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Energies
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Fuel Cell Renewable Hybrid Power Systems 2021
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Fuel Cell Renewable Hybrid Power Systems 2021

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A5: Hydrogen Energy".

Deadline for manuscript submissions: closed (8 September 2023) | Viewed by 15635

Special Issue Editors

Prof. Dr. Nicu Bizon

E-Mail Website
Guest Editor
Faculty of Electronics, Communications and Computers, University of Pitesti, 1 Targu din Vale, 110040 Pitesti, Romania
Interests: power electronics; renewable energy; fuel cell; hybrid power systems; control; optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mihai Oproescu

E-Mail Website
Guest Editor
Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania
Interests: electrical engineering; power electronics; power converters; renewable energy technologies; control systems engineering; MATLAB simulation; power systems simulation; power systems analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The very fast increase in the world’s energy demand over the last decade, and the request for sustainable development, can be approached using microgrids based on hybrid power systems combining renewable energy sources and fuel cell systems.

Thus, to highlight the latest solutions in the implementation of fuel cell renewable hybrid power systems, this Special Issue, entitled “Fuel Cell Renewable Hybrid Power Systems 2021”, has been proposed for the international journal Energies. The present Special Issue of Energies aims to collect innovative solutions and experimental research, as well as state-of-the-art studies, in the following topics:

  • Fuel cell (FC) systems: modeling, control, optimization, and innovative technologies to improve the fuel economy, lifetime, reliability, and safety in operation;
  • Hybrid power systems (HPSs) based on renewable energy sources (RESs) (RES HPS): optimized RES HPSs architectures; global maximum power point tracking (GMPPT) control algorithms to improve energy harvesting from RESs; advanced energy management strategies (EMSs) to optimally ensure the power flow balance on DC (and/or AC bus) for stand-alone RES HPSs or grid-connected RES HPSs (micro-grids);
  • RES HPS with an FC system as a backup energy source (FC RES HPS): innovative solutions to mitigate the RES power variability and load dynamics to energy storage systems (ESSs) by controlling the generated FC power, DC voltage regulation, and/or load pulse mitigation by active control of the power converters from hybrid ESS;
  • FC vehicles (FCVs), electric vehicles (EVs) and hybrid electric vehicles (HEVs): FCV/EV/HEV powertrain, ESSs topologies and hybridization technologies, and EMSs to improve the fuel economy and ESS lifetime;
  • Advanced technologies in microgrids: vehicle-to-everything (V2X), blockchain, smart contracts, cyber-security, etc.;
  • Optimal sizing of FC RES HPSs, FCVs, ESS, and microgrids;
  • Hydrogen production and its specific use in energy applications.

The papers received are subject to a rigorous, but fast, peer review procedure, ensuring the wide dissemination of research results accepted for this Special Issue. I am writing to invite you to submit your original work to this Special Issue. I look forward to receiving your outstanding research outcomes.

Prof. Dr. Nicu Bizon
Prof. Dr. Mihai Oproescu
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

  • Hybrid power systems (HPSs)
  • Renewable energy sources (RESs)
  • Fuel cell (FC) systems
  • Energy management strategies (EMSs)
  • Hybrid energy storage systems (HESSs)
  • Fuel cell vehicles (FCVs)
  • Electric vehicles (EVs)
  • Hybrid electric vehicles (HEVs)
  • Global maximum power point tracking (GMPPT)
  • FC RES microgrids
  • Design and sizing
  • Fuel economy, lifetime, reliability, and safety in operation
  • Vehicle-to-everything (V2X)
  • Blockchain and smart contracts
  • Cyber-security
  • Energy market

Published Papers (4 papers)

