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Electronics
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Selected Papers from Young Researchers in Energy Systems and Management
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Selected Papers from Young Researchers in Energy Systems and Management

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 5885

Special Issue Editors

Dr. Noman Shabbir

E-Mail Website
Guest Editor
FinEst Centre for Smart Cities, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: renewable energy systems; energy management; energy forecasting energy flexibility; AI applications in energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Brunelli

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Trento, I-38123 Trento, Italy
Interests: energy harvesting; ultra-low-power sensors; IoT; energy-neutral devices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main goal of this Special Issue is to offer young researchers an opportunity to share their research results through the MPDI Electronics open access platform, in which they can report their recent achievements in terms of future energy systems, including renewable energy systems, energy management, and energy efficiency.

Energy management and efficiency are of prime importance in the current day scenarios. The installation of nearly zero energy building and future towards energy communities/districts and smart cities make it more important.

The topics included in this Special Issue are renewable energy systems, renewable energy integration, electric vehicles, smart grids, energy flexibility, energy management, nearly zero energy buildings, energy communities, and smart cities. 

Dr. Noman Shabbir
Dr. Davide Brunelli
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. Electronics 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

  • renewable energy systems
  • energy management
  • energy flexibility
  • energy communities
  • smart grids

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

Published Papers (2 papers)

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Research

22 pages, 2018 KiB  
Article
DC-Link Ripple Reduction for Parallel Inverter Systems by a Novel Formulation Using Multiple Space Vector-Based Interleaving Schemes
by Akbar Ali Khan, Nauman Ahmad Zaffar and Muhammad Jahangir Ikram
Electronics 2023, 12(6), 1496; https://doi.org/10.3390/electronics12061496 - 22 Mar 2023
Cited by 3 | Viewed by 2454
Abstract
This paper proposes an analytical formulation-based minimization of DC link current ripples for interleaved parallel inverter systems. Parallel inverter systems find applications in multiple fields. The interleaved superposition of the DC link currents in these systems can potentially be adjusted to mitigate the [...] Read more.
This paper proposes an analytical formulation-based minimization of DC link current ripples for interleaved parallel inverter systems. Parallel inverter systems find applications in multiple fields. The interleaved superposition of the DC link currents in these systems can potentially be adjusted to mitigate the overall harmonics consequently reducing the DC link capacitor size. To this end, a widely used approach in the literature is the Fourier analysis based on interleaving focusing on dominant harmonic mitigation. However, it leaves room for a generic analytical mechanism to provide time shifts leading to an optimal reduction in DC-link ripples. The goal of this work is to target this optimal reduction by utilizing an analytical mechanism. The paper presents an alternate way of DC-link formulation in terms of the piece-wise sinusoids of inverter output currents for space vector modulation-based systems. The formulation is then used to numerically optimize the interleaved shifts for minimum ripples. Moreover, in addition to the traditional concept of fixed time interleaving, a contemporary concept of sequence-based interleaving is utilized, which is anticipated to have more flexibility in the implementation and additional switching synchronism with PWM rectifiers for back–back converters. Therefore, the sequence interleaving has also been utilized in conjunction with the proposed ripple reduction methodology. Further, an underexplored area of using the combined impact of sequence and time interleaving has also been applied in this work. These interleaving methods are shown to provide significantly improved DC-link ripple mitigation, as compared to existing methods, using numerical assessment followed by simulations and experimental evaluation. Full article
(This article belongs to the Special Issue Selected Papers from Young Researchers in Energy Systems and Management)
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24 pages, 6136 KiB  
Article
Forecasting of Wind Speed and Power through FFNN and CFNN Using HPSOBA and MHPSO-BAACs Techniques
by Manzoor Ellahi, Muhammad Rehan Usman, Waqas Arif, Hafiz Fuad Usman, Waheed A. Khan, Gandeva Bayu Satrya, Kamran Daniel and Noman Shabbir
Electronics 2022, 11(24), 4193; https://doi.org/10.3390/electronics11244193 - 15 Dec 2022
Cited by 7 | Viewed by 2674
Abstract
Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed [...] Read more.
Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year’s dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results. Full article
(This article belongs to the Special Issue Selected Papers from Young Researchers in Energy Systems and Management)
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