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Selected Papers from the 5th International Conference on Smart Grid...
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Selected Papers from the 5th International Conference on Smart Grid and Smart Cities (ICSGSC2021)

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 (16 November 2021) | Viewed by 9511

Special Issue Editors

Prof. Dr. Om P. Malik

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
Interests: digital protection; adaptive control; control of renewable power generation and microgrids; AI applications in power system control
Special Issues, Collections and Topics in MDPI journals
Prof. Abdulrahman Khalaf Al-Ali

E-Mail Website
Guest Editor
Computer Science and Engineering Dept., American University of Sharjah, Sharjah, UAE
Interests: cyberphysical systems; smart city applications, including smart grid and smart energy

Special Issue Information

Dear Colleagues,

The application of smart grids and smart cities is now advancing from a conceptual model to the actual development and implementation phase. Many applications, such as smart grid and smart energy, smart homes and smart building, smart farming and smart environment, and smart transportation and smart manufacturing, are gaining ground in academia, industrial research and development, and the business sector. Distributed and decentralized renewable energy resources are needed to complement traditional energy resources. Such energy integration will empower smart city applications where billions of Internet of Things devices and objects require power to operate efficiently. This Special Issue targets a wide range of topics related to smart energy and smart grid supply as well as other smart city applications. Some of the topics included are:

Smart Grid and Smart Energy Topics:

  • Smart and advanced sensors
  • Metering infrastructure
  • Power and energy system applications
  • Impact of smart grid on distributed energy resources    
  • Energy management systems
  • Smart grid interoperability and standards
  • Critical infrastructure resiliency
  • Smart grid security systems

Smart City Topics

  • Smart city roadmap and applications
  • AI, machine learning and deep learning role in smart city applications
  • Fog and cloud computing platforms’ role in smart city applications
  • Smart city cyber security threats and solutions. 
  • Smart city applications’ role in environmental sustainability 

Prof. Dr. Om P. Malik
Prof. Abdulrahman Khalaf Al-Ali
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

  • Smart energy and smart grid
  • Smart homes, smart buildings and smart farming
  • Smart transportation and mobility
  • Smart health and smart manufacturing 
  • AI, machine, and deep learning applications in smart systems 
  • Cyber security threats and solutions for smart applications

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Published Papers (3 papers)

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Research

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19 pages, 4823 KiB  
Article
Fog Computing Approach for Shared Mobility in Smart Cities
by Raafat Aburukba, A. R. Al-Ali, Ahmed H. Riaz, Ahmad Al Nabulsi, Danayal Khan, Shavaiz Khan and Moustafa Amer
Energies 2021, 14(23), 8174; https://doi.org/10.3390/en14238174 - 6 Dec 2021
Cited by 6 | Viewed by 2437
Abstract
Smart transportation a smart city application where traditional individual models are transforming to shared and distributed ownership. These models are used to serve commuters for inter- and intra-city travel. However, short-range urban transportation services within campuses, residential compounds, and public parks are not [...] Read more.
Smart transportation a smart city application where traditional individual models are transforming to shared and distributed ownership. These models are used to serve commuters for inter- and intra-city travel. However, short-range urban transportation services within campuses, residential compounds, and public parks are not explored to their full capacity compared to the distributed vehicle model. This paper aims to explore and design an adequate framework for battery-operated shared mobility within a large community for short-range travel. This work identifies the characteristics of the shared mobility for battery-operated vehicles and accordingly proposes an adequate solution that deals with real-time data collection, tracking, and automated decisions. Furthermore, given the requirement for real-time decisions with low latency for critical requests, the paper deploys the proposed framework within the 3-tier computing model, namely edge, fog, and cloud tiers. The solution design considers the power consumption requirement at the edge by offloading the computational requests to the fog tier and utilizing the LoRaWAN communication technology. A prototype implementation is presented to validate the proposed framework for a university campus using e-bikes. The results show the scalability of the proposed design and the achievement of low latency for requests that require real-time decisions. Full article
(This article belongs to the Special Issue Selected Papers from the 5th International Conference on Smart Grid and Smart Cities (ICSGSC2021))
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16 pages, 1858 KiB  
Article
Probabilistic Load Flow–Based Optimal Placement and Sizing of Distributed Generators
by Ferdous Al Hossain, Md. Rokonuzzaman, Nowshad Amin, Jianmin Zhang, Mahmuda Khatun Mishu, Wen-Shan Tan, Md. Rabiul Islam and Rajib Baran Roy
Energies 2021, 14(23), 7857; https://doi.org/10.3390/en14237857 - 23 Nov 2021
Cited by 6 | Viewed by 2269
Abstract
Distributed generation (DG) is gaining importance as electrical energy demand increases. DG is used to decrease power losses, operating costs, and improve voltage stability. Most DG resources have less environmental impact. In a particular region, the sizing and location of DG resources significantly [...] Read more.
Distributed generation (DG) is gaining importance as electrical energy demand increases. DG is used to decrease power losses, operating costs, and improve voltage stability. Most DG resources have less environmental impact. In a particular region, the sizing and location of DG resources significantly affect the planned DG integrated distribution network (DN). The voltage profiles of the DN will change or even become excessively increased. An enormous DG active power, inserted into an improper node of the distribution network, may bring a larger current greater than the conductor’s maximum value, resulting in an overcurrent distribution network. Therefore, DG sizing and DG location optimization is required for a systematic DG operation to fully exploit distributed energy and achieve mutual energy harmony across existing distribution networks, which creates an economically viable, secure, stable, and dependable power distribution system. DG needs to access the location and capacity for rational planning. The objective function of this paper is to minimize the sum of investment cost, operation cost, and line loss cost utilizing DG access. The probabilistic power flow calculation technique based on the two-point estimation method is chosen for this paper’s load flow computation. The location and size of the DG distribution network are determined using a genetic algorithm in a MATLAB environment. For the optimum solution, the actual power load is estimated using historical data. The proposed system is based on the China distribution system, and the currency is used in Yuan. After DG access, active and reactive power losses are reduced by 53% and 26%, respectively. The line operating cost and the total annual cost are decreased by 53.7% and 12%, respectively. Full article
(This article belongs to the Special Issue Selected Papers from the 5th International Conference on Smart Grid and Smart Cities (ICSGSC2021))
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Review

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19 pages, 2594 KiB  
Review
Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review
by Sanchari Deb
Energies 2021, 14(23), 7833; https://doi.org/10.3390/en14237833 - 23 Nov 2021
Cited by 8 | Viewed by 3966
Abstract
As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is [...] Read more.
As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is an intricate process involving various activities, such as charging station placement, charging demand prediction, and charging scheduling. This planning process involves interactions between power distribution and the road network. The advent of machine learning has made data-driven approaches a viable means for solving charging infrastructure planning problems. Consequently, researchers have started using machine learning techniques to solve the aforementioned problems associated with charging infrastructure planning. This work aims to provide a comprehensive review of the machine learning applications used to solve charging infrastructure planning problems. Furthermore, three case studies on charging station placement and charging demand prediction are presented. This paper is an extension of: Deb, S. (2021, June). Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. In the 2021 5th International Conference on Smart Grid and Smart Cities (ICSGSC) (pp. 16–22). IEEE. I would like to confirm that the paper has been extended by more than 50%. Full article
(This article belongs to the Special Issue Selected Papers from the 5th International Conference on Smart Grid and Smart Cities (ICSGSC2021))
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