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Energies
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Microgrids Control and Optimization
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Microgrids Control and Optimization

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 (15 May 2022) | Viewed by 14119

Special Issue Editors

Dr. Rahmatollah Khezri

E-Mail Website
Guest Editor
College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
Interests: electricity market; intelligent control; load frequency control; planning and operation; renewable energy; smart grid and microgrids
Special Issues, Collections and Topics in MDPI journals
Dr. Arman Oshnoei

E-Mail Website
Guest Editor
Department of Energy, Aalborg University, 9220 Aalborg, Denmark
Interests: load frequency control; energy storage systems; intelligent control; AC/DC microgrids; power system control, power system planning
Special Issues, Collections and Topics in MDPI journals
Dr. Ali Bidram

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, the University of New Mexico, Albuquerque, NM 87131, United States
Interests: power system protection; microgrids; grid integration of renwable energy resources
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Amjad Anvari-Moghaddam

E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Interests: power system operation and planning; microgrids and active energy networks; energy markets and analytics; operations research and its applications to energy systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Behnam Mohammadi-Ivatloo

grade E-Mail Website
Guest Editor
Smart Energy Systems Laboratory, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Interests: smart energy systems, machine learning, uncertainty management, decision making, renewable energy sources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the Guest Editors, we encourage scientists and colleagues to submit their theoretical and applied contributions, as well as review articles, to this Special Issue of Energies entitled “Microgrids Control and Optimization”. This Special Issue aims to explore technologies, methodologies, and solutions for developing new techniques for the efficient, secure, and stable operation of microgrids.

Microgrids are decentralized groups of distributed energy resources and end side consumers. These newly fashioned networks offer some excellent benefits such as high electrification reliability, increased grid efficiency, improved grid resiliency, and decreased environmental issues by making use of renewable resources. Likewise, the microgrids are set up in a way that they can be deployed in both the grid-connected and stand-alone modes of operation. Nonetheless, some technical challenges still stand in the way of successful, reliable utilization of microgrids. The integration of power electronics devices and hence the increased voltage and frequency fluctuations, the changing of power flow patterns, and the stability concerns are recognized as some of the technical obstacles. Hence, microgrids need careful control and optimization to ensure their reliable and cost-effective operation. The main focus of this Special Issue is on microgrids control and optimization.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Control and design of power converters in microgrids;
  • Optimal planning and operation of microgrids;
  • Protection and stability issues in microgrids;
  • Optimal power flow in microgrids;
  • Metaheuristic algorithms for microgrids optimization;
  • Demand response in microgrid;
  • Energy management system for microgrids;
  • Grid integration of microgrids;
  • Power quality issues in microgrids;
  • Reliability and resilience issues in microgrids;
  • Cyber-physical systems, and artificial intelligence in microgrids;
  • Virtual inertia control in microgrids;
  • Economic and environmental analysis in microgrids;
  • Integration of distributed renewable resources in microgrids;
  • Intelligent and robust control of microgrids.

Dr. Rahmatollah Khezri
Dr. Arman Oshnoei
Dr. Ali Bidram
Prof. Dr. Amjad Anvari-Moghaddam
Prof. Dr. Behnam Mohammadi-Ivatloo
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

  • artificial intelligence
  • artificial neural network
  • distributed energy resources
  • demand response
  • electric vehicle
  • energy storage system
  • frequency control
  • fuzzy control
  • internet of things
  • intelligent control
  • metaheuristic algorithms
  • microgrid control
  • multi-agent systems
  • power electronic converter
  • particle swarm optimization
  • microgrid optimization
  • energy management
  • planning
  • optimal control
  • renewable energy
  • grid integration
  • reliability and resiliency
  • robust control
 

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Published Papers (3 papers)

