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Control and Optimization of Microgrids and Renewable Energy Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 7258

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Special Issue Editors

Dr. Sreedhar Madichetty

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Guest Editor

Department of Electronics and Computer Engineering, Ecole Centrale School of Engineering, Mahindra University, Hyderabad, 500043, India
Interests: power electronics; information physical systems and renewable energy systems

Prof. Dr. Abdelkader El Kamel

E-Mail Website
Guest Editor

Centre de Recherche en Informatique, Signal et Automatique de Lille ( CRISTAL ), Villeneuve-d'Ascq, France
Interests: autonomous systems; ITS; control & optimization

Special Issue Information

Dear Colleagues,

Renewable energy-based microgrids are playing a vital role in reduction of energy transmission systems and to produce energy near to consumption points, thus constituting small distribution systems. These are small-scale energy grids that can operate independently or autonomously from the main energy grid. Mainly there are two types, i.e., DC microgrid and AC microgrid. Integration of renewable energy resources introduce numerous challenges to the legacy system are required to achieve affordability, resilience, sustainability, and prosperity. The interconnected microgrids also possess challenges in securing from cyber threats. This Special Issue will include papers related to the control and optimization of microgrids and their applications in industry, transportation, water, waste, and urban and residential infrastructures.

Topics of interest include, but are not limited to, the following aspects of microgrids.

Community microgrids, microgrid clusters, nano-grids;
Cybersecurity aspects in microgrids;
Scheduling, selection, interconnected microgrids, and their interconnection with fuel cells, energy storage systems, other microgrids;
Strategies for advanced control techniques, optimization, and protection;
Technologies, strategies, and policies for more renewable energy penetration;
DC, AC, and hybrid microgrids;
Stability, observability, and resilience of microgrids;
The IoT and energy internet for cluster grids;
Penetration of electric vehicles charging stations;
Space microgrids, ship microgrid, plane microgrids, and their control;
Smart metering and power quality for microgrids;
Reviews on the state of the art in the area of microgrids;
Integration of renewable energy sources, such as wind, tidal, geothermal etc.;
Energy sharing strategies, fault studies, energy optimization.

Dr. Sreedhar Madichetty
Prof. Dr. Abdelkader El Kamel
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

DC microgrid
AC microgrid
cybersecurity in microgrids
control of microgrid
space microgrid

Published Papers (4 papers)

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Research

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17 pages, 14512 KiB

Open AccessArticle

Voltage Stability and Power Sharing Control of Distributed Generation Units in DC Microgrids

by Kafeel Ahmed, Irfan Hussain, Mehdi Seyedmahmoudian, Alex Stojcevski and Saad Mekhilef

Energies 2023, 16(20), 7038; https://doi.org/10.3390/en16207038 - 11 Oct 2023

Cited by 1 | Viewed by 1073

Abstract

Advancements in power conversion efficiency and the growing prevalence of DC loads worldwide have underscored the importance of DC microgrids in modern energy systems. Addressing the challenges of power-sharing and voltage stability in these DC microgrids has been a prominent research focus. Sliding mode control (SMC) has demonstrated remarkable performance in various power electronic converter applications. This paper proposes the integration of universal droop control (UDC) with SMC to facilitate distributed energy resource interfacing and power-sharing control in DC microgrids. Compared to traditional Proportional-Integral (PI) control, the proposed control approach exhibits superior dynamic response characteristics. The UDC is strategically incorporated prior to the SMC and establishes limits on voltage variation and maximum power drawn from the DC–DC converters within the microgrid. A dynamic model of the DC–DC converter is developed as the initial stage, focusing on voltage regulation at the DC link through nonlinear control laws tailored for Distributed Generation (DG)-based converters. The UDC ensures voltage stability in the DC microgrid by imposing predetermined power constraints on the DGs. Comparative evaluations, involving different load scenarios, have been conducted to assess the performance of the proposed UDC-based SMC control in comparison to the PI control-based system. The results demonstrate the superior efficiency of the UDC-based SMC control in handling dynamic load changes. Furthermore, a practical test of the proposed controller has been conducted using a hardware prototype of a DC microgrid. Full article

(This article belongs to the Special Issue Control and Optimization of Microgrids and Renewable Energy Systems)

