Special Issue "Advancing Grid-Connected Renewable Generation Systems 2019"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy".

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editors

Prof. Frede Blaabjerg
grade E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark
Tel. 004521292454; Fax: +45 9815 1411
Interests: power electronics and its applications in motor drives; wind turbines; PV systems; harmonics; reliability of power electronic systems
Special Issues and Collections in MDPI journals
Prof. Dr. Yongheng Yang
E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Tel. + 45 99409766
Interests: power electronics; photovoltaic systems; control of power converters; reliability; power quality
Special Issues and Collections in MDPI journals
Dr. Ariya Sangwongwanich
E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Interests: power electronics; photovoltaic systems; reliability in power electronic systems; power quality; renewable energy integration
Special Issues and Collections in MDPI journals
Dr. Elizaveta Liivik
E-Mail Website
Guest Editor
Department of Electrical Power Engineering and Mechatronics, School of Engineering, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: impedance-source power electronic converters, renewable energy and distributed generation, control and reliability issues of power electronic converters in active distribution networks

Special Issue Information

Dear Colleagues,

Renewables are making pace to become a major source in the energy paradigm, which has been undergoing significant shifts over the past few decades. In order to enable a wide-scale integration of renewables, advanced grid-interfacing control solutions are strongly demanded. On one hand, power conversion efficiency and reliability are the keys aspects that need to be addressed in order to maximize the total energy yield, thereby reducing the overall cost of energy. On the other hand, the stability of the energy system under a high penetration-level of renewable energy is also a concern from the system perspective. This can also be seen in the updated regulation for the grid-connected renewable energy system. Power electronics are the key enabling technology for achieving the above demands, and have been widely used for renewable energy systems such as wind turbine, solar energy, and energy storage systems. Advancing the design, control, operation, and integration of power electronics has the potential to improve the performance of the grid-connected renewable energy systems. This Special Issue, thus, serves to address the present challenging issues regarding the integration of renewable energies into a sustainable and resilient power system. Topics within, but not limited to, grid-connected renewable energy systems, in general, their control, operation, and design, are invited.

Prof. Dr. Frede Blaabjerg
Prof. Dr. Yongheng Yang
Dr. Ariya Sangwongwanich
Dr. Elizaveta Liivik
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 papers will be 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. Applied Sciences 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 1800 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 conversion
  • Power electronics converter topologies and control for grid-connected renewables
  • Energy policies, grid codes, and their development
  • Energy and power management in renewable energy systems
  • Wind power generation (onshore and offshore)
  • Photovoltaic power systems (PV, concentrator PV, and concentrated solar power plants)
  • Fuel cell systems
  • Advanced control for grid-connected renewables
  • Emerging renewable energy technology
  • Wide-bandgap power semiconductor applications in renewable energy systems

Published Papers (5 papers)