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Research

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21 pages, 5476 KiB  
Article
Robust Output Feedback Control of the Voltage Source Inverter in an AC Microgrid
by Hamid Saeed Khan and Attaullah Y. Memon
Energies 2022, 15(15), 5586; https://doi.org/10.3390/en15155586 - 1 Aug 2022
Cited by 2 | Viewed by 1492
Abstract
This paper presents the mathematical model and control of the voltage source inverter (VSI) connected to an alternating current (AC) microgrid. The VSI used in this work was a six-switch three-phase PWM inverter, whose output voltages were controlled in a synchronous ( [...] Read more.
This paper presents the mathematical model and control of the voltage source inverter (VSI) connected to an alternating current (AC) microgrid. The VSI used in this work was a six-switch three-phase PWM inverter, whose output voltages were controlled in a synchronous (dq) reference frame via a sliding mode control strategy. The control strategy required only output voltages; other states of the system were estimated by using a high-gain observer. The power-sharing among multiple inverters was achieved by solving power flow equations of the electrical network. The stability analysis showed that the error was ultimately bound in the case of the real PWM inverter and/or with a nonlinear load in the electrical network. The microgrid was simulated using the SimPowerSystems Toolbox from MATLAB/Simulink. The simulation results show the effectiveness of the proposed control scheme. The output voltage regulation of the inverter and power-sharing was achieved with the ultimately bounded error for the linear load. Later, the nonlinear load was also included in the electrical network and the error was shown to remain ultimately bounded. The output voltage regulation and power-sharing were achieved with the nonlinear load in the system. Full article
(This article belongs to the Special Issue Microgrids Control and Optimization)
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21 pages, 4534 KiB  
Article
Learning-Based Model Predictive Control of DC-DC Buck Converters in DC Microgrids: A Multi-Agent Deep Reinforcement Learning Approach
by Hoda Sorouri, Arman Oshnoei, Mateja Novak, Frede Blaabjerg and Amjad Anvari-Moghaddam
Energies 2022, 15(15), 5399; https://doi.org/10.3390/en15155399 - 26 Jul 2022
Cited by 10 | Viewed by 2958
Abstract
This paper proposes a learning-based finite control set model predictive control (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in a DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented to address one [...] Read more.
This paper proposes a learning-based finite control set model predictive control (FCS-MPC) to improve the performance of DC-DC buck converters interfaced with constant power loads in a DC microgrid (DC-MG). An approach based on deep reinforcement learning (DRL) is presented to address one of the ongoing challenges in FCS-MPC of the converters, i.e., optimal design of the weighting coefficients appearing in the FCS-MPC objective function for each converter. A deep deterministic policy gradient method is employed to learn the optimal weighting coefficient design policy. A Markov decision method formulates the DRL problem. The DRL agent is trained for each converter in the MG, and the weighting coefficients are obtained based on reward computation with the interactions between the MG and agent. The proposed strategy is wholly distributed, wherein agents exchange data with other agents, implying a multi-agent DRL problem. The proposed control scheme offers several advantages, including preventing the dependency of the converter control system on the operating point conditions, plug-and-play capability, and robustness against the MG uncertainties and unknown load dynamics. Full article
(This article belongs to the Special Issue Microgrids Control and Optimization)
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Review

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38 pages, 1162 KiB  
Review
A Review of Total Harmonic Distortion Factors for the Measurement of Harmonic and Interharmonic Pollution in Modern Power Systems
by Angel Arranz-Gimon, Angel Zorita-Lamadrid, Daniel Morinigo-Sotelo and Oscar Duque-Perez
Energies 2021, 14(20), 6467; https://doi.org/10.3390/en14206467 - 9 Oct 2021
Cited by 68 | Viewed by 8120
Abstract
Harmonic distortion is one of the disturbances that most affects the quality of the electrical system. The widespread use of power electronic systems, especially power converters, has increased harmonic and interharmonic emission in a wide range of frequencies. Therefore, there are new needs [...] Read more.
Harmonic distortion is one of the disturbances that most affects the quality of the electrical system. The widespread use of power electronic systems, especially power converters, has increased harmonic and interharmonic emission in a wide range of frequencies. Therefore, there are new needs in the measurement of harmonic distortion in modern electrical systems, such as measurement in the supra-harmonic range (>2 kHz) and the measurement of interharmonics. The International Electrotechnical Commission (IEC) standards define new total harmonic distortion (THD) rates based on the concept of frequency groupings. However, the rates defined in the IEC standards have shortcomings when measuring signals such as those present in the outputs of power systems with abundant interharmonic content and presence of components in the supra-harmonic range. Therefore, in this work, a comparison is made between the different THD factors currently defined, both in the literature and in the standards, to show which of them are the most suitable for assessing harmonic and interharmonic contamination in power system signals such as those present at the output of inverters. Full article
(This article belongs to the Special Issue Microgrids Control and Optimization)
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