensors in the environment, structured, analyzed, stored, managed, and integrated to improve the sustainability, resilience, and reliability of the whole Smart Grid systems, and this will be very challenging. We invite original and unpublished submissions discussing innovative approaches to enhance energy management techniques in the Smart Grid and all relevant applications in ICTs, RESs, electric vehicles, transportation, power electronics, and power systems.

Dr. Dhaou Said
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

  • electric vehicle
  • smart grid
  • supervisory control and data acquisition (SCADA)
  • decentralized techniques
  • blockchain
  • neural networks
  • game theoretic models
  • artificial intelligence techniques for renewable energy systems
  • genetic algorithms
  • deep learning
  • artificial intelligence techniques for power electronics in smart grids
  • fault and fraud detection in smart grid
  • artificial intelligence techniques for energy storage in smart grids
  • artificial intelligence techniques for energy management in smart grids
  • artificial intelligence techniques for smart micro-grids
  • quantum Computing for Smart Grids

Published Papers (2 papers)

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Research

11 pages, 1113 KiB  
Article
Quantum Computing and Machine Learning for Cybersecurity: Distributed Denial of Service (DDoS) Attack Detection on Smart Micro-Grid
by Dhaou Said
Energies 2023, 16(8), 3572; https://doi.org/10.3390/en16083572 - 20 Apr 2023
Cited by 5 | Viewed by 3131
Abstract
Machine learning (ML) is efficiently disrupting and modernizing cities in terms of service quality for mobility, security, robotics, healthcare, electricity, finance, etc. Despite their undeniable success, ML algorithms need crucial computational efforts with high-speed computing hardware to deal with model complexity and commitments [...] Read more.
Machine learning (ML) is efficiently disrupting and modernizing cities in terms of service quality for mobility, security, robotics, healthcare, electricity, finance, etc. Despite their undeniable success, ML algorithms need crucial computational efforts with high-speed computing hardware to deal with model complexity and commitments to obtain efficient, reliable, and resilient solutions. Quantum computing (QC) is presented as a strong candidate to help MLs reach their best performance especially for cybersecurity issues and digital defense. This paper presents quantum support vector machine (QSVM) model to detect distributed denial of service (DDoS) attacks on smart micro-grid (SMG). An evaluation of our approach against a real dataset of DDoS attack instances shows the effectiveness of our proposed model. Finally, conclusions and some open issues and challenges of the fitting of ML with QC are presented. Full article
(This article belongs to the Special Issue Energy Management Optimization and Resource Allocation for Electric Vehicles and Quantum Computing in Smart Grids)
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15 pages, 2151 KiB  
Article
Machine Learning and Game-Theoretic Model for Advanced Wind Energy Management Protocol (AWEMP)
by Imed Khabbouchi, Dhaou Said, Aziz Oukaira, Idir Mellal and Lyes Khoukhi
Energies 2023, 16(5), 2179; https://doi.org/10.3390/en16052179 - 24 Feb 2023
Cited by 7 | Viewed by 1658
Abstract
To meet the target of carbon neutrality by the year 2050 and decrease the dependence on fossil fuels, renewable energy sources (RESs), specifically wind power, and Electric Vehicles (EVs) have to be massively deployed. Nevertheless, the integration of a large amount of wind [...] Read more.
To meet the target of carbon neutrality by the year 2050 and decrease the dependence on fossil fuels, renewable energy sources (RESs), specifically wind power, and Electric Vehicles (EVs) have to be massively deployed. Nevertheless, the integration of a large amount of wind power, with an intermittent nature, into the grid and the variability of the load on the demand side require an efficient and reliable energy management system (EMS) for operation, scheduling, maintenance and energy trading in the modern power system. This article proposes a new Energy Management Protocol (EMP) based on the combination of Machine Learning (ML) and Game-Theoretic (GT) algorithms to manage the operation of the charging/discharging of EVs from an energy storage system (ESS) via EV supply equipment (EVSE) when the main source of energy is wind power. The ESS can be linked to the grid to overcome downtimes of wind power production. Case study results of wind power forecasting using an ML algorithm and 10 min wind measurements, combined with a GT optimization model, showed good performance in the forecasting and management of power dispatching between EVs to ensure the efficient and accurate operation of the power system. Full article
(This article belongs to the Special Issue Energy Management Optimization and Resource Allocation for Electric Vehicles and Quantum Computing in Smart Grids)
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