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Research

Open AccessArticle
Emulation Strategies and Economic Dispatch for Inverter-Based Renewable Generation under VSG Control Participating in Multiple Temporal Frequency Control
Appl. Sci. 2020, 10(4), 1303; https://doi.org/10.3390/app10041303 - 14 Feb 2020
Abstract
As the increasing penetration of inverter-based generation (IBG) and the consequent displacement of traditional synchronous generators (SGs), the system stability and reliability deteriorate for two reasons: first, the overall inertia decreases since the power electronic interfaces (PEIs) are almost inertia-less; second, renewable generation [...] Read more.
As the increasing penetration of inverter-based generation (IBG) and the consequent displacement of traditional synchronous generators (SGs), the system stability and reliability deteriorate for two reasons: first, the overall inertia decreases since the power electronic interfaces (PEIs) are almost inertia-less; second, renewable generation profiles are largely influenced by stochastic meteorological conditions. To strengthen power systems, the concept of the virtual synchronous generator (VSG) has been proposed, which controls the external characteristics of PEIs to emulate those of SGs. Currently, PEIs could perform short-term inertial and primary frequency responses through the VSG algorithm. For renewable energy sources (RES), deloading strategies enable the generation units to possess active power reserves for system frequency responses. Additionally, the deloading strategies could provide the potential for renewable generation to possess long-term frequency regulation abilities. This paper focuses on emulation strategies and economic dispatch for IBG units to perform multiple temporal frequency control. By referring to the well-established knowledge systems of generation and operation in conventional power systems, the current VSG algorithm is extended and complemented by the emulation of secondary and tertiary regulations. The reliability criteria are proposed, considering the loss of load probability (LOLP) and renewable spillage probability (RSP). The reliability criteria are presented in two scenarios, including the renewable units operated in maximum power point tracking (MPPT) and VSG modes. A LOLP-based economic dispatch (ED) approach is solved to acquire the generation and reserve schemes. The emulation strategies and the proposed approach are verified by simulation. Full article
(This article belongs to the Special Issue Advancing Grid-Connected Renewable Generation Systems 2019)
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Open AccessFeature PaperArticle
Global MPPT Based on Machine-Learning for PV Arrays Operating under Partial Shading Conditions
Appl. Sci. 2020, 10(2), 700; https://doi.org/10.3390/app10020700 - 19 Jan 2020
Abstract
A global maximum power point tracking (GMPPT) process must be applied for detecting the position of the GMPP operating point in the minimum possible search time in order to maximize the energy production of a photovoltaic (PV) system when its PV array operates [...] Read more.
A global maximum power point tracking (GMPPT) process must be applied for detecting the position of the GMPP operating point in the minimum possible search time in order to maximize the energy production of a photovoltaic (PV) system when its PV array operates under partial shading conditions. This paper presents a novel GMPPT method which is based on the application of a machine-learning algorithm. Compared to the existing GMPPT techniques, the proposed method has the advantage that it does not require knowledge of the operational characteristics of the PV modules comprising the PV system, or the PV array structure. Additionally, due to its inherent learning capability, it is capable of detecting the GMPP in significantly fewer search steps and, therefore, it is suitable for employment in PV applications, where the shading pattern may change quickly (e.g., wearable PV systems, building-integrated PV systems etc.). The numerical results presented in the paper demonstrate that the time required for detecting the global MPP, when unknown partial shading patterns are applied, is reduced by 80.5%–98.3% by executing the proposed Q-learning-based GMPPT algorithm, compared to the convergence time required by a GMPPT process based on the particle swarm optimization (PSO) algorithm. Full article
(This article belongs to the Special Issue Advancing Grid-Connected Renewable Generation Systems 2019)
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Open AccessFeature PaperArticle
Phase Balancing and Reactive Power Support Services for Microgrids
Appl. Sci. 2019, 9(23), 5067; https://doi.org/10.3390/app9235067 - 24 Nov 2019
Cited by 1
Abstract
Alternating current (AC) microgrids are expected to operate as active components within smart distribution grids in the near future. The high penetration of intermittent renewable energy sources and the rapid electrification of the thermal and transportation sectors pose serious challenges that must be [...] Read more.