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Research

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19 pages, 1806 KiB  
Article
Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks
by Ahmed Sami Alhanaf, Hasan Huseyin Balik and Murtaza Farsadi
Energies 2023, 16(22), 7680; https://doi.org/10.3390/en16227680 - 20 Nov 2023
Cited by 3 | Viewed by 1295
Abstract
Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed [...] Read more.
Effective fault detection, classification, and localization are vital for smart grid self-healing and fault mitigation. Deep learning has the capability to autonomously extract fault characteristics and discern fault categories from the three-phase raw of voltage and current signals. With the rise of distributed generators, conventional relaying devices face challenges in managing dynamic fault currents. Various deep neural network algorithms have been proposed for fault detection, classification, and location. This study introduces innovative fault detection methods using Artificial Neural Networks (ANNs) and one-dimension Convolution Neural Networks (1D-CNNs). Leveraging sensor data such as voltage and current measurements, our approach outperforms contemporary methods in terms of accuracy and efficiency. Results in the IEEE 6-bus system showcase impressive accuracy rates: 99.99%, 99.98% for identifying faulty lines, 99.75%, 99.99% for fault classification, and 98.25%, 96.85% for fault location for ANN and 1D-CNN, respectively. Deep learning emerges as a promising tool for enhancing fault detection and classification within smart grids, offering significant performance improvements. Full article
(This article belongs to the Special Issue Fuel Cell Renewable Hybrid Power Systems 2021)
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25 pages, 3594 KiB  
Article
Direct Power Control Based on Modified Sliding Mode Controller for a Variable-Speed Multi-Rotor Wind Turbine System Using PWM Strategy
by Habib Benbouhenni, Zinelaabidine Boudjema, Nicu Bizon, Phatiphat Thounthong and Noureddine Takorabet
Energies 2022, 15(10), 3689; https://doi.org/10.3390/en15103689 - 18 May 2022
Cited by 22 | Viewed by 2018
Abstract
A robust and improved control scheme of a variable speed multi-rotor wind turbine (MRWT) system with a doubly fed asynchronous generator (DFAG) is displayed in this work. In order to improve the performances and effectiveness of the traditional direct power control (DPC) strategy [...] Read more.
A robust and improved control scheme of a variable speed multi-rotor wind turbine (MRWT) system with a doubly fed asynchronous generator (DFAG) is displayed in this work. In order to improve the performances and effectiveness of the traditional direct power control (DPC) strategy of the DFAG, a new kind of sliding mode controller (SMC) called modified SMC (MSMC) is proposed. The most important advantage of the DPC-MSMC strategy is to reduce the power ripples and improve the quality of the currents provided to the grid. In addition, to control the rotor inverter, a pulse width modulation (PWM) technique is used. The proposed DPC-MSMC strategy was modeled and simulated using MATLAB/Simulink software. The simulation results showed that the ripples in stator currents, active and reactive powers and torque were considerably reduced for the proposed DPC-MSMC strategy compared to the traditional DPC. Additionally, the proposed DPC-MSMC method works excellently to reduce the total harmonic distortion (THD) of the stator current in the case of variable wind speed. On the other hand, a robustness test against parametric variations showed and confirmed the robustness of the proposed technique compared to the classical method. Full article
(This article belongs to the Special Issue Fuel Cell Renewable Hybrid Power Systems 2021)
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20 pages, 3460 KiB  
Article
A Comprehensive Risk Assessment Framework for Synchrophasor Communication Networks in a Smart Grid Cyber Physical System with a Case Study
by Amitkumar V. Jha, Bhargav Appasani, Abu Nasar Ghazali and Nicu Bizon
Energies 2021, 14(12), 3428; https://doi.org/10.3390/en14123428 - 10 Jun 2021
Cited by 13 | Viewed by 1910
Abstract
The smart grid (SG), which has revolutionized the power grid, is being further improved by using the burgeoning cyber physical system (CPS) technology. The conceptualization of SG using CPS, which is referred to as the smart grid cyber physical system (SGCPS), has gained [...] Read more.
The smart grid (SG), which has revolutionized the power grid, is being further improved by using the burgeoning cyber physical system (CPS) technology. The conceptualization of SG using CPS, which is referred to as the smart grid cyber physical system (SGCPS), has gained a momentum with the synchrophasor measurements. The edifice of the synchrophasor system is its communication network referred to as a synchrophasor communication network (SCN), which is used to communicate the synchrophasor data from the sensors known as phasor measurement units (PMUs) to the control center known as the phasor data concentrator (PDC). However, the SCN is vulnerable to hardware and software failures that introduce risk. Thus, an appropriate risk assessment framework for the SCN is needed to alleviate the risk in the protection and control of the SGCPS. In this direction, a comprehensive risk assessment framework has been proposed in this article for three types of SCNs, namely: dedicated SCN, shared SCN and hybrid SCN in an SGCPS. The proposed framework uses hardware reliability as well as data reliability to evaluate the associated risk. A simplified hardware reliability model has been proposed for each of these networks, based on failure probability to assess risk associated with hardware failures. Furthermore, the packet delivery ratio (PDR) metric is considered for measuring risk associated with data reliability. To mimic practical shared and hybrid SCNs, the risk associated with data reliability is evaluated for different background traffics of 70%, 80% and 95% using 64 Kbps and 300 Kbps PMU data rates. The analytical results are meticulously validated by considering a case study of West Bengal’s (a state in India) power grid. With respect to the case study, different SCNs are designed and simulated using the QualNet network simulator. The simulations are performed for dedicated SCN, shared SCN and hybrid SCN with 64 Kbps and 300 Kbps PMU data rates. The simulation results are comprehensively analyzed for risk hedging of the proposed SCNs with data reliability and hardware reliability. To summarize, the mean risk with data reliability (RwDR) as compared to the mean risk with hardware reliability (RwHR) increases in shared SCN and hybrid SCN by a factor of 17.108 and 23.278, respectively. However, minimum RwDR increases in shared and hybrid SCN by a factor of 16.005 and 17.717, respectively, as compared to the corresponding minimum RwHR. The overall analysis reveals that the RwDR is minimum for dedicated SCN, moderate for shared SCN, and highest for hybrid SCN. Full article
(This article belongs to the Special Issue Fuel Cell Renewable Hybrid Power Systems 2021)
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Review

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20 pages, 3296 KiB  
Review
A Comprehensive Survey of Alkaline Electrolyzer Modeling: Electrical Domain and Specific Electrolyte Conductivity
by Frank Gambou, Damien Guilbert, Michel Zasadzinski and Hugues Rafaralahy
Energies 2022, 15(9), 3452; https://doi.org/10.3390/en15093452 - 09 May 2022
Cited by 39 | Viewed by 8832
Abstract
Alkaline electrolyzers are the most widespread technology due to their maturity, low cost, and large capacity in generating hydrogen. However, compared to proton exchange membrane (PEM) electrolyzers, they request the use of potassium hydroxide (KOH) or sodium hydroxide (NaOH) since the electrolyte relies [...] Read more.
Alkaline electrolyzers are the most widespread technology due to their maturity, low cost, and large capacity in generating hydrogen. However, compared to proton exchange membrane (PEM) electrolyzers, they request the use of potassium hydroxide (KOH) or sodium hydroxide (NaOH) since the electrolyte relies on a liquid solution. For this reason, the performances of alkaline electrolyzers are governed by the electrolyte concentration and operating temperature. Due to the growing development of the water electrolysis process based on alkaline electrolyzers to generate green hydrogen from renewable energy sources, the main purpose of this paper is to carry out a comprehensive survey on alkaline electrolyzers, and more specifically about their electrical domain and specific electrolytic conductivity. Besides, this survey will allow emphasizing the remaining key issues from the modeling point of view. Full article
(This article belongs to the Special Issue Fuel Cell Renewable Hybrid Power Systems 2021)
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