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Advanced Application for Renewable Energy Sources: Modelling, Management Control, Data Monitoring, Health Management, Design and Improvement

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

Deadline for manuscript submissions: 18 May 2024 | Viewed by 1491

Special Issue Editors

Dr. Mohamed Louzazni

E-Mail Website
Guest Editor
Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaïb Doukkali University of El Jadida, El Jadida M-24000, Morocco
Interests: performance analysis; monitoring; lifetime analysis; fault detection; control management; power electronics; hybrid renewable energy; mathematical modelling; optimization and meta-heuristic algorithm; computational intelligence; photovoltaic and power energy; forecasting; fuel cell; radar; radio frequency; electromagnetic and electronic
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marco Mussetta

E-Mail Website
Guest Editor
Department of Energy, Politecnico di Milano, 20133 Milano, MI, Italy
Interests: computational intelligence; optimization; machine learning; fuzzy logic; wireless sensor networks; renewable energy; photovoltaics; wind power; forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis M. Fernández-Ramírez

E-Mail Website
Guest Editor
Research Group in Sustainable and Renewable Electrical Technologies (PAIDI-TEP023), Department of Electrical Engineering, Higher Technical School of Engineering of Algeciras, University of Cadiz, Algeciras, Spain
Interests: smart cities; smart grids; microgrids; renewable energy; wind energy; photovoltaic solar energy; energy storage systems; hydrogen and fuel cells; hybrid electric systems; electric vehicles; electric power systems; power converters and energy management/control systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable Energy Sources (RESs) are fast developing as a viable alternative to fossil fuels in the face of global economic instability, climate change, and increased energy consumption. Scientists have been discovering, developing, and modeling various RE technologies in order to improve their performance. Although some RE generators have a nonlinear characteristic in terms of power output as a function of weather conditions, studying and analyzing diverse modes have become a scientific and technological problem. A variety of characteristics must be satisfied in order to ensure the stability and security of RES-based electric power systems such as wind turbines, solar power systems, tidal turbines, and fuel cell generators. The connected RES to the electrical grid needs several operations such as optimization, fault diagnosis and digitalization of the system to be smart. The smart grids provide a high stability of the injected electrical power from RES to small grids. In this regard, modelling, forecasting, management control of hybrid systems, data acquisition, monitoring and fault diagnosis under variable weather conditions are of paramount importance. This Special Issue aims to encourage researchers and practitioners to share and exchange their original and high-quality articles (new theories, methods, techniques, and applications) focusing on innovative renewable energy system modelling, control management and monitoring, forecasting, including energy efficiency improvement and verification. Original research and review articles discussing state-of-the-art research are welcome. The submitted manuscripts for this Special Issue will be peer-reviewed before publication. Potential topics include but are not limited to the following:

  • Modelling and characterization of renewable energy systems;
  • Electronic component diagnosis;
  • Fault-tolerant control strategies;
  • Fault detection and diagnosis of renewable energy systems;
  • Digitalization of renewable energy systems;
  • IoT and Industry 4.0-based sensor technology;
  • Sensors, data acquisition, analysis and monitoring;
  • Advanced optimization and control management;
  • Advanced forecasting methods;
  • Control optimization and stabilization of storage systems;
  • Advanced control strategies for electrical machines in renewable energy applications;
  • Artificial intelligence techniques in smart grids and renewable energy systems.