Alternating current (AC) microgrids are expected to operate as active components within smart distribution grids in the near future. The high penetration of intermittent renewable energy sources and the rapid electrification of the thermal and transportation sectors pose serious challenges that must be addressed by modern distribution system operators. Hence, new solutions should be developed to overcome these issues. Microgrids can be considered as a great candidate for the provision of ancillary services since they are more flexible to coordinate their distributed generation sources and their loads. This paper proposes a method for compensating microgrid power factor and loads asymmetries by utilizing advanced functionalities enabled by grid tied inverters of photovoltaics and energy storage systems. Further, a central controller has been developed for adaptively regulating the provision of both reactive power and phase balancing services according to the measured loading conditions at the microgrid’s point of common coupling. An experimental validation with a laboratory scale inverter and a real time hardware in the loop investigation demonstrates that the provision of such ancillary services by the microgrid can significantly improve the operation of distribution grids in terms of power quality, energy losses and utilization of available capacity. Full article
(This article belongs to the Special Issue Advancing Grid-Connected Renewable Generation Systems 2019)
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Open AccessArticle
Classifying Power Quality Disturbances Based on Phase Space Reconstruction and a Convolutional Neural Network
Appl. Sci. 2019, 9(18), 3681; https://doi.org/10.3390/app9183681 - 05 Sep 2019
Cited by 2
Abstract
This paper presents a hybrid approach combining phase space reconstruction (PSR) with a convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a PSR technique is developed to transform a 1D voltage disturbance signal into a 2D image file. Then, a [...] Read more.
This paper presents a hybrid approach combining phase space reconstruction (PSR) with a convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a PSR technique is developed to transform a 1D voltage disturbance signal into a 2D image file. Then, a CNN model is developed for the image classification. The feature maps are extracted automatically from the image file and different patterns are derived from variables in CNN. A set of synthetic signals, as well as operational measurements, are used to validate the proposed method. Moreover, the test results are also compared with existing methods, including empirical mode decomposition (EMD) with balanced neural tree (BNT), S-transform (ST) with neural network (NN) and decision tree (DT), hybrid ST with DT, adaptive linear neuron (ADALINE) with feedforward neural network (FFNN), and variational mode decomposition (VMD) with deep stochastic configuration network (DSCN). Based on deep learning algorithms, the proposed method is capable of providing more accurate results without any human intervention for PQDs. It also enables the planning of PQ remedy actions. Full article
(This article belongs to the Special Issue Advancing Grid-Connected Renewable Generation Systems 2019)
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Open AccessArticle
GA Optimization Method for a Multi-Vector Energy System Incorporating Wind, Hydrogen, and Fuel Cells for Rural Village Applications
Appl. Sci. 2019, 9(17), 3554; https://doi.org/10.3390/app9173554 - 30 Aug 2019
Abstract
Utilization of renewable energy (e.g., wind, solar, bio-energy) is high on international and governmental agendas. In order to address energy poverty and increase energy efficiency for rural villages, a hybrid distribution generation (DG) system including wind, hydrogen and fuel cells is proposed to [...] Read more.
Utilization of renewable energy (e.g., wind, solar, bio-energy) is high on international and governmental agendas. In order to address energy poverty and increase energy efficiency for rural villages, a hybrid distribution generation (DG) system including wind, hydrogen and fuel cells is proposed to supplement to the main grid. Wind energy is first converted into electrical energy while part of the generated electricity is used for water electrolysis to generate hydrogen for energy storage. Hydrogen is used by fuel cells to convert back to electricity when electrical energy demand peaks. An analytical model has been developed to coordinate the operation of the system involving energy conversion between mechanical, electrical and chemical forms. The proposed system is primarily designed to meet the electrical demand of a rural village in the UK where the energy storage system can balance out the discrepancy between intermittent renewable energy supplies and fluctuating energy demands so as to improve the system efficiency. Genetic Algorithm (GA) is used as an optimization strategy to determine the operational scheme for the multi-vector energy system. In the work, four case studies are carried out based on real-world measurement data. The novelty of this study lies in the GA-based optimization and operational methods for maximized wind energy utilization. This provides an alternative to battery energy storage and can be widely applied to wind-rich rural areas. Full article
(This article belongs to the Special Issue Advancing Grid-Connected Renewable Generation Systems 2019)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Frede Blaabjerg
Title: Thermal Mapping of Power Semiconductors in H-bridge Circuit


2. E. Koutroulis
Title: Application of machine-learning technique for maximizing theenergy production of PV systems under partial shading conditions


3. Elias Kyriakides
Title: Phase balancing and reactive support servicesfor microgrids

 

4. Yongheng Yang

Title: Damping in High-order Filters for Grid-Connected Renewable Energy Systems

 

 

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