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22 pages, 4264 KiB

Open AccessArticle

A Novel Hybrid Control Strategy and Dynamic Performance Enhancement of a 3.3 kW GaN–HEMT-Based iL²C Resonant Full-Bridge DC–DC Power Converter Methodology for Electric Vehicle Charging Systems

by Rajanand Patnaik Narasipuram and Subbarao Mopidevi

Energies 2023, 16(15), 5811; https://doi.org/10.3390/en16155811 - 04 Aug 2023

Cited by 7 | Viewed by 1047

Abstract

The conventional resonant inductor–inductor–capacitor (L²C) DC–DC converters have the major drawbacks of poor regulation, improper current sharing, load current ripples, conduction losses, and limiting the power levels to operate at higher loads for electric vehicle (EV) charging systems. To address the issues of the L²C converter, this paper proposes an interleaved inductor–inductor–capacitor (iL²C) full-bridge (FB) DC–DC converter as an EV charger with wide input voltage conditions. It comprises two L²C converters operating in parallel on the primary side with 8-GaN switches and maintains the single rectifier circuit on the secondary side as common. Further, it introduces the hybrid control strategy called variable frequency + phase shift modulation (VFPSM) technique for iL²C with a constant voltage charging mode operation. The design requirements, modeling, dynamic responses, and operation of an iL²C converter with a controller are discussed. The analysis of the proposed concept designed and simulated with an input voltage of 400 V_in at a load voltage of 48 V₀ presented at different load conditions, i.e., full load (3.3 kW), half load (1.65 kW), and light load (330 W). The dynamic performances of the converter during line and load regulations are presented at assorted input voltages. In addition, to analyze the controller and converter performance, the concept was validated experimentally for wide input voltage applications of 300–500 V_in with a desired output of 48 V₀ at full load condition, i.e., 3.3 kW and the practical efficiency of the iL²C converter was 98.2% at full load. Full article

(This article belongs to the Special Issue Control and Optimization of Microgrids and Renewable Energy Systems)

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17 pages, 533 KiB

Open AccessArticle

A Peer-to-Peer Energy Trading Model for Optimizing Both Efficiency and Fairness

by Eiichi Kusatake, Mitsue Imahori and Norihiko Shinomiya

Energies 2023, 16(14), 5501; https://doi.org/10.3390/en16145501 - 20 Jul 2023

Cited by 1 | Viewed by 1106

Abstract

In recent years, there has been a growing global trend towards transitioning from centralized energy systems to distributed or decentralized models, with the aim of promoting the widespread utilization of renewable energy sources. As a result, the concept of direct energy trading among consumers has garnered considerable attention as a means to effectively harness the potential of distributed energy systems. However, in this decentralized trading scenario, certain consumers may encounter challenges in receiving electricity from their preferred suppliers due to limited supply capacities. As a result of this constraint, there is a reduction in the advantages enjoyed by consumers. While previous studies have predominantly focused on optimizing resource allocation efficiency, the issue of equitable consumer benefits has often been overlooked. Therefore, it is crucial to develop a trading mechanism that considers the preferences of market participants, in addition to balancing supply and demand. Such a mechanism aims to enhance both fairness and efficiency in the market. This paper introduces the formulation of a single-objective optimization and multi-objective optimization problem for an electricity market trading mechanism. To address this challenge, two single-objective algorithms and six evolutionary algorithms (EAs) are employed to solve the optimization problem. By analyzing the simulation results, this study demonstrates the efficacy of the chosen evolutionary algorithms (EAs) and a single-objective optimization approach in effectively optimizing both the utilization of resources and the equitable distribution of consumer benefits. Full article

(This article belongs to the Special Issue Control and Optimization of Microgrids and Renewable Energy Systems)

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Review

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36 pages, 10017 KiB

Open AccessReview

Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review

by Sriranga Suprabhath Koduru, Venkata Siva Prasad Machina and Sreedhar Madichetty

Energies 2023, 16(12), 4573; https://doi.org/10.3390/en16124573 - 07 Jun 2023

Cited by 4 | Viewed by 2902

Abstract

The importance of and need for cyber security have increased in the last decade. The critical infrastructure of the country, modeled with cyber-physical systems (CPS), is becoming vulnerable because of a lack of efficient safety measures. Attackers are becoming more innovative, and attacks are becoming undetectable, thereby causing huge risks to these systems. In this scenario, intelligent and evolving detection methods should be introduced to replace basic and outworn methods. The ability of artificial intelligence (AI) to analyze data and predict outcomes has created an opportunity for researchers to explore the power of AI in cyber security. This article discusses new-age intelligence and smart techniques such as pattern recognition models, deep neural networks, generative adversarial networks, and reinforcement learning for cyber security in CPS. The differences between the traditional security methods used in information technology and the security methods used in CPS are analyzed, and the need for a transition into intelligent methods is discussed in detail. A deep neural network-based controller that detects and mitigates cyber attacks is designed for microgrid systems. As a case study, a stealthy local covert attack that overcomes the existing microgrid protection is modeled. The ability of the DNN controller to detect and mitigate the SLCA is observed. The experiment is performed in a simulation and also in real-time to analyze the effectiveness of AI in cyber security. Full article

(This article belongs to the Special Issue Control and Optimization of Microgrids and Renewable Energy Systems)

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