Dr. Mohamed Louzazni
Prof. Dr. Marco Mussetta
Prof. Dr. Luis M. Fernández-Ramírez
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

  • renewable energy sources
  • variable conditions
  • advanced applications
  • modelling
  • forecasting
  • management control
  • data monitoring
  • fault diagnosis
  • smart grids
  • energy harvesting

Published Papers (2 papers)

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Research

24 pages, 5098 KiB  
Article
Numerical Analysis of Three Vertical Axis Turbine Designs for Improved Water Energy Efficiency
by Derya Karakaya, Aslı Bor and Sebnem Elçi
Energies 2024, 17(6), 1398; https://doi.org/10.3390/en17061398 - 14 Mar 2024
Viewed by 608
Abstract
A hydrokinetic turbine with a vertical axis is specifically designed to harvest the kinetic energy from moving water. In this study, three vertical axis water turbines, namely Gorlov, Darrieus, and Savonius turbines, were compared for their efficiency via numerical modeling for steady-state conditions [...] Read more.
A hydrokinetic turbine with a vertical axis is specifically designed to harvest the kinetic energy from moving water. In this study, three vertical axis water turbines, namely Gorlov, Darrieus, and Savonius turbines, were compared for their efficiency via numerical modeling for steady-state conditions via the ANSYS 2022 R2 Fluent model. The Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) was implemented with an SST k-ω turbulence model. The dynamic mesh technique, which allows modeling according to changes in angular velocity at each time step, was used to simulate flow around the turbines for six different velocities (from 0.5 to 3 m/s). The efficiency of the turbines was compared and the results were analyzed. The pressure, velocity, and turbulence kinetic energy distributions around the rotor were measured at different rotational angles and results indicated a wider operating range for the Darrieus and Gorlov turbines compared to the Savonius turbine. The highest power coefficient of 0.293 was achieved in the model featuring a Darrieus turbine, corresponding to a TSR value of 1.34, compared to 0.208 for the Gorlov and 0.257 for the Savonius turbine, at TSR values of 1.3 and 1.06, respectively. Numerical modeling results pointed to a significantly higher self-starting capacity for the Savonius turbine compared to the others. Full article
(This article belongs to the Special Issue Advanced Application for Renewable Energy Sources: Modelling, Management Control, Data Monitoring, Health Management, Design and Improvement)
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14 pages, 1061 KiB  
Article
Wind Power Bidding Based on an Ensemble Differential Evolution Algorithm with a Problem-Specific Constraint-Handling Technique
by Chao Huang, Zhenyu Zhao, Qingwen Li, Xiong Luo and Long Wang
Energies 2024, 17(2), 380; https://doi.org/10.3390/en17020380 - 12 Jan 2024
Viewed by 503
Abstract
The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant [...] Read more.
The intermittent nature of wind power generation induces great challenges for power bidding in the electricity market. The deployment of battery energy storage can improve flexibility for power bidding. This paper investigates an optimal power bidding strategy for a wind–storage hybrid power plant in the day-ahead electricity market. To handle the challenges of the uncertainties of wind power generation and electricity prices, the optimal bidding problem is formulated as a risk-aware scenario-based stochastic programming, in which a number of scenarios are generated using a copula-based approach to represent the uncertainties. These scenarios consider the temporal correlation of wind power generation and electricity prices between consecutive time intervals. In the stochastic programming, a more practical but nonlinear battery operation cost function is considered, which leads to a nonlinear constrained optimization problem. To solve the nonlinear constrained optimization problem, an ensemble differential evolution (EDE) algorithm is proposed, which makes use of the merits of an ensemble of mutant operators to generate mutant vectors. Moreover, a problem-specific constraint-handling technique is developed. To validate the effectiveness of the proposed EDE algorithm, it is compared with state-of-the-art DE-based algorithms for constrained optimization problems, including a constrained composite DE (C2oDE) algorithm and a novel DE (NDE) algorithm. The experimental results demonstrate that the EDE algorithm is much more reliable and much faster in finding a better bidding strategy against benchmarking algorithms. More precisely, the average values of the success rate are 0.893, 0.667, and 0.96 for C2oDE, NDE, and EDE, respectively. Compared to C2oDE and NDE, the average value of the mean number of function evaluations to succeed with EDE is reduced by 76% and 59%, respectively. Full article
(This article belongs to the Special Issue Advanced Application for Renewable Energy Sources: Modelling, Management Control, Data Monitoring, Health Management, Design and Improvement)
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